Cross-sectional research: A critical perspective, use cases, and recommendations for IS research (2023)

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Article preview International Journal of Information Management Abstract Introduction Section snippets A brief overview of cross-sectional studies Strengths, limitations, and use cases of cross-sectional studies Recommendations for conducting rigorous cross-sectional research Elusive causality and cross-sectional data Concluding remarks References (70) CHEST Journal International Journal of Information Management Computers in Human Behavior International Journal of Information Management International Journal of Information Management International Journal of Information Management The Journal of Strategic Information Systems Computers in Human Behavior Decision Support Systems International Journal of Information Management International Journal of Information Management The Lancet International Journal of Information Management International Journal of Information Management International Journal of Information Management International Journal of Information Management Study designs: Part 2 - Descriptive studies Perspectives in Clinical Research Sample size determination and statistical power analysis in PLS using R: An annotated tutorial Communications of the Association for Information Systems A spotlight on cross-sectional research: Addressing the issues of confounding and adjustment International Journal of Healthcare Management Preregistration of information systems research Communications of the Association for Information Systems Publishing in AMJ —Part 2: Research design Academy of Management Journal Editor's comments: Advancing research transparency at MIS quarterly: A pluralistic approach When constructs become obsolete: A systematic approach to evaluating and updating constructs for information systems research MIS Quarterly Research commentary—Generalizability of information systems research using student subjects - A reflection on our practices and recommendations for future research Information Systems Research Multivariate dependencies Cross-sectional design A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Doctoral dissertation Bias Journal of Epidemiology and Community Health The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies Annals of Internal Medicine Why and how online experiments can benefit information systems research Journal of the Association for Information Systems Belief, attitude, intention and behavior: An introduction to theory and research PLS, small sample size, and statistical power in MIS research Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06) The complexity of establishing causality in a digital environment: an eye to the future Journal of Information Technology Case and Application Research Avoiding bias in observational studies: Part 8 in a series of articles on evaluation of scientific publications Deutsches Arzteblatt International Cited by (0) Recommended articles (6) FAQs Videos
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International Journal of Information Management

Available online 20 January 2023

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(Video) Study Design Part 3 - Cross Sectional Studies

Abstract

Cross-sectional data is pervasive in information systems (IS) research. This editorial reviews cross-sectional studies, summarizes their strengths and limitations, and derives use cases of when cross-sectional data is and is not useful in answering research questions. We raise concerns about assertions of temporal causality using data collected employing cross-sectional methods with no temporal order, which makes cause and effect difficult to establish. Based on our discussion of research using cross-sectional data and its limitations, we offer four recommendations for when and how to use such data: (1) improve credibility by reporting research in detail and transparently, (2) ensure appropriate sampling, (3) take configurational perspectives, and (4) integrate cross-sectional data into mixed- or multi-method designs. By doing so, we help IS researchers position and use cross-sectional studies appropriately within their methodological repertoire.

Introduction

In order to prepare a tasty dish, a chef needs a clear and good recipe, carefully selected ingredients, and a well-equipped kitchen. The dish’s flavor and presentation depend on the chef attending to all three elements of its preparation – following all the proper steps, using high quality ingredients with good flavor, and having all the tools needed to prepare the meal. If the chef skips a step, uses low-quality ingredients, or lacks an essential tool, the dish may well be a failure.

Good research is analogous to cooking. Studies are grounded in clear and solid theory, analyze meaningful data (sets), and apply research methods properly. Numerous Information systems (IS) researchers provide careful guidance on how to develop theory (e.g., contextualization; Hong et al., 2014), advice on how to gather data (Straub, 1989), and when to apply research methods (e.g., qualitative comparative analysis; Mattke et al., 2022; Mattke et al., 2021; Pappas & Woodside, 2021) that can help scholars develop compelling explanations for real-world phenomena. However, if scholars fail to pay sufficient attention to how well different aspects of the research align, e.g., with the research questions and data method, researchers risk overstating conclusions or drawing incorrect inferences.

Data sets are the least elaborated element in IS research. Scholars often fail to sufficiently explain the implications of the data collection technique on theory testing or formally test whether their data meets the quality thresholds needed to conduct analysis. As a result, scholars sometimes rely on sub-optimal data, overuse one form of data collection, or make greater inferences than the data support, an especially great danger with regard to cross-sectional data.

A casual review of research published in major IS journals reveals the pervasiveness of using cross-sectional data. In the International Journal of Information Management (IJIM), for example, more than 85 % of papers published from 2020 to 2022 employ a cross-sectional research design. Editors-in-chief of various journals across different subject disciplines have raised concerns about overusing any one form of data collection (Dwivedi, Hughes, Cheung, et al., 2022). One common mistake is using cross-sectional data inappropriately to answer research questions that implicitly or explicitly point to temporal causality (Bono & McNamara, 2011). Consider a research question exploring whether and how beliefs and intentions shape behaviors. Cross-sectional survey data cannot be analyzed to establish causal relationships, but rather only associations between concepts. Temporal causality between beliefs, intentions, and behavior and how variance in beliefs and intentions relate to behavior can be better proven using multi-wave data (Kim, 2009, Kim and Malhotra, 2005).

Since the great majority of IS research attempts to establish temporal causality (e.g., focuses on cause-effect relationships) or assesses how temporal causality evolves over time, and since most recent IS research analyzes cross-sectional data, it is valuable to review the use of cross-sectional data in IS research. In this editorial, we assert the need in IS research to employ multiple research designs, such as collecting data from the same people in two (Shirish et al., 2021) or more (Adamovic, 2022) waves or using experiments to establish causality (Fink, 2022). While cross-sectional data can be analyzed to demonstrate associations between concepts, causality and cause-effect relationships can be demonstrated effectively using research designs that allow for the temporal ordering of variables.

This editorial provides an overview of cross-sectional studies, presents the strengths and weaknesses of using such data, and provides recommendations for conducting cross-sectional research, thus contributing to IS theory and practice. We hope that this critical review of the use of cross-sectional data in IS research will stimulate a broader conversation about data collection practices.

Section snippets

A brief overview of cross-sectional studies

A cross-sectional study, also known as a prevalence or transverse study, uses a snapshot of participants’ beliefs, behaviors, or other variables of interest of a study population (e.g., a group of individuals or organizations) at a specified point in time (Grimes and Schulz, 2002, Hua and David, 2008) to examine research questions (see Fig. 1).

Descriptive and analytical approaches are commonly used in cross-sectional studies (Kesmodel, 2018, Wang and Cheng, 2020). Descriptive cross-sectional

Strengths, limitations, and use cases of cross-sectional studies

Each cross-sectional approach has strengths and limitations (Fig. 2). We pair these strengths and limitations to offer guidance on when using cross-sectional data is or is not appropriate.

Recommendations for conducting rigorous cross-sectional research

We offer four recommendations to ensure that cross-sectional data is used appropriately (see Table 2).

The first recommendation points to the need for detailed and transparent research reporting. Such reporting includes disclosing the use of cross-sectional data (e.g., in the abstract), formulating appropriate objectives (e.g., referring to relevant practical or theoretical problems), specifying method and findings (e.g., how to reduce biases or handling missing values, providing descriptive

Elusive causality and cross-sectional data

While causality is difficult to establish in purely cross-sectional studies, this does not imply these studies cannot contribute to making a case for causality. As mentioned above, causality requires temporal precedence, covariation, and control of third variables. While the first condition is not met, covariation can easily be established. Similarly, using covariates in multivariate analysis can control for third-variable effects. Even path modeling techniques, like SEM, offer structural

Concluding remarks

Many IS research papers use cross-sectional designs to gather data. While cross-sectional design’s benefits can explain its frequent use (e.g., inexpensive, efficient data collection), it has significant shortcomings, including, among others, the difficulty of deriving cause-effect relationships, the non-existence of a temporal order between the collected variables, and the limited ability to test temporal causality. To realize the benefits of cross-sectional design, we recommend that

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    FAQs

    What is cross-sectional research used for? ›

    Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study.

    Is a cross-sectional study a case study? ›

    Cross sectional studies are used to determine prevalence. They are relatively quick and easy but do not permit distinction between cause and effect. Case controlled studies compare groups retrospectively. They seek to identify possible predictors of outcome and are useful for studying rare diseases or outcomes.

    What is a cross-sectional research study quizlet? ›

    Cross-sectional research involves using different groups of people who differ in the variable of interest but share other characteristics, such as socioeconomic status, educational background, and ethnicity. Cohort. investigate the causes of disease, establishing links between risk factors and health outcomes.

    What type of research is a cross-sectional observational study? ›

    A cross-sectional study is a type of observational research that analyzes data of variables collected at one given point in time across a sample population or a pre-defined subset. This study type is also known as cross-sectional analysis, transverse study, or prevalence study.

    What is the purpose of cross sectional studies quizlet? ›

    Cross sectional studies represent a "snap shot" in time. What can cross sectional studies be used for? To seek associations and to generate and test hypotheses.

    What is the main goal of cross-sectional surveys quizlet? ›

    The goal of a cross-sectional survey is to measure the proportion of a population with a particular exposure or disease.

    How do you identify a cross-sectional study? ›

    In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

    What is an example of a cross-sectional study? ›

    For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes. A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example.

    What is the main strength of a cross-sectional study? ›

    The main strength of cross-sectional studies is that they are relatively quick and inexpensive to conduct. They are the best way to determine the prevalence and can study the associations of multiple exposures and outcomes.

    What is cross-sectional area simple definition? ›

    The cross-sectional area is the area of a two-dimensional shape that is obtained when a three-dimensional object - such as a cylinder - is sliced perpendicular to some specified axis at a point. For example, the cross-section of a cylinder - when sliced parallel to its base - is a circle.

    Which of the following best describes cross-sectional research? ›

    Which of the following best describes cross-sectional research? Groups of different aged people are compared across time.

    What is cross-sectional data type? ›

    Cross-sectional data, or a cross section of a study population, in statistics and econometrics, is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the one point or period of time. The analysis might also have no regard to differences in time.

    What is cross-sectional view? ›

    : a view or drawing that shows what the inside of something looks like after a cut has been made across it. [count]

    Is cross-sectional research Qualitative or quantitative? ›

    An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206).

    What are the advantages of using a cross-sectional research study quizlet? ›

    - The advantage of cross-sectional studies is that it can compare different population groups at a single point in time. This means it allows researchers to compare many different variables at the same time.

    Which of the following is true of a cross-sectional study quizlet? ›

    Which of the following is true of cross-sectional research? It compares people of different ages at the same point in time.

    What is a key feature of a cross-sectional study quizlet? ›

    A key feature of a cross-sectional study is that: A cross-sectional study or survey provides a snapshot of the health of a population, so it assesses prevalence rather than incidence. As a result, it is not as useful as a cohort or case-control study for analytic epidemiology.

    What questions does a cross-sectional study answer? ›

    Cross-sectional study designs are useful when: Answering questions about the incidence or prevalence of a condition, belief or situation. Establishing what the norm is for a specific demographic at a specific time.

    What are two strengths of the cross-sectional design? ›

    4. Strengths and weaknesses of cross-sectional studies. Relatively quick and easy to conduct (no long periods of follow-up). Data on all variables is only collected once.

    Which study uses a cross-sectional design? ›

    Cross-sectional studies are often used in developmental psychology, but this method is also used in many other areas, including social science and education.

    What is a cross-sectional study an example of? ›

    Cross-sectional research studies are a type of descriptive research that provides information from groups. Because it is a snapshot of a moment in time, this type of research cannot be used to define cause and effect relationships between variables.

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