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Cintron, Dakota W.; Montrosse-Moorhead, Bianca – American Journal of Evaluation, 2022
Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim…
Descriptors: Evaluation Research, Evaluation Problems, Evaluation Methods, Models
Jacob M. Schauer; Kaitlyn G. Fitzgerald; Sarah Peko-Spicer; Mena C. R. Whalen; Rrita Zejnullahi; Larry V. Hedges – Grantee Submission, 2021
Several programs of research have sought to assess the replicability of scientific findings in different fields, including economics and psychology. These programs attempt to replicate several findings and use the results to say something about large-scale patterns of replicability in a field. However, little work has been done to understand the…
Descriptors: Statistical Analysis, Research Methodology, Evaluation Methods, Replication (Evaluation)
Tajmel, Tanja – Cultural Studies of Science Education, 2019
With the present forum contribution, I respond to the paper "Discerning contextual complexities in STEM career pathways--Insights from successful Latinas" by Alejandro Gallard Martínez, Wesley Pitts, Silvia Lizette Ramos de Robles, Katie L. Milton Brkich, Belinda Flores Bustos, and Lorena Claeys. I aim to augment the thoughts of Gallard…
Descriptors: STEM Education, Science Careers, Educational Research, Figurative Language
Arnold, Lydia; Norton, Lin – Higher Education Academy, 2018
This resource has been written specifically for higher education practitioners who are interested in improving students' learning experiences through the process of researching their own practice. We use the term 'higher education practitioners' to describe all who work in universities and who have a stake in students' learning experiences.…
Descriptors: Higher Education, Educational Research, Action Research, Definitions
Wise, Alyssa Friend; Shaffer, David Williamson – Journal of Learning Analytics, 2015
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…
Descriptors: Learning Theories, Predictor Variables, Data, Data Analysis
Carter, Nancy; Felton, Nathan; Schwertman, Neil – Journal of Statistics Education, 2014
Engaging students in active learning can enhance their understanding and appreciation of a subject such as statistics. Classroom activities and projects help to engage students and further promote the learning process. In this paper, an activity investigating the influence of population size and wealth on the medal counts from the 2012 London…
Descriptors: Class Activities, Demography, Athletics, Awards
Gee, Kevin A. – American Journal of Evaluation, 2014
The growth in the availability of longitudinal data--data collected over time on the same individuals--as part of program evaluations has opened up exciting possibilities for evaluators to ask more nuanced questions about how individuals' outcomes change over time. However, in order to leverage longitudinal data to glean these important insights,…
Descriptors: Longitudinal Studies, Data Analysis, Statistical Studies, Program Evaluation
Davidson, Christina – English Teaching: Practice and Critique, 2012
This article examines ethnomethodology in order to consider its particular yet under-used perspective within literacy research. Initially, the article outlines ethnomethodology, including its theoretical position and central concepts such as indexicality and reflexivity. Then, selected studies are used to illustrate the application of the…
Descriptors: Research Methodology, Ethnography, Discourse Analysis, Sociocultural Patterns
Boaduo, Nana Adu-Pipim – Educational Research and Reviews, 2011
Two basic data sources required for research studies have been secondary and primary. Secondary data collection helps the researcher to provide relevant background to the study and are, in most cases, available for retrieval from recorded sources. Primary data collection requires the researcher to venture into the field where the study is to take…
Descriptors: Research Problems, Writing Research, Research Methodology, Data Collection
Miner-Romanoff, Karen – Qualitative Report, 2012
The critical and interpretive phenomenological approach is underutilized in the study of crime. This commentary describes this approach, guided by the question, "Why are interpretive phenomenological methods appropriate for qualitative research in criminology?" Therefore, the purpose of this paper is to describe a model of the interpretive…
Descriptors: Crime, Qualitative Research, Data Analysis, Phenomenology
Kavale, Kenneth A.; LeFever, Gretchen B. – Journal of Educational Research, 2007
The authors critiqued the M. K. Lovelace (2005) meta-analysis of the Dunn and Dunn Model of Learning-Style Preferences (DDMLSP). The conclusion that Lovelace reported in her meta-analysis that learning-style instruction is a beneficial form of instructional delivery is unjustified because of critical conceptual and practical problems. Those…
Descriptors: Cognitive Style, Doctoral Dissertations, Meta Analysis, Teaching Methods
Thornton, Thomas L.; Gilden, David L. – Psychological Review, 2007
A long-standing issue in the study of how people acquire visual information centers around the scheduling and deployment of attentional resources: Is the process serial, or is it parallel? A substantial empirical effort has been dedicated to resolving this issue. However, the results remain largely inconclusive because the methodologies that have…
Descriptors: Data Interpretation, Monte Carlo Methods, Cognitive Processes, Research Methodology

Yancey, Bernard D. – New Directions for Institutional Research, 1988
The ultimate goal of the institutional researcher is not always to test a research hypothesis, but more often simply to find an appropriate model to gain an understanding of the underlying characteristics and interrelationships of the data. Exploratory data analysis provides a means of accomplishing this. (Author)
Descriptors: Data Interpretation, Higher Education, Hypothesis Testing, Institutional Research

Hinkle, Dennis E.; And Others – New Directions for Institutional Research, 1988
The data collected in higher education research are not always quantitative or continuous. Statistical methods using the log-linear model provide the institutional researcher with a powerful set of tools for addressing research questions when data are categorical. (Author/MSE)
Descriptors: Data Interpretation, Higher Education, Information Utilization, Institutional Research

Moline, Arlett E. – New Directions for Institutional Research, 1988
Path analysis and linear structural relations (LISREL) provide the institutional researcher with some extremely powerful statistical tools. However, they must be applied and interpreted carefully with a full understanding of their limitations and the statistical assumptions on which they are based. (Author)
Descriptors: Data Interpretation, Higher Education, Institutional Research, Models
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