Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 7 |
Descriptor
Sampling | 7 |
Social Science Research | 7 |
Statistical Inference | 7 |
Statistical Analysis | 4 |
Research Methodology | 3 |
Research Problems | 3 |
Classification | 2 |
Comparative Analysis | 2 |
Monte Carlo Methods | 2 |
Qualitative Research | 2 |
Sample Size | 2 |
More ▼ |
Source
Educational Evaluation and… | 1 |
Educational and Psychological… | 1 |
International Journal of… | 1 |
Oxford Review of Education | 1 |
Practical Assessment,… | 1 |
Qualitative Report | 1 |
Structural Equation Modeling:… | 1 |
Author
Bishara, Anthony J. | 1 |
Collins, Kathleen M. T. | 1 |
Cook, Thomas D. | 1 |
Cooper, Barry | 1 |
Eisermann, Jens | 1 |
Gan, Siyan | 1 |
Glaesser, Judith | 1 |
Hittner, James B. | 1 |
Ke-Hai Yuan | 1 |
Lara, Emily | 1 |
Lee, Hyun Seo | 1 |
More ▼ |
Publication Type
Journal Articles | 7 |
Reports - Research | 5 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Education Level
Adult Education | 1 |
Higher Education | 1 |
Audience
Location
Germany (Berlin) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Meng Qiu; Ke-Hai Yuan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Latent class analysis (LCA) is a widely used technique for detecting unobserved population heterogeneity in cross-sectional data. Despite its popularity, the performance of LCA is not well understood. In this study, we evaluate the performance of LCA with binary data by examining classification accuracy, parameter estimation accuracy, and coverage…
Descriptors: Classification, Sample Size, Monte Carlo Methods, Social Science Research
Yu, Chong Ho; Lee, Hyun Seo; Lara, Emily; Gan, Siyan – Practical Assessment, Research & Evaluation, 2018
Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex…
Descriptors: Data Analysis, Social Sciences, Social Science Research, Models
Neale, Dave – Oxford Review of Education, 2015
Recently, Stephen Gorard has outlined strong objections to the use of significance testing in social research. He has argued, first, that as the samples used in social research are almost always non-random it is not possible to use inferential statistical techniques and, second, that even if a truly random sample were achieved, the logic behind…
Descriptors: Statistical Significance, Statistical Analysis, Sampling, Probability
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
Bishara, Anthony J.; Hittner, James B. – Educational and Psychological Measurement, 2015
It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…
Descriptors: Research Methodology, Monte Carlo Methods, Correlation, Simulation
Pohl, Steffi; Steiner, Peter M.; Eisermann, Jens; Soellner, Renate; Cook, Thomas D. – Educational Evaluation and Policy Analysis, 2009
Adjustment methods such as propensity scores and analysis of covariance are often used for estimating treatment effects in nonexperimental data. Shadish, Clark, and Steiner used a within-study comparison to test how well these adjustments work in practice. They randomly assigned participating students to a randomized or nonrandomized experiment.…
Descriptors: Statistical Analysis, Social Science Research, Statistical Bias, Statistical Inference
Onwuegbuzie, Anthony J.; Collins, Kathleen M. T. – Qualitative Report, 2007
This paper provides a framework for developing sampling designs in mixed methods research. First, we present sampling schemes that have been associated with quantitative and qualitative research. Second, we discuss sample size considerations and provide sample size recommendations for each of the major research designs for quantitative and…
Descriptors: Social Science Research, Qualitative Research, Methods Research, Sample Size