J C F de Winter
Biographic Data
| ID | 8867840 |
|---|---|
| NAME | J C F de Winter |
| GIVEN NAMES | J C F |
| FAMILY NAME | de Winter |
| SIGNATURE | DE WINTER J C F |
| AFFILIATIONS | Delft University of Technology |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2014 |
| H-INDEX | 0 |
Social desirability is the same in offline, online, and paper surveys
Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size
Principal axis factoring (PAF) and maximum likelihood factor analysis (MLFA) are two of the most popular estimation methods in exploratory factor analysis. It is known that PAF is better able to recover weak factors and that the maximum likelihood estimator is asymptotically efficient. However, there is almost no evidence regarding which method should be preferred for different types of factor patterns and sample sizes. Simulations were conducted…
Exploratory Factor Analysis With Small Sample Sizes
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to wh…
No prominent works on this page.
Exploratory Factor Analysis With Small Sample Sizes
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to wh…
Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size
Principal axis factoring (PAF) and maximum likelihood factor analysis (MLFA) are two of the most popular estimation methods in exploratory factor analysis. It is known that PAF is better able to recover weak factors and that the maximum likelihood estimator is asymptotically efficient. However, there is almost no evidence regarding which method should be preferred for different types of factor patterns and sample sizes. Simulations were conducted…
Social desirability is the same in offline, online, and paper surveys
Econometrics (2 works) · Exploratory factor analysis (2 works) · Mathematics (2 works) · Psychometric Methodologies and Testing (2 works) · Sample (material) (2 works) · Sample size determination (2 works) · Statistics (2 works) · Advanced Statistical Modeling Techniques (1 work) · Chemistry (1 work) · Computer Science (1 work)