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A protocol for analyzing repeated measures of online group behavior.

ABSTRACT: In this article, we feature a novel protocol that enables the analysis of repeated measures of online group behavior. The protocol accounts for (1) the nested hierarchy of the data with weeks nested in persons, and persons nested in weeks, and (2) the temporal nature of the behavior at the early, mid, and late periods of each week. To manage and analyze such data in a general way, we first give an illustration of the data structure. Thereafter, we propose a five-step Courtney-Fanguy-Costley protocol that (1) considers the data structure, (2) defines the levels of data, (3) considers variable variation, timing, and necessary aggregation, (4) ensures necessary variation, and (5) specifies null and mixed-effects models. We also provide exemplary R code for readers to replicate our approach.•A general five-step protocol for analyzing repeated measures of online group behavior is offered.•A description of the complex nested data structure is offered.•Users can simulate data in R to run through the protocol.

SUBMITTER: Courtney MGR

PROVIDER: S-EPMC8987404 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A protocol for analyzing repeated measures of online group behavior.

Courtney Matthew G R MGR Costley Jamie J Fanguy Mik M

MethodsX 20220322

In this article, we feature a novel protocol that enables the analysis of repeated measures of online group behavior. The protocol accounts for (1) the nested hierarchy of the data with weeks nested in persons, and persons nested in weeks, and (2) the temporal nature of the behavior at the early, mid, and late periods of each week. To manage and analyze such data in a general way, we first give an illustration of the data structure. Thereafter, we propose a five-step Courtney-Fanguy-Costley prot ...[more]

PMID: 35402171

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Project description:BackgroundMultiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model.MethodsContinuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group.ResultsResults indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance.ConclusionThe time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.

Project description:BackgroundThe assessment of the effectiveness of teaching interventions in enhancing students' understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students' understanding of the PCNE Classification System in pharmaceutical education.MethodsUndergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires.ResultsThe paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire's validity.ConclusionThe implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students' comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

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A protocol for analyzing repeated measures of online group behavior.

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A protocol for analyzing repeated measures of online group behavior.

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