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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
Ehri Ryu – Society for Research on Educational Effectiveness, 2024
Background/Context: Confirmatory factor analysis (CFA) model is a commonly adopted framework to estimate and test a measurement model. Once a well-fitting final CFA model is selected, the selected model may be used to test structural relationships of the latent constructs with other variables, to construct a test with desired reliability and…
Descriptors: Research Problems, Factor Analysis, Scores, Computation
Ting Dai; Yang Du; Jennifer Cromley; Tia Fechter; Frank Nelson – Journal of Experimental Education, 2024
Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle…
Descriptors: Research Problems, Research Design, Data, Matrices
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
Goretzko, David – Educational and Psychological Measurement, 2022
Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process.…
Descriptors: Factor Analysis, Research Problems, Data, Prediction
Myunghwan Hwang; Soyeon Kim; Hyejin Kim; Joohee Han; Hee-Kyung Lee – English Teaching, 2024
This paper evaluates the use of Factor Analysis (FA) in English education research in Korea and suggests improvements in methodology. A detailed coding protocol was used to review 179 FA cases from 12 major English education journals (2014-2023). The review identified several key issues, including small sample sizes and lenient criteria for sample…
Descriptors: Factor Analysis, English (Second Language), Second Language Learning, Second Language Instruction
Su, Dan; Steiner, Peter M. – Sociological Methods & Research, 2020
Factorial surveys use a population of vignettes to elicit respondents' attitudes or beliefs about different hypothetical scenarios. However, the vignette population is frequently too large to be assessed by each respondent. Experimental designs such as randomized block confounded factorial (RBCF) designs, D-optimal designs, or random sampling…
Descriptors: Surveys, Vignettes, Factor Analysis, Research Design
Shi, Dexin; DiStefano, Christine; Zheng, Xiaying; Liu, Ren; Jiang, Zhehan – International Journal of Behavioral Development, 2021
This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML…
Descriptors: Growth Models, Maximum Likelihood Statistics, Factor Analysis, Sample Size
Jennifer Shearman – Sage Research Methods Cases, 2022
This case study describes a Q methodology study which captured and analyzed the viewpoints of 45 UK teachers online. The teachers might liken their participation in the study to a "card sort" activity: their relative placement of statement cards revealed their opinions of mastery in mathematics. Factor analysis of the completed sorts…
Descriptors: Foreign Countries, Online Surveys, Research Methodology, Research Problems
Abascal, Elena; Díaz De Rada, Vidal; García Lautre, Ignacio; Landaluce, M. Isabel – International Journal of Social Research Methodology, 2018
In the field of social sciences, certain tasks, such as the identification of typologies and the characterization of groups of individuals according to a set of questions, tend to pose a challenge for researchers. Further complications arise if the chosen rating scale is from 0 to 10, since the responses can be treated either as metric or…
Descriptors: Social Science Research, Research Problems, Rating Scales, Factor Analysis
Gagnon, Ryan J.; Stone, Garrett A.; Garst, Barry A. – Journal of Outdoor Recreation, Education, and Leadership, 2017
Critically examining common statistical approaches and their strengths and weaknesses is an important step in advancing recreation and leisure sciences. To continue this critical examination and to inform methodological decision making, this study compared three approaches to determine how alternative approaches may result in contradictory…
Descriptors: Recreation, Recreational Programs, Educational Research, Research Methodology
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
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
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
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