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Buckley, Jeffrey; Hyland, Tomás; Seery, Niall – International Journal of Technology and Design Education, 2023
Technology education research is a growing field, with the rate of growth increasing over the last 2 decades. As the field grows, it is paramount that credibility is maintained in published findings. To date there is no evidence to suggest a lack trust is warranted, however in the midst of the replication crisis there is need to ensure continued…
Descriptors: Technology Education, Educational Research, Replication (Evaluation), Credibility
Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
Soysal, Sumeyra; Karaman, Haydar; Dogan, Nuri – Eurasian Journal of Educational Research, 2018
Purpose of the Study: Missing data are a common problem encountered while implementing measurement instruments. Yet the extent to which reliability, validity, average discrimination and difficulty of the test results are affected by the missing data has not been studied much. Since it is inevitable that missing data have an impact on the…
Descriptors: Sample Size, Data Analysis, Research Problems, Error of Measurement
Lane, David; Oswald, Frederick L. – Educational Measurement: Issues and Practice, 2016
The educational literature, the popular press, and educated laypeople have all echoed a conclusion from the book "Academically Adrift" by Richard Arum and Josipa Roksa (which has now become received wisdom), namely, that 45% of college students showed no significant gains in critical thinking skills. Similar results were reported by…
Descriptors: College Students, Critical Thinking, Thinking Skills, Statistical Analysis
Cooper, Barry; Glaesser, Judith – International Journal of Social Research Methodology, 2016
Ragin's Qualitative Comparative Analysis (QCA) is often used with small to medium samples where the researcher has good case knowledge. Employing it to analyse large survey datasets, without in-depth case knowledge, raises new challenges. We present ways of addressing these challenges. We first report a single QCA result from a configurational…
Descriptors: Social Science Research, Robustness (Statistics), Educational Sociology, Comparative Analysis
McNeish, Daniel – Review of Educational Research, 2017
In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…
Descriptors: Models, Statistical Analysis, Sampling, Sample Size
Tang, Yang; Cook, Thomas D.; Kisbu-Sakarya, Yasemin – Society for Research on Educational Effectiveness, 2015
Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…
Descriptors: Pretests Posttests, Statistical Bias, Accuracy, Regression (Statistics)
Rhemtulla, Mijke; Jia, Fan; Wu, Wei; Little, Todd D. – International Journal of Behavioral Development, 2014
We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such…
Descriptors: Data Analysis, Error of Measurement, Models, Longitudinal Studies
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Bernard, Robert M.; Borokhovski, Eugene; Schmid, Richard F.; Tamim, Rana M. – Journal of Computing in Higher Education, 2014
This article contains a second-order meta-analysis and an exploration of bias in the technology integration literature in higher education. Thirteen meta-analyses, dated from 2000 to 2014 were selected to be included based on the questions asked and the presence of adequate statistical information to conduct a quantitative synthesis. The weighted…
Descriptors: Meta Analysis, Bias, Technology Integration, Higher Education
Cheema, Jehanzeb – ProQuest LLC, 2012
This study looked at the effect of a number of factors such as the choice of analytical method, the handling method for missing data, sample size, and proportion of missing data, in order to evaluate the effect of missing data treatment on accuracy of estimation. In order to accomplish this a methodological approach involving simulated data was…
Descriptors: Educational Research, Educational Researchers, Statistical Analysis, Sample Size
Wing, Coady; Cook, Thomas D. – Journal of Policy Analysis and Management, 2013
The sharp regression discontinuity design (RDD) has three key weaknesses compared to the randomized clinical trial (RCT). It has lower statistical power, it is more dependent on statistical modeling assumptions, and its treatment effect estimates are limited to the narrow subpopulation of cases immediately around the cutoff, which is rarely of…
Descriptors: Regression (Statistics), Research Design, Statistical Analysis, Research Problems
Zhuang, Jie; Chen, Peijie; Wang, Chao; Jin, Jing; Zhu, Zheng; Zhang, Wenjie – Research Quarterly for Exercise and Sport, 2013
Purpose: The purpose of this study was to determine which method, individual information-centered (IIC) or group information-centered (GIC), is more efficient in recovering missing physical activity (PA) data. Method: A total of 2,758 Chinese children and youth aged 9 to 17 years old (1,438 boys and 1,320 girls) wore ActiGraph GT3X/GT3X+…
Descriptors: Foreign Countries, Physical Activities, Measurement Equipment, Data Analysis
Gemici, Sinan; Bednarz, Alice; Lim, Patrick – International Journal of Training Research, 2012
Quantitative research in vocational education and training (VET) is routinely affected by missing or incomplete information. However, the handling of missing data in published VET research is often sub-optimal, leading to a real risk of generating results that can range from being slightly biased to being plain wrong. Given that the growing…
Descriptors: Vocational Education, Educational Research, Data, Statistical Analysis
What Works Clearinghouse, 2014
This "What Works Clearinghouse Procedures and Standards Handbook (Version 3.0)" provides a detailed description of the standards and procedures of the What Works Clearinghouse (WWC). The remaining chapters of this Handbook are organized to take the reader through the basic steps that the WWC uses to develop a review protocol, identify…
Descriptors: Educational Research, Guides, Intervention, Classification
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