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Quantitative Research – Methods, Types and Analysis

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Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

• What are the relationships between two or more variables? 
• What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

• Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
• Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

• Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
• Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

• Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
• Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

• Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
• Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

• Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

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• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Research methods--quantitative, qualitative, and more: overview.

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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

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Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

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D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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Quantitative and Qualitative Research

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

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Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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Quantitative research methods

a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time. The goal of gathering this quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Quantitative research techniques include experiments and surveys. 

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What are the strengths of quantitative research.

Professor Norma T. Mertz briefly discusses qualitative research and how it has changed since she entered the field. She emphasizes the importance of defining a research question before choosing a theoretical approach to research.

This is just one segment in a series about quantitative methods. You can find additional videos in our SAGE database, Research Methods: 

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

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Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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What is Quantitative Research? Definition, Examples, Key Advantages, Methods and Best Practices

By Nick Jain

Published on: May 17, 2023

Table of Contents

What is Quantitative Research?

Quantitative research examples, quantitative research: key advantages, quantitative research methodology, 7 best practices to conduct quantitative research.

Quantitative research stands as a powerful research methodology dedicated to the systematic collection and analysis of measurable data. Through rigorous statistical and mathematical techniques, this method extracts insights from structured surveys, controlled experiments, or other defined data-gathering methods.

The primary objective of quantitative research is to measure and quantify variables, relationships, and patterns within the dataset. By testing hypotheses, making predictions, and drawing generalizable conclusions, it plays a crucial role in fields such as psychology, sociology, economics, and education. This approach often involves significant sample sizes, ensuring robust results.

Explore the depth of quantitative research with this comprehensive guide, offering practical examples and applications to demonstrate its real-world impact. Stay updated with the latest trends and developments in quantitative research as we continually refine our insights to provide you with the most relevant and cutting-edge information.

Quantitative Research: Key Characteristics

Below are the key characteristics of quantitative research:

• Objectivity: Quantitative research is grounded in the principles of objectivity and empiricism, which means that the research is focused on observable and measurable phenomena, rather than personal opinions or experiences.
• Structured approach: Quantitative research follows a structured and systematic approach to data collection and analysis, using clearly defined variables, hypotheses, and research questions.
• Numeric data: Quantitative research uses numerical data to describe and analyze the phenomena under study, such as statistical analysis, surveys, and experiments.
• Large sample size: Quantitative research often involves large sample sizes to ensure statistical significance and to generalize findings to a larger population.
• Standardized data collection: Quantitative research typically involves standardized data collection methods, such as surveys or experiments, to minimize potential sources of bias and increase reliability.
• Deductive reasoning: Quantitative research uses deductive reasoning, where the researcher tests a specific hypothesis based on prior knowledge and theory.
• Replication: Quantitative research emphasizes the importance of replication, where other researchers can reproduce the study’s methods and obtain similar results.
• Statistical analysis: Quantitative research involves statistical analysis to analyze the data and test the research hypotheses, often using software programs to assist with data analysis.
• Precision: Quantitative research aims to be precise in its measurement and analysis of data. It seeks to quantify and measure the specific aspects of a phenomenon being studied.
• Generalizability: Quantitative research aims to generalize findings from a sample to a larger population. It seeks to draw conclusions that apply to a broader group beyond the specific sample being studied.

Below are 3 examples of quantitative research:

Example 1: Boosting Employee Performance with Innovative Training Programs

In this quantitative study, we delve into the transformative impact of a cutting-edge training program on employee productivity within corporate environments. Employing a quasi-experimental framework, we meticulously analyze the outcomes of a cohort undergoing innovative training against a control group.

Through advanced statistical methodologies, we unveil actionable insights into performance enhancements, arming organizations with data-driven strategies for workforce development and competitive advantage.

Example 2: Unveiling the Power of Physical Exercise on Mental Well-being

Unlocking the correlation between physical exercise and mental health, this quantitative inquiry stands at the forefront of holistic wellness research.

Through meticulous data collection and rigorous statistical analyses, we dissect the nuanced relationship between exercise regimens and mental well-being indicators.

Our findings not only underscore the profound impact of exercise on psychological resilience but also provide actionable insights for healthcare professionals and individuals striving for optimal mental health.

Example 3: Revolutionizing Education with Innovative Teaching Methodologies

In this groundbreaking study, we embark on a quantitative exploration of the transformative potential of innovative teaching methods on student learning outcomes. Utilizing a quasi-experimental design, we meticulously evaluate the efficacy of novel pedagogical approaches against conventional teaching methodologies.

Through rigorous statistical analyses of pre-test and post-test data, we unearth compelling evidence of enhanced academic performance, paving the way for educational institutions to embrace innovation and elevate learning experiences.

Example 4: Assessing the Impact of Social Media Usage on Academic Performance

In this quantitative research endeavor, we delve into the relationship between social media usage patterns and academic achievement among college students. Employing a correlational research design, we collect data on students’ social media habits and their corresponding GPA scores.

Through regression analysis and other statistical techniques, we uncover insights into how factors such as frequency of social media use, types of platforms utilized, and time spent online correlate with academic performance. These findings provide valuable insights for educators, policymakers, and students themselves to optimize study habits and achieve academic success in the digital age.

Example 5: Analyzing the Effects of Financial Literacy Programs on Saving Behavior

This quantitative study investigates the efficacy of financial literacy programs in promoting responsible saving behavior among young adults. Using a randomized controlled trial methodology, we administer financial education interventions to a sample population and compare their saving habits to a control group over a specified period.

Through statistical analysis of savings rates, expenditure patterns, and financial decision-making, we discern the impact of financial literacy interventions on participants’ saving behaviors. These findings offer valuable implications for financial institutions, policymakers, and educators seeking to empower individuals with the knowledge and skills to make sound financial decisions and achieve long-term financial well-being.

Learn more: What is Quantitative Market Research?

The advantages of quantitative research make it a valuable research method in a variety of fields, particularly in fields that require precise measurement and testing of hypotheses.

• Precision: Quantitative research aims to be precise in its measurement and analysis of data. This can increase the accuracy of the results and enable researchers to make more precise predictions.
• Test hypotheses: Quantitative research is well-suited for testing specific hypotheses or research questions, allowing researchers to draw clear conclusions and make predictions based on the data.
• Quantify relationships: Quantitative research enables researchers to quantify and measure relationships between variables, allowing for more precise and quantitative comparisons.
• Efficiency: Quantitative research often involves the use of standardized procedures and data collection methods, which can make the research process more efficient and reduce the amount of time and resources required.
• Easy to compare: Quantitative research often involves the use of standardized measures and scales, which makes it easier to compare results across different studies or populations.
• Ability to detect small effects: Quantitative research is often able to detect small effects that may not be observable through qualitative research methods, due to the use of statistical analysis and large sample sizes.

Quantitative research is a type of research that focuses on collecting and analyzing numerical data to answer research questions. There are two main methods used to conduct quantitative research:

1. Primary Method

There are several methods of primary quantitative research, each with its own strengths and limitations.

Surveys: Surveys are a common method of quantitative research and involve collecting data from a sample of individuals using standardized questionnaires or interviews. Surveys can be conducted in various ways, such as online, by mail, by phone, or in person. Surveys can be used to study attitudes, behaviors, opinions, and demographics.

One of the main advantages of surveys is that they can be conducted on a large scale, making it possible to obtain representative data from a population. However, surveys can suffer from issues such as response bias, where participants may not provide accurate or truthful answers, and nonresponse bias, where certain groups may be less likely to participate in the survey.

Experiments: Experiments involve manipulating one or more variables to determine their effects on an outcome of interest. Experiments can be carried out in controlled laboratory settings or in real-world field environments. Experiments can be used to test causal relationships between variables and to establish cause-and-effect relationships.

One of the main advantages of experiments is that they provide a high level of control over the variables being studied, which can increase the internal validity of the study. However, experiments can suffer from issues such as artificiality, where the experimental setting may not accurately reflect real-world situations, and demand characteristics, where participants may change their behavior due to the experimental setting.

Observational studies: Observational studies involve observing and recording data without manipulating any variables. Observational studies can be conducted in various settings, such as naturalistic environments or controlled laboratory settings. Observational studies can be used to study behaviors, interactions, and phenomena that cannot be manipulated experimentally.

One of the main advantages of observational studies is that they can provide rich and detailed data about real-world phenomena. However, observational studies can suffer from issues such as observer bias, where the observer may interpret the data in a subjective manner, and reactivity, where the presence of the observer may change the behavior being observed.

Content analysis: Content analysis involves analyzing media or communication content, such as text, images, or videos, to identify patterns or trends. Content analysis can be used to study media representations of social issues or to identify patterns in social media data.

One of the main advantages of content analysis is that it can provide insights into the cultural and social values reflected in media content. However, content analysis can suffer from issues such as the subjectivity of the coding process and the potential for errors or bias in the data collection process.

Psychometrics: Psychometrics involves the development and validation of standardized tests or measures, such as personality tests or intelligence tests. Psychometrics can be used to study individual differences in psychological traits and to assess the validity and reliability of psychological measures.

One of the main advantages of psychometrics is that it can provide a standardized and objective way to measure psychological constructs. However, psychometrics can suffer from issues such as the cultural specificity of the measures and the potential for response bias in self-report measures.

2. Secondary Method

Secondary quantitative research methods involve analyzing existing data that was collected for other purposes. This can include data from government records, public opinion polls, or market research studies. Secondary research is often quicker and less expensive than primary research, but it may not provide data that is as specific to the research question.

One of the main advantages of secondary data analysis is that it can be a cost-effective way to obtain large amounts of data. However, secondary data analysis can suffer from issues such as the quality and relevance of the data, and the potential for missing or incomplete data.

Learn more: What is Quantitative Observation?

Here are the key best practices that should be followed when conducting quantitative research:

1. Clearly define the research question: The research question should be specific, measurable, and focused on a clear problem or issue.

2. Use a well-designed research design: The research design should be appropriate for the research question, and should include a clear sampling strategy, data collection methods, and statistical analysis plan.

3. Use validated and reliable instruments: The instruments used to collect data should be validated and reliable to ensure that the data collected is accurate and consistent.

4. Ensure informed consent: Participants should be fully informed about the purpose of the research, their rights, and how their data will be used. Informed consent should be obtained before data collection begins.

5. Minimize bias: Researchers should take steps to minimize bias in all stages of the research process, including study design, data collection, and data analysis.

6. Ensure data security and confidentiality: Data should be kept secure and confidential to protect the privacy of participants and prevent unauthorized access.

7. Use appropriate statistical analysis: Statistical analysis should be appropriate for the research question and the data collected. Accurate and clear reporting of results is imperative in quantitative research.

Learn more: What is Qualitative Research?

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Types of Research: Quantitative

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Quantitative research is a method of inquiry that focuses on collecting and analyzing numerical data. In other words, it deals with information that can be quantified or measured in numbers. This allows researchers to identify patterns, trends, and relationships between variables.

Here are some key characteristics of quantitative research:

• Numerical data: It relies on data that can be expressed in numbers, such as statistics, survey responses, or experimental results.
• Large samples: Quantitative studies typically involve collecting data from a large sample of participants to ensure generalizability of the findings to a wider population.
• Structured instruments: Data is often collected through standardized methods like surveys, questionnaires, or experiments with predetermined answer choices.
• Statistical analysis: Researchers use statistical techniques to analyze the numerical data and test hypotheses about relationships between variables.

Note: Imposed Numerical Values

Sometimes researchers look at qualities or characteristics that aren't inherently numerical and assign numbers to represent them. For example, a survey might ask you to rate your satisfaction on a scale of 1 to 5 (1 being least satisfied). Here, the numbers represent levels of satisfaction, a concept that isn't inherently numerical.

Randomized Controlled Trials

A randomized controlled trial (RCT) i s a scientific experiment considered the gold standard for testing the effectiveness of interventions like medications, treatments, or even educational programs. It involves splitting participants into groups and randomly assigning them to receive either the intervention being studied (experimental group) or a control condition (control group).

RCTs are a form of quantitative research. They rely on measuring outcomes and statistically analyzing the data to determine if the intervention has a statistically significant effect. This measured data allows researchers to compare the results between the groups objectively and draw conclusions about the effectiveness of the intervention.

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Research Paper Guide

Quantitative Research

Understanding Quantitative Research - Types & Data Collection Techniques

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Ever had a tough time with quantitative research? You're not alone! 

Quantitative research is the process of collecting and analyzing numerical data to understand and study various phenomena using statistical methods. Many find this tedious process tricky. 

But don't worry! 

Our complete guide is here to guide you through the important steps and tricks to handle this challenge with confidence. We've even added some examples to make it easier. 

So, let's dive in and learn together!

• 1. Quantitative Research Definition - What is Quantitative Research?
• 2. Data Collection in Quantitative Research
• 3. Data Analysis in Quantitative Research
• 4. Types of Quantitative Research Methods for Students and Researchers
• 5. Types of Data Collection Methodologies in Quantitative Research
• 6. Quantitative vs. Qualitative Research
• 7. Advantages and Strengths of Quantitative Research
• 8. Disadvantages and Weaknesses of Quantitative Research

Quantitative Research Definition - What is Quantitative Research?

Quantitative research involves gathering and studying numerical data. Its applications include identifying trends, making forecasts, testing cause-and-effect links, and drawing broader conclusions applicable to larger groups.

In this method, researchers employ tools such as surveys, experiments, and observations to gather data. Whereas in qualitative research, you deal with non-numeric data, such as text, video, or audio.

Quantitative research is extensively applied in natural and social sciences, including biology, chemistry, psychology, economics, sociology, and marketing, among others.

Characteristics of Quantitative Research

Here are some distinct quantitative research characteristics:

• Large Sample Sizes: Quantitative studies often involve larger sample sizes, allowing for more robust statistical analyses and generalizability of findings.
• Statistical Analysis: Statistical techniques and tools are extensively used to analyze data, unveiling patterns, relationships, and significance.
• Objective and Replicable: Quantitative research aims for objectivity and replicability. Other researchers should be able to conduct the same study and obtain similar results.
• Closed-Ended Questions: Surveys and questionnaires typically use closed-ended questions with predefined response options, making data analysis more straightforward.
• Quantifiable Variables: Researchers identify and measure variables that can be quantified, such as age, income, or test scores, for precise analysis.
• Hypothesis Testing: It often involves testing hypotheses and making inferences about populations based on sample data.
• Cross-Sectional or Longitudinal: Studies can be cross-sectional (data collected at a single point in time) or longitudinal (data collected over an extended period).
• Generalizability: Quantitative research seeks to generalize findings from a sample to a larger population, provided the sample is representative.

These characteristics make quantitative research different from qualitative research.

Data Collection in Quantitative Research

Data collection is the systematic process of gathering information for research purposes. It is a critical starting point, ensuring that the information gathered is relevant, accurate, and comprehensive.

• Structured Instruments - Quantitative research typically employs structured instruments like surveys and questionnaires. These tools ensure consistency in data gathering by posing the same set of questions to each participant.
• Sampling Methods - Researchers use various sampling techniques, such as random sampling, stratified sampling, or convenience sampling, to select a representative group from the target population.
• Objective Observation - Data collection often involves objective observations of phenomena. This may include recording numerical data, such as counting occurrences or measuring attributes.
• Experimental Control - In experimental research, control over variables is essential. Researchers manipulate one or more variables to observe their impact on the outcome, maintaining control over external factors.

Data Analysis in Quantitative Research

Data analysis is the second important aspect of quantitative research. After collecting the data, the data is analyzed with statistical methods. When analyzing, it is important that the results are relevant and related to the objective and aim of the research.

Below are some common statistical analysis methods that are used to analyze the collected data.

• SWOT Analysis - It stands for Strengths, Weaknesses, Opportunities, and Threats. Businesses use this kind of analysis to evaluate their performance and develop appropriate strategies.
• Conjoint Analysis - This kind of analysis helps businesses to identify how customers make difficult purchasing decisions. The businesses involved in direct sales and purchases know this and use the analysis to make the decisions.
• Cross-tabulation - A preliminary statistical analysis helps understand patterns, trends, and relationships between the various factors of the research.
• TURF Analysis - It stands for Totally Unduplicated Reach and Frequency Analysis. It is conducted to collect and analyze the data and responses of a chosen or favored target group.

Afterward, other methods like inferential statistics could be used to gather the results. 

Types of Quantitative Research Methods for Students and Researchers

‘What are the four types of quantitative research?’

Quantitative research has four distinct types, and all four of them are regarded as primary research methods. Primary quantitative research is more common and useful than secondary research methods. 

It is mainly because, in them, the researcher collects the data directly. He does not depend on previous research and collects the data from scratch. 

Below are the four types of quantitative research methods.

Survey Research 

This type of research is conducted through means of online surveys, online polls, and questionnaires. A group of people is chosen for the survey, and the method is used by big and small organizations and companies. They use it to understand their customers better.

Ideally, the survey is done through face-to-face meetings and interviews. Now, it is conducted through various online methodologies. Below are the common types of surveys.

• Cross-Sectional Survey - This research is conducted on a selected group of people at a certain point in time. The researcher evaluates several things. The selected group of people has similarities in all aspects except the ones chosen by the researcher. This kind of research is used for industries like retail, small-scale businesses, and healthcare industries.
• Longitudinal Survey - This research is based on observing a specific group of people for a set duration. The duration could be days, months, or even years. The researcher observes the change in behavior of the selected group of people.

This kind of research is used in the fields of applied sciences, medicine, and marketing.

Correlational Research 

Correlational research is conducted to identify the relationship between two entities. These entities must be closely related and have a significant impact on each other.

This research is conducted to identify, evaluate, and understand the correlation between the variables and how they depend on each other.

The researchers use mathematical and statistical methods to understand this correlation. Some factors that they consider include relationships, trends, and patterns between these variables.

Sometimes, the researchers make changes in one of the variables to notice the effect on the other one.

Causal-comparative Research 

This research is also known as quasi-experimental research. It is based on the cause and effect relationship between the two variables. Here, one of the variables is dependent on the other one, but the other one is independent. The researcher does not change the independent variable.

The research is not limited to statistical analysis only but includes other groups and variables also. The research could be conducted on the variables, no matter the kind of relationship they have. The statistical analysis method is used to acquire the results.

Experimental Research

This kind of research is based on proving or contradicting a theory or statement. It is also known as true experimentation and is usually focused on single or multiple theories.

The respective theory is not proven yet, and the research method is commonly used in natural sciences.

There could be some theories involved in this research. Due to this, it is more common in social sciences.

Types of Data Collection Methodologies in Quantitative Research

After determining the kind of research, finding the right data collection method is the most important step. Data could be collected through both the sampling and surveys and polls method.

Sampling Data Collection Method

In quantitative research, two types of sampling methods are used: probability and non-probability sampling.

1. Probability Sampling 

The data is collected by sifting some individuals from the general population and creating samples. The individuals, data samples are chosen randomly and without any particular selection criteria.

Probability sampling is further divided into the following kinds.

• Simple Random Sampling - This kind of data selection is the simplest one, and the participants are chosen randomly. This kind of sampling is conducted on a large population.
• Stratified Random Sampling - In this sampling, the population is divided into several groups and strata. The participants for the research are chosen randomly from those groups.
• Cluster Sampling - In cluster sampling, the population is divided into several clusters based on geography and demography.
• Systematic Sampling - In this, the samples from the population are chosen at regular intervals. These intervals are predefined, and usually, they are calculated based on the population or size of the target sample.

2. Non-Probability Sampling 

In this kind of data collection, the researcher uses his knowledge and experience to choose the samples. The researcher is involved and has a set of criteria. Due to this, not all individuals have the chance to be selected for the research.

Below are the main types of non-probability sampling frameworks.

• Convenience Sampling - These kinds of samples are probably the easiest to obtain. They are chosen only because they are the easiest ones to obtain. They are usually closer to the researcher, and these samples are easy to work with because there are no rigid parameters.
• Consecutive Sampling - This is similar to convenience sampling, but the researcher could choose a specific group of people for his research. The researcher could repeat the process with other groups of samples.
• Quota Sampling - The researchers select some specific elements based on the researcher’s target personalities and traits. Based on this, different individuals in the groups have equal chances of getting selected.
• Snowball Sampling - This kind of sampling is done on a target audience or a chosen group that is difficult to contact. In this, the chosen group is difficult to put together.
• Judgemental Sampling - This kind of sample is built based on the researcher’s skills, experience, and preferences.

Survey and Polls Data Collection Method

After the sample or group is chosen, the researcher could use polls or surveys to collect the required research data.

In this kind of research, the data is collected from a selected group of people. The data is used to identify new trends and collect information about different things and topics. Through the survey, the researcher could reach a wider population.

Based on the time allocated for the research, it could be used to collect more information and data.

When creating questions and options for the survey, the researchers use four measurement scales or criteria. These four parameters include nominal, interval, ordinal, and ratio measurement scales. Without them, no multiple-choice questions could be created.

The questions used for the survey must be close-ended. These could be a mix of different kinds of questions, and the responses could be analyzed through different rating scales.

After creating the survey, the next thing is to distribute it. Below are some of the commonly used survey distribution methods.

• Email - The most common method of distributing the survey is email management software to dispense the survey to your selected participants.
• Buying the Respondents - This is also a quite famous and widely used survey distribution method. Select the respondent and have him respond to the survey. Since the respondents would be knowledgeable, they will help in maximizing the results.
• Embedding the Survey on a Website - This is a great way of getting more responses and targeted results. Embedding the survey on a website works because the researcher is at the right place and close to the brand.
• Social Distribution - Distributing the survey through a social media platform helps collect more responses from the right audience.
• QR Code - The survey is stored in the QR code, and it is printed in magazines or on business cards.
• SMS Survey - It is the most convenient way of collecting more responses and data.

Like surveys, polls are also used to collect the data. It also has close-ended quantitative research questions, and election and exit polls are commonly used in this survey.

Quantitative vs. Qualitative Research

Quantitative and qualitative research are major kinds of research. They are mainly used in the subjects that follow detailed research patterns. How does it differ from quantitative research? 

Below is a detailed comparison of the two kinds of research.

Want to know more about the differences between these types of research? Check out this extensive read on qualitative vs. quantitative research to get more insights!

Advantages and Strengths of Quantitative Research

Quantitative research offers several advantages to researchers. Some of the main reasons why researchers use this kind of research are discussed below: 

• The Data Can Be Replicated - The research and study could be replicated. The data collection methods and definitions of the concepts are clear and easy to understand.
• The Results Can Be Compared Easily - The same study could be conducted in different cultural settings and sample groups. The results could also be compared statistically.
• Usage of Large Samples - Data and information from large samples could be processed and analyzed using different research procedures.
• Hypothesis Could be Tested - The researcher could use formal hypothesis testing. He could report the data collection, research variables, research predictions, and testing techniques before forecasting and establishing any conclusion.
• The Data Collection is Quick - The data could be collected easily and from a wider population. The usage of statistical methods and conducting and analyzing results is also easy and to the point.
• The Data Analysis is Inclusive - Quantitative data and research offer a wider population for sampling. They could be analyzed through research and analysis procedures.

Due to all of these advantages, researchers prefer using this kind of research method. It is easy to sample, collect, and analyze data and repeat the procedure easily.

Disadvantages and Weaknesses of Quantitative Research

Despite the benefits for the researchers, quantitative research design has some limitations. It may not be suitable for more complex and detailed kinds of topics.

Below are some common quantitative research limitations.

• Superficial - since the research includes limited and precise research samples. In quantitative research, the research is presented in numbers. They could be explained in detail through qualitative data and research.
• Limited Focus - the focus is narrow and limited, and the researcher would have to ignore other relevant and important variables.
• Biased Structure - structural biases could exist and affect sampling methods, data collection, and measurement results.
• Lack of Proper Conditions - sometimes, quantitative research may not include other important factors to collect the data.

Due to these reasons, quantitative research is not an ideal choice for detailed kinds of research. For them, qualitative research works better.

To help you further, we have added some useful examples of quantitative research here.

Quantitative Research Examples

Below are some helpful quantitative research examples to help you understand it better.

Sample Quantitative Research

Quantitative Research Example for Students

Now that you've got the hang of how to do quantitative research and why it's valuable, you're all set to begin your research journey.

The qualitative research method shows the idea and perception of your targeted audience. However, not every student is able to choose the right approach while writing a research paper. It requires a thorough understanding of both qualitative research and quantitative research methods.

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• Open access
• Published: 01 May 2024

Health system lessons from the global fund-supported procurement and supply chain investments in Zimbabwe: a mixed methods study

• Abaleng Lesego 1 ,
• Lawrence P. O. Were 1 , 2 ,
• Tsion Tsegaye 1 ,
• Rafiu Idris 3 ,
• Linden Morrison 3 ,
• Tatjana Peterson 3 ,
• Sheza Elhussein 3 ,
• Esther Antonio 4 ,
• Godfrey Magwindiri 4 ,
• Ivan Dumba 5 ,
• Cleyland Mtambirwa 5 ,
• Newman Madzikwa 5 ,
• Raiva Simbi 5 ,
• Misheck Ndlovu 6 &
• Tom Achoki 1  

BMC Health Services Research volume  24 , Article number:  557 ( 2024 ) Cite this article

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Metrics details

The Global Fund partnered with the Zimbabwean government to provide end-to-end support to strengthen the procurement and supply chain within the health system. This was accomplished through a series of strategic investments that included infrastructure and fleet improvement, training of personnel, modern equipment acquisition and warehouse optimisation. This assessment sought to determine the effects of the project on the health system.

This study employed a mixed methods design combining quantitative and qualitative research methods. The quantitative part entailed a descriptive analysis of procurement and supply chain data from the Zimbabwe healthcare system covering 2018 – 2021. The qualitative part comprised key informant interviews using a structured interview guide. Informants included health system stakeholders privy to the Global Fund-supported initiatives in Zimbabwe. The data collected through the interviews were transcribed in full and subjected to thematic content analysis.

Approximately 90% of public health facilities were covered by the procurement and distribution system. Timeliness of order fulfillment (within 90 days) at the facility level improved from an average of 42% to over 90% within the 4-year implementation period. Stockout rates for HIV drugs and test kits declined by 14% and 49% respectively. Population coverage for HIV treatment for both adults and children remained consistently high despite the increasing prevalence of people living with HIV. The value of expired commodities was reduced by 93% over the 4-year period.

Majority of the system stakeholders interviewed agreed that support from Global Fund was instrumental in improving the country's procurement and supply chain capacity. Key areas include improved infrastructure and equipment, data and information systems, health workforce and financing. Many of the participants also cited the Global Fund-supported warehouse optimization as critical to improving inventory management practices.

It is imperative for governments and donors keen to strengthen health systems to pay close attention to the procurement and distribution of medicines and health commodities. There is need to collaborate through joint planning and implementation to optimize the available resources. Organizational autonomy and sharing of best practices in management while strengthening accountability systems are fundamentally important in the efforts to build institutional capacity.

Peer Review reports

The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund), together with its Zimbabwean national and international stakeholders have continued to invest in health system strengthening to improve public health in the country. These investments have been guided by systemwide strategic assessments to understand the fundamental challenges facing the Zimbabwean health system [ 1 , 2 , 3 , 4 ].

Given the strategic importance of access to essential medicines, vaccines, and other health technologies as a strategic pillar of any health system [ 4 , 5 ], the Global Fund supported the Ministry of Health and Child Care (MOHCC) through the United Nations Development Program (UNDP) to undertake a comprehensive assessment of the national procurement and supply chain management (PSCM) system in 2013 [ 6 ]. This assessment aimed to develop a strategic vision and costed action plan for improvement. This covered both upstream and downstream aspects of PSCM. The strategic purpose was to help the MOHCC launch a coordinated approach to invest in PSCM improvements and enhance coordination and cooperation in managing all health commodities across the health system [ 3 , 6 , 7 ].

Subsequently, the Global Fund supported several initiatives aimed at the realization of the improvements proposed by the comprehensive assessment undertaken by UNDP [ 6 ]. These initiatives primarily encompassed end-to-end supply chain assistance in critical areas, including demand quantification and forecasting, warehousing capacity development, fleet improvement, distribution systems, and waste management systems. Other support aspects focused on warehouse optimization, data and information management systems, and personnel training [ 7 , 8 ]. In general, warehouse optimization is the process of improving the efficiency and effectiveness of warehouse operations. It involved refining workflows, leveraging technology, enhancing spatial utilization, and ensuring precise inventory management [ 6 , 7 ].

More specifically, the Global Fund provided funds for the construction of warehouses for the National Pharmaceutical Company of Zimbabwe (NATPHARM) to facilitate the smooth handling of health commodities. Additionally, this support extended to water supplies (in terms of sinking boreholes) for various warehouses, water tanks, and booster pumps for the other branches. Global Fund also supported NATPHARM in constructing two incinerators in the two main cities, Harare, and Bulawayo to handle pharmaceutical waste effectively [ 9 , 10 ].

Through Global Fund support, NATPHARM also received modern warehouse equipment such as forklifts, pallet jacks and rolling ladders, and data and information management system support for better visibility and effective handling of health commodities. The support further extended to optimising the Harare branch warehouse with modern receiving and transit capabilities to serve other feeder locations and the procurement of modern delivery trucks to facilitate the distribution of commodities. Global Fund support also extended to personnel training and retention for the effective functioning of NATPHARM and the broader procurement and supply system [ 3 , 6 , 9 ].

Focusing on quality assurance and safety of medicines and other health commodities, Global Fund supported the Medicines Control Authority of Zimbabwe (MCAZ) to develop capacity in quality testing of all commodities procured through grants. MCAZ was also supported with the installation of solar panels that allowed for an uninterrupted power supply to facilitate smooth operations at the organization. The Global Fund support was also critical in the upgrading of the biology and chemistry laboratories to attain WHO prequalification standards [ 8 , 9 , 11 ]. Similarly, there was direct support from the Global Fund to facilitate pharmacovigilance activities, such as adverse drug reactions reporting using electronic systems. All these measures were meant to ensure that the medicines and health commodities consumed in the Zimbabwean health system were safe and quality-assured [ 4 , 5 , 9 , 11 ].

The overarching project by the Global Fund to support the Zimbabwean government was designed and implemented in response to the nationally identified gaps and opportunities that were established through various research and consultative efforts [ 3 , 4 , 6 , 8 ]. The project was kickstarted in the first quarter of 2019 and continued through 2021, with various project components being implemented in a phased approach to achieve the national targets [ 3 , 4 , 7 , 10 , 11 ]. To ensure effective coordination, accountability, and avoidance of duplicative efforts, the project was designed and implemented in close coordination with other partners represented in the National Health Development Partners Coordination Forum (HDPCF), Health Sector Technical Working Group (HSTWG), and the Global Fund Country Coordination Mechanism (CCM), among others [ 3 , 4 , 6 , 8 , 10 ]. There was regular reporting to the respective coordination mechanisms to track progress as well as troubleshoot any implementation issues as they arose [ 6 , 9 ].

Overall, the gaps identified through the various assessments commissioned by the Global Fund included a lack of effective coordination, poor inventory and order management, human resource constraints, and warehousing and storage inefficiencies [ 12 , 13 ]. Therefore, the focus of the Global Fund support was to retool the Zimbabwean PSCM system to be efficient, cost-effective and responsive to the population's health needs, particularly in the face of global epidemics and pandemics such as HIV/AIDS and COVID-19 [ 14 , 15 , 16 , 17 , 18 ].

In an attempt to resolve these issues, healthcare systems around the world are working on streamlining their supply chains through various health system strengthening measures [ 19 , 20 , 21 , 22 ]. Therefore, the objective of this analysis was to assess the overall effects of the Global Fund-supported investments in the Zimbabwean PSCM system and document the lessons learned to inform future programming efforts to strengthen healthcare systems.

The assessment covered a period of 2018 -2021 and employed both qualitative and quantitative research methods. Figure 1 . illustrates the convergent mixed methods study design that was applied. In this study design, both the qualitative and quantitative data collection and analyses are implemented simultaneously, and the insights merged to provide a fuller picture [ 23 ].

Convergent mixed methods design

The quantitative part of the study entailed collecting and analysing administrative data covering priority indicators that are routinely reported on the Zimbabwe PSCM. Meanwhile, the qualitative part comprised of key informant interviews (KII) focusing on stakeholders within the healthcare system to give perspective to the observed data trends. Insight from the two parts of the analysis were merged and subjected to comparative assessment and interpretation to ensure that a consistent picture emerged [ 23 , 24 ]. More details on the methods are provided in later sections.

Analytical framework

The overall analytical approach espoused in this assessment was anchored on the logical relationships of the building blocks of the health system as described by the World Health Organization (WHO) health system framework [ 5 ]. Figure 2 shows the analytical framework, which illustrates the results chain cascading from the Global Fund-supported initiatives to the expected improvements in intermediate and long-term outcomes related to PSCM, including the availability of medicines, reduced wastage, and overall improvements in population-level coverage [ 4 , 5 ].

Analytic framework

Overall, the framework graphically displays the results Global Fund intended to achieve through its support to the Zimbabwean PSCM. The "theory of change" that underlies the Global Fund’s strategy is revealed through the arrows in the diagram that identify “causal” linkages through which various intermediate results interact to make progress toward the overall goal of improving health system performance [ 3 , 5 ].

Quantitative research

The quantitative research entailed a detailed descriptive analysis of the operational data that was routinely reported across the Zimbabwe PSCM system. Table 1 . shows some of the key performance indicators (KPI) that were considered in our analysis.

Data collection, management and analysis

The data used in this analysis were obtained from the routinely reported operational data that included the NATPHARM-operated warehouses and healthcare facilities in the country. The data were extracted from the various data management systems operated by the different institutions, cleaned, and collated into a comprehensive dataset in the form of a spreadsheet covering the period of the assessment. The database was examined for completeness and accuracy by cross-referencing the corresponding progress reports for specific periods. Trends of priority indicators were compared over time, as they related to Global Fund support to the PSCM space.

Qualitative research

The qualitative assessment entailed KIIs with health system stakeholders who were knowledgeable and intimately involved in the Global Fund-supported initiatives and its intended beneficiaries. This included provincial and district management teams, hospital and clinic personnel, and other stakeholders in the Zimbabwe health system. Informed consent was obtained from each study participant involved in the study. The data collection protocol ensured that all study participants fully understood the objectives of the study and consented verbally to provide the required information.

As previously stated, the literature review helped map and identify critical organisations involved in the PSCM space, and more specifically, those involved in the procurement and health system strengthening activities supported by the Global Fund. A full list of those organisations is provided on Table  2 .

Sampling techniques

Convenience purposive sampling was used to select key informants and in-depth interviews [ 23 ]. Our sample was supplemented using snowball sampling methods (also called chain sampling). The initial respondents referred other potential respondents until no new information was forthcoming or achieved saturation. Efforts were made to be all-inclusive, involving various stakeholder groups and organisations intimately linked to the operations of the Zimbabwe PSCM landscape.

This comprised of KIIs using a structured interview guide that covered various thematic areas relevant to the assessment to obtain a comprehensive perspective of the impact of the Global Fund-supported initiatives in the country. In its development, testing and validation, the key informant guide was pretested and adapted to ensure suitability for the task. In view of the restrictions imposed to prevent the spread of COVID-19 infections at the time of the study, some KIIs were conducted online using multimedia channels such as Zoom, Skype, and telephonically.

Three research assistants supported the two project leaders in conducting the KIIs. After each interview, all notes taken by the research assistant were checked by the two project leaders to ensure completeness and readability to minimise recording errors. In addition, a tape recorder was used for interviews to assist with reference post data collection. All the recordings were stored in a pin-protected cloud storage which was only accessible by the two project evaluation leaders. Qualitative data obtained from the KIIs were transcribed in full and then manually analysed applying thematic content analysis. Where there was a divergence of opinion, an agreement was established through discussion with three members of the project evaluation team. In thematic analysis, data from interview transcripts were grouped into similar concepts. This approach was appropriate for semi-structured expert interviews as it is used to code text with a predefined coding system that can then be refined and completed with new themes emerging [ 23 , 24 ]. Our initial coding system was defined during the desk review stage and continuously updated in the successive phases of data collection employing a deductive approach of qualitative research. The emerging themes were not preconceived (desk review) but emerged from the data during the coding process, while the global themes were the highest-order themes that emerged from the data and were broad enough to capture the essence of the entire dataset [ 23 ]. The codes are presented in a tabular format in the results section below.

This section presents both the quantitative and qualitative research results from the study. The quantitative results comprise of trends of the priority operational PSCM indicators for the relevant period. The qualitative results present the perspectives of the key health stakeholders involved in the Zimbabwe healthcare system.

Quantitative results

Table 3 shows that the total warehouse capacity across the Zimbabwean health system increased by 37.8% between 2018 and 2021.

Of the 1500 public health facilities in Zimbabwe coverage by the PSCM system was consistently high between the years 2018 and 2021, averaging 94%, and increasing by 13.6% over the same period. However, order fulfillment rate within 90 days, for 1410 reporting health facilities was consistently below 50% from 2018 to 2020, despite the reported high coverage for the health facilities by the PSCM in the country. Notably, this indicator showed remarkable improvement to 91% in 2021, from an average of 42% from the previous three years. More specifically, the order fill rate for Tenofovir 300mg/ lamivudine 300mg/efavirenz 600mg (TLE 600mg) improved despite the significant drop observed in 2020. However, when comparing 2018 and 2021, the order fill rate for this specific HIV drug increased by around 36.5%, while the stockout rates for the same drug at the central stores declined by about 14.5% over the same period.

Table 3 further shows a 44% drop of order fill rates for the Determine HIV Test Kit between 2018 and 2020 for the 1410 reporting health facilities, only to recover in the year 2021, where order fill rates improved to 83%. At the same time, the stockout rates for the Determine HIV Test Kit at the central stores declined by 49% between 2018 and 2021.

Figure  3 shows the estimated average population coverage for HIV treatment for adults and children, from 2018 to 2020, at 92% and 71%, respectively. The figure shows that there was limited variation in the population level coverage over the years, despite the estimated increase in the number of people living with HIV over the same period. The national target for this indicator is 95%.

Adult and paediatric HIV treatment population coverage

Figure  4 shows the proportion of the value of the expired stock in the 7 warehouses, over three years, between 2019 and 2021, which demonstrates a declining trend over time. The highest expiry was in quarter 4 2019 at 1.9%, compared with the lowest in quarter 3 2021 at 0.1%. This represents a 93% reduction in value of expired stock.

Percentage value of expired stock

Figure  5 shows the combined stock-taking variance valued in United States Dollar terms across 7 warehouses over a three-year period. The stock variance shows a declining trend over the three-year period to negligible values at the end of 2021.

Stock-taking variance

Figure  6 shows the temporal trend of the number of days that it took NATPHARM to resolve the stock variances across the different warehouses in the country. Overall, there is a decline from the average of 8 days from the December 2018 stocktake (with Harare warehouse as an outliner at 25 days), to an average of 1 day in the December 2021 stocktake, where all warehouses converge.

Duration to resolve stock variance

Table 4 shows the funding levels in USD$ to support the diagnostic capacity for Covid-19, comprising of the polymerase chain reaction test (PCR) and rapid diagnostic tests (RDT). The table further shows the PSCM related costs, the total test done, and positive cases identified over the two-year period. The total funding between 2020 and 2021 increased by 290%, with testing levels increasing by 490% over the same period. The average Covid-19 positivity rate in 2020 was 6.4% while the positivity rate for 2021, was 3.1%, indicating a greater than 50% drop.

Qualitative results

Most of the participants interviewed acknowledged that the Global Fund support to NATPHARM and the broader Zimbabwean health system had been central in improving the overall performance of the health system through improved availability of essential medicines and other health commodities. This was largely achieved by ramping up the various components of the PSCM value chain and related operations, leading to efficiency, effectiveness and reliability.

Table 5 shows the codes, emerging and global themes from the thematic content analysis. The emerging themes revolved around the lack of infrastructure and equipment curtailing warehouse operations before the Global Fund support. Data gaps and poor product visibility were also emerging themes, as were the effects of the old fleet on the overall supply and distribution system. Similarly, issues of infrastructure, capacity, and personnel training gaps emerged as crucial themes hindering quality assurance within the PSCM. Global themes also largely focused on infrastructural inadequacy leading to underperformance. Improvements leading to better handling of commodities; data and information systems, enhancing visibility and supporting accuracy in forecasts; improvements in the distribution systems enabled by newer fleets also featured as global themes. Similarly, better trained and motivated personnel, able to perform critical functions; capacity to ensure the quality and safety of medicines and other health commodities; and the need for effective multistakeholder partnerships to improve effectiveness and sustainability of health systems, were key themes.

NATPHARM operations

According to the NATPHARM management, warehouse improvement and optimisation exercise resulted in better visibility and improved efficiency in the operations related to the commodity handling across the entire value chain. More specifically, the processes related to stock taking improved markedly over time according to the reports presented by various organizations that had been commissioned to undertake the stock audits.

“ …. warehouse optimisation supported implementing an inventory management system which conformed with bin location and variant codes, according to different donors. The result was improved, faster and more accurate stock takes, a sharp reduction of variances and more streamlined order processing ” Participant, NATPHARM.

Further, it was reported by various participants that order processing and deliveries had improved to be timely and on schedule as a result of the improved visibility and efficiency harnessed across the PSCM. Similarly, there was consensus that receiving processes and documentation had significantly improved through the support offered by Global Fund particularly towards warehouse optimization. The improvements in the data management systems and related trainings were also cited as contributory to the overall trend that was observed.

“ Reporting quality has greatly improved and is now timely, accurate and complete. This helps in accurate forecasting of demand, which in turn avoids unnecessary wastage and expiries” Participant MOHCC.

There was consensus from the majority of participants interviewed that the fleet improvements had improved the availability of essential commodities vital for the effective management of high burden diseases; HIV/AIDS, Malaria and Tuberculosis in Zimbabwe. According to participants from a local health facility, this was evidenced by low stockout rates for the key commodities needed to manage these three conditions effectively. The new fleet was reported to facilitate deliveries from various warehouses to the recipient health facilities on a regular basis. This level of distributional access coupled with better demand forecasting as a result of improved data use, was noted as critical in the improved availability of medicines and health commodities at the health facility levels.

Further, respondents in the leadership of NATPHARM revealed that the Global Fund support had benefited the overall financial position of the organisation by tapping into efficiencies harnessed through the various measures that have been implemented. Some of the support measures that resulted in efficiency improvements include, the warehouse optimization, pharmaceutical waste management and fleet improvements, which ultimately reduced operational costs.

For example, it was noted that running a newer fleet of vehicles led to lower maintenance and fuelling costs than previously was the case, when deliveries were done using older vehicles. Similarly, it was noted that pharmaceutical waste resulting from expired medicines and other health commodities was expensive to store and dispose, particularly when engaging third party organizations. However, this additional cost was reportedly in the decline, as a result of the investment in the incinerators for waste management.

“ The provision of incinerators for waste management has resulted in huge savings in terms of the cost of waste destruction. It has also resulted in significant compliance with environmental health regulations.”, Participant, NATPHARM.

MCAZ operations

Majority of the participants agreed that the Global Fund support to MCAZ strengthened its overall capacity to handle the requisite safety and quality assurance needs to effectively support the procurement functions for medicines and other health commodities within the country and regionally. The installation of solar panels to provide uninterrupted electricity power supply for the operations of the organization was cited as a huge advantage allowing for improved performance, in a country where power supply is unreliable. Similarly, other participants cited, the support for MCAZ laboratories to obtain the WHO prequalification status, as a major step towards effectiveness and sustainability for the organization; citing the fact that MCAZ is offering quality assurance services regionally at a fee.

“ We [MCAZ] now have the capacity to conduct the safety and quality assurance tests needed to support the procurement of commodities in the country and the region. We [MCAZ] even recently won the tender to support the regional procurement activities ”, Participant, MCAZ.

Based on the results framework provided in Fig.  3 , there is clear evidence that the Global Fund-supported initiatives resulted in positive improvements in the overall performance of the Zimbabwean PSCM system. However, it is important to recognise some of the assessment’s limitations in interpreting these findings. First, the results reported are for a limited observation period and a limited set of indicators, which are largely confined to the national level analysis, missing out on granular subnational and commodity-specific analysis that could be more informative. Secondly, this study was not conceptualised before the onset of the intervention reported here (i.e., Global Fund-supported initiatives), and therefore, no specific steps were taken to develop an appropriate prospective research design and data collection strategy to support a more rigorous assessment. Therefore, the study relied on secondary PSCM data that were sparse and covered a limited period. Third, the study could be subject to confounding relationships with other concurrent interventions being implemented by other health system stakeholders that have direct or indirect effects on the PSCM system, complicating impact attribution to specific interventions. Forth, the analysis focused only on a narrow subset of medicines and commodities related to HIV/AIDS and COVID-19. However, despite these limitations, every effort has been made to use the most up-to-date and complete information available, including validation using official reports and collaborative reported data with key informant interviews.

The estimated population coverage for HIV treatment for both adults and children remained consistently high despite the increasing prevalence in the country. It was estimated that adults living with HIV increased by 10% from a baseline of 2018, to reach 1.3M in 2020, while children living with HIV increased by 24%, from a baseline of 2018, to reach 75 000 in 2020 [ 3 , 4 , 9 ]. As a key last mile population outcome, it can be rightly assumed that high HIV treatment coverage in the Zimbabwean system emanated from strengthened inventory management functionality and improved delivery of orders supported by a modern fleet of vehicles, which allowed for meeting the supply target of four quarterly rounds [ 3 , 8 ]. Population coverage is an important performance measure for a health system. It unites two important concepts; need and utilisation of an intervention to improve health [ 25 ]. In our case, the intervention is HIV treatment and the population in need is those living with HIV needing treatment; and the proportion with access and able to use the treatment they need, represents population coverage. This is a fundamentally important consideration as various health systems, including low- and middle-income countries, are making universal health coverage (UHC) efforts. There is no question, that improved access to essential medicines and other health technologies is a fundamental cornerstone towards UHC [ 1 , 5 , 26 ].

Other intermediate indicators that are critical for progress towards improved availability of medicines and other health commodities and hence UHC, also showed significant improvements that could be attributed to Global Fund-supported initiatives. For example, reduced wastage and decreasing value of expired health commodities reported, point towards improving efficiency across the value chain. As noted earlier, efficiency is one of the fundamental expectations of an effective health system outlined in the WHO health system framework [ 3 , 5 ]. The diminishing value of expiries could be ascribed to various factors, including the improved workflow processes and data accuracy at NATPHARM. This improvement which is associated with better visibility of commodities across the value chain could be attributed to investments made by Global Fund such as the enterprise resource planning platform, coupled with concomitant training and supervision.

Through Global Fund’s assistance to NATPHARM, automation of tasks such as stock management, ordering, and other operational activities was central and contributory to driving the observed improvements in the handling of commodities; reduction of wastage and expiries and improving availability. Similarly, better inventory management and warehouse optimization activities such as decongestion resulted in quicker, timely, more accurate, and well-documented stock takes, improving overall commodity management.

Variances between stock on hand and physical counts were used to determine whether facilities are conducting period checks on their stocks and therefore calculating monthly consumption of commodities accurately. As such the variance across commodities should be zero. Low variance indicates that the stocks at hand are generally similar and do not vary widely from the physical stock counts, while high variance indicates that the respective values have greater variability and are more widely dispersed from one another. There is clear evidence pointing towards the reduction in stock variances when comparing stock on hand and physical counts across the different warehouses in the country over time. This trend can be attributed to better visibility of commodities at the warehouses and training of personnel which was supported by the Global Fund [ 2 , 9 ]. Similarly, the number of days it took the NATPHARM personnel to resolve stock variances showed a dramatic reduction, from an average of 8 days to 1 day in a span of 3 years. This observed trend could also further support the claim that overall, the Global Fund supported initiatives produced the desired results.

With the advent of Covid-19, the effects of the Global Fund support on the PSCM became evident considering the robust response the country was able to mount particularly in terms of diagnostics [ 9 ]. The country was able to rapidly roll out COVID-19 testing, reaching many people between 2020 and 2021. Similarly, the Covid-19 positivity rates declined from 6.4% to about 3.1% over the same period. High positivity rates may indicate that the health system is only testing the sickest patients who seek medical attention and is not casting a wide enough net to know how much of the virus is spreading within its communities. A low rate of positivity on the other hand, can be seen as a sign that a health system has sufficient testing capacity for the size of the Covid-19 outbreak and is testing enough of its population to make informed decisions about reopening the economy. The WHO guidance is that countries which have conducted extensive testing for COVID-19, should remain at 5% or lower positivity rate for at least 14 days.

Safe pharmaceutical waste management and disposal is a primary consideration of any effective health system in completing the PSCM loop [ 27 ]. The Global Fund supported the investment in MOHCC operated incinerators. These investments could largely be associated with reduction in the cost of storage, handling and disposal of the expired stock, particularly when considering that certain space was rented from third parties which often charged a premium. Safe pharmaceutical waste disposal also became more priority with the increased supplies that resulted from the efforts to tackle the Covid-19 pandemic.

Despite signs of progress, there was temporary faltering of indicators- namely, order fill and stockout rates; associated with key commodities for effective management of HIV in the year 2020, warranting an explanation. The drop in Tenofovir/Lamivudine/Efavirenz (TLE 600mg) in 2020 could be linked to several factors. In the year 2019, the Zimbabwe MOHCC adopted new treatment regimens containing Dolutegravir. This means, newly HIV positive clients were started on Dolutegravir regimen as standard of care rather than the previous first line treatment which then surged Tenofovir/Lamivudine/Dolutegravir 50mg order fill rate, while having the opposite effect on the old regimen. Lastly, the effects of COVID-19 pandemic cannot be underestimated as the global supply chain systems were logged with delays which caused disruptions and inefficiencies in health systems in many countries [ 28 ]. In the same period, Determine HIV Test Kit rebounded from stocking out in central stores because of strengthened warehouse optimization activities, including better inventory management, purposeful stock taking, and approval processes contributed to the lowering of stockout rates.

The Global Fund-supported initiatives were also instrumental in building capacity by training key personnel for the effective implementation of activities related to the procurement and supply chain management function [ 8 , 29 , 30 ]. Better quantification and forecasting capabilities (due to data availability through e-LMIS and personnel training), improved warehousing capacity to hold a wide portfolio of products, and direct delivery to facilities through a modern fleet could have contributed to the high population coverage reported [ 26 , 30 , 31 ]. According to the WHO health system framework, effective leadership is required to coordinate all the functions of the health system in order to achieve the desired outcomes [ 5 ]. Therefore, it is sensible to conclude that, the reported health system improvements could not have happened without effective leadership and well-trained staff tasked with coordination and management across the PSCM value chain. It can be further inferred that the training and capacity development measures offered to the NATPHARM personnel were consequential in supporting the broader health system to meet its overall objectives, including improving PSCM performance [ 2 , 7 , 29 ].

Similarly, adequate infrastructure, equipment, data, and information management systems are crucial ingredients for a well-functioning health system, according to the WHO health system framework [ 4 , 5 , 9 ]. The Global Fund-supported initiatives were central in supporting these aspects of the health system through improved warehousing capacity, of modern equipment, installation of solar panels, fleet improvement and deployment of an electronic-logistic management information system (e-LMIS). The cumulative benefits of these investments include optimised procurement and distribution of commodities leading to a reduction in stockout rates and timely order refills to meet the population health needs [ 9 ].

Based on these findings, it would be reasonable to conclude that the Global Fund-supported initiatives in Zimbabwe contributed positively to strengthening the health system, particularly through the improved performance of the various indicators linked to the PSCM system at national and regional warehouses, as well as health facilities. Considering the prevailing health needs in the country, the implementation of this project and the manner of investments provide a basis and playbook for further support to make progress. This is particularly true considering the various competing priorities in the Zimbabwean healthcare system amidst resource constraints [ 3 , 6 , 8 ]. This was largely underpinned on the overarching focus on UHC and the critical role that an effective PSCM plays towards that very objective [ 3 , 12 , 13 , 14 ].

The Global Fund-supported project in Zimbabwe worked through the existing national coordination mechanisms where various key stakeholders, including MOHCC and NATPHARM, were involved in all key strategic planning and implementation decisions, ensuring country leadership and ownership. It was clear from the outset that this approach required sound partnership, transparency, and accountability among all the involved stakeholders, to deliberate and find common ground, guided by the overarching objective to make progress towards UHC.

The question of securing the gains and ensuring sustainability is fundamental for donor supported health programs in low- and middle- income countries. To make progress, it is imperative for health system stakeholders, including governments and donor organizations that are keen to sustainably strengthen health systems to pay close attention to critical areas like the procurement and distribution of health commodities. It is critical to collaborate with key stakeholders through joint planning and implementation to optimize the available resources. Organizational autonomy coupled with strong data driven accountability systems and the sharing of best management practices are fundamentally important in this discourse.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request and once written permission is obtained from NATPHARM.

Abbreviations

Country Coordination Mechanism

Health Development Partners Coordination Forum

Health Sector Technical Working Group

Key Informant Interview

Medicines Control Authority of Zimbabwe

Ministry of Health and Child Care

National Pharmaceutical Company of Zimbabwe

Polymerase Chain Reaction

Procurement and Supply Chain Management

Rapid Diagnostic Tests

Tenofovir Lamivudine Efavirenz

Universal Health Coverage

United Nations Development Program

World Health Organization

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Arney L, Yadav P, Miller R, Wilkerson T. Strategic contracting practices to improve procurement of health commodities. Glob Health Sci Pract. 2014;2(3):295–306. https://doi.org/10.9745/GHSP-D-14-00068 .

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Acknowledgements

The authors would like to thank the participants from the different organizations in Zimbabwe that provided feedback during the study. They are also grateful to the management of the various organizations that allowed their staff to participate and provided premises and other resources that were used during the interviews. Gratitude to Sarah Gurrib who proofread the manuscript and offered useful comments.

The study was funded by the Global Fund to Fight AIDs, Tuberculosis and Malaria. The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the article. All authors had full access to study data and had final responsibility for the decision to submit for publication.

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Africa Institute for Health Policy, P.O. Box 57266-00200, Nairobi, Kenya

Abaleng Lesego, Lawrence P. O. Were, Tsion Tsegaye & Tom Achoki

Department of Health Sciences & Department of Global Health, Boston University, Boston, U.S.A.

Lawrence P. O. Were

Global Fund to Fight AIDs, Tuberculosis and Malaria, Geneva, Switzerland

Rafiu Idris, Linden Morrison, Tatjana Peterson & Sheza Elhussein

PricewaterhouseCoopers, Harare, Zimbabwe

Esther Antonio & Godfrey Magwindiri

National Pharmaceutical Company of Zimbabwe, Harare, Zimbabwe

Ivan Dumba, Cleyland Mtambirwa, Newman Madzikwa & Raiva Simbi

Medicines Control Authority of Zimbabwe, Harare, Zimbabwe

Misheck Ndlovu

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Contributions

Tom Achoki (TA), Rafiu Idris (RI), Lawrence Were (LW) and Abaleng Lesego (AL) conceptualized and designed the study. AL, TA, and Godfrey Magwindiri (GM) collected and analyzed data. TA and AL drafted the manuscript. Tsion Tsegaye (TT), Linden Morrison (LM), Tatjana Peterson (TP), Sheza Elhussein (SE), Esther Antonio(EA), Ivan Dumba (ID), Cleyland Mtambirwa (CM), Newman Madzikwa(NM), Raiva Simbi (RS), Misheck Ndlovu (MN) and LW did the critical revisions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tom Achoki .

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Ethics approval and consent to participate.

Ethical approval including the consent procedure for participants was obtained from the Ethics Committee of the MOHCC Department of Research and NATPHARM, Harare Zimbabwe. The conduct and methods of this study adhered to the tenets outlined in the Declaration of Helsinki. Informed consent was obtained from each study participant involved in the study. The data collection protocol ensured that all study participants fully understood the objectives of the study and consented in writing to provide the required information. Before the interview commenced, the participants also consented verbally and confirmed that they had understood the objectives of the study and that they could opt out of the interview at any time without prejudice. No sensitive or personally identifying information was collected regarding the study participants.

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Not applicable.

Competing Interests

RI, LM, TP and SE declare that they are fulltime employees at the Global Fund to Fight AIDs, Tuberculosis and Malaria. EA and GM declare that they are fulltime employees at PricewaterhouseCoopers. ID, CM, NM and RS are fulltime employees at National Pharmaceutical Company of Zimbabwe. The rest of the authors declare that they have no competing interests.

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Lesego, A., Were, L.P.O., Tsegaye, T. et al. Health system lessons from the global fund-supported procurement and supply chain investments in Zimbabwe: a mixed methods study. BMC Health Serv Res 24 , 557 (2024). https://doi.org/10.1186/s12913-024-11028-6

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Received : 06 March 2023

Accepted : 22 April 2024

Published : 01 May 2024

DOI : https://doi.org/10.1186/s12913-024-11028-6

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Qualitative Research Design

This course is part of Qualitative Research Design and Methods for Public Health Specialization

Taught in English

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Instructor: Karen Andes, PhD

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(103 reviews)

What you'll learn

Design a qualitative research project to respond to specific public health problems/questions.

Design strategies and instruments for qualitative data collection linked to study objectives and appropriate for the population of interest.

Address ethical concerns in qualitative research design and implementation.

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There are 6 modules in this course

This course introduces qualitative research, compares and contrasts qualitative and quantitative research approaches, and provides an overview of qualitative methods for data collection. It outlines a step-by-step approach to qualitative research design that begins by identifying a public health topic of interest, works to hone in on a specific research problem, and then specifies research questions, objectives, and specific aims. The course emphasizes the iterative nature of research design in qualitative inquiry and highlights the importance of specifying a population of interest, an appropriate sampling strategy, and potential approaches to recruitment. It introduces the relationship between these considerations and key concepts such as saturation and transferability in qualitative research. Finally, the course considers ethical concerns specific to qualitative research and potential solutions. Learners of this course will not only be able to put what they learn into practice, but they'll also develop a portfolio of qualitative research materials for career advancement.

Introduction to Qualitative Research

In this first week, you'll get the chance to explore characteristics and approaches of both qualitative and quantitative research, understand their differences, and acknowledge how both are complementary.

What's included

6 videos 4 readings 1 quiz 1 peer review

6 videos • Total 20 minutes

• Welcome to the Course! • 2 minutes • Preview module
• What Is Research? • 2 minutes
• What Is Qualitative Research? • 3 minutes
• How Is Qualitative Different From Quantitative? • 4 minutes
• Five Basic Approaches to Qualitative Research • 4 minutes
• Qualitative and Quantitative As Complementary Methods • 3 minutes

4 readings • Total 145 minutes

• Course Outline and Grading Information • 5 minutes
• Introduction to Qualitative Research • 20 minutes
• Qualitative Inquiry • 90 minutes
• Mixed Methods Design • 30 minutes

1 quiz • Total 20 minutes

• Practice • 20 minutes

1 peer review • Total 60 minutes

• Week 1: Comparing Quantitative and Qualitative Studies • 60 minutes

Qualitative Methods

This week, we'll look at two types of data that can be collected and dive into the three main data collection methods used in qualitative studies. Finally, we'll wrap up by discussing the concept of saturation and consider the lingering question, "how much data is enough?"

6 videos 2 readings 1 quiz 1 peer review

6 videos • Total 15 minutes

• A Look at This Week • 2 minutes • Preview module
• Types of Data • 1 minute
• Observation • 4 minutes
• Interviews • 3 minutes
• Focus Group Discussions • 2 minutes
• Saturation: How Much Data to Collect? • 2 minutes

2 readings • Total 135 minutes

• Data Collection • 90 minutes
• Saturation Point • 45 minutes
• Week 2: Exploring Qualitative Methods • 60 minutes

Objective-Driven Design

In our third week, we'll discuss how to develop a problem statement from a topic of interest, craft research questions and aims, and discuss how this process all relates to objective-driven design.

5 videos 2 readings 1 quiz 1 peer review

5 videos • Total 21 minutes

• A Look at This Week • 3 minutes • Preview module
• What Is Objective-Driven Design? • 1 minute
• Uncovering a Problem • 6 minutes
• Developing & Refining a Problem Statement • 3 minutes
• Developing Research Questions & Specific Aims • 6 minutes

2 readings • Total 75 minutes

• Setting Up a Qualitative Project • 30 minutes
• Resources for Developing Your Research Problem & Question • 45 minutes
• Week 3: Identifying Your Research Problem • 60 minutes

Methods, Population, Sampling, & Recruitment

For our fourth week, we'll take a look at how to choose data collection methods that are best suited for your aims, explore the various sampling and recruitment strategies to select participants, and finally consider how research design is an iterative process.

• A Look at This Week • 1 minute • Preview module
• Aligning Methods with Aims • 2 minutes
• Identifying a Setting and Honing in on a Population • 2 minutes
• Sampling Strategies • 6 minutes
• Recruitment Strategies • 2 minutes
• Iterative Design • 0 minutes

2 readings • Total 95 minutes

• Resources for Selecting Appropriate Methods • 35 minutes
• Resources for Identifying Your Strategies • 60 minutes

1 quiz • Total 30 minutes

• Practice • 30 minutes
• Week 4: Identifying Your Methods, Population, Sampling, & Recruitment Strategies • 60 minutes

Research Ethics

Our fifth week is all about ethical considerations in qualitative research. As researchers, we need to be aware and take precautions to ensure our work with human subjects is never exploitative, deceitful, or harmful. We'll go over the key ethical principles to keep in mind when beginning a qualitative study.

4 videos 3 readings 1 quiz 1 peer review 1 discussion prompt

4 videos • Total 12 minutes

• Overview of Research Ethics • 4 minutes
• Key Issues and Benefits of Qualitative Research • 5 minutes
• Strategies for IRBs and Ethics Committees • 1 minute

3 readings • Total 130 minutes

• CITI Certification for Human Subjects Research • 10 minutes
• Studies with Questionable Ethics • 30 minutes
• Resources for Ethics in Qualitative Research • 90 minutes
• Week 5: Considering Ethics in Research • 60 minutes

1 discussion prompt • Total 10 minutes

• Ethics Around the World • 10 minutes

Course Project and Design Examples

In this final week, you'll combine all items of your research design for a final submission. We'll also review real cases of graduates in the MPH program to see the different approaches that can be taken.

5 videos 2 readings 1 peer review

5 videos • Total 47 minutes

• Elements of Research Design: An Example (from Tamar Goldenberg's MPH Thesis Research) • 5 minutes
• Candace Girod: Menstrual Hygiene Management at Schools in Nairobi, Kenya • 13 minutes
• Wendy Avila: Exclusive Breast-Feeding among Women in Managua, Nicaragua • 15 minutes
• Jasmine Kelly: Maternal & Child Nutrition in Rural Tanzania • 11 minutes

2 readings • Total 60 minutes

• Interview Series • 30 minutes
• Preparing Your Research Design • 30 minutes
• Week 6: Your Qualitative Research Design • 60 minutes

Instructor ratings

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103 reviews

Reviewed on Oct 2, 2023

It was really a difficult nut to crack but a very useful and descriptive course for those people who wants to do research

Reviewed on Jul 15, 2021

Excellent Course. Improved my understanding of Qualitative Research Design through actually doing it for my research.

Reviewed on May 31, 2023

This course is very use full and practical course.

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