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Jamelia Harris – Field Methods, 2024
Not knowing the population size is a common problem in data-limited contexts. Drawing on work in Sierra Leone, this short take outlines a four-step solution to this problem: (1) estimate the population size using expert interviews; (2) verify estimates using interviews with participants sampled; (3) triangulate using secondary data; and (4)…
Descriptors: Foreign Countries, Sample Size, Surveys, Computation
Guest, Greg; Namey, Emily; McKenna, Kevin – Field Methods, 2017
Few empirical studies exist to guide researchers in determining the number of focus groups necessary for a research study. The analyses described here provide foundational evidence to help researchers in this regard. We conducted a thematic analysis of 40 focus groups on health-seeking behaviors of African American men in Durham, North Carolina.…
Descriptors: Focus Groups, Sample Size, Evidence Based Practice, Thematic Approach
Orcan, Fatih – ProQuest LLC, 2013
Parceling is referred to as a procedure for computing sums or average scores across multiple items. Parcels instead of individual items are then used as indicators of latent factors in the structural equation modeling analysis (Bandalos 2002, 2008; Little et al., 2002; Yang, Nay, & Hoyle, 2010). Item parceling may be applied to alleviate some…
Descriptors: Structural Equation Models, Evaluation Methods, Simulation, Sample Size
Lee, HwaYoung; Beretvas, S. Natasha – Educational and Psychological Measurement, 2014
Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…
Descriptors: Item Analysis, Factor Structure, Bayesian Statistics, Goodness of Fit
Dunst, Carl J.; Hamby, Deborah W. – Journal of Intellectual & Developmental Disability, 2012
This paper includes a nontechnical description of methods for calculating effect sizes in intellectual and developmental disability studies. Different hypothetical studies are used to illustrate how null hypothesis significance testing (NHST) and effect size findings can result in quite different outcomes and therefore conflicting results. Whereas…
Descriptors: Intervals, Developmental Disabilities, Statistical Significance, Effect Size
Ellington, Linda F. – ProQuest LLC, 2011
Statistical theory and its application provide the foundation to modern systematic inquiry in the behavioral, physical and social sciences disciplines (Fisher, 1958; Wilcox, 1996). It provides the tools for scholars and researchers to operationalize constructs, describe populations, and measure and interpret the relations between populations and…
Descriptors: Equivalency Tests, Social Sciences, Sample Size, Probability
Sueiro, Manuel J.; Abad, Francisco J. – Educational and Psychological Measurement, 2011
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…
Descriptors: Goodness of Fit, Item Response Theory, Nonparametric Statistics, Probability
Kazi, Mansoor A. F.; Pagkos, Brian; Milch, Heidi A. – Research on Social Work Practice, 2011
Objectives: The purpose of this study was to develop a realist evaluation paradigm in social work evidence-based practice. Method: Wraparound (at Gateway-Longview Inc., New York) used a reliable outcome measure and an electronic database to systematically collect and analyze data on the interventions, the client demographics and circumstances, and…
Descriptors: Intervals, Data Analysis, Social Work, Sample Size
Yuan, Ke-Hai – Multivariate Behavioral Research, 2008
In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…
Descriptors: Monte Carlo Methods, Graduate Students, Social Sciences, Data Analysis
Nylund, Karen L.; Asparouhov, Tihomir; Muthen, Bengt O. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study…
Descriptors: Test Items, Monte Carlo Methods, Program Effectiveness, Data Analysis
Peer reviewedAiken, Lewis R. – Educational and Psychological Measurement, 1985
Three numerical coefficients for analyzing the validity and reliability of ratings are described. Each coefficient is computed as the ratio of an obtained to a maximum sum of differences in ratings. The coefficients are also applicable to the item analysis, agreement analysis, and cluster or factor analysis of rating-scale data. (Author/BW)
Descriptors: Computer Software, Data Analysis, Factor Analysis, Item Analysis
Ingels, Steven J.; Quinn, Peggy – 1996
The National Education Longitudinal Studies Program (NELS) is a long-term effort that encompasses the educational experience of youth from three decades. This report is based on the National Education Longitudinal Study of 1988 (NELS:88), a component of the NELS series that began as a national probability sample of eighth grade schools and…
Descriptors: Data Analysis, Educational Experience, Elementary Secondary Education, Eligibility
Peer reviewedBarbella, Peter; And Others – Mathematics Teacher, 1990
Demonstrates a statistically valid method allowing students to explore randomization. Described are two examples: counting techniques for a small set of data and simulation for a large sample. (YP)
Descriptors: Data Analysis, Data Interpretation, Mathematical Concepts, Mathematical Logic

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