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Research Design Notes

Cross-Sectional Research

  • Definition: a snapshot in time of a particular group; data are collected on one or more variables for a single time-period.
  • Primary use: mostly for exploratory research.
  • Example: Survey of university students’ attitudes toward climate change and their pro-environmental behaviors.
  • Key assumption: there are no dramatic day-to-day fluctuations implied by the design, but results will reflect the time at which the data were collected.
  • Time-specificity: findings from a cross-sectional study at a specific time are expected to be unique to that time, especially after major events (e.g., a disaster).
  • History effect: the influence of events or circumstances outside the research study on the variables of interest (e.g., national security attitudes measured on September 10 vs. September 12).
  • Cross-sectional vs. longitudinal distinction (overview):
    • Cross-sectional design collects data at a single time period for one or more variables.
    • Longitudinal design collects data for two or more time periods, enabling tracking of changes over time.

Cross-Sectional vs Longitudinal

  • Cross-sectional: data collected at a single time period for one or more variables.
  • Longitudinal: data collected for two or more time periods, allowing researchers to track changes in the variables.

Longitudinal Research

  • Definition: collect data for two or more time periods.
  • Two main purposes:
    • To investigate changes in variables over time.
    • To establish time order (causal relationships) between variables.
  • Types of longitudinal designs:
    • Trend studies
    • Panel studies
    • Cohort studies
    • Cross-lagged studies

Trend Studies

  • Description: gather data from a particular population characterized by a specific variable (e.g., level of education, political position).
  • Method: draw a different sample from the population each time to determine trends in data.
  • Advantages:
    • Easy to maintain sample size across waves.
    • Easy to track changes in the study variables.
  • Disadvantages:
    • Different samples may contain different individual characteristics, introducing error.
  • Example: polling data.

Panel Studies

  • Description: gather data from a group of individuals who are recruited and retained to answer questions over time.
  • Advantages:
    • Helps researchers identify factors of influence within the research sample.
    • The same sample characteristics tend to persist over time.
  • Disadvantages:
    • Requires substantial time, energy, and resources.
    • High drop-out rate.
    • Participants may become familiar with measurements, potentially biasing responses.
  • Example: surveying a group of 10 class representatives about their satisfaction with their high school experience over time.

Cohort Studies

  • Description: focus on the experiences of people who belong to the same cohort (a cohort is a group with a shared experience).
  • Example: All freshmen in Fall 2020 who experienced the transition to online classes and their overall success after college.
  • Sampling: the cohort is sampled at each time point (Time 1, Time 2, Time 3), with each time point potentially using a different sample (e.g., 300 random samples of class of 2024 at each time).
  • Advantages:
    • Maintains a recognizable group across time.
    • Easy to track changes in variables within the cohort.
  • Disadvantages:
    • Typically expensive in terms of time, money, and energy.
    • Over time, researchers have less control over variables as the cohort ages.
  • Cohort effect: a research result that occurs due to the characteristics of the cohort being studied, rather than due to the variables of interest.
  • Example: studying the benefits of using a new AI tool across Baby Boomers vs. Gen Z.

Cross-Lagged Studies

  • Design: measure an independent variable (IV) and a dependent variable (DV) at two points in time from the same sample.
  • Procedure: IV and DV measured at Time 1, and again at Time 2 (IVT1, DVT1; IVT2, DVT2).
  • Purpose: to draw conclusions about causality by establishing time order.
  • Significance: the design is often described as the only survey design that permits researchers to assess causality; it is foundational for experiments in social science research.
  • Advantages:
    • Helps identify potential causal factors within the research sample.
    • The same sample characteristics tend to persist over time.
  • Disadvantages:
    • Requires substantial time, energy, and resources.
    • High drop-out rate.
    • Variables may be influenced by confounding variables not accounted for in the design.

Example 1

  • Dr. Russell’s research team surveys a cohort of children about their television viewing habits and their predisposition to violence at age 5 (Time 1) and again at age 10 (Time 2).
  • Objective: assess the relationship between TV viewing and any change in predisposition to violence over time.

Choosing A Design

  • Consider available resources and research objectives:
    • Exploration and explanation: Cross-sectional study.
    • Explanation, prediction, and control: Longitudinal study.
  • What do researchers want to do?
    • Snapshot status of one or more variables: Cross-sectional.
    • Changes in one or more variables, causality: Longitudinal.
  • Note: There is little or nothing that cross-sectional research can do that longitudinal cannot.

Practice Questions

Question 1

  • Scenario: You are hired at Apple to learn about the current status of existing products and what people want to see in the future.
  • Question: Which design is appropriate for this project?
  • Options: a. Cross-sectional b. Longitudinal c. Sensational d. Nothing
  • Answer: a. Cross-sectional
  • Rationale: To capture current status and desires for the future in a single snapshot; longitudinal would be needed only if tracking changes over time.

Question 2

  • Scenario: The university will evaluate a course at the end of the semester with one survey.
  • Question: This course evaluation survey is which type of research?
  • Options: a. Expensive b. Cross-sectional c. Longitudinal d. Meaningless
  • Answer: b. Cross-sectional
  • Rationale: One survey taken at a single time point fits cross-sectional design.

Question 3

  • Scenario: To learn how students’ evaluation of COMG 102 changes throughout the semester.
  • Question: What design should be used?
  • Options: a. Expensive b. Cross-sectional c. Longitudinal d. Meaningless
  • Answer: c. Longitudinal
  • Rationale: Observing change over time requires data from two or more time points.

Question 4

  • Scenario: What is the least number of times the university should conduct the survey to learn about change over the semester?
  • Options: a. Once b. Twice c. Three times d. Four times
  • Answer: b. Twice
  • Rationale: To assess change, at least two time points are needed (e.g., Time 1 and Time 2).

Wrap-up

  • Key takeaway: Research design choices depend on what you want to measure and how many times you collect data.
  • Cross-sectional vs longitudinal: One-time snapshot vs two or more time points.
  • Longitudinal designs offer several variants (Trend, Panel, Cohort, Cross-lagged) each with specific strengths, weaknesses, and cost considerations.
  • Final guidance: Choose a design based on resources and the research goal (exploration, explanation, prediction, control, or causality).