comparative survey research design

Testing for Measurement Invariance with Many Groups

Chapter 2 comparative survey research.

Cross-national and cross-cultural comparative surveys are a very important resource for the Social Sciences. According to the Overview of Comparative Surveys Worldwide , more than 90 cross-national comparative surveys have been conducted around the world since 1948.

Even though surveys can aim to fulfill different purposes, generally they aim to estimate population means, totals or distributions or relationships between variables. A comparative survey will aim to compare these levels or relationships across groups (national or otherwise).

Figure 2.1: Comparative percentages by country regarding immigration tolerance, from the European Social Survey Round 7. Source: Dimiter Toshkov 2020

Figure 2.1 shows a rather common application of a comparative survey. The groups, in this case European countries, are compared on their percentage shares on the answer to the question about allowing more immigrants.

However, what we see in this graph is only the final abstraction of very long process that typically surveys, and most particularly cross-national surveys, must go through. This process is sometimes called survey lifecycle and goes from design to dissemination.

Figure 2.2: Survey Lifecycle. Source: Cross Cultural Survey Guidelines

2.1 Survey Error

Survey error is any error arising from the survey process that contributed to the deviation of an estimate from its true parameter values. (Biemer 2016 )

But regardless of how much we can try to prevent it, survey errors in one form or another will always occur. And survey errors might affect the estimates and their comparability.

This applies both to when we compare data from different surveys and comparisons of sub-groups within the same survey.

The comparability of survey measurements is an issue that should be thoughtfully considered before drawing substantive conclusions from comparative surveys.

Figure 2.3: Slighty problematic survey question. Source: badsurveyq

Survey error can be classified in two components:

Random error is caused by any factors that randomly affect measurement of the variable

Systematic error is caused by any factors that systematically affect measurement of the variable

Survey group comparability problems come from systematic error or “Bias” 1 .

What is particular about comparative surveys is that there are at least two different survey statistics. Therefore, each one of these statistics is subject to different sources of error. If the overall statistics are differently affected by the error, this will cause some form of “bias” in the comparison.

In other words, besides substantive differences between survey statistics, there might be systematic differences caused by survey error.

2.2 Total Survey Error framework

Total survey error is the accumulation of all errors that may arise in the design, collection, processing and analysis of survey data.

The Total Survey Error offers an elegant framework to describe survey errors and the several sources of error that are rooted within the survey lifecycle.

Figure 2.4: Source: Groves and Lyberg ( 2010 )

As it can be seen in figure 2.4, there is a “representation” and a “measurement” side.

Systematic representation errors include:

• coverage error
• sampling error
• nonresponse

Systematic error in measurement include:

• measurement error

Measurement error includes: response error, interviewer-induced response effects, social desirability, methods effects, response styles.

Here we will focus on the Measurement side

2.3 The “Bias” framework

The TSE error classification is analogous to the “Bias” framework in the field of cross-cultural psychology. Under this framework, Vijver and Leung ( 1997 ) distinguished between “construct”, “item”, and “method” bias which are essencially similar to the TSE’s validity, measurement error and all the remaining errors.

The bias framework is developed from the perspective of cross-cultural psychology and attempts to provide a comprehensive taxonomy of all systematic sources of error that can challenge the inferences drawn from cross-cultural studies (Vijver and Leung 1997 , 2000 ; Van de Vijver and Poortinga 1997 ; Vijver and Tanzer 2004 ) .

2.3.1 Construct Bias

Construct bias is present if the underlying construct measured is not the same across cultures.

• It can occur if a construct is differently defined or only has a partial overlap across cultural groups.

Varying definitions of happiness in Western and East Asian cultures (Uchida, Norasakkunkit, and Kitayama 2004 ) . In Western cultures, happiness tends to be defined in terms of individual achievement, whereas in East Asian cultures happiness is defined in terms of interpersonal connectedness.

2.3.2 Method Bias

Sample Bias: is the incomparability of samples due to cross-cultural variations in characteristics, such as different educational levels, students versus the general population, and urban versus rural residents

Instrument bias: involves systematic errors derived from instrument characteristics such as self-report bias in Likert-type scale measures. The systematic tendency of respondents to endorse certain response options on some basis other than the target construct (i.e., response styles) may affect the validity of cross- cultural comparisons (Herk, Poortinga, and Verhallen 2004 ) .

Administration Bias: stems from administration conditions (e.g., data collection modes, group versus individual assessment), ambiguous instructions, interaction between administrators and respondents (e.g., halo effects), and communication problems (e.g., language differences, taboo topic).

2.3.3 Item Bias

Occurs when an item has a different meaning across cultures. An item of a scale is biased if persons with the same target trait level, but coming from different cultures, are not equally likely to endorse the item ( Vijver and Leung ( 1997 ) ; Vijver ( 2013 ) ).

Item bias can arise from poor translation, inapplicability of item contents in different cultures, or from items that trigger additional traits or have words with ambiguous connotations.

2.4 Preventing survey comparability problems

Following the TSE framework, the best way to reduce eventual comparability issues in survey data is to reduce the survey error to the very minimum and assuring that the persistent errors are most likely similar across the groups.

There is a vast literature discussing how to reduce TSE. However, two issues are particularly relevant to cross-cultural/national surveys.

2.4.1 Translation

TRAPD - Translation, Review, Adjudication, Pretesting, and Documentation

This method was proposed by Harkness, Vijver, and Mohler ( 2003 )

Team approach to survey translation:

T ranslators produce, independently from each other, initial translations

R eviewers review translations with the translators

A djudicator (one or more) decides whether the translation is ready

P retesting is the next step before going out to the field

D ocumentation should be constant during the entire process

2.4.2 Question coding system: SQP

It offers an additional way to check question comparability by taking into account the different characteristics of the questions in the original and adapted versions.

https://sqp.upf.edu/

Biemer, Paul P. 2016. “Total Survey Error Paradigm: Theory and Practice.” In The Sage Handbook of Survey Methodology , 122–41. London: SAGE Publications Ltd. https://doi.org/10.4135/9781473957893.n10 .

Groves, R. M., and L. Lyberg. 2010. “Total Survey Error: Past, Present, and Future.” Public Opinion Quarterly 74 (5): 849–79. https://doi.org/10.1093/poq/nfq065 .

Harkness, Janet A., Fons J. R. van de Vijver, and Peter Ph. Mohler. 2003. Cross-Cultural Survey Methods . Hoboken, NJ: Wiley.

Herk, Hester van, Ype H Poortinga, and Theo M M Verhallen. 2004. “Response Styles in Rating Scales: Evidence of Method Bias in Data From Six EU Countries.” Journal of Cross-Cultural Psychology 35 (3): 346–60. https://doi.org/10.1177/0022022104264126 .

Uchida, Yukiko, Vinai Norasakkunkit, and Shinobu Kitayama. 2004. “Cultural Constructions of Happiness: Theory and Empirical Evidence.” Journal of Happiness Studies 5 (February): 223–39. https://doi.org/10.1007/s10902-004-8785-9 .

Van de Vijver, Fons, and Ype Poortinga. 1997. “Towards an Integrated Analysis of Bias in Cross-Cultural Assessment.” European Journal of Psychological Assessment 13 (January): 29–37. https://doi.org/10.1027/1015-5759.13.1.29 .

Vijver, Fons J R van de. 2013. “Item Bias.” Major Reference Works. https://doi.org/doi:10.1002/9781118339893.wbeccp309 .

Vijver, Fons J R van de, and Kwok Leung. 1997. Methods and data analysis for cross-cultural research. Cross-Cultural Psychology Series, Vol 1. Thousand Oaks, CA, US: Sage Publications, Inc.

Vijver, Fons J R van de, and Kwok Leung. 2000. “Methodological issues in psychological research on culture.” Journal of Cross-Cultural Psychology 31 (1): 33–51. https://doi.org/10.1177/0022022100031001004 .

Vijver, Fons van de, and Norbert K Tanzer. 2004. “Bias and equivalence in cross-cultural assessment: An overview.” European Review of Applied Psychology / Revue Européenne de Psychologie Appliquée 54 (2): 119–35. https://doi.org/10.1016/j.erap.2003.12.004 .

systematic error and “Bias” are terms used interchangeably in the literature and they refer to deviations that are not due to chance alone. ↩︎

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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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48 Comparative Opinion Surveys

John Curtice is Professor of Politics at Strathclyde University, Glasgow.

• Published: 02 September 2009
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This article discusses comparative opinion surveys and comparative survey research. It first identifies the problems of comparative surveys and how these problems can be solved or overcome. It then tries to determine whether these surveys should include more or fewer countries during the research process. The last two sections discuss the need for data on the countries included in the survey or data set and the how the comparative survey data is analyzed.

And what should they know of England, who only England know? (Kipling 1910)

1 Why do Comparative Opinion Research?

The study of mass political behavior has a deceptively simple objective—to establish the causes and consequences of the political values and behaviors of the general population. 1 It faces one major obstacle—the sheer size of (most) general populations. Its ability to overcome that obstacle rests heavily on the power of the sample survey. Statistical theory demonstrates that inferences about the characteristics of a large population can in fact be drawn from the evidence of relatively small samples drawn randomly from that population. True, there is some uncertainty associated with those inferences, but its degree is known. Moreover that uncertainty is centered on the true value in the population as a whole. So, for example, if 50 percent of a random sample of 1,000 people has a particular characteristic, there is a 95 percent chance that between 47 and 53 percent actually have that characteristic amongst the population from which the sample was drawn. Armed with that knowledge, the student of political behavior has been able since the advent of the sample survey in the 1930s to make empirically substantiated statements about mass publics on the basis of information gathered from just a thousand people or so.

This approach does, however, beg one important question—what is the population about which we want to make empirically substantiated statements? Sample surveys are commonly conducted within the confines of a particular state. For many purposes this is perfectly acceptable. If, for example, we want to understand why people vote the way that they do in US presidential elections, a survey based on a random sample of the population of the United States is likely to be perfectly adequate. But the study of political behavior has loftier ambitions than simply explaining how people behave in particular countries. It wishes to be able to make statements about behavior in general. Yet we cannot assume that what is true in one country necessarily holds elsewhere. Perhaps, for example, how people vote in US presidential elections is influenced by circumstances that are particular to those elections and thus is different from how people vote in other elections elsewhere.

So survey research that crosses the boundaries of the nation‐state is essential to the study of political behavior, as Kipling recognized in the above quotation. At minimum if we are to be able to make statements about how people behave politically that are generally empirically substantiated, they need to be tested in a wide variety of social and political environments. Yet in practice cross‐national research has much more to offer than this. If political behavior is influenced by the circumstances in which it takes place, its study needs to be pursued using a research design in which those circumstances vary. This condition is often not fulfilled by research conducted within one country. For example, it is often argued that people are more likely to participate in elections if a system of proportional representation is in place than if a majoritarian system is in use (see chapter by Blais in this volume; Blais and Dobryzynska 1998 ; Franklin 2002 ; Norris 2004 ). Yet, elections within any one country are typically held using either one kind of system or the other, not a mixture of the two. Thus, the study of the impact of electoral systems on turnout at elections almost inevitably requires us to undertake comparative research. Much the same can be said of almost any characteristic that does not usually vary within a country.

Moreover, there is more than one way in which circumstances that vary from one country to another can affect behavior. For example, one way in which the use of proportional representation might increase turnout is simply by increasing the proportion of all kinds of people who vote—young and old, those interested in politics and those less so, strong party identifiers and weak identifiers, etc.—by more or less the same amount. In short, a particular kind of electoral system simply influences the overall level of turnout rather than the kind of person who votes. But an alternative possibility is that the use of proportional representation increases turnout amongst some groups more than others. Perhaps, for example, it has more impact on those belonging to groups who are less likely to vote, such as younger people, the politically uninterested and weak identifiers (Fisher et al., 2006) . If that happens, then the electoral system a country uses not only affects the level of participation but its relationship with other variables, such as age, political interest, and strength of identification. The ability of comparative survey research to assess the degree to which relationships may be contingent on national circumstance is at least as important as the opportunities it opens up to assess the impact of national circumstance on the overall incidence of behaviors and attitudes.

Indeed, the ability of comparative research to uncover the impact of circumstances that vary between countries but do not differ within them means that such research should be of interest even to the student whose concern is confined to understanding behavior in one particular country. If, for example, some of the most profound influences upon how people vote in US presidential elections are features that are common across the United States, such as the relative weakness of the country's political parties or the ability of candidates to purchase air time on television, then US presidential elections can only be adequately analyzed and understood if they are compared with elections elsewhere where these circumstances do not pertain. In other words, comparative survey research can enable us better to understand not only the general but the particular as well.

So comparative cross‐national mass political research brings three main benefits. First, it enables us to assess the empirical generalizability of claims that we might make about the causes and consequences of political attitudes and behavior. Second, it enables us to widen the range of contextual influences on attitudes and behavior that can be analyzed, in particular making it possible to assess the impact of influences that are largely invariant within countries but do vary between them. Third, it can even contribute to the study of behavior within a particular country by providing points of comparison that make it possible to assess the impact of that country's particular social and political circumstances on behavior within that country.

2 The Problems of Comparative Surveys

The benefits of comparative analyses rest on a crucial assumption—that we can make valid comparisons between the results obtained by surveys conducted in different countries. All forms of survey research are subject to potential sources of bias that mean they fail to provide as reliable a guide to the characteristics of a population as sampling theory would lead us to expect ( Groves 1987 , 1989 ). In particular, some parts of a population may be omitted from or under‐represented in the coverage of a survey. Meanwhile, the questions asked in a survey may fail to measure accurately what the researcher may have been aiming to measure. The particular difficulty that arises in comparative cross‐national research is not that these problems exist, but that their incidence may vary from country to country. As a result we may wonder whether the differences between the results of a survey conducted in two or more countries reflect artifactual differences in how the survey was conducted in the two countries rather than real differences between their populations (Heath, Fisher, and Smith 2005) .

Between‐country variation in measurement error is perhaps the most obvious pitfall of comparative survey research. Clearly if different questions are asked in different countries, any differences in response may simply reflect differences in question wording. Thus most exercises in comparative survey research are based on a common questionnaire that ideally is administered in an identical manner in every country. But one immediately obvious limitation to the fulfillment of this ideal is that people in different countries speak different languages. Even if a questionnaire is translated faithfully from its internationally agreed original, differences in the structure of different languages and in the connotations associated with different words in different languages may well mean that the cognitive and affective meaning of a question in the minds of respondents varies from country to country. Meanwhile attempts to develop batteries of questions designed to measure adherence to underlying values such as equality or social liberalism may be undermined by the fact that the degree to which any particular question taps adherence to such values varies from country to country. More difficult still is the possibility that a concept may not exist in certain cultures. Previous research on attitudes towards religion, for example, has had to cope with the difficulty that the concept of “God” does not exist in Japanese culture (Jowell 1998) .

Less obvious, but no less important however, are differences between countries in survey practice. Survey research is commonly organized on a national basis. That is, while one fieldwork organization will usually be responsible for conducting a comparative survey in its country, a different organization will undertake the survey in another country, etc. In any event, even if this were not the case the way in which a survey is conducted may have to vary from country to country because of national differences of practice and circumstance. For example, the ability to undertake pure random sampling depends on the existence of (and access to) a full (and accurate) population register, or failing that a full list of households or addresses from which a random sample of individuals can be generated. Sampling procedures must inevitably vary depending on the existence and the form of such information. Meanwhile, in some countries securing interviews in rural areas may be difficult either because such populations are geographically widely spread or because of relatively low rates of literacy. Equally, differences of geography may mean that the degree to which samples are geographically clustered in order to keep survey costs down has to vary. Of course, both differences of national circumstance and in the quality of interviewing may produce substantial differences in response rates—and thus different levels of exposure to the possibility that samples may be biased because of differential non‐response.

In short—and the above discussion is far from exhaustive—comparative opinion research is exposed to the severe problem that alongside the substantive differences between countries whose impact such research is designed to discern there may well coexist methodological differences that in themselves make countries appear more or less similar to each other than in reality is the case. The comparative researcher's task is already often difficult enough because countries typically do not differ substantively in just one respect, but several, thereby potentially making it difficult to discern which substantive difference might account for any particular difference between the results of surveys conducted in two or more countries. Now it appears that in practice we cannot be sure that any difference we might uncover is not simply an artifact of difference in survey method rather than a real difference. The apparent analytic power of comparative opinion research appears to have crumbled all too readily in our hands.

3 Overcoming the Problems

How might that power possibly be restored? One obvious possibility is to reduce the degree of heterogeneity in survey practice. This is very much the approach that has been adopted by one recently instigated cross‐national collaboration, the European Social Survey (European Social Survey n.d. a ). One of its avowed aims has been to ensure that each participating survey is conducted to more or less the same high standard. Thus, for example, not only are strict guidelines for the implementation of random sampling laid down, but how it is proposed to implement those guidelines in each country has to be agreed by a coordinating methodological committee (Lynn et al. 2004) . Amongst the key features of these guidelines are that all interviewing has to take place face to face, with both a minimum target response rate of 70 percent, and a minimum effective sample size (that is after taking into account the impact of any geographical clustering of interviews and any unequal selection probabilities), of 1,500 (European Social Survey n.d. b ). Meanwhile, questionnaires are independently translated by two native speakers and then any differences are resolved by those with knowledge of survey design and the research topic as well as the languages in question. (For more on translation strategies see Smith 2002.)

Such an approach to comparative survey research places a premium on the quality of the work in those countries that do participate in a survey rather than on ensuring coverage of as many countries as possible—though in the event when the first ESS survey was conducted in 2002 no less than twenty‐two European countries endeavored to meet the organizers' exacting standards while twenty‐six did so in 2004. (Surveys are being conducted every other year.) In any case, there are other reasons why we might limit the range of countries included in a program of comparative opinion research. First, the more diverse the countries being covered, the greater the difficulty of ensuring that the questions asked have the same meaning to respondents across language and culture. Even if there are attempts to ensure similarity of meaning, when there is a diverse set of countries comparability may only be achieved at the cost of producing questions that are so general and abstract that respondents everywhere may have some difficulty discerning their meaning (Kuechler 1998) . Second, the larger the number of countries included, the greater the likelihood that those who attempt to analyze the resulting data do not have sufficient understanding of the social, political, economic, and cultural attributes of each country to be able to interpret the data sensitively and sensibly (Jowell 1998) . In short it could be argued that the approach of the first ever major piece of comparative opinion research, Almond and Verba's Civic Culture study (Almond and Verba 1963) , which confined its attention to five countries chosen for their theoretical interest, provides a model as to how comparative survey research should be conducted.

Indeed, it is interesting to note that many of the more recently instigated programs of comparative survey research have been “regional” rather than “global” in character (also see chapter by Kittilson in this volume). The trend began with the instigation of a range of “barometer” surveys in central and eastern Europe following the collapse of the Berlin Wall (Centre for the Study of Public Policy n.d.). An annual “Latinobarometer” that now covers eighteen countries in Latin America began in 1995 (Latinobarómetro Corporation n.d.), an “Afrobarometer” started in 1999 with twelve countries, with as many as eighteen participating in the third round conducted in 2005–6 (Afrobarometer n.d. a ), while no less than two collaborations, the East Asia Barometer and the Asia Barometer, have been instigated (in 2001 and annually since 2003 respectively), each covering around a dozen or so countries in overlapping parts of eastern and southern Asia (East Asia Barometer n.d.; Asia Barometer n.d.). These regional collaborations vary in the degree to which they have attempted to impose similarity of methodological rigor across their component countries, with perhaps the most impressive being the attempts of the Afrobarometer to promote the same high standards in countries that often lack a tradition of high‐quality survey research or indeed much of the infrastructure required to implement random sampling (Afrobarometer n.d. b ). Such regional collaborations enable these surveys to focus on those topics that are of common concern in their parts of the world rather than pursuing an agenda that simply reflects the intellectual concerns and assumptions of advanced industrial democracies.

In fact, the ability of the European Social Survey to obtain methodological consistency and rigor is dependent not so much on its regional character as on the organizational structure it has been able to develop. Unlike other collaborations, it has access to funding from cross‐national political and scientific institutions such as the European Union and the European Science Foundation. 2 Amongst other things this ensures that it has a comparatively well‐funded permanent secretariat and central infrastructure. Meanwhile, funding for the various national surveys typically comes from national scientific funding councils who are willing to support the high standards that ESS demands. In contrast, most comparative research projects are voluntary collaborations between national survey teams and have a relatively limited secretariat that may well be provided by just one country.

Indeed, of the three truly “global” comparative research survey collaborations that currently exist, two do not even attempt to undertake complete whole surveys within each country. Rather, both the International Social Survey Programme (ISSP), a collaboration between general social surveys, and the Comparative Study of Electoral Systems Project (CSES), a collaboration between academic national election studies, are collaborations between national surveys that ask about their own domestic agendas but agree to devote a part of their survey to a module of common questions. Indeed many of the surveys involved in these two collaborations are well‐established enterprises. Thus the ISSP was instigated in 1984 by three existing social surveys, the US General Social Survey, the British Social Attitudes survey, and the German ALLBUSS, together with survey researchers from the Australian National University, though it now has as many as forty‐one participants who collaborate annually. The CSES, which began in 1996, includes amongst its membership most of the long‐running national election studies in the well‐established democracies, as well as studies in countries where democratic elections let alone election studies have a much shorter history. Altogether, its first module covered elections in as many as thirty‐four countries, the second in over forty.

Collaborations between existing national studies have one key advantage. They are relatively inexpensive, as they do not require the full cost of mounting a survey to be found in every participating country. But they are inevitably limited in the degree of methodological consistency that can be achieved. Faced with the choice between maintaining existing domestic practices and changing those practices to meet international requirements the former pressure will tend to be greater, especially if changing the way an established survey is conducted might compromise the integrity of a domestic time series. Given that constraint the ISSP has relatively strict requirements that a survey must be able to satisfy before it is admitted to its membership (International Social Survey Programme 2003) . All surveys are, for example, meant to use random sampling and undertake at least a thousand interviews. Meanwhile, the questions in each annual module (with each module covering a rotating cycle of subjects) are agreed collaboratively by all of the participating members, thereby helping to ensure that they are crafted with sensitivity to cultural and linguistic differences. They are also all asked together in the same order in a block, albeit either face to face or as part of a self‐completion supplement. Nevertheless, the program does not have any rules on how questionnaires should be translated while an examination of the methodology actually being employed by its members revealed that not all of them necessarily followed the principles of random sampling that the program was meant to uphold (Park and Jowell 1997) .

The CSES is even less rigorous. While it supposedly requires its members to administer the module as whole in a block, not all of them follow this requirement. The module may be administered face to face, by telephone, or by self‐completion questionnaire (either as a supplement to a face‐to‐face survey or as part of a mail‐back survey). It does not insist on random sampling nor does it have any rules on translation. This latitude in part reflects the fact that, as we have already noted, the project has had to accept the fact that already well‐established national election studies are less willing to compromise their own domestic time series by changing how their surveys are conducted. In part, too, the secretariat lacks either the resources or indeed the authority in what is a voluntary collaboration to insist on greater conformity to a set of common standards. The one crucial requirement that the project does have, however, is that fieldwork should take place in the period immediately after a national parliamentary or presidential election, thereby enabling the project to capture as accurately as possible in each country the attitudes and behavior of the electorate at the occasion of an election. This makes the CSES a unique resource for the comparative study of electoral behavior, though it does mean that the fieldwork for each module has to be spread out over a five‐year period in order to ensure that an election has been held in each country that wishes to participate.

The comparative project whose subject matter covers political attitudes and behavior that has the widest reach of all, however, is the World Values Survey. This began life as a solely European survey (the European Values Survey) in the early 1980s, designed to look at social and moral values in the then western Europe. However, it was then promoted by Ronald Inglehart at the University of Michigan and adopted in a dozen non‐European countries. Thereafter the collaboration has blossomed (though the European countries continue to have their own organization and secretariat). Now, after four rounds, each around five years apart, it is being conducted in nearly eighty countries. While unlike both CSES and ISSP a whole survey is commissioned especially for the purpose, the project is reliant on teams within each country to raise the necessary funds and in practice the project is not notable for the similarity of methods employed in each country. Thus, while the survey work is nearly always undertaken face to face, it only aspires to follow random sampling “as closely as possible” (World Values Survey n.d. b ), while some samples are not fully nationally representative (Inglehart 1997, 346) . Meanwhile amongst the thirty‐two European countries that fielded the fourth round of the survey in 1999, around half back‐translated the questionnaire into English, while the other half did not. Nearly two‐thirds added one or more country‐specific questions in the middle of the common module. Equally, a third used some form of quota control at some point in the sampling process and around two‐thirds allowed some form of substitution for non‐contacts, while the remainder did not implement such procedures (European Values Study 1999) .

4 Many Countries or Few?

One feature that these three truly intercontinental projects have in common is that they challenge the earlier notion that in comparative survey research less may be better. They all suffer, even if rather less so in the case of ISSP, from between‐country methodological pluralism. They also appear to encourage the user to analyze data from countries about which he or she may know little or nothing. Yet, despite the undoubted disadvantages of their methodological diversity, these exercises are still highly valuable.

To see why this is the case we may perhaps need to remind ourselves as to the analytic purchase that we argued earlier comparative opinion research brings. This is that comparative survey research enables us to examine the links between circumstances that vary between countries (but usually not within them) and both the incidence of various political attitudes and behaviors and the relationships between them. From this perspective our interest in, for example, the United States lies not in the United States per se but in the politically relevant attributes that it has, such as the fact that it is a federal country, has a presidential system of government, uses a single‐member plurality electoral system or that health care is primarily funded by private insurance. Equally our interest in, for example, Sweden may lie in the fact that is a unitary state, has a parliamentary system of government, uses a party list electoral system, or that the state funds most health care.

In short, countries may be regarded as cases with theoretically relevant attributes. We can then assess the impact of these attributes by coding each country accordingly and including the resulting variables in our data analyses. While the coding of each attribute needs to be conducted accurately it does not require expert knowledge of the social, economic, political, and cultural attributes of a country. Note further that in such analyses any particular attribute may well be present in more than one country, and indeed will probably be present in several. Thus instead of being reliant on the evidence of just one country to assess the impact of a particular attribute we should have available to us the evidence of a number. This means we can begin to assess whether a general relationship exists between the presence of a particular circumstance and a particular attitude or behavior—and it is identifying the existence of such relationships that we have argued is the central task of the study of political behavior.

How does this mitigate the dangers of methodological pluralism? Quite simply because there is safety in numbers. While the results of a survey in any one country with a particular attribute may be more artifact than fact, the probability that this is true of all countries with that attribute is far less. So long as differences of methodological approach are not strongly correlated with the presence or absence of the attribute of interest, then those differences of approach cannot be responsible for any relationship that may be uncovered between that attribute and a particular attitude or behavior. The presence of methodological diversity will probably result in greater error variance between countries and, as a result, real relationships may well be attenuated in the survey data. Nevertheless, the more countries that a program of comparative research covers, the more likely it is to be insulated against the danger that substantive conclusions are drawn on the basis of artifactual differences.

5 The Need for Data about Countries

One important implication, however, flows from this approach. Comparative opinion research cannot be conducted using survey data alone. Rather it needs to analyze survey data alongside systematically collected and coded data that give details of the attributes of the countries included in the data set. In short, measurement of the (particularly national) context within which the survey data have been collected should be an integral part of any exercise in comparative opinion research. Note indeed that such data could also include information on the key attributes of the methodology deployed in each country, thereby making it possible to include in analyses the possible impact of between‐country methodological differences.

In practice, however, only two of the projects referred to so far include in their activities the provision to the wider community of relevant data about the context within which the data have been collected. The Comparative Study of Electoral Systems Project not only provides extensive information on the electoral system and constitutional structure of each country that is surveyed, but also information on the political parties, the issues at each election, and the election outcome, including some data at the level of the electoral district rather than just the country as a whole. Meanwhile the European Social Survey provides some social and economic indicators for each participating country, including some population data at regional level, as well as information on key events that took place in each country during the course of fieldwork. At the same time, the ESS has compiled an impressive set of web links to sources of data and information about individual countries, including data provided by key international organizations such as UNESCO and the OECD (European Social Survey 2003) . 3

6 Analyzing Comparative Survey Data

The challenges of comparative survey research are not, however, confined to the conduct of fieldwork or the collection of contextual data. There are also important questions about how best to analyze such data. Note first of all that the data may be regarded as either a sample of individuals or a sample of countries. In the former case, however, a pooled individual level data set from a comparative survey research project cannot be regarded as a simple random sample of individuals. The respondents to the surveys are not independent of each other, but rather are clustered by nation. This has to be allowed for either by using a multi‐level model (Snijders and Bosker 1999) or by using statistical routines that take into account the clustered nature of the samples (Seligson 2004) . Meanwhile, in the case of the latter approach at least some consideration has to be given as to the weight that each country's sample should have in the analysis. If some countries have included more respondents in their surveys than others, respondents from those countries will have more impact on any estimates derived from the survey data unless this imbalance is altered. One possibility is that the sample sizes should be weighted to be proportionate to population; this might be done if there is a wish to make statements about some coherent geographical entity such as the European Union. Another possibility is to regard each national sample as a separate reading of the phenomena under investigation and to equalize the sample sizes for each country

In addition, comparative survey data can be regarded as a sample of countries. At its simplest this means deriving frequencies and means from the individual‐level data for each country. The relationships between these readings across countries may then be analyzed, or analyzed in tandem with data about those countries from other sources. This, for example, is the approach that has been adopted by Inglehart in some of his most striking analyses of data from the World Values surveys, such as examining the relationship between the importance of postmaterialist values and affluence (Inglehart and Abramson 1995) , civic norms, and the longevity of democratic institutions (Inglehart 1997) , and between the degree of emphasis placed on self‐expression and the openness and accountability of a country's political institutions (Inglehart and Welzel 2004) . Such analyses may well uncover relationships that do not appear at the individual level—or indeed fail to corroborate relationships that do appear at the individual level. Such instances can tell us a great deal about the nature of the processes that underlie such relationships, and thus both forms of analysis need to be conducted if the full power of comparative opinion research is to be utilized.

Indeed, not only should individual‐level and aggregate‐level analysis be conducted, but they should also be brought together. Earlier in this chapter we noted that one of the possible roles of comparative opinion research is to identify the degree to which relationships, such as that between interest in politics and turnout, are contingent upon circumstance, such as the kind of electoral system in place. This implies bringing together aggregate data about a country (which may either be derived from the survey itself or from another source) and individual‐level data about the strength of the relationship between two or more variables. This may be done in more than one way (Franzese 2005) . One is to undertake a pooled individual‐level analysis in which interaction terms between aggregate‐level national circumstance and one or more independent individual‐level variables are included in the modeling. Another is to estimate the individual‐level relationship in each country, and then analyze the resulting data alongside other relevant country‐level data at the aggregate (country) level (Curtice forthcoming; Lewis and Linzer 2005 ; Jusko and Shiveley 2005 ).

7 Conclusion

Comparative opinion research is potentially a highly powerful instrument. Indeed it is difficult to see how the aspiration of political science to be able to make empirically sustained generalizations about what influences and structures political behavior can be achieved without it. Thus the substantial increase in the amount of such research that has occurred over the last decade or so represents a significant organizational advance in the study of political behavior.

Yet at the same time it is also methodologically at least a potentially fragile endeavor. Most comparative survey research is a voluntary collaboration between national teams, each of which operates in different circumstances and cultures. As a result there is a tendency for survey research to be undertaken differently in different countries—and even if the same survey instrument is administered in the same manner everywhere there is no guarantee that it has the same meaning for respondents everywhere. Such methodological diversity means that our attempts to study what makes countries different run the risk of being confounded by differences between countries in how surveys are conducted. It certainly means that much comparative opinion research tolerates a degree of methodological inconsistency that would not usually be tolerated on a national survey.

There are two possible responses to this problem. One is to attempt to secure greater consistency of methodological approach—and at a high standard. This is the route that has been taken by the European Social Survey, which is undoubtedly methodologically the most impressive exercise in comparative opinion research that has been undertaken to date. Yet it remains to be seen whether such an exercise can be conducted outside the unique circumstances created in Europe by the existence of a relatively powerful cross‐national institution such as the European Union. And if we are to reap fully the benefits of comparative opinion research we need to maximize the variety of countries (and thus of circumstance) that are covered. This suggests that, despite their methodological diversity, there will continue to be an important role for the substantively more diverse global endeavors too.

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This chapter focuses on the methods and logic of comparative survey research. The chapter by Kittilson describes the wealth of comparative surveys that are available to researchers.

The influence that the existence of the European Union has had on comparative survey research is underlined by the fact that the Union has commissioned, funded, and undertaken the longest‐running and most intense program of cross‐national research anywhere in the world (European Commission n.d.). While its Eurobarometer surveys (including associated surveys in candidate members of the Union) are primarily concerned with the policy need of the Union to chart public support for its activities and institutions, the series has proved to be an invaluable resource in academic research ( e.g. Inglehart 1990 ; Kaase and Newton 1995 ). The surveys have been conducted twice a year since 1973. The initial survey covered the nine countries that were members of the EU at that time; it now covers the current twenty‐five members.

The introduction of direct elections to the Union's European Parliament in 1979 has also stimulated cross‐national survey research on voting behavior and attitudes at those elections, some of which research used the Eurobarometer as its survey platform, under the auspices of the European Election Study (European Elections Study n.d.; van der Eijk and Franklin 1996) .

The European Election Study has on one or more occasions undertaken content analysis of both media output at the time of European Parliament elections and of party manifestos, thereby making it possible to link survey results to the different national media contexts to which voters were exposed. The 1994 study also undertook surveys of candidates, thereby facilitating a study of political representation in the EU.

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Characteristics of a Comparative Research Design

Hannah richardson, 28 jun 2018.

Comparative research essentially compares two groups in an attempt to draw a conclusion about them. Researchers attempt to identify and analyze similarities and differences between groups, and these studies are most often cross-national, comparing two separate people groups. Comparative studies can be used to increase understanding between cultures and societies and create a foundation for compromise and collaboration. These studies contain both quantitative and qualitative research methods.

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• Comparative Quantitative
• Comparative Qualitative
• When to Use It
• When Not to Use It

1 Comparative Quantitative

Quantitative, or experimental, research is characterized by the manipulation of an independent variable to measure and explain its influence on a dependent variable. Because comparative research studies analyze two different groups -- which may have very different social contexts -- it is difficult to establish the parameters of research. Such studies might seek to compare, for example, large amounts of demographic or employment data from different nations that define or measure relevant research elements differently.

However, the methods for statistical analysis of data inherent in quantitative research are still helpful in establishing correlations in comparative studies. Also, the need for a specific research question in quantitative research helps comparative researchers narrow down and establish a more specific comparative research question.

2 Comparative Qualitative

Qualitative, or nonexperimental, is characterized by observation and recording outcomes without manipulation. In comparative research, data are collected primarily by observation, and the goal is to determine similarities and differences that are related to the particular situation or environment of the two groups. These similarities and differences are identified through qualitative observation methods. Additionally, some researchers have favored designing comparative studies around a variety of case studies in which individuals are observed and behaviors are recorded. The results of each case are then compared across people groups.

3 When to Use It

Comparative research studies should be used when comparing two people groups, often cross-nationally. These studies analyze the similarities and differences between these two groups in an attempt to better understand both groups. Comparisons lead to new insights and better understanding of all participants involved. These studies also require collaboration, strong teams, advanced technologies and access to international databases, making them more expensive. Use comparative research design when the necessary funding and resources are available.

4 When Not to Use It

Do not use comparative research design with little funding, limited access to necessary technology and few team members. Because of the larger scale of these studies, they should be conducted only if adequate population samples are available. Additionally, data within these studies require extensive measurement analysis; if the necessary organizational and technological resources are not available, a comparative study should not be used. Do not use a comparative design if data are not able to be measured accurately and analyzed with fidelity and validity.

• 1 San Jose State University: Selected Issues in Study Design
• 2 University of Surrey: Social Research Update 13: Comparative Research Methods

About the Author

Hannah Richardson has a Master's degree in Special Education from Vanderbilt University and a Bacheor of Arts in English. She has been a writer since 2004 and wrote regularly for the sports and features sections of "The Technician" newspaper, as well as "Coastwach" magazine. Richardson also served as the co-editor-in-chief of "Windhover," an award-winning literary and arts magazine. She is currently teaching at a middle school.

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Internationally Comparative Research Designs in the Social Sciences: Fundamental Issues, Case Selection Logics, and Research Limitations

International vergleichende Forschungsdesigns in den Sozialwissenschaften: Grundlagen, Fallauswahlstrategien und Grenzen

• Abhandlungen
• Published: 29 April 2019
• Volume 71 , pages 75–97, ( 2019 )

Cite this article

• Achim Goerres 1 ,
• Markus B. Siewert 2 &
• Claudius Wagemann 2  

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17 Citations

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This paper synthesizes methodological knowledge derived from comparative survey research and comparative politics and aims to enable researches to make prudent research decisions. Starting from the data structure that can occur in international comparisons at different levels, it suggests basic definitions for cases and contexts, i. e. the main ingredients of international comparison. The paper then goes on to discuss the full variety of case selection strategies in order to highlight their relative advantages and disadvantages. Finally, it presents the limitations of internationally comparative social science research. Overall, the paper suggests that comparative research designs must be crafted cautiously, with careful regard to a variety of issues, and emphasizes the idea that there can be no one-fits-all solution.

Zusammenfassung

Dieser Beitrag bietet eine Synopse zentraler methodischer Aspekte der vergleichenden Politikwissenschaft und Umfrageforschung und zielt darauf ab, Sozialwissenschaftler zu reflektierten forschungspraktischen Entscheidungen zu befähigen. Ausgehend von der Datenstruktur, die bei internationalen Vergleichen auf verschiedenen Ebenen vorzufinden ist, werden grundsätzliche Definitionen für Fälle und Kontexte, d. h. die zentralen Bestandteile des internationalen Vergleichs, vorgestellt. Anschließend wird die gesamte Bandbreite an Strategien zur Fallauswahl diskutiert, wobei auf ihre jeweiligen Vor- und Nachteile eingegangen wird. Im letzten Teil werden die Grenzen international vergleichender Forschung in den Sozialwissenschaften dargelegt. Der Beitrag plädiert für ein umsichtiges Design vergleichender Forschung, welches einer Vielzahl von Aspekten Rechnung trägt; dabei wird ausdrücklich betont, dass es keine Universallösung gibt.

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Cross-National Comparative Research—Analytical Strategies, Results, and Explanations

Hans-Jürgen Andreß, Detlef Fetchenhauer & Heiner Meulemann

Theory Development in Comparative Social Research

Clemens Kroneberg

Multilevel Structural Equation Modeling for Cross-National Comparative Research

Bart Meuleman

One could argue that there are no N  = 1 studies at all, and that every case study is “comparative”. The rationale for such an opinion is that it is hard to imagine a case study which is conducted without any reference to other cases, including theoretically possible (but factually nonexisting) ideal cases, paradigmatic cases, counterfactual cases, etc.

This exposition might suggest that only the combinations of “most independent variables vary and the outcome is similar between cases” and “most independent variables are similar and the outcome differs between cases” are possible. Ragin’s ( 1987 , 2000 , 2008 ) proposal of QCA (see also Schneider and Wagemann 2012 ) however shows that diversity (Ragin 2008 , p. 19) can also lie on both sides. Only those designs in which nothing varies, i. e. where the cases are similar and also have similar outcomes, do not seem to be very analytically interesting.

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Acknowledgements

Equal authors listed in alphabetical order. We would like to thank Ingo Rohlfing, Anne-Kathrin Fischer, Heiner Meulemann and Hans-Jürgen Andreß for their detailed feedback, and all the participants of the book workshop for their further comments. We are grateful to Jonas Elis for his linguistic suggestions.

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Fakultät für Gesellschaftswissenschaften, Institut für Politikwissenschaft, Universität Duisburg-Essen, Lotharstr. 65, 47057, Duisburg, Germany

Achim Goerres

Fachbereich Gesellschaftswissenschaften, Institut für Politikwissenschaft, Goethe-Universität Frankfurt, Theodor-W.-Adorno Platz 6, 60323, Frankfurt am Main, Germany

Markus B. Siewert & Claudius Wagemann

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Goerres, A., Siewert, M.B. & Wagemann, C. Internationally Comparative Research Designs in the Social Sciences: Fundamental Issues, Case Selection Logics, and Research Limitations. Köln Z Soziol 71 (Suppl 1), 75–97 (2019). https://doi.org/10.1007/s11577-019-00600-2

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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