Aptitude by Treatment Interactions and Matthew Effects in Graduate-Level Cooperative-Learning Groups

Onwuegbuzie, Anthony J.; Collins, Kathleen M. T.; Elbedour, Salman

doi:10.1080/00220670309598811

Cited by 13 publications

(13 citation statements)

References 31 publications

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“…Onwuegbuzie, Collins, and their colleagues (Collins, Onwuegbuzie, & DaRos-Voseles, 2004; DaRos-Voseles, Collins, & Onwuegbuzie, 2005; DaRos-Voseles, Collins, Onwuegbuzie, & Jiao, 2008; DaRos-Voseles, Onwuegbuzie, & Collins, 2003; Jiao, Collins, & Onwuegbuzie, 2008; Onwuegbuzie, 2001; Onwuegbuzie & Collins, 2002; Onwuegbuzie, Collins, & Elbedour, 2003; Onwuegbuzie & DaRos-Voseles, 2001) have provided much evidence of the important role that group dynamics play in determining group outcomes. Thus, it is reasonable to expect the composition of the focus group to influence the quality of responses given by one or more of the participants.…”

Section: Micro-interlocutor Analysis: a New Methods Of Analysis For Fomentioning

confidence: 99%

A Qualitative Framework for Collecting and Analyzing Data in Focus Group Research

Onwuegbuzie

Dickinson

Leech

et al. 2009

International Journal of Qualitative Methods

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Despite the abundance of published material on conducting focus groups, scant specific information exists on how to analyze focus group data in social science research. Thus, the authors provide a new qualitative framework for collecting and analyzing focus group data. First, they identify types of data that can be collected during focus groups. Second, they identify the qualitative data analysis techniques best suited for analyzing these data. Third, they introduce what they term as a micro-interlocutor analysis, wherein meticulous information about which participant responds to each question, the order in which each participant responds, response characteristics, the nonverbal communication used, and the like is collected, analyzed, and interpreted. They conceptualize how conversation analysis offers great potential for analyzing focus group data. They believe that their framework goes far beyond analyzing only the verbal communication of focus group participants, thereby increasing the rigor of focus group analyses in social science research.

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Section: Micro-interlocutor Analysis: a New Methods Of Analysis For Fomentioning

confidence: 99%

A Qualitative Framework for Collecting and Analyzing Data in Focus Group Research

Onwuegbuzie

Dickinson

Leech

et al. 2009

International Journal of Qualitative Methods

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1,250

859

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“…Another example of a study using MMR examined the relationship between the mean group midterm scores (aptitude, the IV) and group article critique scores (outcome, the DV) for several groups of graduate students (Onwuegbuzie, Collins, & Elbedour, 2003). They were interested in whether the treatment (MV), which was the amount of heterogeneity (variance) in the group's midterm scores moderated the relationship between aptitude and the critique score.…”

Section: Moderated Multiple Regression (Mmr)mentioning

confidence: 99%

Estimating individual-level interaction effects in multilevel models: a Monte Carlo simulation study with application

Lorah

2017

Journal of Applied Statistics

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Moderated multiple regression (MMR) provides a useful framework for understanding moderator variables. When the relationship between an independent variable (predictor) and a dependent variable (criterion) varies as a function of a third variable, that third variable is considered a moderator of the predictor-criterion relationship. Moderated relationships can also be examined within datasets that include nesting, for example individuals might be nested within groups. However, the literature is not clear on the best way to assess data for significant moderating effects, particularly within a multilevel modeling framework. This study explores potential ways to test moderation at the individual level (level 1) within a 2-level multilevel modeling framework, with varying effect sizes, cluster sizes, and numbers of clusters that represent realistic conditions in applied educational research. The study examines five potential methods for testing interaction effects: the Wald test, F-test, likelihood ratio test, BIC, and AIC.For each method, the simulation study examines how Type I error rates vary as a function of number of clusters and cluster size and how power varies as a function of number of clusters, cluster size, and interaction effect size. Following the simulation study, an applied study uses real data to assess interaction effects using the same five methods. Results indicate that the Wald test, F-test, and likelihood ratio test all perform similarly in terms of Type I error rates and power. Type I error rates for the AIC are more liberal, and for the BIC typically more conservative. A four-step procedure for applied researchers interested in examining interaction effects in multi-level models is provided. gender is present. Further, the data for this study (Litzler et al., 2014) included students nested within multiple schools, indicating that a multilevel model would be appropriate, given the non-independence of individual student observations. Moderator hypotheses such as these can be examined with moderated multiple regression models and this can be done within a multilevel framework for data that includes nesting. The results of these types of models can represent important and relevant contributions to the literature. This study will investigate methods for testing multilevel moderated regression models with interaction effects at the individual level only (or more generally, at level-one only). Within this study, this model includes a continuous dependent variable, a continuous independent variable, and a binary moderator variable. Methods for testing this interaction effect will be evaluated on Type I error rates and power based on a simulation study and an applied study will follow to illustrate the technique. An introduction to moderation, multilevel models, and testing for interaction effects follows in this chapter.

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“…This line of thinking applies even when dealing with well-known instructional ideas. For example, there is much empirical support for cooperative learning at both the secondary (e.g., Ginsburg-Block, Rohrbeck, & Fantuzzo, 2006;Johnson & Johnson, 2009;Rohrbeck, Ginsburg-Block, Fantuzzo, & Miller, 2003) and postsecondary (e.g., Onwuegbuzie, Collins, & Elbedour, 2003;Onwuegbuzie, Collins, & Jiao, 2009;Onwuegbuzie & DaRos-Voseles, 2001) levels; however, it appears from a quick review of search engines that little is known about how cooperative learning works in different international settings compared to status quo instruction. Therefore, it is plausible that cooperative learning is not better compared to what is already being undertaken.…”

Section: Program Evaluation and The Ethics Of Random Assignmentmentioning

confidence: 99%

“…And this confounding is even more problematic when the intervention is administered to groups over a long period of time because the number of confounding variables increases as a function of time (McMillan, 2000). Consequently, as noted by Onwuegbuzie et al (2003), the internal validity of the findings stemming from the majority of the intervention research-including RCTs-in the area of cooperative learning is seriously threatened because of this treatment replication error.…”

Section: A General Design Shell For a School-level Rctmentioning

confidence: 99%

Causal Inference and Assessing Learning Outcomes Across Multiple Methods: A Primer for International Evaluators in Education

Hitchcock¹,

Onwuegbuzie²,

David³

et al. 2018

IJMRA

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Aptitude by Treatment Interactions and Matthew Effects in Graduate-Level Cooperative-Learning Groups

Cited by 13 publications

References 31 publications

A Qualitative Framework for Collecting and Analyzing Data in Focus Group Research

A Qualitative Framework for Collecting and Analyzing Data in Focus Group Research

Estimating individual-level interaction effects in multilevel models: a Monte Carlo simulation study with application

Causal Inference and Assessing Learning Outcomes Across Multiple Methods: A Primer for International Evaluators in Education

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