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Showing all 10 results Save | Export
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Stephanie Wermelinger; Marco Bleiker; Moritz M. Daum – Infant and Child Development, 2025
Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11…
Descriptors: Infants, Young Children, Research Problems, Factor Analysis
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Yan Xia; Selim Havan – Educational and Psychological Measurement, 2024
Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…
Descriptors: Data Interpretation, Factor Analysis, Statistical Inference, Research Problems
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Stapleton, Laura M.; McNeish, Daniel M.; Yang, Ji Seung – Educational Psychologist, 2016
Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003; Sterba, 2009). The overarching goal of this article is to suggest when it is appropriate…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Statistical Data, Multivariate Analysis
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Jia, Fan; Moore, E. Whitney G.; Kinai, Richard; Crowe, Kelly S.; Schoemann, Alexander M.; Little, Todd D. – International Journal of Behavioral Development, 2014
Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This article explores this question by using…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
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Brodeur, Pascale; Larose, Simon; Tarabulsy, George; Feng, Bei; Forget-Dubois, Nadine – Mentoring & Tutoring: Partnership in Learning, 2015
Researchers suggest that certain supportive behaviors of mentors could increase the benefits of school-based mentoring for youth. However, the literature contains few validated instruments to measure these behaviors. In our present study, we aimed to construct and validate a tool to measure the supportive behaviors of mentors participating in…
Descriptors: Foreign Countries, Mentors, Motivation, College Students
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Keaton, Shaughan A.; Bodie, Graham D. – International Journal of Listening, 2013
This article investigates the quality of social scientific listening research that reports numerical data to substantiate claims appearing in the "International Journal of Listening" between 1987 and 2011. Of the 225 published articles, 100 included one or more studies reporting numerical data. We frame our results in terms of eight…
Descriptors: Periodicals, Journal Articles, Listening, Social Science Research
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Koopman, Raymond F. – Psychometrika, 1976
This note proposes an alternative implementation of the regression method which should be slightly faster than the principal components methods for estimating missing data. (RC)
Descriptors: Comparative Analysis, Data Analysis, Factor Analysis, Multiple Regression Analysis
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Deal, James E. – Journal of Marriage and the Family, 1995
Dealing with data from multiple family members presents problems for researchers, as many of the techniques available for dealing with such data are problematic, and there is no way that is unique to each family. Proposes and gives an example of the use of Q factor analysis as a means of combining data from multiple family members in a unique way…
Descriptors: Correlation, Data Analysis, Data Interpretation, Factor Analysis
Remer, Rory; Burton, Nancy – 1971
The relative precision of four methods of estimating missing data in principal components analysis was investigated. Artificial data with known characteristics, obtained from Cattell's "Plasmode: 30-10-4-2," was used with one third of the data on half of the variables being systematically eliminated. The four methods of missing data estimation…
Descriptors: Comparative Analysis, Computation, Correlation, Data Analysis
Hambleton, Ronald K.; Rogers, H. Jane – 1986
The general goal of this paper is to help researchers conduct appropriately designed goodness of fit studies for item response model applications. The specific purposes are to describe: (1) an up-to-date set of promising and useful methods for addressing a variety of goodness of fit questions; and (2) current research studies to advance this set…
Descriptors: Data Analysis, Educational Research, Factor Analysis, Goodness of Fit