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Descriptive Analysis: How-To, Types, Examples

PESTLEanalysis Team

We review the basics of descriptive analysis, including what exactly it is, what benefits it has, how to do it, as well as some types and examples.

From diagnostic to predictive, there are many different types of data analysis . Perhaps the most straightforward of them is descriptive analysis, which seeks to describe or summarize past and present data, helping to create accessible data insights. In this short guide, we'll review the basics of descriptive analysis, including what exactly it is, what benefits it has, how to do it, as well as some types and examples.

What Is Descriptive Analysis?

Descriptive analysis, also known as descriptive analytics or descriptive statistics, is the process of using statistical techniques to describe or summarize a set of data. As one of the major types of data analysis, descriptive analysis is popular for its ability to generate accessible insights from otherwise uninterpreted data.

Unlike other types of data analysis, the descriptive analysis does not attempt to make predictions about the future. Instead, it draws insights solely from past data, by manipulating in ways that make it more meaningful.

Benefits of Descriptive Analysis

Descriptive analysis is all about trying to describe or summarize data. Although it doesn't make predictions about the future, it can still be extremely valuable in business environments . This is chiefly because descriptive analysis makes it easier to consume data, which can make it easier for analysts to act on.

Another benefit of descriptive analysis is that it can help to filter out less meaningful data. This is because the statistical techniques used within this type of analysis usually focus on the patterns in data, and not the outliers.

Types of Descriptive Analysis

According to CampusLabs.com , descriptive analysis can be categorized as one of four types. They are measures of frequency, central tendency, dispersion or variation, and position.

Measures of Frequency

In descriptive analysis, it's essential to know how frequently a certain event or response occurs. This is the purpose of measures of frequency, like a count or percent. For example, consider a survey where 1,000 participants are asked about their favourite ice cream flavor. A list of 1,000 responses would be difficult to consume, but the data can be made much more accessible by measuring how many times a certain flavor was selected.

Measures of Central Tendency

In descriptive analysis, it's also worth knowing the central (or average) event or response. Common measures of central tendency include the three averages — mean, median, and mode. As an example, consider a survey in which the height of 1,000 people is measured. In this case, the mean average would be a very helpful descriptive metric.

Measures of Dispersion

Sometimes, it may be worth knowing how data is distributed across a range. To illustrate this, consider the average height in a sample of two people. If both individuals are six feet tall, the average height is six feet. However, if one individual is five feet tall and the other is seven feet tall, the average height is still six feet. In order to measure this kind of distribution, measures of dispersion like range or standard deviation can be employed.

Measures of Position

Last of all, descriptive analysis can involve identifying the position of one event or response in relation to others. This is where measures like percentiles and quartiles can be used.

descriptive-analysis-charts

How to Do Descriptive Analysis

Like many types of data analysis, descriptive analysis can be quite open-ended. In other words, it's up to you what you want to look for in your analysis. With that said, the process of descriptive analysis usually consists of the same few steps.

  • Collect data

The first step in any type of data analysis is to collect the data. This can be done in a variety of ways, but surveys and good old fashioned measurements are often used.

Another important step in descriptive and other types of data analysis is to clean the data. This is because data may be formatted in inaccessible ways, which will make it difficult to manipulate with statistics. Cleaning data may involve changing its textual format, categorizing it, and/or removing outliers.

  • Apply methods

Finally, descriptive analysis involves applying the chosen statistical methods so as to draw the desired conclusions. What methods you choose will depend on the data you are dealing with and what you are looking to determine. If in doubt, review the four types of descriptive analysis methods explained above.

When to Do Descriptive Analysis

Descriptive analysis is often used when reviewing any past or present data. This is because raw data is difficult to consume and interpret, while the metrics offered by descriptive analysis are much more focused.

Descriptive analysis can also be conducted as the precursor to diagnostic or predictive analysis , providing insights into what has happened in the past before attempting to explain why it happened or predicting what will happen in the future.

Descriptive Analysis Example

As an example of descriptive analysis, consider an insurance company analyzing its customer base.

The insurance company may know certain traits about its customers, such as their gender, age, and nationality. To gain a better profile of their customers, the insurance company can apply descriptive analysis.

Measures of frequency can be used to identify how many customers are under a certain age; measures of central tendency can be used to identify who most of their customers are; measures of dispersion can be used to identify the variation in, for example, the age of their customers; finally, measures of position can be used to compare segments of customers based on specific traits.

Final Thoughts

Descriptive analysis is a popular type of data analysis. It's often conducted before diagnostic or predictive analysis, as it simply aims to describe and summarize past data.

To do so, descriptive analysis uses a variety of statistical techniques, including measures of frequency, central tendency, dispersion, and position. How exactly you conduct descriptive analysis will depend on what you are looking to find out, but the steps usually involve collecting, cleaning, and finally analyzing data.

In any case, this business analysis process is invaluable when working with data.

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What is Descriptive Research? Definition, Methods, Types and Examples

What is Descriptive Research? Definition, Methods, Types and Examples

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.

Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.

After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.

Table of Contents

What is descriptive research?

If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.

descriptive analysis in research methodology

Importance of descriptive research

Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:

Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.

Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.

Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.

Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.

Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.

When to use descriptive research design?

Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.

Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:

  • In the last two decades, what changes have occurred in patterns of urban gardening in Mumbai?
  • What are the differences in climate change perceptions of farmers in coastal versus inland villages in the Philippines?

Characteristics of descriptive research

Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.

  • Quantitative nature: Some descriptive research types involve quantitative research methods to gather quantifiable information for statistical analysis of the population sample.
  • Qualitative nature: Some descriptive research examples include those using the qualitative research method to describe or explain the research problem.
  • Observational nature: This approach is non-invasive and observational because the study variables remain untouched. Researchers merely observe and report, without introducing interventions that could impact the subject(s).
  • Cross-sectional nature: In descriptive research, different sections belonging to the same group are studied, providing a “snapshot” of sorts.
  • Springboard for further research: The data collected are further studied and analyzed using different research techniques. This approach helps guide the suitable research methods to be employed.

Types of descriptive research

There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.

  • Surveys: This involves collecting data through questionnaires or interviews to gather qualitative and quantitative data.
  • Observational studies: This involves observing and collecting data on a particular population or phenomenon without influencing the study variables or manipulating the conditions. These may be further divided into cohort studies, case studies, and cross-sectional studies:
  • Cohort studies: Also known as longitudinal studies, these studies involve the collection of data over an extended period, allowing researchers to track changes and trends.
  • Case studies: These deal with a single individual, group, or event, which might be rare or unusual.
  • Cross-sectional studies : A researcher collects data at a single point in time, in order to obtain a snapshot of a specific moment.
  • Focus groups: In this approach, a small group of people are brought together to discuss a topic. The researcher moderates and records the group discussion. This can also be considered a “participatory” observational method.
  • Descriptive classification: Relevant to the biological sciences, this type of approach may be used to classify living organisms.

Descriptive research methods

Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.

  • Surveys: This method involves the collection of data through questionnaires or interviews. Surveys may be done online or offline, and the target subjects might be hyper-local, regional, or global.
  • Observational studies: These entail the direct observation of subjects in their natural environment. These include case studies, dealing with a single case or individual, as well as cross-sectional and longitudinal studies, for a glimpse into a population or changes in trends over time, respectively. Participatory observational studies such as focus group discussions may also fall under this method.

Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.

Examples of descriptive research

Now, let’s consider some descriptive research examples.

  • In social sciences, an example could be a study analyzing the demographics of a specific community to understand its socio-economic characteristics.
  • In business, a market research survey aiming to describe consumer preferences would be a descriptive study.
  • In ecology, a researcher might undertake a survey of all the types of monocots naturally occurring in a region and classify them up to species level.

These examples showcase the versatility of descriptive research across diverse fields.

Advantages of descriptive research

There are several advantages to this approach, which every researcher must be aware of. These are as follows:

  • Owing to the numerous descriptive research methods and types, primary data can be obtained in diverse ways and be used for developing a research hypothesis .
  • It is a versatile research method and allows flexibility.
  • Detailed and comprehensive information can be obtained because the data collected can be qualitative or quantitative.
  • It is carried out in the natural environment, which greatly minimizes certain types of bias and ethical concerns.
  • It is an inexpensive and efficient approach, even with large sample sizes

Disadvantages of descriptive research

On the other hand, this design has some drawbacks as well:

  • It is limited in its scope as it does not determine cause-and-effect relationships.
  • The approach does not generate new information and simply depends on existing data.
  • Study variables are not manipulated or controlled, and this limits the conclusions to be drawn.
  • Descriptive research findings may not be generalizable to other populations.
  • Finally, it offers a preliminary understanding rather than an in-depth understanding.

To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.

Frequently asked questions

When should researchers conduct descriptive research.

Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.

What is the difference between descriptive and exploratory research?

Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.

What is the difference between descriptive and experimental research?

Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.

Is descriptive research only for social sciences?

No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.

How important is descriptive research?

The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.

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  • What is descriptive research?

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Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

Analyze your descriptive research

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Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

  • Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

  • Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

  • Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

  • Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

  • Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

  • Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

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Descriptive Statistics | Definitions, Types, Examples

Published on July 9, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.

Table of contents

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, other interesting articles, frequently asked questions about descriptive statistics.

There are 3 main types of descriptive statistics:

  • The distribution concerns the frequency of each value.
  • The central tendency concerns the averages of the values.
  • The variability or dispersion concerns how spread out the values are.

Types of descriptive statistics

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

  • Go to a library
  • Watch a movie at a theater
  • Visit a national park

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A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages. This is called a frequency distribution .

  • Simple frequency distribution table
  • Grouped frequency distribution table
Gender Number
Male 182
Female 235
Other 27

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Library visits in the past year Percent
0–4 6%
5–8 20%
9–12 42%
13–16 24%
17+ 8%

Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

Mean number of library visits
Data set 15, 3, 12, 0, 24, 3
Sum of all values 15 + 3 + 12 + 0 + 24 + 3 = 57
Total number of responses = 6
Mean Divide the sum of values by to find : 57/6 =

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then , the median is the number in the middle. If there are two numbers in the middle, find their mean.

Median number of library visits
Ordered data set 0, 3, 3, 12, 15, 24
Middle numbers 3, 12
Median Find the mean of the two middle numbers: (3 + 12)/2 =

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Mode number of library visits
Ordered data set 0, 3, 3, 12, 15, 24
Mode Find the most frequently occurring response:

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s or SD ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

  • List each score and find their mean.
  • Subtract the mean from each score to get the deviation from the mean.
  • Square each of these deviations.
  • Add up all of the squared deviations.
  • Divide the sum of the squared deviations by N – 1.
  • Find the square root of the number you found.
Raw data Deviation from mean Squared deviation
15 15 – 9.5 = 5.5 30.25
3 3 – 9.5 = -6.5 42.25
12 12 – 9.5 = 2.5 6.25
0 0 – 9.5 = -9.5 90.25
24 24 – 9.5 = 14.5 210.25
3 3 – 9.5 = -6.5 42.25
= 9.5 Sum = 0 Sum of squares = 421.5

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

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descriptive analysis in research methodology

Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

Visits to the library
6
Mean 9.5
Median 7.5
Mode 3
Standard deviation 9.18
Variance 84.3
Range 24

If you were to only consider the mean as a measure of central tendency, your impression of the “middle” of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to outliers , you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read “across” the table to see how the independent and dependent variables relate to each other.

Number of visits to the library in the past year
Group 0–4 5–8 9–12 13–16 17+
Children 32 68 37 23 22
Adults 36 48 43 83 25

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

Visits to the library in the past year (Percentages)
Group 0–4 5–8 9–12 13–16 17+
Children 18% 37% 20% 13% 12% 182
Adults 15% 20% 18% 35% 11% 235

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables . It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

Descriptive statistics: Scatter plot

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Statistical power
  • Pearson correlation
  • Degrees of freedom
  • Statistical significance

Methodology

  • Cluster sampling
  • Stratified sampling
  • Focus group
  • Systematic review
  • Ethnography
  • Double-Barreled Question

Research bias

  • Implicit bias
  • Publication bias
  • Cognitive bias
  • Placebo effect
  • Pygmalion effect
  • Hindsight bias
  • Overconfidence bias

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

  • Distribution refers to the frequencies of different responses.
  • Measures of central tendency give you the average for each response.
  • Measures of variability show you the spread or dispersion of your dataset.
  • Univariate statistics summarize only one variable  at a time.
  • Bivariate statistics compare two variables .
  • Multivariate statistics compare more than two variables .

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  • Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

  • How has the London housing market changed over the past 20 years?
  • Do customers of company X prefer product Y or product Z?
  • What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
  • What are the most popular online news sources among under-18s?
  • How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

  • Describing the demographics of a country or region
  • Gauging public opinion on political and social topics
  • Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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descriptive analysis in research methodology

Quant Analysis 101: Descriptive Statistics

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Kerryn Warren (PhD) | October 2023

If you’re new to quantitative data analysis , one of the first terms you’re likely to hear being thrown around is descriptive statistics. In this post, we’ll unpack the basics of descriptive statistics, using straightforward language and loads of examples . So grab a cup of coffee and let’s crunch some numbers!

Overview: Descriptive Statistics

What are descriptive statistics.

  • Descriptive vs inferential statistics
  • Why the descriptives matter
  • The “ Big 7 ” descriptive statistics
  • Key takeaways

At the simplest level, descriptive statistics summarise and describe relatively basic but essential features of a quantitative dataset – for example, a set of survey responses. They provide a snapshot of the characteristics of your dataset and allow you to better understand, roughly, how the data are “shaped” (more on this later). For example, a descriptive statistic could include the proportion of males and females within a sample or the percentages of different age groups within a population.

Another common descriptive statistic is the humble average (which in statistics-talk is called the mean ). For example, if you undertook a survey and asked people to rate their satisfaction with a particular product on a scale of 1 to 10, you could then calculate the average rating. This is a very basic statistic, but as you can see, it gives you some idea of how this data point is shaped .

Descriptive statistics summarise and describe relatively basic but essential features of a quantitative dataset, including its “shape”

What about inferential statistics?

Now, you may have also heard the term inferential statistics being thrown around, and you’re probably wondering how that’s different from descriptive statistics. Simply put, descriptive statistics describe and summarise the sample itself , while inferential statistics use the data from a sample to make inferences or predictions about a population .

Put another way, descriptive statistics help you understand your dataset , while inferential statistics help you make broader statements about the population , based on what you observe within the sample. If you’re keen to learn more, we cover inferential stats in another post , or you can check out the explainer video below.

Why do descriptive statistics matter?

While descriptive statistics are relatively simple from a mathematical perspective, they play a very important role in any research project . All too often, students skim over the descriptives and run ahead to the seemingly more exciting inferential statistics, but this can be a costly mistake.

The reason for this is that descriptive statistics help you, as the researcher, comprehend the key characteristics of your sample without getting lost in vast amounts of raw data. In doing so, they provide a foundation for your quantitative analysis . Additionally, they enable you to quickly identify potential issues within your dataset – for example, suspicious outliers, missing responses and so on. Just as importantly, descriptive statistics inform the decision-making process when it comes to choosing which inferential statistics you’ll run, as each inferential test has specific requirements regarding the shape of the data.

Long story short, it’s essential that you take the time to dig into your descriptive statistics before looking at more “advanced” inferentials. It’s also worth noting that, depending on your research aims and questions, descriptive stats may be all that you need in any case . So, don’t discount the descriptives! 

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The “Big 7” descriptive statistics

With the what and why out of the way, let’s take a look at the most common descriptive statistics. Beyond the counts, proportions and percentages we mentioned earlier, we have what we call the “Big 7” descriptives. These can be divided into two categories – measures of central tendency and measures of dispersion.

Measures of central tendency

True to the name, measures of central tendency describe the centre or “middle section” of a dataset. In other words, they provide some indication of what a “typical” data point looks like within a given dataset. The three most common measures are:

The mean , which is the mathematical average of a set of numbers – in other words, the sum of all numbers divided by the count of all numbers. 
The median , which is the middlemost number in a set of numbers, when those numbers are ordered from lowest to highest.
The mode , which is the most frequently occurring number in a set of numbers (in any order). Naturally, a dataset can have one mode, no mode (no number occurs more than once) or multiple modes.

To make this a little more tangible, let’s look at a sample dataset, along with the corresponding mean, median and mode. This dataset reflects the service ratings (on a scale of 1 – 10) from 15 customers.

Example set of descriptive stats

As you can see, the mean of 5.8 is the average rating across all 15 customers. Meanwhile, 6 is the median . In other words, if you were to list all the responses in order from low to high, Customer 8 would be in the middle (with their service rating being 6). Lastly, the number 5 is the most frequent rating (appearing 3 times), making it the mode.

Together, these three descriptive statistics give us a quick overview of how these customers feel about the service levels at this business. In other words, most customers feel rather lukewarm and there’s certainly room for improvement. From a more statistical perspective, this also means that the data tend to cluster around the 5-6 mark , since the mean and the median are fairly close to each other.

To take this a step further, let’s look at the frequency distribution of the responses . In other words, let’s count how many times each rating was received, and then plot these counts onto a bar chart.

Example frequency distribution of descriptive stats

As you can see, the responses tend to cluster toward the centre of the chart , creating something of a bell-shaped curve. In statistical terms, this is called a normal distribution .

As you delve into quantitative data analysis, you’ll find that normal distributions are very common , but they’re certainly not the only type of distribution. In some cases, the data can lean toward the left or the right of the chart (i.e., toward the low end or high end). This lean is reflected by a measure called skewness , and it’s important to pay attention to this when you’re analysing your data, as this will have an impact on what types of inferential statistics you can use on your dataset.

Example of skewness

Measures of dispersion

While the measures of central tendency provide insight into how “centred” the dataset is, it’s also important to understand how dispersed that dataset is . In other words, to what extent the data cluster toward the centre – specifically, the mean. In some cases, the majority of the data points will sit very close to the centre, while in other cases, they’ll be scattered all over the place. Enter the measures of dispersion, of which there are three:

Range , which measures the difference between the largest and smallest number in the dataset. In other words, it indicates how spread out the dataset really is.

Variance , which measures how much each number in a dataset varies from the mean (average). More technically, it calculates the average of the squared differences between each number and the mean. A higher variance indicates that the data points are more spread out , while a lower variance suggests that the data points are closer to the mean.

Standard deviation , which is the square root of the variance . It serves the same purposes as the variance, but is a bit easier to interpret as it presents a figure that is in the same unit as the original data . You’ll typically present this statistic alongside the means when describing the data in your research.

Again, let’s look at our sample dataset to make this all a little more tangible.

descriptive analysis in research methodology

As you can see, the range of 8 reflects the difference between the highest rating (10) and the lowest rating (2). The standard deviation of 2.18 tells us that on average, results within the dataset are 2.18 away from the mean (of 5.8), reflecting a relatively dispersed set of data .

For the sake of comparison, let’s look at another much more tightly grouped (less dispersed) dataset.

Example of skewed data

As you can see, all the ratings lay between 5 and 8 in this dataset, resulting in a much smaller range, variance and standard deviation . You might also notice that the data are clustered toward the right side of the graph – in other words, the data are skewed. If we calculate the skewness for this dataset, we get a result of -0.12, confirming this right lean.

In summary, range, variance and standard deviation all provide an indication of how dispersed the data are . These measures are important because they help you interpret the measures of central tendency within context . In other words, if your measures of dispersion are all fairly high numbers, you need to interpret your measures of central tendency with some caution , as the results are not particularly centred. Conversely, if the data are all tightly grouped around the mean (i.e., low dispersion), the mean becomes a much more “meaningful” statistic).

Key Takeaways

We’ve covered quite a bit of ground in this post. Here are the key takeaways:

  • Descriptive statistics, although relatively simple, are a critically important part of any quantitative data analysis.
  • Measures of central tendency include the mean (average), median and mode.
  • Skewness indicates whether a dataset leans to one side or another
  • Measures of dispersion include the range, variance and standard deviation

If you’d like hands-on help with your descriptive statistics (or any other aspect of your research project), check out our private coaching service , where we hold your hand through each step of the research journey. 

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ed

Good day. May I ask about where I would be able to find the statistics cheat sheet?

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Right above you comment 🙂

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Good job. you saved me

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Brilliant and well explained. So much information explained clearly!

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descriptive analysis in research methodology

What is Descriptive Research and How is it Used?

descriptive analysis in research methodology

Introduction

What does descriptive research mean, why would you use a descriptive research design, what are the characteristics of descriptive research, examples of descriptive research, what are the data collection methods in descriptive research, how do you analyze descriptive research data, ensuring validity and reliability in the findings.

Conducting descriptive research offers researchers a way to present phenomena as they naturally occur. Rooted in an open-ended and non-experimental nature, this type of research focuses on portraying the details of specific phenomena or contexts, helping readers gain a clearer understanding of topics of interest.

From businesses gauging customer satisfaction to educators assessing classroom dynamics, the data collected from descriptive research provides invaluable insights across various fields.

This article aims to illuminate the essence, utility, characteristics, and methods associated with descriptive research, guiding those who wish to harness its potential in their respective domains.

descriptive analysis in research methodology

At its core, descriptive research refers to a systematic approach used by researchers to collect, analyze, and present data about real-life phenomena to describe it in its natural context. It primarily aims to describe what exists, based on empirical observations .

Unlike experimental research, where variables are manipulated to observe outcomes, descriptive research deals with the "as-is" scenario to facilitate further research by providing a framework or new insights on which continuing studies can build.

Definition of descriptive research

Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon.

The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.

The difference between descriptive and exploratory research

While both descriptive and exploratory research seek to provide insights into a topic or phenomenon, they differ in their focus. Exploratory research is more about investigating a topic to develop preliminary insights or to identify potential areas of interest.

In contrast, descriptive research offers detailed accounts and descriptions of the observed phenomenon, seeking to paint a full picture of what's happening.

The evolution of descriptive research in academia

Historically, descriptive research has played a foundational role in numerous academic disciplines. Anthropologists, for instance, used this approach to document cultures and societies. Psychologists have employed it to capture behaviors, emotions, and reactions.

Over time, the method has evolved, incorporating technological advancements and adapting to contemporary needs, yet its essence remains rooted in describing a phenomenon or setting as it is.

descriptive analysis in research methodology

Descriptive research serves as a cornerstone in the research landscape for its ability to provide a detailed snapshot of life. Its unique qualities and methods make it an invaluable method for various research purposes. Here's why:

Benefits of obtaining a clear picture

Descriptive research captures the present state of phenomena, offering researchers a detailed reflection of situations. This unaltered representation is crucial for sectors like marketing, where understanding current consumer behavior can shape future strategies.

Facilitating data interpretation

Given its straightforward nature, descriptive research can provide data that's easier to interpret, both for researchers and their audiences. Rather than analyzing complex statistical relationships among variables, researchers present detailed descriptions of their qualitative observations . Researchers can engage in in depth analysis relating to their research question , but audiences can also draw insights from their own interpretations or reflections on potential underlying patterns.

Enhancing the clarity of the research problem

By presenting things as they are, descriptive research can help elucidate ambiguous research questions. A well-executed descriptive study can shine light on overlooked aspects of a problem, paving the way for further investigative research.

Addressing practical problems

In real-world scenarios, it's not always feasible to manipulate variables or set up controlled experiments. For instance, in social sciences, understanding cultural norms without interference is paramount. Descriptive research allows for such non-intrusive insights, ensuring genuine understanding.

Building a foundation for future research

Often, descriptive studies act as stepping stones for more complex research endeavors. By establishing baseline data and highlighting patterns, they create a platform upon which more intricate hypotheses can be built and tested in subsequent studies.

descriptive analysis in research methodology

Descriptive research is distinguished by a set of hallmark characteristics that set it apart from other research methodologies . Recognizing these features can help researchers effectively design, implement , and interpret descriptive studies.

Specificity in the research question

As with all research, descriptive research starts with a well-defined research question aiming to detail a particular phenomenon. The specificity ensures that the study remains focused on gathering relevant data without unnecessary deviations.

Focus on the present situation

While some research methods aim to predict future trends or uncover historical truths, descriptive research is predominantly concerned with the present. It seeks to capture the current state of affairs, such as understanding today's consumer habits or documenting a newly observed phenomenon.

Standardized and structured methodology

To ensure credibility and consistency in results, descriptive research often employs standardized methods. Whether it's using a fixed set of survey questions or adhering to specific observation protocols, this structured approach ensures that data is collected uniformly, making it easier to compare and analyze.

Non-manipulative approach in observation

One of the standout features of descriptive research is its non-invasive nature. Researchers observe and document without influencing the research subject or the environment. This passive stance ensures that the data gathered is a genuine reflection of the phenomenon under study.

Replicability and consistency in results

Due to its structured methodology, findings from descriptive research can often be replicated in different settings or with different samples. This consistency adds to the credibility of the results, reinforcing the validity of the insights drawn from the study.

descriptive analysis in research methodology

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Numerous fields and sectors conduct descriptive research for its versatile and detailed nature. Through its focus on presenting things as they naturally occur, it provides insights into a myriad of scenarios. Here are some tangible examples from diverse domains:

Conducting market research

Businesses often turn to data analysis through descriptive research to understand the demographics of their target market. For instance, a company launching a new product might survey potential customers to understand their age, gender, income level, and purchasing habits, offering valuable data for targeted marketing strategies.

Evaluating employee behaviors

Organizations rely on descriptive research designs to assess the behavior and attitudes of their employees. By conducting observations or surveys , companies can gather data on workplace satisfaction, collaboration patterns, or the impact of a new office layout on productivity.

descriptive analysis in research methodology

Understanding consumer preferences

Brands aiming to understand their consumers' likes and dislikes often use descriptive research. By observing shopping behaviors or conducting product feedback surveys , they can gauge preferences and adjust their offerings accordingly.

Documenting historical patterns

Historians and anthropologists employ descriptive research to identify patterns through analysis of events or cultural practices. For instance, a historian might detail the daily life in a particular era, while an anthropologist might document rituals and ceremonies of a specific tribe.

Assessing student performance

Educational researchers can utilize descriptive studies to understand the effectiveness of teaching methodologies. By observing classrooms or surveying students, they can measure data trends and gauge the impact of a new teaching technique or curriculum on student engagement and performance.

descriptive analysis in research methodology

Descriptive research methods aim to authentically represent situations and phenomena. These techniques ensure the collection of comprehensive and reliable data about the subject of interest.

The most appropriate descriptive research method depends on the research question and resources available for your research study.

Surveys and questionnaires

One of the most familiar tools in the researcher's arsenal, surveys and questionnaires offer a structured means of collecting data from a vast audience. Through carefully designed questions, researchers can obtain standardized responses that lend themselves to straightforward comparison and analysis in quantitative and qualitative research .

Survey research can manifest in various formats, from face-to-face interactions and telephone conversations to digital platforms. While surveys can reach a broad audience and generate quantitative data ripe for statistical analysis, they also come with the challenge of potential biases in design and rely heavily on respondent honesty.

Observations and case studies

Direct or participant observation is a method wherein researchers actively watch and document behaviors or events. A researcher might, for instance, observe the dynamics within a classroom or the behaviors of shoppers in a market setting.

Case studies provide an even deeper dive, focusing on a thorough analysis of a specific individual, group, or event. These methods present the advantage of capturing real-time, detailed data, but they might also be time-intensive and can sometimes introduce observer bias .

Interviews and focus groups

Interviews , whether they follow a structured script or flow more organically, are a powerful means to extract detailed insights directly from participants. On the other hand, focus groups gather multiple participants for discussions, aiming to gather diverse and collective opinions on a particular topic or product.

These methods offer the benefit of deep insights and adaptability in data collection . However, they necessitate skilled interviewers, and focus group settings might see individual opinions being influenced by group dynamics.

Document and content analysis

Here, instead of generating new data, researchers examine existing documents or content . This can range from studying historical records and newspapers to analyzing media content or literature.

Analyzing existing content offers the advantage of accessibility and can provide insights over longer time frames. However, the reliability and relevance of the content are paramount, and researchers must approach this method with a discerning eye.

descriptive analysis in research methodology

Descriptive research data, rich in details and insights, necessitates meticulous analysis to derive meaningful conclusions. The analysis process transforms raw data into structured findings that can be communicated and acted upon.

Qualitative content analysis

For data collected through interviews , focus groups , observations , or open-ended survey questions , qualitative content analysis is a popular choice. This involves examining non-numerical data to identify patterns, themes, or categories.

By coding responses or observations , researchers can identify recurring elements, making it easier to comprehend larger data sets and draw insights.

Using descriptive statistics

When dealing with quantitative data from surveys or experiments, descriptive statistics are invaluable. Measures such as mean, median, mode, standard deviation, and frequency distributions help summarize data sets, providing a snapshot of the overall patterns.

Graphical representations like histograms, pie charts, or bar graphs can further help in visualizing these statistics.

Coding and categorizing the data

Both qualitative and quantitative data often require coding. Coding involves assigning labels to specific responses or behaviors to group similar segments of data. This categorization aids in identifying patterns, especially in vast data sets.

For instance, responses to open-ended questions in a survey can be coded based on keywords or sentiments, allowing for a more structured analysis.

Visual representation through graphs and charts

Visual aids like graphs, charts, and plots can simplify complex data, making it more accessible and understandable. Whether it's showcasing frequency distributions through histograms or mapping out relationships with networks, visual representations can elucidate trends and patterns effectively.

In the realm of research , the credibility of findings is paramount. Without trustworthiness in the results, even the most meticulously gathered data can lose its value. Two cornerstones that bolster the credibility of research outcomes are validity and reliability .

Validity: Measuring the right thing

Validity addresses the accuracy of the research. It seeks to answer the question: Is the research genuinely measuring what it aims to measure? In descriptive research, where the objective is to paint an authentic picture of the current state of affairs, ensuring validity is crucial.

For instance, if a study aims to understand consumer preferences for a product category, the questions posed should genuinely reflect those preferences and not veer into unrelated territories. Multiple forms of validity, including content, criterion, and construct validity, can be examined to ensure that the research instruments and processes are aligned with the research goals.

Reliability: Consistency in findings

Reliability, on the other hand, pertains to the consistency of the research findings. When a study demonstrates reliability, this suggests that others could repeat the study and the outcomes would remain consistent across repetitions.

In descriptive research, factors like the clarity of survey questions , the training of observers , and the standardization of interview protocols play a role in enhancing reliability. Techniques such as test-retest and internal consistency measurements can be employed to assess and improve reliability.

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descriptive analysis in research methodology

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descriptive analysis in research methodology

Home Market Research Research Tools and Apps

Descriptive Analysis: What It Is + Best Research Tips

Descriptive analysis summarize the attributes of a data set. It uses frequency, central tendency, dispersion, & position measurements.

Leading statistical analysis usually begins with a descriptive analysis. It is also known as descriptive analytics or descriptive statistics. It helps you think about how to utilize your data, help you identify exceptions and mistakes, and see how variables are related, putting you in a position to lead future statistical research.

Keeping raw data in a format that makes it easy to understand and analyze, i.e., rearranging, sorting, and changing data so that it can tell you something useful about the data it contains.

Descriptive analysis is one of the most crucial phases of statistical data analysis. It provides you with a conclusion about the distribution of your data and aids in detecting errors and outliers. It lets you spot patterns between variables, preparing you for future statistical analysis.

In this blog, we will discuss descriptive analysis and the best tips for researchers.

What is Descriptive Analysis?

Descriptive analysis is a sort of data research that aids in describing, demonstrating, or helpfully summarizing data points so those patterns may develop that satisfy all of the conditions of the data.

It is the technique of identifying patterns and links by utilizing recent and historical data. Because it identifies patterns and associations without going any further, it is frequently referred to as the most basic data analysis .

When describing change over time, this analysis is beneficial. It utilizes patterns as a jumping-off point for further research to inform decision-making. When done systematically, they are not tricky or tiresome.

Data aggregation and mining are two methods used in descriptive analysis to generate historical data. Information is gathered and sorted in data aggregation to simplify large datasets. Data mining is the next analytical stage, which entails searching the data for patterns and significance. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

Types of Descriptive Analysis

A variety of empirical methodologies support practical descriptive analyses. The most popular descriptive work tools are simple statistics representing core trends and variations (such as means, medians, and modes), which may be highly useful for explaining data.

It is the responsibility of the descriptive researcher to condense the body of data into a form that the audience will find helpful. This data reduction does not mean a situation or phenomenon should be equally weighted in all its components.

Instead, it concentrates on the most critical aspects of the phenomenon as it is and, more generally, the context of real-world practice in which a research study is to be read. The four types of descriptive analysis methods are:

01. Measurements of Frequency

Understanding how often a particular event or reaction is likely to occur is crucial for descriptive analysis. The main goal of frequency measurements is to provide something like a count or a percentage.

02. Measures of Central Tendency

Finding the central (or average) tendency or response is crucial in descriptive analysis. Three standards—mean, median, and mode—are used to calculate central tendency.

03. Measures of Dispersion

At times, understanding how data is distributed throughout a range is crucial. This kind of distribution may be measured using dispersion metrics like range or standard deviation.

04. Measures of Position

Finding a value’s or response’s location concerning other matters is another aspect of descriptive analysis. In this area of knowledge, metrics like quartiles and percentiles are beneficial.

How to Conduct a Descriptive Analysis?

Descriptive analysis is an important phase in data exploration that involves summarizing and describing the primary properties of a dataset. It provides vital insights into the data’s frequency distribution, central tendency, dispersion, and identifying position. It assists researchers and analysts in better understanding their data.

Conducting a descriptive analysis entails several critical phases, which we will discuss below.

Step 1: Data Collection

Before conducting any analysis, you must first collect relevant data. This process involves identifying data sources, selecting appropriate data-collecting methods, and verifying that the data acquired accurately represents the population or topic of interest.

You can collect data through surveys, experiments, observations, existing databases, or other data collection methods .

Step 2: Data Preparation

Data preparation is crucial for ensuring the dataset is clean, consistent, and ready for analysis. This step covers the following tasks:

  • Data Cleaning: Handle missing values, exceptions, and errors in the dataset. Input missing values or develop appropriate statistical techniques for dealing with them.
  • Data Transformation: Convert data into an appropriate format. Examples of this are changing data types, encoding categorical variables, or scaling numerical variables.
  • Data Reduction: For large datasets, try reducing their size by sampling or aggregation to make the analysis more manageable.

Step 3: Apply Methods

In this step, you will analyze and describe the data using a variety of methodologies and procedures. The following are some common descriptive analysis methods:

  • Frequency Distribution Analysis: Create frequency tables or bar charts to show the number or proportion of occurrences for each category for categorical variables.
  • Measures of Central Tendency: Calculate numerical variables’ mean, median, and mode to determine the center or usual value.
  • Measures of Dispersion: Calculate the range, variance, and standard deviation to examine the dispersion or variability of the data.
  • Measures of Position: Identify the position of a single value or its response to others.

Identify which variables are important to your descriptive analysis and research questions. Various methods are used for numerical and categorical variables, so it is essential to distinguish between them.

  • After the data set has been analyzed, researchers may interpret the findings in light of the goals. The analysis was successful if the conclusions were what was anticipated. Otherwise, they must search for weaknesses in their strategy and repeat these processes to get better outcomes.

Step 4: Summary Statistics and Visualization

Descriptive statistics refers to a set of methods for summarizing and describing the main characteristics of a dataset. Summarize the data through statistics and visualization. This step involves the following tasks:

  • Summary Statistics: Summarize your findings clearly and concisely.
  • Data Visualization: Use various charts and plots to visualize the data. Create histograms, box plots, scatter plots, or line charts for numerical data. Use bar charts, pie charts, or stacked bar charts for categorical data.

Best Research Tips to Complete Descriptive Analysis

Moreover, what researchers can do to complete descriptive analysis are:

  • They must specify the purpose of the in-depth analysis , the goals, the direction they will take, the things they must overlook, and the format in which the data must be provided.
  • They must gather data after identifying the goals. This is a critical phase since collecting incorrect data might lead them far from their objective.
  • Cleaning up the data is the next stage. When working with massive data sets, data cleansing may become challenging. The working data set’s noise or irrelevant information might skew the findings. Researchers should clean the data following the specifications for reliable results.
  • Different descriptive techniques are used once the data has been cleaned. In the form of in-depth descriptive summaries, the descriptive analysis highlights the fundamental characteristics of the data.
  • When you’re presenting your analysis to non-technical stakeholders and teams, it might be challenging to communicate the findings. Data visualization helps to complete this task efficiently. To give the results, researchers might use a variety of data visualization approaches, such as charts, pie charts, graphs, and others.

Descriptive analysis is a crucial research approach, regardless of whether the researcher wants to discover causal relationships between variables, explain population patterns, or develop new metrics for basic phenomena. When used correctly, it may significantly contribute to various descriptive and causal research investigations.

Looking at the correct data and evaluating it is pretty valuable for researchers and marketers. You may gather research data and execute complex analysis within the tool with an established research platform like QuestionPro, which enables you to get the insights that matter.

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Research Method

Home » Descriptive Statistics – Types, Methods and Examples

Descriptive Statistics – Types, Methods and Examples

Table of Contents

Descriptive Statistics

Descriptive Statistics

Descriptive statistics is a branch of statistics that deals with the summarization and description of collected data. This type of statistics is used to simplify and present data in a manner that is easy to understand, often through visual or numerical methods. Descriptive statistics is primarily concerned with measures of central tendency, variability, and distribution, as well as graphical representations of data.

Here are the main components of descriptive statistics:

  • Measures of Central Tendency : These provide a summary statistic that represents the center point or typical value of a dataset. The most common measures of central tendency are the mean (average), median (middle value), and mode (most frequent value).
  • Measures of Dispersion or Variability : These provide a summary statistic that represents the spread of values in a dataset. Common measures of dispersion include the range (difference between the highest and lowest values), variance (average of the squared differences from the mean), standard deviation (square root of the variance), and interquartile range (difference between the upper and lower quartiles).
  • Measures of Position : These are used to understand the distribution of values within a dataset. They include percentiles and quartiles.
  • Graphical Representations : Data can be visually represented using various methods like bar graphs, histograms, pie charts, box plots, and scatter plots. These visuals provide a clear, intuitive way to understand the data.
  • Measures of Association : These measures provide insight into the relationships between variables in the dataset, such as correlation and covariance.

Descriptive Statistics Types

Descriptive statistics can be classified into two types:

Measures of Central Tendency

These measures help describe the center point or average of a data set. There are three main types:

  • Mean : The average value of the dataset, obtained by adding all the data points and dividing by the number of data points.
  • Median : The middle value of the dataset, obtained by ordering all data points and picking out the one in the middle (or the average of the two middle numbers if the dataset has an even number of observations).
  • Mode : The most frequently occurring value in the dataset.

Measures of Variability (or Dispersion)

These measures describe the spread or variability of the data points in the dataset. There are four main types:

  • Range : The difference between the largest and smallest values in the dataset.
  • Variance : The average of the squared differences from the mean.
  • Standard Deviation : The square root of the variance, giving a measure of dispersion that is in the same units as the original dataset.
  • Interquartile Range (IQR) : The range between the first quartile (25th percentile) and the third quartile (75th percentile), which provides a measure of variability that is resistant to outliers.

Descriptive Statistics Formulas

Sure, here are some of the most commonly used formulas in descriptive statistics:

Mean (μ or x̄) :

The average of all the numbers in the dataset. It is computed by summing all the observations and dividing by the number of observations.

Formula : μ = Σx/n or x̄ = Σx/n (where Σx is the sum of all observations and n is the number of observations)

The middle value in the dataset when the observations are arranged in ascending or descending order. If there is an even number of observations, the median is the average of the two middle numbers.

The most frequently occurring number in the dataset. There’s no formula for this as it’s determined by observation.

The difference between the highest (max) and lowest (min) values in the dataset.

Formula : Range = max – min

Variance (σ² or s²) :

The average of the squared differences from the mean. Variance is a measure of how spread out the numbers in the dataset are.

Population Variance formula : σ² = Σ(x – μ)² / N Sample Variance formula: s² = Σ(x – x̄)² / (n – 1)

(where x is each individual observation, μ is the population mean, x̄ is the sample mean, N is the size of the population, and n is the size of the sample)

Standard Deviation (σ or s) :

The square root of the variance. It measures the amount of variability or dispersion for a set of data. Population Standard Deviation formula: σ = √σ² Sample Standard Deviation formula: s = √s²

Interquartile Range (IQR) :

The range between the first quartile (Q1, 25th percentile) and the third quartile (Q3, 75th percentile). It measures statistical dispersion, or how far apart the data points are.

Formula : IQR = Q3 – Q1

Descriptive Statistics Methods

Here are some of the key methods used in descriptive statistics:

This method involves arranging data into a table format, making it easier to understand and interpret. Tables often show the frequency distribution of variables.

Graphical Representation

This method involves presenting data visually to help reveal patterns, trends, outliers, or relationships between variables. There are many types of graphs used, such as bar graphs, histograms, pie charts, line graphs, box plots, and scatter plots.

Calculation of Central Tendency Measures

This involves determining the mean, median, and mode of a dataset. These measures indicate where the center of the dataset lies.

Calculation of Dispersion Measures

This involves calculating the range, variance, standard deviation, and interquartile range. These measures indicate how spread out the data is.

Calculation of Position Measures

This involves determining percentiles and quartiles, which tell us about the position of particular data points within the overall data distribution.

Calculation of Association Measures

This involves calculating statistics like correlation and covariance to understand relationships between variables.

Summary Statistics

Often, a collection of several descriptive statistics is presented together in what’s known as a “summary statistics” table. This provides a comprehensive snapshot of the data at a glanc

Descriptive Statistics Examples

Descriptive Statistics Examples are as follows:

Example 1: Student Grades

Let’s say a teacher has the following set of grades for 7 students: 85, 90, 88, 92, 78, 88, and 94. The teacher could use descriptive statistics to summarize this data:

  • Mean (average) : (85 + 90 + 88 + 92 + 78 + 88 + 94)/7 = 88
  • Median (middle value) : First, rearrange the grades in ascending order (78, 85, 88, 88, 90, 92, 94). The median grade is 88.
  • Mode (most frequent value) : The grade 88 appears twice, more frequently than any other grade, so it’s the mode.
  • Range (difference between highest and lowest) : 94 (highest) – 78 (lowest) = 16
  • Variance and Standard Deviation : These would be calculated using the appropriate formulas, providing a measure of the dispersion of the grades.

Example 2: Survey Data

A researcher conducts a survey on the number of hours of TV watched per day by people in a particular city. They collect data from 1,000 respondents and can use descriptive statistics to summarize this data:

  • Mean : Calculate the average hours of TV watched by adding all the responses and dividing by the total number of respondents.
  • Median : Sort the data and find the middle value.
  • Mode : Identify the most frequently reported number of hours watched.
  • Histogram : Create a histogram to visually display the frequency of responses. This could show, for example, that the majority of people watch 2-3 hours of TV per day.
  • Standard Deviation : Calculate this to find out how much variation there is from the average.

Importance of Descriptive Statistics

Descriptive statistics are fundamental in the field of data analysis and interpretation, as they provide the first step in understanding a dataset. Here are a few reasons why descriptive statistics are important:

  • Data Summarization : Descriptive statistics provide simple summaries about the measures and samples you have collected. With a large dataset, it’s often difficult to identify patterns or tendencies just by looking at the raw data. Descriptive statistics provide numerical and graphical summaries that can highlight important aspects of the data.
  • Data Simplification : They simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary, making it easier to understand and interpret the dataset.
  • Identification of Patterns and Trends : Descriptive statistics can help identify patterns and trends in the data, providing valuable insights. Measures like the mean and median can tell you about the central tendency of your data, while measures like the range and standard deviation tell you about the dispersion.
  • Data Comparison : By summarizing data into measures such as the mean and standard deviation, it’s easier to compare different datasets or different groups within a dataset.
  • Data Quality Assessment : Descriptive statistics can help identify errors or outliers in the data, which might indicate issues with data collection or entry.
  • Foundation for Further Analysis : Descriptive statistics are typically the first step in data analysis. They help create a foundation for further statistical or inferential analysis. In fact, advanced statistical techniques often assume that one has first examined their data using descriptive methods.

When to use Descriptive Statistics

They can be used in a wide range of situations, including:

  • Understanding a New Dataset : When you first encounter a new dataset, using descriptive statistics is a useful first step to understand the main characteristics of the data, such as the central tendency, dispersion, and distribution.
  • Data Exploration in Research : In the initial stages of a research project, descriptive statistics can help to explore the data, identify trends and patterns, and generate hypotheses for further testing.
  • Presenting Research Findings : Descriptive statistics can be used to present research findings in a clear and understandable way, often using visual aids like graphs or charts.
  • Monitoring and Quality Control : In fields like business or manufacturing, descriptive statistics are often used to monitor processes, track performance over time, and identify any deviations from expected standards.
  • Comparing Groups : Descriptive statistics can be used to compare different groups or categories within your data. For example, you might want to compare the average scores of two groups of students, or the variance in sales between different regions.
  • Reporting Survey Results : If you conduct a survey, you would use descriptive statistics to summarize the responses, such as calculating the percentage of respondents who agree with a certain statement.

Applications of Descriptive Statistics

Descriptive statistics are widely used in a variety of fields to summarize, represent, and analyze data. Here are some applications:

  • Business : Businesses use descriptive statistics to summarize and interpret data such as sales figures, customer feedback, or employee performance. For instance, they might calculate the mean sales for each month to understand trends, or use graphical representations like bar charts to present sales data.
  • Healthcare : In healthcare, descriptive statistics are used to summarize patient data, such as age, weight, blood pressure, or cholesterol levels. They are also used to describe the incidence and prevalence of diseases in a population.
  • Education : Educators use descriptive statistics to summarize student performance, like average test scores or grade distribution. This information can help identify areas where students are struggling and inform instructional decisions.
  • Social Sciences : Social scientists use descriptive statistics to summarize data collected from surveys, experiments, and observational studies. This can involve describing demographic characteristics of participants, response frequencies to survey items, and more.
  • Psychology : Psychologists use descriptive statistics to describe the characteristics of their study participants and the main findings of their research, such as the average score on a psychological test.
  • Sports : Sports analysts use descriptive statistics to summarize athlete and team performance, such as batting averages in baseball or points per game in basketball.
  • Government : Government agencies use descriptive statistics to summarize data about the population, such as census data on population size and demographics.
  • Finance and Economics : In finance, descriptive statistics can be used to summarize past investment performance or economic data, such as changes in stock prices or GDP growth rates.
  • Quality Control : In manufacturing, descriptive statistics can be used to summarize measures of product quality, such as the average dimensions of a product or the frequency of defects.

Limitations of Descriptive Statistics

While descriptive statistics are a crucial part of data analysis and provide valuable insights about a dataset, they do have certain limitations:

  • Lack of Depth : Descriptive statistics provide a summary of your data, but they can oversimplify the data, resulting in a loss of detail and potentially significant nuances.
  • Vulnerability to Outliers : Some descriptive measures, like the mean, are sensitive to outliers. A single extreme value can significantly skew your mean, making it less representative of your data.
  • Inability to Make Predictions : Descriptive statistics describe what has been observed in a dataset. They don’t allow you to make predictions or generalizations about unobserved data or larger populations.
  • No Insight into Correlations : While some descriptive statistics can hint at potential relationships between variables, they don’t provide detailed insights into the nature or strength of these relationships.
  • No Causality or Hypothesis Testing : Descriptive statistics cannot be used to determine cause and effect relationships or to test hypotheses. For these purposes, inferential statistics are needed.
  • Can Mislead : When used improperly, descriptive statistics can be used to present a misleading picture of the data. For instance, choosing to only report the mean without also reporting the standard deviation or range can hide a large amount of variability in the data.

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Chapter 14 Quantitative Analysis Descriptive Statistics

Numeric data collected in a research project can be analyzed quantitatively using statistical tools in two different ways. Descriptive analysis refers to statistically describing, aggregating, and presenting the constructs of interest or associations between these constructs. Inferential analysis refers to the statistical testing of hypotheses (theory testing). In this chapter, we will examine statistical techniques used for descriptive analysis, and the next chapter will examine statistical techniques for inferential analysis. Much of today’s quantitative data analysis is conducted using software programs such as SPSS or SAS. Readers are advised to familiarize themselves with one of these programs for understanding the concepts described in this chapter.

Data Preparation

In research projects, data may be collected from a variety of sources: mail-in surveys, interviews, pretest or posttest experimental data, observational data, and so forth. This data must be converted into a machine -readable, numeric format, such as in a spreadsheet or a text file, so that they can be analyzed by computer programs like SPSS or SAS. Data preparation usually follows the following steps.

Data coding. Coding is the process of converting data into numeric format. A codebook should be created to guide the coding process. A codebook is a comprehensive document containing detailed description of each variable in a research study, items or measures for that variable, the format of each item (numeric, text, etc.), the response scale for each item (i.e., whether it is measured on a nominal, ordinal, interval, or ratio scale; whether such scale is a five-point, seven-point, or some other type of scale), and how to code each value into a numeric format. For instance, if we have a measurement item on a seven-point Likert scale with anchors ranging from “strongly disagree” to “strongly agree”, we may code that item as 1 for strongly disagree, 4 for neutral, and 7 for strongly agree, with the intermediate anchors in between. Nominal data such as industry type can be coded in numeric form using a coding scheme such as: 1 for manufacturing, 2 for retailing, 3 for financial, 4 for healthcare, and so forth (of course, nominal data cannot be analyzed statistically). Ratio scale data such as age, income, or test scores can be coded as entered by the respondent. Sometimes, data may need to be aggregated into a different form than the format used for data collection. For instance, for measuring a construct such as “benefits of computers,” if a survey provided respondents with a checklist of b enefits that they could select from (i.e., they could choose as many of those benefits as they wanted), then the total number of checked items can be used as an aggregate measure of benefits. Note that many other forms of data, such as interview transcripts, cannot be converted into a numeric format for statistical analysis. Coding is especially important for large complex studies involving many variables and measurement items, where the coding process is conducted by different people, to help the coding team code data in a consistent manner, and also to help others understand and interpret the coded data.

Data entry. Coded data can be entered into a spreadsheet, database, text file, or directly into a statistical program like SPSS. Most statistical programs provide a data editor for entering data. However, these programs store data in their own native format (e.g., SPSS stores data as .sav files), which makes it difficult to share that data with other statistical programs. Hence, it is often better to enter data into a spreadsheet or database, where they can be reorganized as needed, shared across programs, and subsets of data can be extracted for analysis. Smaller data sets with less than 65,000 observations and 256 items can be stored in a spreadsheet such as Microsoft Excel, while larger dataset with millions of observations will require a database. Each observation can be entered as one row in the spreadsheet and each measurement item can be represented as one column. The entered data should be frequently checked for accuracy, via occasional spot checks on a set of items or observations, during and after entry. Furthermore, while entering data, the coder should watch out for obvious evidence of bad data, such as the respondent selecting the “strongly agree” response to all items irrespective of content, including reverse-coded items. If so, such data can be entered but should be excluded from subsequent analysis.

Missing values. Missing data is an inevitable part of any empirical data set. Respondents may not answer certain questions if they are ambiguously worded or too sensitive. Such problems should be detected earlier during pretests and corrected before the main data collection process begins. During data entry, some statistical programs automatically treat blank entries as missing values, while others require a specific numeric value such as -1 or 999 to be entered to denote a missing value. During data analysis, the default mode of handling missing values in most software programs is to simply drop the entire observation containing even a single missing value, in a technique called listwise deletion . Such deletion can significantly shrink the sample size and make it extremely difficult to detect small effects. Hence, some software programs allow the option of replacing missing values with an estimated value via a process called imputation . For instance, if the missing value is one item in a multi-item scale, the imputed value may be the average of the respondent’s responses to remaining items on that scale. If the missing value belongs to a single-item scale, many researchers use the average of other respondent’s responses to that item as the imputed value. Such imputation may be biased if the missing value is of a systematic nature rather than a random nature. Two methods that can produce relatively unbiased estimates for imputation are the maximum likelihood procedures and multiple imputation methods, both of which are supported in popular software programs such as SPSS and SAS.

Data transformation. Sometimes, it is necessary to transform data values before they can be meaningfully interpreted. For instance, reverse coded items, where items convey the opposite meaning of that of their underlying construct, should be reversed (e.g., in a 1-7 interval scale, 8 minus the observed value will reverse the value) before they can be compared or combined with items that are not reverse coded. Other kinds of transformations may include creating scale measures by adding individual scale items, creating a weighted index from a set of observed measures, and collapsing multiple values into fewer categories (e.g., collapsing incomes into income ranges).

Univariate Analysis

Univariate analysis, or analysis of a single variable, refers to a set of statistical techniques that can describe the general properties of one variable. Univariate statistics include: (1) frequency distribution, (2) central tendency, and (3) dispersion. The frequency distribution of a variable is a summary of the frequency (or percentages) of individual values or ranges of values for that variable. For instance, we can measure how many times a sample of respondents attend religious services (as a measure of their “religiosity”) using a categorical scale: never, once per year, several times per year, about once a month, several times per month, several times per week, and an optional category for “did not answer.” If we count the number (or percentage) of observations within each category (except “did not answer” which is really a missing value rather than a category), and display it in the form of a table as shown in Figure 14.1, what we have is a frequency distribution. This distribution can also be depicted in the form of a bar chart, as shown on the right panel of Figure 14.1, with the horizontal axis representing each category of that variable and the vertical axis representing the frequency or percentage of observations within each category.

descriptive analysis in research methodology

Figure 14.1. Frequency distribution of religiosity.

With very large samples where observations are independent and random, the frequency distribution tends to follow a plot that looked like a bell-shaped curve (a smoothed bar chart of the frequency distribution) similar to that shown in Figure 14.2, where most observations are clustered toward the center of the range of values, and fewer and fewer observations toward the extreme ends of the range. Such a curve is called a normal distribution.

Central tendency is an estimate of the center of a distribution of values. There are three major estimates of central tendency: mean, median, and mode. The arithmetic mean (often simply called the “mean”) is the simple average of all values in a given distribution. Consider a set of eight test scores: 15, 22, 21, 18, 36, 15, 25, 15. The arithmetic mean of these values is (15 + 20 + 21 + 20 + 36 + 15 + 25 + 15)/8 = 20.875. Other types of means include geometric mean (n th root of the product of n numbers in a distribution) and harmonic mean (the reciprocal of the arithmetic means of the reciprocal of each value in a distribution), but these means are not very popular for statistical analysis of social research data.

The second measure of central tendency, the median , is the middle value within a range of values in a distribution. This is computed by sorting all values in a distribution in increasing order and selecting the middle value. In case there are two middle values (if there is an even number of values in a distribution), the average of the two middle values represent the median. In the above example, the sorted values are: 15, 15, 15, 18, 22, 21, 25, 36. The two middle values are 18 and 22, and hence the median is (18 + 22)/2 = 20.

Lastly, the mode is the most frequently occurring value in a distribution of values. In the previous example, the most frequently occurring value is 15, which is the mode of the above set of test scores. Note that any value that is estimated from a sample, such as mean, median, mode, or any of the later estimates are called a statistic .

Dispersion refers to the way values are spread around the central tendency, for example, how tightly or how widely are the values clustered around the mean. Two common measures of dispersion are the range and standard deviation. The range is the difference between the highest and lowest values in a distribution. The range in our previous example is 36-15 = 21.

The range is particularly sensitive to the presence of outliers. For instance, if the highest value in the above distribution was 85 and the other vales remained the same, the range would be 85-15 = 70. Standard deviation , the second measure of dispersion, corrects for such outliers by using a formula that takes into account how close or how far each value from the distribution mean:

descriptive analysis in research methodology

Figure 14.2. Normal distribution.

descriptive analysis in research methodology

Table 14.1. Hypothetical data on age and self-esteem.

The two variables in this dataset are age (x) and self-esteem (y). Age is a ratio-scale variable, while self-esteem is an average score computed from a multi-item self-esteem scale measured using a 7-point Likert scale, ranging from “strongly disagree” to “strongly agree.” The histogram of each variable is shown on the left side of Figure 14.3. The formula for calculating bivariate correlation is:

descriptive analysis in research methodology

Figure 14.3. Histogram and correlation plot of age and self-esteem.

After computing bivariate correlation, researchers are often interested in knowing whether the correlation is significant (i.e., a real one) or caused by mere chance. Answering such a question would require testing the following hypothesis:

H 0 : r = 0

H 1 : r ≠ 0

H 0 is called the null hypotheses , and H 1 is called the alternative hypothesis (sometimes, also represented as H a ). Although they may seem like two hypotheses, H 0 and H 1 actually represent a single hypothesis since they are direct opposites of each other. We are interested in testing H 1 rather than H 0 . Also note that H 1 is a non-directional hypotheses since it does not specify whether r is greater than or less than zero. Directional hypotheses will be specified as H 0 : r ≤ 0; H 1 : r > 0 (if we are testing for a positive correlation). Significance testing of directional hypothesis is done using a one-tailed t-test, while that for non-directional hypothesis is done using a two-tailed t-test.

In statistical testing, the alternative hypothesis cannot be tested directly. Rather, it is tested indirectly by rejecting the null hypotheses with a certain level of probability. Statistical testing is always probabilistic, because we are never sure if our inferences, based on sample data, apply to the population, since our sample never equals the population. The probability that a statistical inference is caused pure chance is called the p-value . The p-value is compared with the significance level (α), which represents the maximum level of risk that we are willing to take that our inference is incorrect. For most statistical analysis, α is set to 0.05. A p-value less than α=0.05 indicates that we have enough statistical evidence to reject the null hypothesis, and thereby, indirectly accept the alternative hypothesis. If p>0.05, then we do not have adequate statistical evidence to reject the null hypothesis or accept the alternative hypothesis.

The easiest way to test for the above hypothesis is to look up critical values of r from statistical tables available in any standard text book on statistics or on the Internet (most software programs also perform significance testing). The critical value of r depends on our desired significance level (α = 0.05), the degrees of freedom (df), and whether the desired test is a one-tailed or two-tailed test. The degree of freedom is the number of values that can vary freely in any calculation of a statistic. In case of correlation, the df simply equals n – 2, or for the data in Table 14.1, df is 20 – 2 = 18. There are two different statistical tables for one-tailed and two -tailed test. In the two -tailed table, the critical value of r for α = 0.05 and df = 18 is 0.44. For our computed correlation of 0.79 to be significant, it must be larger than the critical value of 0.44 or less than -0.44. Since our computed value of 0.79 is greater than 0.44, we conclude that there is a significant correlation between age and self-esteem in our data set, or in other words, the odds are less than 5% that this correlation is a chance occurrence. Therefore, we can reject the null hypotheses that r ≤ 0, which is an indirect way of saying that the alternative hypothesis r > 0 is probably correct.

Most research studies involve more than two variables. If there are n variables, then we will have a total of n*(n-1)/2 possible correlations between these n variables. Such correlations are easily computed using a software program like SPSS, rather than manually using the formula for correlation (as we did in Table 14.1), and represented using a correlation matrix, as shown in Table 14.2. A correlation matrix is a matrix that lists the variable names along the first row and the first column, and depicts bivariate correlations between pairs of variables in the appropriate cell in the matrix. The values along the principal diagonal (from the top left to the bottom right corner) of this matrix are always 1, because any variable is always perfectly correlated with itself. Further, since correlations are non-directional, the correlation between variables V1 and V2 is the same as that between V2 and V1. Hence, the lower triangular matrix (values below the principal diagonal) is a mirror reflection of the upper triangular matrix (values above the principal diagonal), and therefore, we often list only the lower triangular matrix for simplicity. If the correlations involve variables measured using interval scales, then this specific type of correlations are called Pearson product moment correlations .

Another useful way of presenting bivariate data is cross-tabulation (often abbreviated to cross-tab, and sometimes called more formally as a contingency table). A cross-tab is a table that describes the frequency (or percentage) of all combinations of two or more nominal or categorical variables. As an example, let us assume that we have the following observations of gender and grade for a sample of 20 students, as shown in Figure 14.3. Gender is a nominal variable (male/female or M/F), and grade is a categorical variable with three levels (A, B, and C). A simple cross-tabulation of the data may display the joint distribution of gender and grades (i.e., how many students of each gender are in each grade category, as a raw frequency count or as a percentage) in a 2 x 3 matrix. This matrix will help us see if A, B, and C grades are equally distributed across male and female students. The cross-tab data in Table 14.3 shows that the distribution of A grades is biased heavily toward female students: in a sample of 10 male and 10 female students, five female students received the A grade compared to only one male students. In contrast, the distribution of C grades is biased toward male students: three male students received a C grade, compared to only one female student. However, the distribution of B grades was somewhat uniform, with six male students and five female students. The last row and the last column of this table are called marginal totals because they indicate the totals across each category and displayed along the margins of the table.

descriptive analysis in research methodology

Table 14.2. A hypothetical correlation matrix for eight variables.

descriptive analysis in research methodology

Table 14.3. Example of cross-tab analysis.

Although we can see a distinct pattern of grade distribution between male and female students in Table 14.3, is this pattern real or “statistically significant”? In other words, do the above frequency counts differ from that that may be expected from pure chance? To answer this question, we should compute the expected count of observation in each cell of the 2 x 3 cross-tab matrix. This is done by multiplying the marginal column total and the marginal row total for each cell and dividing it by the total number of observations. For example, for the male/A grade cell, expected count = 5 * 10 / 20 = 2.5. In other words, we were expecting 2.5 male students to receive an A grade, but in reality, only one student received the A grade. Whether this difference between expected and actual count is significant can be tested using a chi-square test . The chi-square statistic can be computed as the average difference between observed and expected counts across all cells. We can then compare this number to the critical value associated with a desired probability level (p < 0.05) and the degrees of freedom, which is simply (m-1)*(n-1), where m and n are the number of rows and columns respectively. In this example, df = (2 – 1) * (3 – 1) = 2. From standard chi-square tables in any statistics book, the critical chi-square value for p=0.05 and df=2 is 5.99. The computed chi -square value, based on our observed data, is 1.00, which is less than the critical value. Hence, we must conclude that the observed grade pattern is not statistically different from the pattern that can be expected by pure chance.

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Understanding Descriptive Method in Research: A Comprehensive Guide

Research methods: the descriptive method.

Research methods play a crucial role in the field of academia, providing a systematic approach to investigating phenomena. One such method, the descriptive method, serves as a foundational tool for researchers across various disciplines. In this comprehensive guide, we delve into the intricacies of the descriptive method in research, exploring its significance, applications, and best practices. By understanding the descriptive method, researchers can effectively summarize, organize, and interpret data to draw meaningful conclusions. Whether you are a seasoned researcher or a novice in the field, this guide aims to demystify the complexities of descriptive research, offering valuable insights to enhance your research endeavors. Join us on this enlightening journey as we unravel the nuances of the descriptive method and equip you with the knowledge and skills to conduct research that is both rigorous and insightful.

Descriptive Research in Focus

Descriptive research is a fundamental research design that aims to meticulously describe the characteristics of a population or phenomenon under study. In this section, we delve deeper into the methods and approaches commonly employed in descriptive research.

Observational Techniques in Descriptive Research

Observational techniques play a pivotal role in descriptive research by allowing researchers to directly observe subjects in their natural settings without any interference. By keenly observing and documenting behaviors, events, or other relevant aspects, researchers can gain valuable insights into natural occurrences. This method is particularly useful for studying human behavior, social interactions, and environmental dynamics.

Data Collection Methods in Descriptive Research

Descriptive research heavily relies on a variety of data collection methods to gather information systematically. Surveys, interviews, questionnaires, and analysis of existing records are among the commonly used techniques. Surveys and questionnaires are effective tools for collecting data from a large and diverse sample of participants, providing a broad perspective on the subject of study. On the other hand, interviews offer a more personalized approach, allowing researchers to delve deeply into the thoughts, opinions, and experiences of a smaller group of individuals.

Comparison with Other Research Methodologies

Descriptive research is distinct from exploratory and explanatory research methodologies. While exploratory research aims to uncover new insights and formulate hypotheses, and explanatory research seeks to establish causal relationships between variables, descriptive research focuses on portraying existing phenomena accurately. By emphasizing the depiction of reality as it is, descriptive research offers valuable insights into the characteristics and behaviors of a population or phenomenon without altering any variables.

Moreover, descriptive research can be further categorized into subtypes such as case studies, surveys, and correlational studies, each offering unique advantages in different research contexts. Case studies provide an in-depth analysis of a particular individual, group, or event, offering rich qualitative data. Surveys, on the other hand, enable researchers to collect quantitative data from a large sample, facilitating generalizations about a population. Correlational studies explore the relationships between variables, shedding light on potential associations.

Descriptive research serves as a cornerstone in research methodology, offering a comprehensive understanding of subjects through meticulous observation techniques and diverse data collection methods. By providing detailed descriptions and insights into various phenomena, descriptive research contributes significantly to the body of knowledge in numerous fields.

Applications and Benefits of Descriptive Research

Descriptive research is a type of research that aims to describe characteristics of a population or phenomenon being studied. It focuses on answering questions of who, what, where, when, and how. This section explores the various applications and benefits of descriptive research.

Real-world Examples of Descriptive Research:

  • Descriptive research is commonly used in fields such as marketing, sociology, education, and psychology. For example, a marketing team may conduct a survey to describe the demographics and preferences of their target market. This information can help in developing targeted marketing strategies.

Advantages and Limitations of Descriptive Research:

  • Advantages of descriptive research include providing a snapshot of a phenomenon, identifying patterns and trends, and being relatively easy to conduct. However, limitations may include a lack of depth in understanding causality and the potential for bias in data collection.

Importance of Descriptive Research in Decision Making:

  • Descriptive research plays a crucial role in decision-making processes. By providing valuable insights into the characteristics of a population or phenomenon, it helps organizations make informed decisions. For instance, a company may use descriptive research to understand customer satisfaction levels and make improvements to their products or services.

Types of Descriptive Research:

  • Descriptive research can take various forms, including observational studies, surveys, and case studies. Observational studies involve observing and describing behavior without influencing it. Surveys collect data from a sample of individuals to describe characteristics of a larger population. Case studies focus on in-depth analysis of a single individual, group, or event.

Challenges in Descriptive Research:

  • While descriptive research offers valuable insights, researchers may face challenges such as ensuring the representativeness of the sample, minimizing bias in data collection, and interpreting the results accurately. Overcoming these challenges is essential to ensure the reliability and validity of the research findings.

Future Trends in Descriptive Research:

  • With advancements in technology and data analytics, the field of descriptive research is evolving. Big data analytics and machine learning techniques are being increasingly used to analyze large datasets and extract meaningful insights. The future of descriptive research lies in leveraging these tools to gain a deeper understanding of complex phenomena.

Descriptive research serves as a foundational method in various disciplines, providing valuable information for decision-making and problem-solving. Understanding its applications, advantages, limitations, and evolving trends is essential for researchers and practitioners alike.

Implementing Descriptive Research in Practice

Descriptive research is a crucial aspect of any research project as it helps in providing a detailed account of a situation. In this section, we will explore the steps to conduct descriptive research, best practices and considerations, as well as the tools and technologies that can aid in conducting descriptive research.

Steps to Conduct Descriptive Research:

  • Define the research objectives clearly.
  • Choose the appropriate research design.
  • Select the sample size and sampling technique.
  • Collect data through surveys, observations, or existing sources.
  • Analyze the data using statistical tools.
  • Summarize the findings and draw conclusions.

Best Practices and Considerations:

  • Ensure the research is relevant and focused.
  • Use reliable sources for data collection.
  • Maintain objectivity throughout the research process.
  • Validate the findings through peer reviews.
  • Ethical considerations in data collection and analysis.
  • Consider the limitations of descriptive research, such as the inability to establish causation.

Tools and Technologies for Descriptive Research:

  • Statistical software like SPSS, SAS, or R for data analysis.
  • Survey tools such as SurveyMonkey or Google Forms for data collection.
  • Data visualization tools like Tableau or Power BI for presenting findings.
  • Online databases and repositories for accessing secondary data.
  • Utilize text mining and sentiment analysis tools for deeper insights from textual data.
  • Consider the use of geographic information systems (GIS) for spatial analysis in descriptive research.

Descriptive research is valuable in providing a snapshot of a particular phenomenon or situation, allowing researchers to explore characteristics, behaviors, and relationships within a specific context. Researchers should also consider the importance of data quality, ensuring that the information collected is accurate and reliable. Additionally, incorporating a mix of quantitative and qualitative data can offer a comprehensive understanding of the research subject.

When conducting descriptive research, it is essential to consider the various types of research designs available, such as cross-sectional, longitudinal, or case study designs, depending on the research objectives and the nature of the phenomenon under study. Each design has its strengths and limitations, and researchers must choose the most appropriate design to address their research questions effectively.

Moreover, researchers should pay attention to the sampling techniques employed in descriptive research to ensure the sample is representative of the population being studied. Common sampling methods include random sampling, stratified sampling, and convenience sampling, each suitable for different research contexts.

In addition to traditional data collection methods like surveys and observations, researchers can also leverage advanced technologies such as wearable devices, sensors, and social media analytics to gather real-time data and insights. These innovative approaches can provide richer and more dynamic data, enhancing the depth of descriptive research findings.

Furthermore, the integration of data visualization techniques in descriptive research can aid in presenting complex data in a visually appealing and understandable manner. Visual representations such as charts, graphs, and interactive dashboards can facilitate the communication of research findings to diverse audiences, making the information more accessible and engaging.

Implementing descriptive research in practice requires careful planning, adherence to best practices, and utilization of appropriate tools and technologies. By following a systematic approach, researchers can uncover valuable insights, identify patterns and trends, and contribute meaningfully to their respective fields of study. Descriptive research serves as a foundational method for exploring phenomena, generating hypotheses, and informing decision-making processes, making it an indispensable tool in the research toolkit.

In the realm of research, mastering the art of crafting a compelling scientific abstract is paramount for effectively communicating the essence of your study. As PhD students venture into the world of academic publications, understanding the key components of an abstract – from the background to the conclusions – is crucial. The webpage at. Avidnote serves as a valuable resource, offering insightful guidance on structuring abstracts and providing practical tips for clarity and engagement. By honing this skill through practice and analysis of existing abstracts, researchers can enhance their ability to succinctly convey the significance of their work. Embrace the opportunity to refine your abstract writing skills and captivate your audience with precision and conciseness. Visit the provided link to delve deeper into the art of crafting impactful scientific abstracts.

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Descriptive Statistics: Reporting the Answers to the 5 Basic Questions of Who, What, Why, When, Where, and a Sixth, So What?

Affiliation.

  • 1 From the Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, Texas.
  • PMID: 28891910
  • DOI: 10.1213/ANE.0000000000002471

Descriptive statistics are specific methods basically used to calculate, describe, and summarize collected research data in a logical, meaningful, and efficient way. Descriptive statistics are reported numerically in the manuscript text and/or in its tables, or graphically in its figures. This basic statistical tutorial discusses a series of fundamental concepts about descriptive statistics and their reporting. The mean, median, and mode are 3 measures of the center or central tendency of a set of data. In addition to a measure of its central tendency (mean, median, or mode), another important characteristic of a research data set is its variability or dispersion (ie, spread). In simplest terms, variability is how much the individual recorded scores or observed values differ from one another. The range, standard deviation, and interquartile range are 3 measures of variability or dispersion. The standard deviation is typically reported for a mean, and the interquartile range for a median. Testing for statistical significance, along with calculating the observed treatment effect (or the strength of the association between an exposure and an outcome), and generating a corresponding confidence interval are 3 tools commonly used by researchers (and their collaborating biostatistician or epidemiologist) to validly make inferences and more generalized conclusions from their collected data and descriptive statistics. A number of journals, including Anesthesia & Analgesia, strongly encourage or require the reporting of pertinent confidence intervals. A confidence interval can be calculated for virtually any variable or outcome measure in an experimental, quasi-experimental, or observational research study design. Generally speaking, in a clinical trial, the confidence interval is the range of values within which the true treatment effect in the population likely resides. In an observational study, the confidence interval is the range of values within which the true strength of the association between the exposure and the outcome (eg, the risk ratio or odds ratio) in the population likely resides. There are many possible ways to graphically display or illustrate different types of data. While there is often latitude as to the choice of format, ultimately, the simplest and most comprehensible format is preferred. Common examples include a histogram, bar chart, line chart or line graph, pie chart, scatterplot, and box-and-whisker plot. Valid and reliable descriptive statistics can answer basic yet important questions about a research data set, namely: "Who, What, Why, When, Where, How, How Much?"

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Descriptive Statistics

Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.

Descriptive statistics are typically distinguished from inferential statistics . With descriptive statistics you are simply describing what is or what the data shows. With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what’s going on in our data.

Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures. Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average. This single number is simply the number of hits divided by the number of times at bat (reported to three significant digits). A batter who is hitting .333 is getting a hit one time in every three at bats. One batting .250 is hitting one time in four. The single number describes a large number of discrete events. Or, consider the scourge of many students, the Grade Point Average (GPA). This single number describes the general performance of a student across a potentially wide range of course experiences.

Every time you try to describe a large set of observations with a single indicator you run the risk of distorting the original data or losing important detail. The batting average doesn’t tell you whether the batter is hitting home runs or singles. It doesn’t tell whether she’s been in a slump or on a streak. The GPA doesn’t tell you whether the student was in difficult courses or easy ones, or whether they were courses in their major field or in other disciplines. Even given these limitations, descriptive statistics provide a powerful summary that may enable comparisons across people or other units.

Univariate Analysis

Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at:

  • the distribution
  • the central tendency
  • the dispersion

In most situations, we would describe all three of these characteristics for each of the variables in our study.

The Distribution

The distribution is a summary of the frequency of individual values or ranges of values for a variable. The simplest distribution would list every value of a variable and the number of persons who had each value. For instance, a typical way to describe the distribution of college students is by year in college, listing the number or percent of students at each of the four years. Or, we describe gender by listing the number or percent of males and females. In these cases, the variable has few enough values that we can list each one and summarize how many sample cases had the value. But what do we do for a variable like income or GPA? With these variables there can be a large number of possible values, with relatively few people having each one. In this case, we group the raw scores into categories according to ranges of values. For instance, we might look at GPA according to the letter grade ranges. Or, we might group income into four or five ranges of income values.

CategoryPercent
Under 35 years old9%
36–4521%
46–5545%
56–6519%
66+6%

One of the most common ways to describe a single variable is with a frequency distribution . Depending on the particular variable, all of the data values may be represented, or you may group the values into categories first (e.g. with age, price, or temperature variables, it would usually not be sensible to determine the frequencies for each value. Rather, the value are grouped into ranges and the frequencies determined.). Frequency distributions can be depicted in two ways, as a table or as a graph. The table above shows an age frequency distribution with five categories of age ranges defined. The same frequency distribution can be depicted in a graph as shown in Figure 1. This type of graph is often referred to as a histogram or bar chart.

Distributions may also be displayed using percentages. For example, you could use percentages to describe the:

  • percentage of people in different income levels
  • percentage of people in different age ranges
  • percentage of people in different ranges of standardized test scores

Central Tendency

The central tendency of a distribution is an estimate of the “center” of a distribution of values. There are three major types of estimates of central tendency:

The Mean or average is probably the most commonly used method of describing central tendency. To compute the mean all you do is add up all the values and divide by the number of values. For example, the mean or average quiz score is determined by summing all the scores and dividing by the number of students taking the exam. For example, consider the test score values:

The sum of these 8 values is 167 , so the mean is 167/8 = 20.875 .

The Median is the score found at the exact middle of the set of values. One way to compute the median is to list all scores in numerical order, and then locate the score in the center of the sample. For example, if there are 500 scores in the list, score #250 would be the median. If we order the 8 scores shown above, we would get:

There are 8 scores and score #4 and #5 represent the halfway point. Since both of these scores are 20 , the median is 20 . If the two middle scores had different values, you would have to interpolate to determine the median.

The Mode is the most frequently occurring value in the set of scores. To determine the mode, you might again order the scores as shown above, and then count each one. The most frequently occurring value is the mode. In our example, the value 15 occurs three times and is the model. In some distributions there is more than one modal value. For instance, in a bimodal distribution there are two values that occur most frequently.

Notice that for the same set of 8 scores we got three different values ( 20.875 , 20 , and 15 ) for the mean, median and mode respectively. If the distribution is truly normal (i.e. bell-shaped), the mean, median and mode are all equal to each other.

Dispersion refers to the spread of the values around the central tendency. There are two common measures of dispersion, the range and the standard deviation. The range is simply the highest value minus the lowest value. In our example distribution, the high value is 36 and the low is 15 , so the range is 36 - 15 = 21 .

The Standard Deviation is a more accurate and detailed estimate of dispersion because an outlier can greatly exaggerate the range (as was true in this example where the single outlier value of 36 stands apart from the rest of the values. The Standard Deviation shows the relation that set of scores has to the mean of the sample. Again lets take the set of scores:

to compute the standard deviation, we first find the distance between each value and the mean. We know from above that the mean is 20.875 . So, the differences from the mean are:

Notice that values that are below the mean have negative discrepancies and values above it have positive ones. Next, we square each discrepancy:

Now, we take these “squares” and sum them to get the Sum of Squares (SS) value. Here, the sum is 350.875 . Next, we divide this sum by the number of scores minus 1 . Here, the result is 350.875 / 7 = 50.125 . This value is known as the variance . To get the standard deviation, we take the square root of the variance (remember that we squared the deviations earlier). This would be SQRT(50.125) = 7.079901129253 .

Although this computation may seem convoluted, it’s actually quite simple. To see this, consider the formula for the standard deviation:

  • X is each score,
  • X̄ is the mean (or average),
  • n is the number of values,
  • Σ means we sum across the values.

In the top part of the ratio, the numerator, we see that each score has the mean subtracted from it, the difference is squared, and the squares are summed. In the bottom part, we take the number of scores minus 1 . The ratio is the variance and the square root is the standard deviation. In English, we can describe the standard deviation as:

the square root of the sum of the squared deviations from the mean divided by the number of scores minus one.

Although we can calculate these univariate statistics by hand, it gets quite tedious when you have more than a few values and variables. Every statistics program is capable of calculating them easily for you. For instance, I put the eight scores into SPSS and got the following table as a result:

MetricValue
N8
Mean20.8750
Median20.0000
Mode15.00
Standard Deviation7.0799
Variance50.1250
Range21.00

which confirms the calculations I did by hand above.

The standard deviation allows us to reach some conclusions about specific scores in our distribution. Assuming that the distribution of scores is normal or bell-shaped (or close to it!), the following conclusions can be reached:

  • approximately 68% of the scores in the sample fall within one standard deviation of the mean
  • approximately 95% of the scores in the sample fall within two standard deviations of the mean
  • approximately 99% of the scores in the sample fall within three standard deviations of the mean

For instance, since the mean in our example is 20.875 and the standard deviation is 7.0799 , we can from the above statement estimate that approximately 95% of the scores will fall in the range of 20.875-(2*7.0799) to 20.875+(2*7.0799) or between 6.7152 and 35.0348 . This kind of information is a critical stepping stone to enabling us to compare the performance of an individual on one variable with their performance on another, even when the variables are measured on entirely different scales.

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  • > Statistics

An Overview of Descriptive Analysis

  • Ayush Singh Rawat
  • Mar 31, 2021

An Overview of Descriptive Analysis title banner

Nowadays, Big Data and Data Science have become high volume keywords. They tend to become extensively researched and this makes this data to be processed and studied with scrutiny. One of the techniques to analyse this data is Descriptive Analysis.

This data needs to be analysed to provide great insights and influential trends that allows the next batch of content to be made in accordance to the general population’s liking or dis-liking.

Introduction

The conversion of raw data into a form that will make it easy to understand & interpret, ie., rearranging, ordering, and manipulating data to provide insightful information about the provided data.

Descriptive Analysis is the type of analysis of data that helps describe, show or summarize data points in a constructive way such that patterns might emerge that fulfill every condition of the data.

It is one of the most important steps for conducting statistical data analysis . It gives you a conclusion of the distribution of your data, helps you detect typos and outliers, and enables you to identify similarities among variables, thus making you ready for conducting further statistical analyses.   

Techniques for Descriptive Analysis

Data aggregation and data mining are two techniques used in descriptive analysis to churn out historical data. In Data aggregation, data is first collected and then sorted in order to make the datasets more manageable.

Descriptive techniques often include constructing tables of quantiles and means, methods of dispersion such as variance or standard deviation, and cross-tabulations or "crosstabs" that can be used to carry out many disparate hypotheses. These hypotheses often highlight differences among subgroups.

Measures like segregation, discrimination, and inequality are studied using specialised descriptive techniques. Discrimination is measured with the help of audit studies or decomposition methods. More segregation on the basis of type or inequality of outcomes need not be wholly good or bad in itself, but it is often considered a marker of unjust social processes; accurate measurement of the different steps across space and time is a prerequisite to understanding these processes.

A table of means by subgroup is used to show important differences across subgroups, which mostly results in inference and conclusions being made. When we notice a gap in earnings, for example, we naturally tend to extrapolate reasons for those patterns complying. 

But this also enters the province of measuring impacts which requires the use of different techniques. Often, random variation causes difference in means, and statistical inference is required to determine whether observed differences could happen merely due to chance.

A crosstab or two-way tabulation is supposed to show the proportions of components with unique values for each of two variables available, or cell proportions. For example, we might tabulate the proportion of the population that has a high school degree and also receives food or cash assistance, meaning a crosstab of education versus receipt of assistance is supposed to be made. 

Then we might also want to examine row proportions, or the fractions in each education group who receive food or cash assistance, perhaps seeing assistance levels dip extraordinarily at higher education levels.

Column proportions can also be examined, for the fraction of population with different levels of education, but this is the opposite from any causal effects. We might come across a surprisingly high number or proportion of recipients with a college education, but this might be a result of larger numbers of people being college graduates than people who have less than a high school degree.

(Must check: 4 Types of Data in Statistics )

Types of Descriptive Analysis

Descriptive analysis can be categorized into four types which are measures of frequency, central tendency, dispersion or variation, and position. These methods are optimal for a single variable at a time.

the photo represents the different types of Descriptive analysis techniques, namely; Measures of frequency, measures of central tendency, measures of dispersion, measures of position, contingency tables and scatter plots.

Different types of Descriptive Analysis

Measures of Frequency

In descriptive analysis, it’s essential to know how frequently a certain event or response is likely to occur. This is the prime purpose of measures of frequency to make like a count or percent. 

For example, consider a survey where 500 participants are asked about their favourite IPL team. A list of 500 responses would be difficult to consume and accommodate, but the data can be made much more accessible by measuring how many times a certain IPL team was selected.

Measures of Central Tendency

In descriptive analysis, it’s also important to find out the Central (or average) Tendency or response. Central tendency is measured with the use of three averages — mean, median, and mode. As an example, consider a survey in which the weight of 1,000 people is measured. In this case, the mean average would be an excellent descriptive metric to measure mid-values.

Measures of Dispersion

Sometimes, it is important to know how data is divided across a range. To elaborate this, consider the average weight in a sample of two people. If both individuals are 60 kilos, the average weight will be 60 kg. However, if one individual is 50 kg and the other is 70 kg, the average weight is still 60 kg. Measures of dispersion like range or standard deviation can be employed to measure this kind of distribution.

Measures of Position

Descriptive analysis also involves identifying the position of a single value or its response in relation to others. Measures like percentiles and quartiles become very useful in this area of expertise.

Apart from it, if you’ve collected data on multiple variables, you can use the Bivariate or Multivariate descriptive statistics to study whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two different variables to see if they seem to have a pattern and vary together. You can also test and compare the central tendency of the two variables before carrying out further types of statistical analysis .

Multivariate analysis is the same as bivariate analysis but it is carried out for more than two variables. Following 2 methods are for bivariate analysis.

Contingency table

In a contingency table, each cell represents the combination of the two variables. Naturally, an independent variable (e.g., gender) is listed along the vertical axis and a dependent one is tallied along the horizontal axis (e.g., activities). You need to read “across” the table to witness how the two variables i.e. independent and dependent variables relate to each other.

A table showing a tally of different gender with number of activities

Scatter plots

A scatter plot is a chart that enables you to see the relationship between two or three different variables. It’s a visual rendition of the strength of a relationship.

In a scatter plot, you are supposed to plot one variable along the x-axis and another one along the y-axis. Each data point is denoted by a point in the chart.

the photo is a scatter plot representation for the different hours of sleep a person needs to acquire by the different age in his lifespan

The scatter plot shows the hours of sleep needed per day by age, Source

(Recommend Blog: Introduction to Bayesian Statistics )

Advantages of Descriptive Analysis

High degree of objectivity and neutrality of the researchers are one of the main advantages of Descriptive Analysis. The reason why researchers need to be extra vigilant is because descriptive analysis shows different characteristics of the data extracted and if the data doesn’t match with the trends then it will lead to major dumping of data.

Descriptive analysis is considered to be more vast than other quantitative methods and provide a broader picture of an event or phenomenon. It can use any number of variables or even a single number of variables to conduct a descriptive research. 

This type of analysis is considered as a better method for collecting information that describes relationships as natural and exhibits the world as it exists. This reason makes this analysis very real and close to humanity as all the trends are made after research about the real-life behaviour of the data.

It is considered useful for identifying variables and new hypotheses which can be further analyzed through experimental and inferential studies. It is considered useful because the margin for error is very less as we are taking the trends straight from the data properties.

This type of study gives the researcher the flexibility to use both quantitative and qualitative data in order to discover the properties of the population.

For example, researchers can use both case study which is a qualitative analysis and correlation analysis to describe a phenomena in its own way. Using the case studies for describing people, events, institutions enables the researcher to understand the behavior and pattern of the concerned set to its maximum potential. 

In the case of surveys which consist of one of the main types of Descriptive Analysis, the researcher tends to gather data points from a relatively large number of samples unlike experimental studies that generally need smaller samples.

This is an out and out advantage of the survey method over other descriptive methods that it enables researchers to study larger groups of individuals with ease. If the surveys are properly administered, it gives a broader and neater description of the unit under research.

(Also check: Importance of Statistics for Data Science )

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descriptive analysis in research methodology

  • Open access
  • Published: 09 July 2024

Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre—Northern Tanzania

  • Manasseh J. Mwanswila   ORCID: orcid.org/0000-0003-3378-2865 1 , 2 ,
  • Henry A. Mollel 2 &
  • Lawrencia D. Mushi 2  

BMC Health Services Research volume  24 , Article number:  785 ( 2024 ) Cite this article

Metrics details

The Tanzania healthcare system is beset by prolonged waiting time in its hospitals particularly in the outpatient departments (OPD). Previous studies conducted at Kilimanjaro Christian Medical Centre (KCMC) revealed that patients typically waited an average of six hours before receiving the services at the OPD making KCMC have the longest waiting time of all the Zonal and National Referral Hospitals. KCMC implemented various interventions from 2016 to 2021 to reduce the waiting time. This study evaluates the outcome of the interventions on waiting time at the OPD.

This is an analytical cross-sectional mixed method using an explanatory sequential design. The study enrolled 412 patients who completed a structured questionnaire and in-depth interviews (IDI) were conducted among 24 participants (i.e., 12 healthcare providers and 12 patients) from 3rd to 14th July, 2023. Also, a documentary review was conducted to review benchmarks with regards to waiting time. Quantitative data analysis included descriptive statistics, bivariable and multivariable. All statistical tests were conducted at 5% significance level. Thematic analysis was used to analyse qualitative data.

The findings suggest that post-intervention of technical strategies, the overall median OPD waiting time significantly decreased to 3 h 30 min IQR (2.51–4.08), marking a 45% reduction from the previous six-hour wait. Substantial improvements were observed in the waiting time for registration (9 min), payment (10 min), triage (14 min for insured patients), and pharmacy (4 min). Among the implemented strategies, electronic medical records emerged as a significant predictor to reduced waiting time (AOR = 2.08, 95% CI, 1.10–3.94, p -value = 0.025). IDI findings suggested a positive shift in patients' perceptions of OPD waiting time. Problems identified that still need addressing include, ineffective implementation of block appointment and extension of clinic days was linked to issues of ownership, organizational culture, insufficient training, and ineffective follow-up. The shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays.

The established technical strategies have been effective in reducing waiting time, although further action is needed to attain the global standard of 30 min to 2 h OPD waiting time.

Peer Review reports

The Tanzanian healthcare system is beset by prolonged waiting times in its hospitals, particularly in the outpatient departments. The reported contributing factors include the increased need for healthcare due to uncontrolled population growth, an inadequate number of medical experts, underdeveloped healthcare systems, and ineffective referral systems [ 1 ]. The audit report from the Ministry of Health on the management of referral and emergency healthcare services at zonal and regional referral hospitals showed a high OPD waiting time. Previous studies suggest that the average waiting time at, Muhimbili National Hospital OPD was 4 – 6 h; Muloganzila Zonal Referral Hospital was 3 – 4 h; Bugando Medical Centre was 2.5 h, Mbeya Zonal Hospital was 3 – 4 h and Kilimanjaro Christian Medical Centre (KCMC) was 6 h [ 1 , 2 ]. According to these data, KCMC has the longest waiting time of any zonal and National referral hospital in Tanzania. In response to the long waiting time, KCMC implemented a series of interventions that were incorporated into the strategic plan from 2016 to 2021. The interventions included the use of a block appointment system, the transition from paper to electronic medical records (EMRs), the extension of clinic days and the acquisition of modern diagnostic equipment.

Effective scheduling is crucial to minimize patient waiting times. Appointment systems should include rules for setting appointments and sequencing patients' arrivals, aligning them with doctors' schedules. Studies have shown that optimizing block appointment scheduling can significantly reduce patient waiting times without increasing physician idle time [ 3 , 4 , 5 ]. Effective appointment scheduling has been shown to significantly reduce patient waiting time in outpatient facilities. A study conducted in the USA demonstrated that planning appointment slots can decrease waiting time by as much as 56%.This evidence suggests that optimizing block appointment scheduling is a viable strategy to enhance outpatient efficiency [ 6 ]. Another study in Sri Lanka, demonstrated that implementing a well-structured appointment scheduling system could reduce total patient waiting time by over 60%. Therefore, adopting a block appointment system allows for more efficient allocation of resources and scheduling, ultimately enhancing the overall patient experience and optimizing healthcare delivery [ 7 , 8 ]. In Mozambique they introduced a block appointment scheduling system to evaluate its impact on waiting time. The findings revealed a reduction in waiting time by 1 h and 40 min (100 min) The study concluded that by introducing block appointment scheduling, patient arrivals were distributed more evenly throughout the day, resulting in reduced waiting times [ 9 ].

The implementation of electronic medical records (EMRs) has been shown to offer significant advantages in healthcare delivery, particularly in less developed nations. Evidence indicates that EMRs can decrease patient waiting time, lower hospital operating costs and communication between departments; enable doctors to share best practices. Unlike paper-based records, EMRs provide greater flexibility and leverage, enhancing overall healthcare efficiency [ 10 ]. Long waiting times in the OPDs are often exacerbated by inefficiencies in managing patient records. A tertiary medical college hospital in Mangalore, Karnataka, evaluated patient waiting and identified disorganized manual files as a primary cause of delays. These findings underscore the disadvantages of paper-based records and suggest that implementing electronic medical records (EMRs) can greatly enhance efficiency [ 11 ]. Reducing outpatient waiting times is a critical challenge for healthcare systems. Evidence from a study in Korea demonstrated that implementing EMRs can significantly reduce waiting time by nearly 60% and enhance operational efficiency. [ 12 ]. Addressing long waiting time in the OPD is essential for enhancing patient satisfaction and healthcare efficiency. A systematic survey study aimed at utilizing various models to shorten OPD waiting time found that healthcare providers significantly favored electronic medical records (EMRs) over manual records. The primary reasons cited were significant time savings and a consequent reduction in long waiting time.

[ 13 ]. The issue of long waiting time in outpatient departments (OPDs) is a prevalent problem faced by healthcare facilities worldwide. A study conducted in Brazil applied Lean thinking and an action research strategy to address patient flow issues and identify the causes of prolonged waiting time at the OPD. The study's findings highlighted that many hospitals globally are tackling this issue by investing in electronic medical records (EMRs) to transition away from manual medical records. This evidence suggests that implementing technical strategies, such as EMRs, can significantly improve patient flow and reduce waiting times [ 14 ].

Extending clinic days throughout the week has been found to be more effective in reducing waiting times than extending clinic hours. Studies have demonstrated substantial reductions in patient waiting times and increased patient satisfaction following the extension of clinic days. In Canada the study found that extending clinic day was more effective in reducing waiting time than extending clinic hours. Extending clinic days resulted in a 26% reduction in average waiting time, whereas extending clinic hours led to a 16% reduction. This research provides valuable insights for healthcare administrators seeking to optimize clinic operations and enhance patient experience [ 15 ]. At a tertiary care hospital in Oman the findings revealed a substantial 56% reduction in patient waiting time following the extension of clinic days. Additionally, patient feedback indicated a high level of satisfaction with the extended clinic days, with 97% of patients reporting satisfaction with the service [ 16 ]. Extending clinic days throughout the week has demonstrated promising results in a study conducted at a tertiary care hospital in India. The findings revealed a noteworthy 46% reduction in average patient waiting time following the extension of clinic days. This substantial decrease underscores the effectiveness of extending clinic hours in streamlining patient flow and improving efficiency. Consequently, these results provide compelling evidence supporting the rationale for extending clinic days throughout the week as a viable intervention to alleviate patient waiting times and enhance overall healthcare service delivery [ 17 ].

Utilizing modern equipment in healthcare settings has shown significant potential in reducing patient waiting times. A study conducted at a tertiary care hospital in Italy evaluated the effectiveness of modern equipment on patient. The findings indicated a notable reduction in patient waiting time, with an average decrease of 14 min per patient following the introduction of modern equipment. These results suggest that integrating modern equipment into can be a highly effective intervention for improving operational efficiency and reducing patient waiting time [ 18 ]. Modern equipment can be instrumental in reducing patient waiting times. A tertiary care hospital in Pakistan revealed that one of the primary causes of prolonged waiting time was the lack of adequate examination equipment. By addressing the equipment deficiencies highlighted in the study, healthcare providers can significantly reduce waiting times, thereby improving patient satisfaction and overall efficiency. Therefore, investing in modern equipment is justified as a strategic intervention to enhance patient flow and optimize healthcare service delivery [ 19 , 20 , 21 , 22 ]. Modern equipment is essential for reducing patient waiting times in healthcare facilities. An audit assessment conducted in zonal hospitals in Tanzania by the Ministry of Health revealed that outdated equipment, such as x-ray machines, significantly contributed to long waiting time. The limited capacity of these machines meant that only a certain number of patients could be attended to each day, and the equipment required rest periods to avoid overheating. These findings underscore the necessity of updating and maintaining modern medical equipment to improve patient throughput and reduce waiting times [ 2 ].

In Tanzania the Ministry of Health has not established the gold standard waiting time for patients to wait for services at the OPD [ 2 ]. However the United States Institute of Medicine (IOM) has established their gold standard patient waiting time at the OPD which suggests that medical care should be provided to at least 90% of patients no later than 30 min after their scheduled appointment time [ 23 , 24 ]. The Patient's Charter of UK, has recommended the same standard as the IOM [ 25 ]. The absence of a gold standard waiting time carries several significant implications. It results in inconsistent patient experiences with unpredictable waiting time across facilities, leading to frustration and dissatisfaction. Prolonged and varied waiting time can compromise the quality of care, affecting patient outcomes. Inefficient resource allocation becomes a challenge, hampering the ability to determine staffing and infrastructure needs [ 26 ]. This lack of a benchmark reduces accountability, and healthcare providers may not be incentivized to improve waiting time. It adversely affects patient satisfaction, the reputation of healthcare providers, and can exacerbate healthcare disparities. [ 19 ]. Hence, the findings from this research will provide valuable insights to the hospital management, enabling them to reinforce substantial improvements in patient waiting time and target areas where progress has been limited within the OPD at KCMC.

The objective of this study is to assess the patient waiting time at KCMC after intervention. Thus, the specific objectives were to determine the OPD patient waiting time since the inception of implementation of the interventions and to assess the effect of technical strategies on patient waiting time.

Design and methods

The study was conducted at Kilimanjaro Christian Medical Centre (KCMC) Outpatient department. KCMC is located in the foothills of the snow-capped Mount Kilimanjaro. It is one among the six zonal consultant hospitals in Tanzania. It was established in 1971 as a Zonal Referral Consultant hospital owned by the Evangelical Lutheran Church of Tanzania (ELCT) under the Good Samaritan Foundation (GSF). The referral hospital was established in order to serve the northern, eastern and central zone of Tanzania. Its record in medical services, research, and education has significant influence in Tanzania, East Africa and beyond. It serves a potential catchment population of 15 million people with 630 official bed capacity. The hospital has a number of clinical departments namely, General Surgery, Orthopaedic and Trauma, Dental, Dermatology, Paediatric, Eye, Otorhinolaryngology, Obstetric and Gynaecological and Internal Medicine. There are 1300 staff seeing about 1200 outpatients and 800 inpatients. The hospital has 100 specialists, 52 medical doctors, 465 nurses and the remaining 643 are paramedical and supporting staff. This area was chosen because the outpatient department at KCMC sees a high volume of patients on a regular basis from diverse backgrounds, including rural and urban populations of Tanzania as well as neighbouring countries. For instance in the year 2022, a total of 301,091 patients attended KCMC hospital, of which 92% ( n  = 277,013) attended the OPD. This high patient volume made it a suitable location for studying patient waiting time.

Study design

This was an outcome evaluation whereby an analytical cross-sectional design was used to examine the subject matter. This study employed a mixed method explanatory sequential evaluation approach.

Population and sampling

The study surveyed 412 patients quantitatively and conducted qualitative interviews with 12 patients and 12 healthcare providers. In addition patients who were involved in quantitative were not involved in the qualitative sample. The quantitative sample size was obtained using the following formula [ 27 ]:

n  = sample size.

Z = is the standard normal deviation which is 1.96 for a 95% confidence interval.

P = is the percentage of patients attending the OPD at KCMC is estimated to be 0.5, attributed to the absence of prior research data.

d = is the margin of error, which is 5% (0.05).

Therefore, the minimum sample size for this study was 384 patients approximated to be 422 after adjustment for a 10 percent non response rate.

Quantitative sampling

The systematic sampling process was designed to select 412 patients for interviews for working 10 days, with a daily minimum patient arrival of 500 patients. The daily interview target was calculated by dividing the total number of patients (412) by the number of days (10), resulting in an average of 41.2 interviews per day.

The systematic sampling process began with setting up a consent desk and queue number system. Patients were informed about the survey, and consent was obtained. Each patient was assigned a unique queue number upon arrival.

To determine the sampling interval, the total daily patients (500) were divided by the daily interview targets (41 or 42 patients). This resulted in a sampling interval of approximately 12. A random starting point between 1 and 12 was selected, and from this point, every 12th patient was chosen for the interview.

For the daily interview allocation, 42 patients were interviewed on the first 5 days, and 41 patients were interviewed on the remaining 5 days. This method ensured an even distribution of interviews and a representative sample for the survey.

Qualitative sample size

This study adopted a sample size of 12 respondents for the qualitative data collection, because it has been suggested that in practical research data saturation in a relatively homogeneous population can be achieved with this sample size [ 28 ]. Therefore, twelve (12) healthcare providers at the OPD and twelve (12) patients were selected making a total sample size of 24 for qualitative study.

Qualitative sampling

To select 12 healthcare providers purposive sampling was employed. We targeted specific roles to ensure a comprehensive representation of the outpatient department: doctors, nurses, management, cashiers, and medical records personnel. The selection included 3 doctors, 3 nurses, 2 management personnel, 2 cashiers, and 2 medical records personnel. Doctors were chosen based on their direct patient interaction and diverse specializations within outpatient care. Nurses were selected to represent varying levels of experience, from junior to senior roles. Management personnel were chosen for their administrative and operational oversight responsibilities. Cashiers who handle patient transactions and medical records personnel involved in managing patient records were also included. This purposive sampling strategy aimed to capture a holistic view of the outpatient department's operations and challenges, providing valuable insights for the study. Also to select 12 patients we used convenience sampling. We chose individuals based on their accessibility and willingness to participate at the outpatient department. This approach involved approaching patients who were readily available and consented to participate in the study. The sampling process took place over several days, with researchers stationed in the waiting area to identify potential participants. Patients were approached in a systematic manner, ensuring a mix of different ages, genders, and medical conditions to achieve a varied sample. Each patient was briefly informed about the study's purpose and asked for their consent to participate. Those who agreed were included in the sample until the target of 12 patients was reached. This method was chosen for its practicality and ease of implementation, allowing researchers to quickly gather insights from a diverse group of patients without the need for complex selection criteria.

Inclusion criteria

The study focused on patients aged 18 and older who attended the OPD during the data collection period.

Exclusion criteria

Patients below 18 years or who were severely ill or had scheduled admission appointments were excluded, as well as first time attendees (new patients) because they lacked prior experience with the implemented interventions.

Data collection tools and procedures

The researcher developed a structured questionnaire as a data collection tool. The tool had socio-demographic characteristics which included age, gender, marital status, education level, occupation, place of address, mode of payment and year of attendance at KCMC. The measurement scale for technical strategy was typically ordinal, based on fourteen (14) Likert scale questions with response options of 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree. Allowing patients to indicate their level of agreement or disagreement with statements related to technical strategies. Additionally, strongly disagree and disagree were consolidated as disagree and neutral, agree and strongly agree were consolidated as agree following the approach used in a previous study [ 29 ]. The internal reliability of the fourteen items used to assess effectiveness of technical strategies on reducing patient waiting time was measured using Cronbach’s alpha which was found to be 0.940. The survey included questions on arrival time, time the queue number was issued, registration waiting time, payment waiting time, triage waiting time, waiting time to see the doctor, pharmacy waiting time, laboratory waiting time, radiology waiting time and exit time. This data was collected from patients who attended clinics such as the general OPD clinic, orthopedic clinic, Medical clinic, surgical clinic, Urology clinic, Ear, Nose & Throat, Diabetic, cardiac clinic, Neurology and Neurosurgery. Waiting time was measured with a stopwatch.

Semi-structured guides for conducting in-depth interviews with patients and healthcare providers were developed. The interview guide had questions on socio-demographic and technical strategies such as the new block appointment system, use of EMR, extension of clinic days and availability of modern diagnostic equipment.

Also, the researcher conducted a documentary review, analyzing written records detailing time allocation before the studied event. This approach offered insights into past practices, aiding pattern and trend analysis. It involved reviewing benchmarks like a six-hour average waiting time, median waiting time for specific clinics, and total treatment duration for patients in various clinics. The six-hour benchmark was derived from the Ministry of Health's assessment report on OPD waiting time at KCMC and patients' information was not matched or linked to this report. Therefore, we considered the six-hour mark as our reference point." The data collection was conducted for two consecutive weeks from 3rd July to 14th July 2023.

Data analysis

Quantitative data.

The data collected were imported to the STATA programme (version 18.0) for further analysis. Descriptive Statistics: The analysis began with the presentation of data using various methods, including figures, graphs, and frequency distributions. The effect each response was rated on a scale of 1 to 5. Subsequently, cut-off points were utilized for each area to categorize the effectiveness of each intervention strategies as follows: 1–1.8 (very low), 1.8–2.6 (low), 2.6–3.4 (medium), 3.4–4.2 (high), and 4.2–5 (very high) [ 30 ]. Also, in this study, efficacy was determined by calculating the percentage reduction in OPD waiting time achieved through the implementation of intervention strategies. The current overall OPD waiting time (as shown in Table  4 ) was used as the numerator and the 6-h benchmark as the denominator [ 2 ].

The study defined the dependent variable as follows: overall patient waiting time, which was captured using a stopwatch, was categorized as a binary dummy variable. A value of 1 represented OPD waiting time less than 3 h, while a value of 0 indicated OPD waiting time exceeding 3 h. Comparison with Standards: The analysis involved evaluating OPD waiting time against established benchmarks. This included comparing the waiting time with the standards outlined in the Patients Charter of the United Kingdom (UK) and the recommendations from the United States Institute of Medicine (IOM), which advocate that at least 90% of patients should receive medical care within 30 min of their scheduled appointment time. Additionally, the study compared the observed 6-h waiting time, set as outpatient waiting time at KCMC Zonal Hospital, to assess whether there was any reduction post-intervention. Statistical Tests: To explore potential associations between dependent and independent variables, statistical tests were employed. Logistic regression analysis, encompassing both bivariate and multivariate analyses, was conducted. The multivariable analysis included all variables with p  < 0.200 as identified during the bivariable analysis. It was further adjusted for sex, level of education, and mode of payment. All statistical analyses were conducted at a significance level of 0.05. These analytical steps were taken to provide a comprehensive assessment of the effect of the intervention on patient waiting time.

Qualitative data

All interview transcripts were transcribed verbatim and translated into English. In order to maintain the original meaning back translation was employed. The analysis was done using the English transcript. Thematic data analysis was employed using both deductive and inductive reasoning. Consequently, a preliminary codebook for data analysis was developed, aligning with the study objectives, after which the final codebook was imported into Atlas.ti 7.0 qualitative data analysis computer software. Inductive coding was assigned to text segments which built on emerged new themes that were not pre-determined. The codes were sorted into categories then were clustered into sub-themes which were aligned into themes. The entire process of analysis was iterative. In ensuring rigor, validity, and the mitigation of bias in the qualitative component, it was considered important to ensure the credibility, transferability, dependability, and confirmability of qualitative component to enhance its trustworthiness [ 31 , 32 ]. In this study, credibility ensured that the data accurately reflects the real experiences and perceptions of those involved in the waiting process, allowing for subsequent decision-making. Transferability sought to make the findings relevant and to be applied to various healthcare settings beyond the specific study setting, ensuring that solutions can be adapted and implemented effectively in different contexts. Dependability ensured that the methods used to reduce waiting time were consistent and reliable over time, thus enabling the replication of the study's results. Confirmability ensures that the strategies for reducing waiting time are grounded in the data collected, rather than being influenced by the researchers' biases, thus enhancing the trustworthiness and effectiveness of the research findings in addressing waiting time issues in healthcare settings, thereby increasing the objectivity and validity of the research.

Ethical clearance

The Clearance Committee from Mzumbe University from the Directorates of Research, Publication and Postgraduate Studies provided ethical clearance with reference number MU/DPGS/INT/38/Vol. IV/236. Subsequently, the proposal was submitted for evaluation to the College Research Ethics and Review Committee (CRERC) at Kilimanjaro Christian Medical University College – Moshi. The CRERC granted approval, as indicated by certificate number 2639. Additionally, the data collection procedure received endorsement from the directors of KCMC Hospital reference number KCMC/P.1/Vol. XII. Prior to data collection, participants provided written informed consent. To ensure respondents’ autonomy, patients were fully informed about the purpose and nature of the study and provided with the option to withdraw at any time without any impact on their medical care. Patients were then questioned after completing their medical care. Also interviews were conducted in a private office within the OPD premises.

In this study, the initial calculated sample size was 422 patients. However, out of this group, only 412 patients consented to participate and completed the questionnaire. This resulted in a response rate of 97.6%. The median age was 52 (IQR, 38–65), with the majority aged over sixty. Over half were female (53.6%, n  = 221), and the majority were married (76%, n  = 313). Most had a basic education, including primary (44.7%, n  = 184) and secondary education (26.7%, n  = 110). More than half were peasant farmers (52.4%, n  = 218), and the vast majority (94.7%, n  = 338) resided within the KCMC catchment area. The majority were insurance patients (82.0%, n  = 338), and more than two-thirds (66.5%, n  = 274) had attended KCMC before the intervention's inception (Table  1 ).

Demographic characteristics in the qualitative sample for healthcare providers

A total of 12 healthcare providers were enrolled of whom half were male (50%, n  = 6) and half (50%, n  = 6) were female (Table  2 ).

Demographic characteristics in the qualitative sample for patients

A total of 12 patients were enrolled of whom half were male (50%, n  = 6) and half (50%, n  = 6) were female (Table  3 ).

Sub-themes from the in-depth interviews

During IDIs sub-themes that emerged were; ownership, training, organization culture, ineffective follow up, effective follow up and enhanced process simplification (Table  4 ).

OPD waiting time since the inception of implementation of the interventions

Following the intervention, the overall median waiting time in the OPD was 3.30 h IQR (2.51–4.08) a reduction of 2.30 h after the intervention.

The median waiting time for registration was 9 min IOR (0.03–0.15). For payment, the median waiting time was 10 min IOR (0.07–0.15). For triage patients using out-of-pocket payments experienced median waiting time of 17 min IQR (0.05–0.19) while those with insurance had median waiting time of 14 min IQR (0.06–0.19) and the median waiting time to see a doctor was 1.36 h IQR (0.51–2.01). The time from arrival to actually seeing a doctor was measured at 3.08 h IQR (2.13–3.30). Furthermore, the median consultation time was 19 min IQR (0.15–0.24), waiting time at the pharmacy was 4 min IQR (0.02–0.06), at the laboratory it was 31 min IQR (0.20–0.37) and waiting time at Radiology varied based on the specific service. X-ray services in different rooms had average waiting time ranging from 35 min to 1.15 h with varying IQR (0.23–2.19). Ultrasound services had median waiting time of 32 min (Table  5 ).

Qualitative findings

Registration (medical records department).

The adoption of electronic medical records (EMRs) appears to have enhanced the overall efficiency of the KCMC OPD registration process, benefiting both patients and staff.

"I have been receiving treatment here at KCMC for over 20 years. In the past, in the medical records department, it was necessary to have someone, a staff member, whom you would contact in advance, preferably three days before your clinic day, so that they could start looking for your file. This way, you could save time waiting. However, nowadays, this process is no longer in place. When I arrive, I simply present my card, and in no time, I'm on my way to the next area. There's no longer any time wasted at the reception." (IDI – Male Patient, aged 67 years)

Another interviewee added that:

"Nowadays, with the system in place, the process is streamlined, allowing me to efficiently register as many patients as possible in a short amount of time. I no longer have to leave the reception area to search for files, which has significantly improved the efficiency of the registration process." (IDI – Male healthcare provider (HCP), aged 45 years)

Waiting time to see the doctor

The issue of waiting time for patients to see the doctor has emerged as a significant concern within the healthcare facility. This concern is consistently echoed in both the quantitative data and qualitative interviews.

For example a female HCP aged 40 years reported:

" […] commencing clinics promptly can be challenging for doctors, as it is crucial for them to first participate in the morning report, which provides essential updates on the status of hospitalized patients." (IDI – male HCP, aged 40 years).

After probing as to why the medical staff cannot split into two teams of doctors so that one team could attend to outpatients the response was as follows:

"We have a limited number of doctors, making it challenging to divide them into two groups. Moreover, admitted patients demand our additional attention, as some rely on oxygen for breathing, while others are too ill to walk. Unlike outpatients, the majority of whom can independently come for treatment, we kindly request their understanding as we prioritize the care of our admitted patients." (IDI – male HCP, aged 40 years).

A female patient aged 53 years gave some observations.

“[….] Mmh! I want to highlight that delay in seeing the doctor can have serious consequences. It can lead to a worsening of symptoms or conditions, increase stress levels, and ultimately result in reduced satisfaction with the healthcare service. It's imperative that we address these extended waiting times. This is crucial not just for the comfort of the patient, but also to ensure that medical care is administered in a timely and effective manner.” (IDI – female patient, aged 53 years).

In the pharmacy department, there has been a notable improvement in waiting time. Patients now experience a comfortable and efficient process, with minimal time spent before receiving their prescribed medications.

"With the use of a computerized system, things have been greatly simplified. The waiting time to collect medicine has become short. When I come here, I wait for just a little while and quickly get my medicine." (IDI – Male patient, aged 45 years).
“Apart from using the computerized system in place, which has simplified things, the hospital administration has managed to establish three additional pharmacies apart from this one, thus reducing congestion in a single pharmacy, as it used to be in the past. That's why now a patient can be served quickly.” (IDI – male HCP, aged 50).

Laboratory department

In the laboratory department, the waiting time has been a subject of varying experiences among patients. Some patients have reported relatively short waiting periods, while others have encountered longer waits.

“I have been patiently waiting for a long time to be called for my tests, I’ve not yet been called up to now.” (IDI – female patient, aged 43 years).

Another interviewee shared that:

"I've noticed that one of the main reasons for long waiting time at the laboratory here is the limited space. The laboratory rooms at the Outpatient Department (OPD) have remained the same since the hospital was established, which means they can only accommodate a small number of patients at a time. This often leads to a backlog of patients waiting to get their tests done. It's clear that expanding the laboratory facilities is crucial to reduce these extended waiting time and ensure more efficient service delivery for everyone” (IDI – male HCP, aged 55 years).

Radiology department

Despite having modern diagnostic equipment, which appears to have significantly contributed to reducing patient waiting time, there are still instances where patients experience long waiting time in the radiology department.

"For me, even though waiting for an X-ray may take some time, I don't mind the wait. I've noticed a significant improvement in waiting time compared to before. In addition nowadays, when I have an X-ray, I can also consult with my doctor on the same day, which wasn't possible in the past” (IDI – male patient, aged 40 years).

One interviewee highlighted a crucial factor contributing to the extended waiting time at the radiology department and pointed out that:

“The same rooms at the radiology department are utilized for both outpatient and inpatient cases. As a result, priority is often given to the admitted patients, leading to longer waiting time for those seeking outpatient radiology services. This dual-use of facilities poses a challenge in managing patient flow and significantly contributes to the observed delays in the radiology department”. (IDI – female HCP, aged 49 years)

Patient OPD waiting time with Six (6) and Three (3) Hours Threshold

Not a single patient managed to complete the treatment within the recommended 30-min window following their scheduled appointment. When assessed based on the KCMC benchmark of a 6-h timeframe, the vast majority of patients (98.3%, n  = 407, 95% CI, 97.0%-99.5%) indicated that they received the OPD services within a period of less than six hours. However, when the time threshold was further reduced to three hours, 31% ( n  = 128, 95% CI, 26.6%-35.6%) of all surveyed patients reported that they received OPD services within a duration of fewer than three hours (Fig.  1 ).

figure 1

Patient OPD waiting time with six (6) and three (3) hours threshold ( n  = 412)

Furthermore, during the in-depth interviews (IDIs), patients emphasized receiving OPD services within a timeframe of below three hours.

For instance, a 58-year-old female patient remarked:

“ Certainly, drawing from my extensive experience of over 15 years attending KCMC hospital, I can attest to the positive changes in the waiting time for OPD services. Patients, including myself, are genuinely appreciative of this effective reduction in waiting time. I personally find it remarkable that I can now complete all the necessary OPD services in just about three hours, which is a stark contrast to the longer waiting periods we used to endure. This improvement has undoubtedly enhanced the overall patient experience and contributes positively to our healthcare journey”. (IDI – female patient, aged 58 years)

Effect of technical strategies on patient waiting time

Descriptive statistics of the technical strategies.

The study assessed the effectiveness of various technical strategies on reducing patient waiting time, categorized into four domains: block appointment, implementation of electronic medical records (EMR), extension of clinic days throughout the week, and utilization of modern diagnostic tools. The self-reported data were analyzed using mean scores and standard deviations, measured on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The effectiveness of strategies in reducing patient waiting time was categorized as follows: very low (1–1.8), low (1.8–2.6), medium (2.6–3.4), high (3.4–4.2), and very high (4.2–5).

Overall, the average effectiveness of technical strategies in reducing patient waiting time was found to be very high with a mean score of 4.27 (SD = 0.904) with a descriptive equivalent of “very high”. Specifically, the new block appointment system obtained a mean score of 4.36 (SD = 0.856) with a descriptive equivalent of “high”. Additionally, the introduction of hourly appointments demonstrated positive effects with a mean score of 4.18 (SD = 1.024) with a descriptive equivalent of “high”. The transition from paper based to electronic medical records was also effective and obtained a mean score of 4.09 (SD = 1.033) with a descriptive equivalent of “high”. Moreover, the extension of clinic days obtained a mean score of 4.31 (SD = 0.832) with a descriptive equivalent of “very high”. Finally, the availability of modern diagnostic services, achieving a mean score of 4.30 (SD = 0.861) with a descriptive equivalent of “very high” (Table  6 ).

Bivariable analysis of technical strategies and patient waiting time

Bivariable regression analysis established a significant association between new block appointment system (OR 3.34; CI 1.28–8.77: p  = 0.014), hourly appointment system (OR 2.49; CI 1.01–6.13; p  = 0.047) and patient waiting time (Table  7 ).

Multivariable analysis between technical strategies and patient waiting time

Multivariable logistic analysis was employed to determine which technical strategy played a significant role in reducing patient waiting time. The findings in the adjusted odds ratio indicate that there was an association between reduction of patient waiting time and migrating from paper based to electronic medical records, thus electronic medical records remained a significant factor for patient waiting (AOR = 2.08, 95% CI, 1.10–3.94, p -value = 0.025). However, the introduction of the new block appointment system demonstrated a higher likelihood for a positive effect on reducing waiting time, although the findings were not statistically significant (AOR = 2.49; 95% CI, 0.68–9.10, p -value = 0.168) (Table  8 ).

Qualitative findings with regards to technical strategies

  • Block appointment

Based on the findings from in-depth interviews, both patients and healthcare providers expressed varying opinions on the block appointment system.

One female patient aged 62 years said that:

"Over the years, I have become accustomed to coming in the morning. I can't come at other time besides the morning; it would disrupt my plans." (IDI, female patient, aged 62 years).

A male healthcare provider aged 39 years shared the experience:

“ The truth is, we haven't been very successful in using block appointments. We tried it on the first and second days, but things went back to how they were before. The problem is, patients arrive very early in the morning, and you find them all crowded, waiting for service. Once a patient arrives, they must be attended to. We've realized that this block appointment system requires the whole team to be involved, from medical records (reception) to doctors, nurses, and the patients themselves” (IDI – male HCP, aged 51 years).

However, it's important to note that amidst these negative perspectives, several interviewees also acknowledged the positive effect of the system.

“ Since the introduction of the appointment system in 2020, we've observed a significant reduction in patient waiting time, which has led to quicker and more efficient service delivery for patients. When a patient arrives, the waiting area is usually less crowded. Furthermore, doctors now have more spaced-out appointments, allowing them to devote ample time to each patient.” (IDI – female HCP, aged 47 years).
  • Electronic medical records

The quantitative finding regarding migrating from paper based to electronic medical records aligns with our qualitative findings.

"From my experience, dealing with physical files presented its own set of challenges. There was a lengthy process, and files were prone to being misplaced, including important test results. Sometimes, files would be delayed in reaching the clinic. This was particularly problematic for patients who arrived early; if their files couldn't be located promptly, it would cause a delay. However, with the new system in place, everything operates swiftly and efficiently. The system has truly revolutionized the process” (IDI – female HCP, aged 55 years)

One interviewee shared the experience.

“When I come for treatment nowadays, I no longer experience the frustration of my test results going missing or my file being unavailable." (IDI – male patient, aged 50 years)

Extension of clinic days

The implementation of the daily clinic schedule has yielded mixed results.

One interviewee stated that:

"In our department, the limited number of staff has posed a challenge. Conducting daily clinics becomes demanding, as the same doctor is tasked with conducting ward rounds, making decisions for admitted patients, and performing surgery. However, once we have an adequate staff complement, we can begin seeing patients on a daily basis." (IDI – male HCP, aged 42 years)

On the contrary, extension of clinic days has proven to be a highly beneficial strategy in our facility serving as one of the key strategies to address patient waiting time.

“It has significantly reduced the patient waiting time. In the past, clinics used to run until 6 pm in the evening. Since they implemented the daily clinic schedule, patients are now seen earlier, and the clinics end earlier. This is because patients have been scheduled throughout the week”. (IDI – female HCP, aged 48 years)

Another interviewee supported that.

“It has helped in limiting the number of patients flocking to a single clinic, but it doesn't necessarily reduce patient waiting time." (IDI – male HCP, aged 51 years)

One interviewee shared the experience:

"These days, I finish my treatments earlier than I used to.” (IDI – male patient, aged 49 years)

Availability of modern diagnostic equipment

The integration of modern diagnostic equipment stands as a substantial contributor to the reduction of patient waiting time. This positive trend is supported by both our quantitative and qualitative findings, affirming the significance of having advanced diagnostic tools readily accessible within our healthcare facility.

" Nowadays, the procedure has become significantly more simplified. You just need to consult the system to retrieve the patient's results. When you open it, you can readily peruse the information, making the process more efficient. If I require additional specifics about the condition, it's easy to locate them in the patient's file. I simply access it in the system, and their image is readily available, leading to a substantial time-saving." (IDI – male HCP, aged 40 years)

More experience is shared from a male patient aged 45 years.

"I now do my investigations on the same day and return to the doctor for my results. This contrasts with the past when I needed to be scheduled for a different day to pick up the results. This has resulted in a considerable time-saving." (IDI – male patient, aged 45 years)

OPD waiting time

Following the intervention, it was observed that the overall median waiting time in the OPD was reduced to 3.30 h in contrast to the previous six-hour (6) waiting time prior to the intervention, showing the effectiveness of the intervention achieving a reduction of waiting time by 45%. This improvement is significant and suggests that the interventions have had a positive effect.

These findings align with other research involved adding more human resources and changing business and management practices. The findings demonstrated a significant success in reducing wait time in the USA, China, Sri Lanka and Taiwan by 15%, 78%, 60%, and 50%, respectively [ 33 ].

The study at KCMC found low median waiting time of 9 min for registration. This is not congruent with findings in China and Saudi Arabia where registration time were notably higher [ 24 , 34 ]. In Ethiopia, waiting time varied, with some patients waiting over an hour [ 35 ], while another study reported a median wait of 18 min [ 36 ]. In Kenya, registration waiting time were even shorter 5.8 min [ 37 ]. These discrepancies could be explained by variations in patient flow management techniques or data collection techniques. Overall, the study shows that the waiting time for registration has significantly decreased at KCMC, clearly demonstrating the efficiency of the technical strategies that have been put in place to cut down on waiting time.

In terms of payment processing, the median waiting time was 10 min. Although it appears majority of patients were insured, the mode of payments had no significant association with waiting time. This suggests that insured patients were handled just as quickly as patients paying with cash. These results are relatively congruent with a study conducted in a Tertiary Care Hospital in Pune, India, where patients spent an average of 7 min at the cashier [ 26 ]. This shared emphasis on streamlined payment processes underscores their significance in enhancing the patient experience, reinforcing the importance of efficient payment processing in healthcare settings.

At the triage area, patients paying cash had a median waiting time of 17 min, while insured patients experienced slightly shorter median waiting time of 14 min. These results are congruent with a study by [ 38 ], who found that insured patients at a hospital in Northeast Thailand had an average triage waiting time of 13 min. The consistency in findings between these studies suggests that insurance status may play a role in patient waiting time, with insured patients benefiting from somewhat more efficient service and well streamlined patient flow. However, it's important to note that regional contexts may influence waiting time, and these results may vary in different healthcare settings and countries.

The median waiting time before seeing a doctor from arrival to consultation was 3.08 h. These results resonate with research from Nigeria, where 38% of respondents waited for over 2 h for a consultation [ 39 , 40 ] found an average waiting time of 137.02 ± 53.64 min before seeing a doctor. In contrast, some studies reported shorter waiting time, such as 40 min in India [ 26 ], over 90% of patients waiting for more than 20 min in Saudi Arabia [ 24 ] and more than half of patients waiting for over 60 min in Ethiopia [ 35 ]. Involvement of doctors in teaching students, long ward rounds, staff constraints and prioritizing inpatients over outpatients could all contribute to doctors coming late to the clinics, thus, causing increased stress, discomfort, and impatience among patients.

The study's findings emphasize a positive aspect of healthcare delivery at KCMC, specifically in pharmacy services, with remarkably short median waiting time of 4 min. This aligns with research in Iran by [ 41 ], which also reported the pharmacy as having the shortest average waiting time of 5 ± 3 min and in Kenya, where patients experienced a similar pattern with an average waiting time of 5.5 min [ 37 ]. However, these results contrast with a study in a Tertiary Care Hospital in Pune, India, revealing a 15-min average waiting time at the pharmacy [ 26 ]. In Ethiopia, [ 35 ] found that only 23.6% of patients received their prescribed drugs within ≤ 30 min, while a comparable number received them within 30–60 min or > 60 min. During interview, patients commended the computerized system's effectiveness in streamlining the medication collection process, which the study attributes to its implementation. In addition, three new pharmacies have been added to the existing one, reducing congestion and allowing patients to receive faster service. The aforementioned positive results serve as evidence of the efficiency with which KCMC's pharmacy services have integrated technology.

The median waiting time at the laboratory department was 31 min. This is congruent with studies done in Ethiopia which reported a similar median of waiting time of 31 min, reflecting consistency in laboratory waiting time within Ethiopian healthcare settings [ 36 ]. Similarly, another study noted that 58.1% of patients received laboratory services within 30 to 60 min, with only 12.0% within ≤ 30 min [ 35 ]. On the contrary, in Nigeria a study revealed a longer waiting time, with patients waiting over 50 min on average for laboratory services. This suggests that KCMC’s laboratory waiting time maybe more favourable when compared to other hospitals [ 42 ]. Nevertheless, another study reported an average waiting time which was significantly shorter, 12.75 min which suggest that there may be variations in waiting time between KCMC and Indian healthcare facility. The reason for the long waiting time at KCMC could be due to the limited space within the laboratory rooms resulting in the accommodation of fewer patients at any given time. This emphasized the necessity of expanding facilities to improve the effectiveness of service delivery [ 26 ].

Waiting time at the Radiology department showed significant differences depending on which investigation was ordered. Thus the median waiting time for X-ray services varied between rooms, from 35 min to 1.15 h, whereas the median waiting time for ultrasound services was 32 min. Important insights into patient experiences were obtained through in-depth interviews. Some patients expressed contentment with the waiting time for X-rays because they were able to get the results on the same day and continue with further treatment from their doctors on the very same day. Various studies revealed differing median radiology waiting time. Iran reported 27 min ± 11 [ 41 ] while India recorded 36.05 min [ 26 ], Ethiopia’s studies indicated 33 min [ 36 ] and 60 min [ 35 ], all indicating relatively shorter waiting time. Conversely, Nigeria showed the longest waiting time for radiological services at 77 min [ 42 ]. There were issues identified within KCMC's Radiology department, such as the dual use of rooms for outpatient and inpatient cases, which prioritized admitted patients and resulted in longer wait time for outpatients. This organizational practice complicates patient flow management and contributes considerably to perceived delays in the radiology department. The findings emphasize that waiting time in Radiology are influenced by resource availability, facility organization, and patient flow management.

Technical strategies on patient waiting time

The implemented block appointment system appears to have the potential to improve waiting time, even though the effect was not statistically significant. Early patient arrivals continue to be problematic, which emphasizes how crucial it is to provide efficient patient education and coordination in order to reap the full rewards of this system. Similar findings in Nigeria demonstrate that appointments with specific time are uncommon, resulting in early patient arrivals and possible delays in the start of services [ 8 ]. However, in other nations where it has been used, the block appointment system has proved to be successful. Research conducted in the United States [ 43 ] and the United Kingdom [ 44 ] have demonstrated its effectiveness in reducing patient wait time. In Thailand [ 5 ] and Sri Lanka [ 7 ] demonstrated the possible advantages of carefully planned scheduling by demonstrating how the use of appointment systems can dramatically reduce average waiting time. Block appointment scheduling also successfully spread out patient arrivals throughout the day, as shown by a pilot study conducted in Mozambique, which significantly decreased waiting time [ 9 ]. Hence, coordinated efforts involving medical records, physicians, nurses, and patients themselves are needed to operate the system.

The transition from paper to electronic medical records had a significant and positive impact on reducing long waiting time at the OPD. Various studies underlined the benefits of electronic medical records over paper-based systems, including how it can improve patient waiting time, increase efficiency, and improve the delivery of healthcare services [ 10 , 11 ] and [ 12 ]. Another study highlighted the preference for electronic health records among healthcare providers due to their efficiency and speed in patient care. By eliminating labour intensive procedures, space limitations, and document misplacement problems associated with manual filing systems, the switch to electronic records helped to create more efficient and productive operations [ 13 ]. The entire patient experience was greatly enhanced since patients were no longer frustrated by lost records or delayed test results. The implementation of electronic health records has proven to be beneficial in reducing extended wait time in outpatient clinics, as evidenced by a study carried out in Brazil [ 14 ].

The extension of clinic days yielded a mean score of 4.31 (SD = 0.832) signifying positive effect. Similarly, qualitative findings from healthcare providers and patients shed light on the effect of extending clinic days. The department's small staffing posed a significant challenge, as doctors had to manage multiple responsibilities, such as ward rounds, decision-making for admitted patients, and surgery. These findings are not congruent with those of other locations where clinic days have been extended. For instance, a study suggested that extending clinic days was more effective, resulting in a 26% reduction in average waiting time [ 15 ]. Additionally, another study found a significant 56% reduction in average waiting time after extending clinic days, coupled with high patient satisfaction rates [ 16 ]. Similarly in other study extending clinic days resulted in an astounding 46% decrease in average waiting time. The study also found that patient satisfaction was high and that the number of patients seen each day had increased [ 17 ].

The availability of modern diagnostic services had a mean score of 4.30 (SD = 0.861), signifying a positive effect. This demonstrates that advanced diagnostic equipment played a significant role in streamlining healthcare processes and enhancing efficiency. Qualitative findings from both healthcare providers and patients supported this, highlighting how digital systems and modern equipment simplified procedures and expedited healthcare services. Access to electronic patient information and test results contributed to time savings. These findings are congruent with studies conducted in Italy [ 18 ], Pakistan [ 19 , 20 ], and Iran [ 21 ], which all demonstrated reductions in waiting time following the acquisition of modern equipment. A study from India also supported the positive impact of modern equipment on patient waiting time [ 22 ]. Additionally, audit assessments in Tanzania by the Ministry of Health and equipment-related observations in zonal hospitals emphasized the critical role of modern equipment in healthcare settings. Outdated equipment can lead to extended patient waiting time, underscoring the importance of maintaining and upgrading diagnostic facilities to improve healthcare efficiency and patient care [ 2 ].

The implemented technical strategies resulted in a significant reduction in overall OPD waiting time to an average of 3.30 h, marking a 45% reduction from the previous six-hour wait. While there have been notable improvements in registration, payment, triage, and pharmacy services, issues remain in doctor consultations, laboratory, and radiology services, resulting in extended waiting time for some patients. The adoption of electronic medical records emerged as the most effective technical strategy, emphasizing its critical role in improving OPD efficiency. Despite these advancements, additional improvements are required to meet the global standard of waiting time ranging from 30 min to 2 h. Nevertheless, ineffective implementation of block appointment and extension of clinic days appears to stem from lack of ownership and proactive involvement by hospital managers in driving these strategies forward. Furthermore, the hospital's dominant organizational culture seemed to be resistant to change, which could hinder the effective implementation of these strategies. The results indicated a possible training shortfall, suggesting that personnel may not have had enough training to properly adopt and implement these new strategies. Moreover, there was a lack of effective follow-up and management strategies by hospital managers, potentially hindering the sustained implementation of these strategies. Moreover, the shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays, impacting waiting time. Alongside, a comprehensive review of the diagnostic service structure might be necessary to alleviate delays and streamline services for both inpatient and outpatient care.

Limitations of the study

Since only one hospital was involved in the study, generalization to cover the rest of Tanzania remains uncertain. Additionally, there was a chance that selection bias might have impacted the findings.

Availability of data and materials

Data is available upon request from the corresponding author.

Abbreviations

College research ethics and review committee

Healthcare provider

In-depth interview

Institute of medicine

Interquartile range

Kilimanjaro Christian Medical Centre

Ministry of health, community development, gender, elderly and children

Mzumbe University

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Acknowledgements

We extend our gratitude to the patients who participated in this study and the research assistants who contributed to data collection namely, Geofrey A. Sikaluzwe, Mbayani J. Kivuyo, Richard Hezron Mwamahonje, Emmanuel M. Mabula, Abel E. Lucas, Amos Francis, Dr. (Mrs) Angela Savage for proof reading and Dr. Bernard Njau for his continuity guidance. Also Dr. Theresia Mkenda for availing us with research assistants.

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Manasseh J. Mwanswila, Henry A. Mollel & Lawrencia D. Mushi

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M.J.M conceptualized and conducted the study, handling data collection, analysis, and initial manuscript drafting. H.A.M and L.D.M provided oversight and reviewed the process from proposal to final manuscript. All authors reviewed the manuscript.

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The Clearance Committee from Mzumbe University from the Directorates of Research, Publication and Postgraduate Studies provided ethical clearance with reference number MU/DPGS/INT/38/Vol. IV/236. Subsequently, the proposal was submitted for evaluation to the College Research Ethics and Review Committee (CRERC) at Kilimanjaro Christian Medical University College – Moshi. The CRERC granted approval, as indicated by certificate number 2639. Additionally, the data collection procedure received endorsement from the directors of KCMC Hospital reference number KCMC/P.1/Vol. XII. Prior to data collection, participants provided written informed consent. To ensure respondent autonomy, patients were fully informed about the purpose and nature of the study and provided with the option to withdraw at any time without any impact on their medical care. Patients were then questioned as completing their medical care.

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Mwanswila, M.J., Mollel, H.A. & Mushi, L.D. Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre—Northern Tanzania. BMC Health Serv Res 24 , 785 (2024). https://doi.org/10.1186/s12913-024-11231-5

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Descriptive Statistics

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Descriptive statistics in research: a critical component of data analysis.

15 min read With any data, the object is to describe the population at large, but what does that mean and what processes, methods and measures are used to uncover insights from that data? In this short guide, we explore descriptive statistics and how it’s applied to research.

What do we mean by descriptive statistics?

With any kind of data, the main objective is to describe a population at large — and using descriptive statistics, researchers can quantify and describe the basic characteristics of a given data set.

For example, researchers can condense large data sets, which may contain thousands of individual data points or observations, into a series of statistics that provide useful information on the population of interest. We call this process “describing data”.

In the process of producing summaries of the sample, we use measures like mean, median, variance, graphs, charts, frequencies, histograms, box and whisker plots, and percentages. For datasets with just one variable, we use univariate descriptive statistics. For datasets with multiple variables, we use bivariate correlation and multivariate descriptive statistics.

Want to find out the definitions? Univariate descriptive statistics: this is when you want to describe data with only one characteristic or attribute

Bivariate correlation: this is when you simultaneously analyze (compare) two variables to see if there is a relationship between them

Multivariate descriptive statistics: this is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable

Then, after describing and summarising the data, as well as using simple graphical analyses, we can start to draw meaningful insights from it to help guide specific strategies. It’s also important to note that descriptive statistics can employ and use both quantitative and qualitative research .

Describing data is undoubtedly the most critical first step in research as it enables the subsequent organisation, simplification and summarisation of information — and every survey question and population has summary statistics. Let’s take a look at a few examples.

Examples of descriptive statistics

Consider for a moment a number used to summarise how well a striker is performing in football — goals scored per game. This number is simply the number of shots taken against how many of those shots hit the back of the net (reported to three significant digits). If a striker is scoring 0.333, that’s one goal for every three shots. If they’re scoring one in four, that’s 0.250.

A classic example is a student’s grade point average (GPA). This single number describes the general performance of a student across a range of course experiences and classes. It doesn’t tell us anything about the difficulty of the courses the student is taking, or what those courses are, but it does provide a summary that enables a degree of comparison with people or other units of data.

Ultimately, descriptive statistics make it incredibly easy for people to understand complex (or data intensive) quantitative or qualitative insights across large data sets.

Take your research and subsequent analysis to the next level

Types of descriptive statistics

To quantitatively summarise the characteristics of raw, ungrouped data, we use the following types of descriptive statistics:

  • Measures of Central Tendency ,
  • Measures of Dispersion and
  • Measures of Frequency Distribution.

Following the application of any of these approaches, the raw data then becomes ‘grouped’ data that’s logically organised and easy to understand. To visually represent the data, we then use graphs, charts, tables etc.

Let’s look at the different types of measurement and the statistical methods that belong to each:

Measures of Central Tendency are used to describe data by determining a single representative of central value. For example, the mean, median or mode.

Measures of Dispersion are used to determine how spread out a data distribution is with respect to the central value, e.g. the mean, median or mode. For example, while central tendency gives the person the average or central value, it doesn’t describe how the data is distributed within the set.

Measures of Frequency Distribution are used to describe the occurrence of data within the data set (count).

The methods of each measure are summarised in the table below:

Measures of Central Tendency Measures of Dispersion Measures of Frequency Distribution
Mean Range Count
Median Standard deviation
Mode Quartile deviation
Variance
Absolute deviation

Mean: The most popular and well-known measure of central tendency. The mean is equal to the sum of all the values in the data set divided by the number of values in the data set.

Median: The median is the middle score for a set of data that has been arranged in order of magnitude. If you have an even number of data, e.g. 10 data points, take the two middle scores and average the result.

Mode: The mode is the most frequently occurring observation in the data set.  

Range: The difference between the highest and lowest value.

Standard deviation: Standard deviation measures the dispersion of a data set relative to its mean and is calculated as the square root of the variance.

Quartile deviation : Quartile deviation measures the deviation in the middle of the data.

Variance: Variance measures the variability from the average of mean.

Absolute deviation: The absolute deviation of a dataset is the average distance between each data point and the mean.

Count: How often each value occurs.

Scope of descriptive statistics in research

Descriptive statistics (or analysis) is considered more vast than other quantitative and qualitative methods as it provides a much broader picture of an event, phenomenon or population.

But that’s not all: it can use any number of variables, and as it collects data and describes it as it is, it’s also far more representative of the world as it exists.

However, it’s also important to consider that descriptive analyses lay the foundation for further methods of study. By summarising and condensing the data into easily understandable segments, researchers can further analyse the data to uncover new variables or hypotheses.

Mostly, this practice is all about the ease of data visualisation. With data presented in a meaningful way, researchers have a simplified interpretation of the data set in question. That said, while descriptive statistics helps to summarise information, it only provides a general view of the variables in question.

It is, therefore, up to the researchers to probe further and use other methods of analysis to discover deeper insights.

Things you can do with descriptive statistics:

  • Define subject characteristics: If a marketing team wanted to build out accurate buyer personas for specific products and industry verticals, they could use descriptive analyses on customer datasets (procured via a survey) to identify consistent traits and behaviours.

They could then ‘describe’ the data to build a clear picture and understanding of who their buyers are, including things like preferences, business challenges, income and so on.

  • Measure data trends

Let’s say you wanted to assess propensity to buy over several months or years for a specific target market and product. With descriptive statistics, you could quickly summarise the data and extract the precise data points you need to understand the trends in product purchase behaviour.

  • Compare events, populations or phenomena

How do different demographics respond to certain variables? For example, you might want to run a customer study to see how buyers in different job functions respond to new product features or price changes. Are all groups as enthusiastic about the new features and likely to buy? Or do they have reservations? This kind of data will help inform your overall product strategy and potentially how you tier solutions.

  • Validate existing conditions

When you have a belief or hypothesis but need to prove it, you can use descriptive techniques to ascertain underlying patterns or assumptions.

  • Form new hypotheses

With the data presented and surmised in a way that everyone can understand (and infer connections from), you can delve deeper into specific data points to uncover deeper and more meaningful insights — or run more comprehensive research.

Guiding your survey design to improve the data collected

To use your surveys as an effective tool for customer engagement and understanding, every survey goal and item should answer one simple, yet highly important question:

“What am I really asking?”

It might seem trivial, but by having this question frame survey research, it becomes significantly easier for researchers to develop the right questions that uncover useful, meaningful and actionable insights.

Planning becomes easier, questions clearer and perspective far wider and yet nuanced.

Hypothesise — what’s the problem that you’re trying to solve? Far too often, organisations collect data without understanding what they’re asking, and why they’re asking it.

Finally, focus on the end result. What kind of data do you need to answer your question? Also, are you asking a quantitative or qualitative question? Here are a few things to consider:

  • Clear questions are clear for everyone. It takes time to make a concept clear
  • Ask about measurable, evident and noticeable activities or behaviours.
  • Make rating scales easy. Avoid long lists, confusing scales or “don’t know” or “not applicable” options.
  • Ensure your survey makes sense and flows well. Reduce the cognitive load on respondents by making it easy for them to complete the survey.
  • Read your questions aloud to see how they sound.
  • Pretest by asking a few uninvolved individuals to answer.

Furthermore…

As well as understanding what you’re really asking, there are several other considerations for your data:

  • Keep it random

How you select your sample is what makes your research replicable and meaningful. Having a truly random sample helps prevent bias, increasingly the quality of evidence you find.

  • Plan for and avoid sample error

Before starting your research project, have a clear plan for avoiding sample error. Use larger sample sizes, and apply random sampling to minimise the potential for bias.

  • Don’t over sample

Remember, you can sample 500 respondents selected randomly from a population and they will closely reflect the actual population 95% of the time.

  • Think about the mode

Match your survey methods to the sample you select. For example, how do your current customers prefer communicating? Do they have any shared characteristics or preferences? A mixed-method approach is critical if you want to drive action across different customer segments.

Use a survey tool that supports you with the whole process

Surveys created using a survey research software can support researchers in a number of ways:

  • Employee satisfaction survey template
  • Employee exit survey template
  • Customer satisfaction (CSAT) survey template
  • Ad testing survey template
  • Brand awareness survey template
  • Product pricing survey template
  • Product research survey template
  • Employee engagement survey template
  • Customer service survey template
  • NPS survey template
  • Product package testing survey template
  • Product features prioritisation survey template

These considerations have been included in Qualtrics’ survey software , which summarises and creates visualisations of data, making it easy to access insights, measure trends, and examine results without complexity or jumping between systems.

Uncover your next breakthrough idea with Stats iQ™

What makes Qualtrics so different from other survey providers is that it is built in consultation with trained research professionals and includes high-tech statistical software like Qualtrics Stats iQ .

With just a click, the software can run specific analyses or automate statistical testing and data visualisation. Testing parameters are automatically chosen based on how your data is structured (e.g. categorical data will run a statistical test like Chi-squared), and the results are translated into plain language that anyone can understand and put into action.

  • Get more meaningful insights from your data

Stats iQ includes a variety of statistical analyses, including: describe, relate, regression, cluster, factor, TURF, and pivot tables — all in one place!

  • Confidently analyse complex data

Built-in artificial intelligence and advanced algorithms automatically choose and apply the right statistical analyses and return the insights in plain english so everyone can take action.

  • Integrate existing statistical workflows

For more experienced stats users, built-in R code templates allow you to run even more sophisticated analyses by adding R code snippets directly in your survey analysis.

         Advanced statistical analysis methods available in Stats iQ

Regression analysis – Measures the degree of influence of independent variables on a dependent variable (the relationship between two or multiple variables).

Analysis of Variance (ANOVA) test – Commonly used with a regression study to find out what effect independent variables have on the dependent variable. It can compare multiple groups simultaneously to see if there is a relationship between them.

Conjoint analysis – Asks people to make trade-offs when making decisions, then analyses the results to give the most popular outcome. Helps you understand why people make the complex choices they do.

T-Test – Helps you compare whether two data groups have different mean values and allows the user to interpret whether differences are meaningful or merely coincidental.

Crosstab analysis – Used in quantitative market research to analyse categorical data – that is, variables that are different and mutually exclusive, and allows you to compare the relationship between two variables in contingency tables.

Go from insights to action

Now that you have a better understanding of descriptive statistics in research and how you can leverage statistical analysis methods correctly, now’s the time to utilise a tool that can take your research and subsequent analysis to the next level.

Try out a Qualtrics survey software demo so you can see how it can take you through descriptive research and further research projects from start to finish.

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A Meta-Analysis of 46 Career Pathways Impact Evaluations

Publication info, research methodology, country, state or territory, description, other products.

The career pathways approach to workforce development emerged to help workers with lower levels of formal education advance to better paying jobs by earning in-demand postsecondary credentials. The approach involves articulated steps of education, training, and jobs within an industry sector or occupational cluster, combined with other services and employer connections to support participant success. To advance the evidence base in the career pathways field, the Descriptive & Analytical Career Pathways Project (D&A CP Project) includes three sub-studies, each addressing different evidence gaps through distinct data sources and methods.

The D&A CP Project Meta-Analysis Study is designed to answer two research questions: (1) What is the overall impact of the career pathways approach on participants’ educational progress and labor market outcomes?, and (2) Which characteristics of career pathways programs are most closely associated with impacts? To answer these questions, this meta-analysis summarizes 46 impact evaluations that focus on programs that embed elements of the career pathways approach. The programs that are at the center of the 46 evaluations in this meta-analysis are diverse across a wide variety of dimensions—including what they offer, how they provide those offerings, who they serve, and their local contexts.

Based on robust evidence, the meta-analysis reports the average impacts from these 46 evaluations, revealing that the career pathways approach leads to large educational progress gains, large gains in industry-specific employment, small gains in general employment, small gains in short-term earnings, and no meaningful gains in medium/longer-term earnings. Additional, exploratory analyses identify some factors that appear to be associated with smaller or larger impacts in the evaluated programs.

The Meta-Analysis Study deliverables include a brief and comprehensive report and public use data. Additionally, the D&A CP Project produced a career pathways timeline as well as an early brief describing highlights from a scan of the research and an accompanying research and evaluation matrix.

The other two sub-studies in the D&A CP Project include a Career Trajectories and Occupational Transitions (CTOT) Study and Machine Learning Study.

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Research on the manifestation and formation mechanism of new characteristics of land disputes: evidence from the yangtze river economic belt, china.

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Characteristics of Pre-Service Chemistry Teachers' Mechanistic Reasoning In Organic Chemistry Tasks: An Eye-Tracking Study

  • Published: 08 July 2024

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descriptive analysis in research methodology

  • Jianqiang Ye   ORCID: orcid.org/0000-0002-0672-6385 1 ,
  • Yubin Zheng 1 ,
  • Min Zhan 1 ,
  • Yiling Zhou 1 ,
  • Long Li 1 &
  • Dimei Chen 1  

Organic chemistry is challenging for novices as it involves a large quantity of organic reactions. Effective learning requires not only profound theoretical knowledge but also the ability to reason about causal mechanisms. This study investigated pre-service chemistry teachers' mechanistic reasoning and the implicit cognitive process. Participants ( N  = 33) were asked to complete three tasks, which required them to explain chemical phenomena or analyze chemical reactions. This work analyzed the components involved in participants' explanations based on the discourse analysis framework and evaluated the mechanistic reasoning by identifying the causal relationship between different components. An eye-tracking method was employed to recognize the mental activity underlying participants' performance. Four parameters, percentage of dwell time, percentage of fixation count, heat maps, and average pupil size, were used to conduct quantitative analyses on the data collected from the eye-tracker. Each parameter on predefined areas of interest was compared to identify the information that participants paid more attention to and bore more cognitive load while reasoning. The results revealed that pre-service chemistry teachers demonstrate four different types of reasoning in organic chemistry tasks: descriptive, relational, simple causal, and mechanistic reasoning. Pre-service chemistry teachers were more concerned with key information and symbolic representations. It was symbolic representations that increased cognitive load.

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Ye, J., Zheng, Y., Zhan, M. et al. Characteristics of Pre-Service Chemistry Teachers' Mechanistic Reasoning In Organic Chemistry Tasks: An Eye-Tracking Study. Res Sci Educ (2024). https://doi.org/10.1007/s11165-024-10185-2

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