11.4 Repeated measures designs
11 min read•august 21, 2024
Repeated measures designs are a powerful tool in communication research, allowing researchers to track changes over time or across conditions. These designs enhance the ability to detect subtle effects and reduce the impact of individual differences on research outcomes.
Understanding various types of repeated measures designs, such as within-subjects designs, longitudinal studies, and time series designs, enables researchers to choose the most appropriate method for their specific research questions. Each type offers unique advantages in studying communication phenomena.
Types of repeated measures
- Repeated measures designs form a crucial component of Advanced Communication Research Methods, allowing researchers to track changes over time or across conditions
- These designs enhance the ability to detect subtle effects and reduce the impact of individual differences on research outcomes
- Understanding various types of repeated measures designs enables researchers to choose the most appropriate method for their specific research questions
Within-subjects designs
Top images from around the web for Within-subjects designs
Frontiers | A Cognitive-Emotional Model to Explain Message Framing Effects: Reducing Meat ... View original
Is this image relevant?
r - Plotting individual responses for two-way repeated measures design with geom_path from ... View original
Is this image relevant?
A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time ... View original
Is this image relevant?
Frontiers | A Cognitive-Emotional Model to Explain Message Framing Effects: Reducing Meat ... View original
Is this image relevant?
r - Plotting individual responses for two-way repeated measures design with geom_path from ... View original
Is this image relevant?
1 of 3
Top images from around the web for Within-subjects designs
Frontiers | A Cognitive-Emotional Model to Explain Message Framing Effects: Reducing Meat ... View original
Is this image relevant?
r - Plotting individual responses for two-way repeated measures design with geom_path from ... View original
Is this image relevant?
A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time ... View original
Is this image relevant?
Frontiers | A Cognitive-Emotional Model to Explain Message Framing Effects: Reducing Meat ... View original
Is this image relevant?
r - Plotting individual responses for two-way repeated measures design with geom_path from ... View original
Is this image relevant?
1 of 3
- Participants serve as their own control, experiencing all experimental conditions
- Reduces the impact of individual differences on results
- Typically involves shorter time intervals between measurements (hours or days)
- Requires fewer participants compared to between-subjects designs
- Examples include:
- Testing the effectiveness of different message framing techniques on the same group of participants
- Evaluating changes in attitude after exposure to various types of media content
Longitudinal studies
- Tracks the same variables in the same group of participants over an extended period
- Allows for the observation of developmental trends and long-term changes
- Often spans months, years, or even decades
- Valuable for studying phenomena like:
- Changes in media consumption habits across different life stages
- Evolution of public opinion on social issues over time
Time series designs
- Involves multiple observations of a single unit (individual, group, or system) over time
- Focuses on identifying patterns, trends, or cycles in the data
- Can be used to assess the impact of interventions or events on a variable of interest
- Applications in communication research include:
- Analyzing social media engagement rates before and after a marketing campaign
- Studying the frequency of specific news topics over an extended period
Advantages of repeated measures
- Repeated measures designs offer significant benefits in Advanced Communication Research Methods, enhancing the quality and efficiency of studies
- These advantages contribute to more robust and reliable findings in communication research
- Understanding these benefits helps researchers make informed decisions about study design
Increased statistical power
- Detects smaller effect sizes with fewer participants
- Reduces error variance by controlling for individual differences
- Allows for more sensitive analyses of subtle changes or effects
- Particularly useful in communication studies where effects may be nuanced (persuasion, attitude change)
Reduced participant variability
- Each participant serves as their own control, minimizing between-subject variability
- Eliminates the need to match participants across conditions
- Improves the ability to isolate the effects of experimental manipulations
- Especially beneficial in studies of media effects or message processing
Cost-effectiveness
- Requires fewer participants to achieve the same statistical power as between-subjects designs
- Reduces recruitment costs and time investment
- Allows for more efficient use of limited resources (lab space, equipment)
- Enables researchers to conduct more comprehensive studies within budget constraints
Disadvantages of repeated measures
- While repeated measures designs offer many advantages, they also come with specific challenges
- Understanding these limitations is crucial for researchers in Advanced Communication Research Methods
- Awareness of these issues allows for better study design and more accurate interpretation of results
Carryover effects
- Influence of earlier conditions or measurements on subsequent ones
- Can lead to biased results if not properly controlled
- Particularly problematic in studies involving learning or memory (message recall, skill acquisition)
- Mitigation strategies include:
- the order of conditions
- Introducing longer intervals between measurements
Practice effects
- Improvement in performance due to familiarity with tasks or measures
- Can mask or exaggerate the true effects of experimental manipulations
- Common in studies involving repeated cognitive tasks or surveys
- Strategies to minimize include:
- Using parallel forms of measures
- Incorporating practice trials before the actual experiment
Fatigue effects
- Decreased performance or engagement due to repeated testing or prolonged participation
- Can lead to less reliable data in later stages of the study
- Particularly relevant in lengthy experiments or studies with multiple complex tasks
- Mitigation approaches include:
- Incorporating breaks between measurements
- Limiting the duration of individual sessions
- Using varied and engaging tasks to maintain participant interest
Design considerations
- Careful planning of repeated measures designs is essential in Advanced Communication Research Methods
- These considerations help ensure the validity and reliability of research findings
- Implementing these strategies can significantly improve the quality of repeated measures studies
Counterbalancing techniques
- Systematically varies the order of conditions or stimuli across participants
- Helps control for order effects and
- Common methods include:
- Latin square designs
- Complete counterbalancing
- Partial counterbalancing
- Example: Rotating the order of exposure to different media messages across participant groups
Washout periods
- Introduces time intervals between measurements or conditions
- Allows for the effects of previous treatments or stimuli to dissipate
- Crucial in studies involving lasting effects (attitude change, learning)
- Length of washout periods depends on the nature of the study and variables measured
- Example: Allowing a week between exposures to different persuasive messages in a campaign effectiveness study
Number of measurement points
- Determines the granularity and comprehensiveness of the data collected
- Balances the need for detailed information with practical constraints
- Considerations for choosing the number of measurement points:
- Research question and hypotheses
- Expected rate of change in the variables of interest
- Participant burden and potential attrition
- Available resources and time constraints
- Example: Deciding whether to measure social media engagement daily, weekly, or monthly in a
Statistical analysis methods
- Advanced Communication Research Methods employs sophisticated statistical techniques for analyzing repeated measures data
- These methods allow researchers to account for the unique structure of repeated measures designs
- Understanding these analytical approaches is crucial for accurate interpretation and reporting of results
Repeated measures ANOVA
- Analyzes differences in mean scores across multiple time points or conditions
- Accounts for the dependency between measurements from the same participants
- Key components include:
- Within-subjects factors
- Between-subjects factors (if applicable)
- Interaction effects
- Assumptions:
- Normality of residuals
- Example application: Comparing the effectiveness of different message framing techniques across multiple time points
Mixed-effects models
- Combines fixed effects (experimental manipulations) and random effects (individual differences)
- Allows for the inclusion of time-varying covariates
- Handles missing data more effectively than traditional ANOVA
- Provides flexibility in modeling various covariance structures
- Useful for analyzing complex repeated measures designs with nested data structures
- Example: Studying the impact of media exposure on political attitudes over time, accounting for individual differences and contextual factors
Multilevel modeling
- Analyzes hierarchical data structures common in repeated measures designs
- Accounts for dependencies within and between levels of analysis
- Allows for the examination of both within-subject and between-subject variability
- Can handle unequal numbers of observations per participant
- Particularly useful for longitudinal studies with varying measurement intervals
- Example: Investigating how individual-level media consumption patterns influence group-level opinion dynamics over time
Assumptions and violations
- Understanding the assumptions underlying repeated measures analyses is crucial in Advanced Communication Research Methods
- Violations of these assumptions can lead to biased results and incorrect interpretations
- Researchers must be able to identify and address potential violations to ensure the validity of their findings
Sphericity assumption
- Requires equal variances of the differences between all pairs of repeated measures
- Critical for the validity of repeated measures ANOVA
- Violation can lead to inflated Type I error rates
- Can be tested using Mauchly's test of sphericity
- Commonly violated in practice, especially with larger numbers of repeated measures
- Example: Ensuring that the variability in attitude scores is consistent across all pairs of measurement time points in a longitudinal study
Compound symmetry
- A stricter form of sphericity requiring equal variances and covariances across all repeated measures
- Rarely met in practice but often assumed by simpler statistical models
- Violation can result in biased parameter estimates and incorrect standard errors
- More flexible covariance structures (unstructured, autoregressive) can be used when compound symmetry is violated
- Example: Assessing whether the relationship between media exposure and political knowledge remains consistent across multiple time points
Corrections for violations
- Adjustments made to degrees of freedom when sphericity is violated
- Common corrections include:
- Greenhouse-Geisser correction (more conservative)
- Huynh-Feldt correction (less conservative)
- Alternative approaches:
- Using multivariate tests (Pillai's trace, Wilks' lambda) which do not assume sphericity
- Employing mixed-effects models with appropriate covariance structures
- Example: Applying the Greenhouse-Geisser correction to adjust the degrees of freedom in a repeated measures ANOVA examining changes in media trust over multiple time points
Interpreting results
- Proper interpretation of results is a critical skill in Advanced Communication Research Methods
- Understanding the nuances of repeated measures analyses helps researchers draw accurate conclusions
- Careful consideration of various effects and their practical significance enhances the value of research findings
Main effects vs interactions
- Main effects represent the overall impact of a single independent variable
- Interactions indicate how the effect of one variable depends on the levels of another
- In repeated measures designs:
- Within-subjects main effects show changes across time or conditions
- Between-subjects main effects (if applicable) show differences between groups
- Interactions can reveal complex patterns of change over time or across conditions
- Example: Distinguishing between the overall effect of message type (main effect) and how this effect changes over time (interaction) in a persuasion study
Post-hoc comparisons
- Conducted after finding significant main effects or interactions
- Help identify specific differences between conditions or time points
- Common methods include:
- Bonferroni correction
- Tukey's HSD
- Sidak correction
- Importance of controlling for familywise error rate in multiple comparisons
- Example: Determining which specific time points show significant differences in media consumption patterns after finding a significant main effect of time
Effect sizes in repeated measures
- Quantify the magnitude of observed effects, complementing statistical significance
- Partial eta-squared (η²p) commonly used for repeated measures ANOVA
- Cohen's d can be adapted for paired comparisons in repeated measures
- Interpretation guidelines:
- Small effect: η²p ≈ 0.01, d ≈ 0.2
- Medium effect: η²p ≈ 0.06, d ≈ 0.5
- Large effect: η²p ≈ 0.14, d ≈ 0.8
- Example: Reporting the effect size of a media literacy intervention on critical thinking skills measured at multiple time points
Reporting repeated measures
- Clear and comprehensive reporting is essential in Advanced Communication Research Methods
- Proper documentation ensures reproducibility and facilitates meta-analyses
- Following established guidelines enhances the clarity and impact of research findings
APA format guidelines
- Adhere to the latest APA style manual for reporting statistical results
- Key elements to include:
- Degrees of freedom (with corrections if applicable)
- F-values or t-values
- p-values (exact values when possible)
- Effect sizes
- Report assumptions checks and any violations
- Clearly state the type of analysis used (repeated measures ANOVA, )
- Example: F(2.34, 70.20) = 4.56, p = .01, η²p = .13 (with Greenhouse-Geisser correction)
Graphical representations
- Use visual aids to enhance understanding of complex repeated measures results
- Common graph types:
- Line graphs for showing changes over time
- Bar graphs for comparing means across conditions
- Error bars to represent confidence intervals or standard errors
- Ensure clear labeling of axes, conditions, and time points
- Consider using color or patterns to differentiate between groups or conditions
- Example: Creating a line graph showing changes in social media engagement across multiple time points for different age groups
Tables for repeated measures
- Organize complex data in a clear, concise format
- Include descriptive statistics (means, standard deviations) for each condition and time point
- Present inferential statistics (F-values, p-values, effect sizes) in a separate table or integrated format
- Use notes to explain any abbreviations or special considerations
- Ensure consistency in decimal places and formatting throughout the table
- Example: Creating a table summarizing means and standard deviations of attitude scores across three time points and two experimental conditions
Advanced applications
- Advanced Communication Research Methods often involves complex research designs
- These advanced applications allow for more nuanced and comprehensive investigations
- Understanding these designs enhances researchers' ability to address sophisticated research questions
Crossover designs
- Participants receive different treatments in a specific sequence over time
- Combines features of within-subjects and between-subjects designs
- Allows for the assessment of both treatment effects and period effects
- Particularly useful in media effects studies or communication intervention research
- Example: Evaluating the effectiveness of two different public health communication strategies, with each participant experiencing both strategies in a randomized order
Repeated measures in factorial designs
- Incorporates both within-subjects and between-subjects factors
- Allows for the examination of complex interactions between time-varying and time-invariant variables
- Enhances the ability to detect both main effects and interaction effects
- Requires careful consideration of design balance and power
- Example: Investigating the impact of message framing (within-subjects) and individual personality traits (between-subjects) on persuasion over multiple time points
Repeated measures in mixed methods
- Integrates quantitative repeated measures with qualitative data collection
- Provides a more comprehensive understanding of phenomena under study
- Qualitative data can help explain patterns observed in quantitative repeated measures
- Approaches include:
- Sequential designs (quantitative followed by qualitative or vice versa)
- Concurrent designs (collecting both types of data simultaneously)
- Example: Combining a longitudinal survey on media trust with in-depth interviews at key time points to explore the reasons behind observed changes in trust levels
Ethical considerations
- Ethical considerations are paramount in Advanced Communication Research Methods, especially in repeated measures designs
- These considerations ensure the protection of participants and the integrity of the research process
- Addressing ethical concerns is crucial for conducting responsible and valuable communication research
Participant burden
- Repeated measures designs often require more time and effort from participants
- Strategies to minimize burden:
- Carefully consider the frequency and duration of measurements
- Use efficient data collection methods (online surveys, mobile apps)
- Provide adequate compensation for time and effort
- Balance research needs with participant well-being
- Example: Implementing shorter, more frequent surveys instead of lengthy questionnaires in a longitudinal study of social media use
Data privacy in longitudinal studies
- Longitudinal designs involve collecting and storing personal data over extended periods
- Implement robust data protection measures:
- Use secure, encrypted storage systems
- Anonymize or pseudonymize data where possible
- Limit access to identifiable information
- Develop clear data retention and destruction policies
- Inform participants about data handling procedures and their rights
- Example: Using unique identifiers instead of names to link participant data across multiple time points in a study on political communication
Informed consent for multiple timepoints
- Participants must understand the full scope of their involvement in repeated measures studies
- Key elements to include in the consent process:
- Clear explanation of the study duration and number of measurement points
- Description of what will be required at each time point
- Information about potential risks and benefits of long-term participation
- Procedures for withdrawing from the study at any point
- Consider implementing ongoing consent processes for long-term studies
- Example: Obtaining initial consent for a year-long study on media effects, with brief re-consent procedures at each quarterly measurement point
Key Terms to Review (20)
Anova for repeated measures: ANOVA for repeated measures is a statistical technique used to analyze data where the same subjects are measured multiple times under different conditions or over time. This method helps in assessing whether there are statistically significant differences in the means of the dependent variable across the different conditions while accounting for the correlation between repeated measures on the same subjects.
Carryover Effects: Carryover effects refer to the influence that prior treatments or conditions can have on subsequent measures or outcomes in a research study. This is especially significant in designs where the same participants are exposed to multiple conditions, as the effects from one condition can persist and impact performance or responses in later conditions. These effects can lead to confounding variables, making it harder to isolate the true effect of the experimental manipulation.
Correlation between measures: Correlation between measures refers to the statistical relationship that exists when two or more variables move in relation to each other. In repeated measures designs, this concept is critical because it helps researchers understand how changes in one variable may correspond with changes in another, which is especially important when measuring the same subjects across different conditions or time points.
Counterbalancing: Counterbalancing is a method used in experimental designs, particularly in repeated measures studies, to control for the potential effects of confounding variables by systematically varying the order of treatment conditions. This approach helps to ensure that any differences observed in the dependent variable can be attributed more confidently to the independent variable rather than the order in which treatments are presented. By balancing the sequence of conditions across participants, counterbalancing minimizes bias and enhances the validity of the findings.
Crossover Design: Crossover design is a type of experimental design where participants receive multiple treatments in a sequential manner, allowing each participant to serve as their own control. This method enhances the efficiency of the study by reducing the variability among participants since each participant is exposed to all conditions. It's particularly useful for studying the effects of different interventions on the same subjects over time.
Fatigue effects: Fatigue effects refer to the decline in performance or response that can occur when participants are subjected to multiple tasks or conditions over time. This phenomenon is particularly relevant in research designs where the same subjects are tested repeatedly, leading to potential decreases in motivation, focus, or physical endurance as the study progresses. Recognizing fatigue effects is crucial for researchers to ensure that data collected is valid and reliable.
Increased statistical power: Increased statistical power refers to the probability that a study will correctly reject a false null hypothesis, thus detecting an effect when one truly exists. This concept is crucial in research designs, especially when it comes to repeated measures and within-subjects designs, where the same subjects are measured multiple times under different conditions. Higher statistical power enhances the reliability of findings and reduces the likelihood of Type II errors, ultimately leading to more valid conclusions about the effects being studied.
Jacob Cohen: Jacob Cohen was a prominent statistician known for his contributions to statistical power analysis and effect size measurement. His work significantly influenced how researchers interpret the strength of relationships and the impact of interventions in psychological and social sciences, particularly emphasizing the importance of effect sizes in repeated measures designs and other research methodologies.
John Tukey: John Tukey was a renowned American statistician known for his significant contributions to the field of statistics, particularly in exploratory data analysis and the development of the box plot. His work emphasized the importance of visualizing data to uncover patterns and insights, which is especially relevant in repeated measures designs where multiple observations are made on the same subjects. Tukey's innovative approaches have influenced how researchers analyze and interpret data across various fields.
Longitudinal Study: A longitudinal study is a research design that involves repeated observations of the same variables over a period of time, often years or even decades. This method is particularly useful for tracking changes and developments within subjects, making it a key approach in understanding trends and causal relationships. By collecting data from the same participants at multiple time points, researchers can identify patterns over time and assess how variables interact and influence one another.
Matched pairs design: Matched pairs design is a type of experimental design where participants are paired based on certain characteristics, such as age, gender, or other relevant variables, to ensure that each pair is similar in those respects. This method helps to control for variables that could confound results, allowing for a clearer understanding of the treatment effect. It is often used in repeated measures designs where each participant experiences different conditions, enhancing the reliability and validity of the findings.
Mixed-effects model: A mixed-effects model is a statistical technique used to analyze data that involves both fixed and random effects, allowing researchers to account for variability at different levels within their data. This method is particularly useful for repeated measures designs, where multiple observations are made from the same subjects over time or under different conditions. By incorporating both types of effects, mixed-effects models provide a more flexible and accurate way to understand complex data structures and relationships.
Practice effects: Practice effects refer to the improvements in participants' performance on a task due to repeated exposure to the task rather than actual changes in the underlying ability or skill. This phenomenon is particularly important in experimental designs where the same participants are tested multiple times, influencing the interpretation of results. Recognizing practice effects helps researchers understand how learning or familiarity with the task might skew findings and highlights the need for careful control measures.
Random assignment: Random assignment is a procedure used in experiments where participants are randomly allocated to different groups or conditions to ensure that each participant has an equal chance of being placed in any group. This technique helps to eliminate bias and control for variables that could affect the outcome, allowing researchers to make valid causal inferences about the effects of experimental manipulations.
Reduced error variance: Reduced error variance refers to the decrease in variability in the data that can be attributed to measurement errors or extraneous influences, enhancing the precision of statistical analyses. This concept is particularly crucial in research designs that involve repeated measures, as it allows for a clearer understanding of the treatment effects by controlling for individual differences across measurements.
Repeated measures in factorial designs: Repeated measures in factorial designs refer to a research method where the same subjects are exposed to multiple conditions or treatments across different levels of independent variables. This approach allows researchers to examine the interactions between factors while controlling for individual differences, as each participant serves as their own control, leading to increased statistical power and reduced variability in the data.
Repeated measures in mixed methods: Repeated measures in mixed methods refers to a research design where the same subjects are assessed multiple times across different conditions or time points, integrating both qualitative and quantitative data. This approach allows researchers to gain a deeper understanding of changes over time or the effects of different interventions while taking advantage of the strengths of both qualitative and quantitative methodologies.
Sphericity: Sphericity is a statistical assumption that refers to the equality of variances of the differences between all possible pairs of conditions in a repeated measures design. It plays a crucial role in the analysis of variance (ANOVA) when using repeated measures, as violations of this assumption can lead to incorrect conclusions about the data. When sphericity holds, it ensures that the results of statistical tests are valid, providing reliable insights into the effects of independent variables across multiple conditions.
Time Series Design: Time series design is a research method that involves collecting data at multiple time points to observe changes and trends over time. This approach allows researchers to examine the effects of interventions or events by comparing data before and after the occurrence, making it particularly useful for identifying causal relationships in non-experimental settings. By analyzing patterns over time, this design helps in understanding dynamics and fluctuations in behavior or phenomena.
Within-subjects design: Within-subjects design is an experimental approach where the same participants are exposed to all levels of the independent variable, allowing researchers to directly compare effects across conditions. This design minimizes individual differences as each participant acts as their own control, making it particularly useful in understanding variations in behavior or response over multiple conditions or time points.