Kim May
Biographic Data
| ID | 3801236 |
|---|---|
| NAME | Kim May |
| GIVEN NAMES | Kim |
| FAMILY NAME | May |
| SIGNATURE | MAY K |
| AFFILIATIONS | College of Charleston |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 22 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1991 |
| LATEST PUBLICATION YEAR | 2012 |
| H-INDEX | 1 |
How Accurate Is the Pearson r -from-Z Approximation? A Monte Carlo Simulation Study
The Pearson r-from-Z approximation estimates the sample correlation (as an effect size measure) from the ratio of two quantities: the standard normal deviate equivalent (Z-score) corresponding to a one-tailed p-value divided by the square root of the total (pooled) sample size. The formula has utility in meta-analytic work when reports of research contain minimal statistical information. Although simple to implement, the accuracy of the Pearson r…
Sample Size Determination for Health Psychology Interventions with Binomially Distributed Outcomes
Health intervention outcomes are often assessed as binomially distributed variables. In designing such interventions it is important to model the pre-intervention rate of the target behavior when performing sample size calculations. Unfortunately, the majority of sample size programs model post-intervention outcomes only, which results in exaggerated sample size estimates. An exception is Yoo and Spoth's (1993) conditional binomial method of samp…
A Monte Carlo Evaluation of Tests for Comparing Dependent Correlations
The authors conducted a Monte Carlo simulation of 8 statistical tests for comparing dependent zero-order correlations. In particular, they evaluated the Type I error rates and power of a number of test statistics for sample sizes (Ns) of 20, 50, 100, and 300 under 3 different population distributions (normal, uniform, and exponential). For the Type I error rate analyses, the authors evaluated 3 different magnitudes of the predictor-criterion corr…
Measuring Change Conventionally and Adaptively
The ordinary difference or gain score is known generally to be unreliable. However, it is not widely known that when a difference score is used to measure change, the difficulty of the pretest can bias the amount of gain observed in groups that differ in initial achievement. This type of bias we call scale distortion. Using item response theory, one may compute gain scores based on differences in estimated Os (the latent trait being measured). Th…
A Note on Statistics for Comparing Dependent Correlations
The four statistics examined are available for use in comparing two dependent correlation coefficients (correlations between two predictors and a common criterion from a single sample wherein the predictors themselves may be correlated). There has been much past discussion in the literature of the properties and appropriate situations for these statistics. Two somewhat counterintuitive results are given here; both are examined using variables of …
Exaggerated social control and its relationship to the type a behavior pattern as measured by the structured interview
A Monte Carlo Evaluation of Tests for Comparing Dependent Correlations
The authors conducted a Monte Carlo simulation of 8 statistical tests for comparing dependent zero-order correlations. In particular, they evaluated the Type I error rates and power of a number of test statistics for sample sizes (Ns) of 20, 50, 100, and 300 under 3 different population distributions (normal, uniform, and exponential). For the Type I error rate analyses, the authors evaluated 3 different magnitudes of the predictor-criterion corr…
How Accurate Is the Pearson r -from-Z Approximation? A Monte Carlo Simulation Study
The Pearson r-from-Z approximation estimates the sample correlation (as an effect size measure) from the ratio of two quantities: the standard normal deviate equivalent (Z-score) corresponding to a one-tailed p-value divided by the square root of the total (pooled) sample size. The formula has utility in meta-analytic work when reports of research contain minimal statistical information. Although simple to implement, the accuracy of the Pearson r…
Exaggerated social control and its relationship to the type a behavior pattern as measured by the structured interview
A Note on Statistics for Comparing Dependent Correlations
The four statistics examined are available for use in comparing two dependent correlation coefficients (correlations between two predictors and a common criterion from a single sample wherein the predictors themselves may be correlated). There has been much past discussion in the literature of the properties and appropriate situations for these statistics. Two somewhat counterintuitive results are given here; both are examined using variables of …
Measuring Change Conventionally and Adaptively
The ordinary difference or gain score is known generally to be unreliable. However, it is not widely known that when a difference score is used to measure change, the difficulty of the pretest can bias the amount of gain observed in groups that differ in initial achievement. This type of bias we call scale distortion. Using item response theory, one may compute gain scores based on differences in estimated Os (the latent trait being measured). Th…
A Monte Carlo Evaluation of Tests for Comparing Dependent Correlations
The authors conducted a Monte Carlo simulation of 8 statistical tests for comparing dependent zero-order correlations. In particular, they evaluated the Type I error rates and power of a number of test statistics for sample sizes (Ns) of 20, 50, 100, and 300 under 3 different population distributions (normal, uniform, and exponential). For the Type I error rate analyses, the authors evaluated 3 different magnitudes of the predictor-criterion corr…
Sample Size Determination for Health Psychology Interventions with Binomially Distributed Outcomes
Health intervention outcomes are often assessed as binomially distributed variables. In designing such interventions it is important to model the pre-intervention rate of the target behavior when performing sample size calculations. Unfortunately, the majority of sample size programs model post-intervention outcomes only, which results in exaggerated sample size estimates. An exception is Yoo and Spoth's (1993) conditional binomial method of samp…
How Accurate Is the Pearson r -from-Z Approximation? A Monte Carlo Simulation Study
The Pearson r-from-Z approximation estimates the sample correlation (as an effect size measure) from the ratio of two quantities: the standard normal deviate equivalent (Z-score) corresponding to a one-tailed p-value divided by the square root of the total (pooled) sample size. The formula has utility in meta-analytic work when reports of research contain minimal statistical information. Although simple to implement, the accuracy of the Pearson r…
Mathematics (5 works) · Statistics (5 works) · Psychology (4 works) · Sample size determination (4 works) · Physics (3 works) · Statistical Methods in Clinical Trials (3 works) · Advanced Statistical Modeling Techniques (2 works) · Econometrics (2 works) · Monte Carlo method (2 works) · Statistical physics (2 works)