Ken Kelley
Biographic Data
| ID | 5381502 |
|---|---|
| NAME | Ken Kelley |
| GIVEN NAMES | Ken |
| FAMILY NAME | Kelley |
| SIGNATURE | KELLEY K |
| AFFILIATIONS | University of Notre Dame |
| ORCID | 0000-0002-4756-8360 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 21 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2005 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Multilevel Modeling Using R
Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in …
Indirect Effects in Sequential Mediation Models: Evaluating Methods for Hypothesis Testing and Confidence Interval Formation
Complex mediation models, such as a two-mediator sequential model, have become more prevalent in the literature. To test an indirect effect in a two-mediator model, we conducted a large-scale Monte Carlo simulation study of the Type I error, statistical power, and confidence interval coverage rates of 10 frequentist and Bayesian confidence/credible intervals (CIs) for normally and nonnormally distributed data. The simulation included never-studie…
Who says there’s a problem? Preferences on the sending and receiving of prohibitive voice
Which employees are likely to warn leaders about threats to the workplace? When employees do speak up, will these messages gain the leader’s interest? In this article, we rely on theories of power to predict how employee characteristics (work prevention regulatory focus, closeness to the leader (leader-member exchange) and rank) influence whether employees send messages about threats (prohibitive voice). We also explore whether employee character…
Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations.
Clustered data are common in many fields. Some prominent examples of clustering are employees clustered within supervisors, students within classrooms, and clients within therapists. Many methods exist that explicitly consider the dependency introduced by a clustered data structure, but the multitude of available options has resulted in rigid disciplinary preferences. For example, those working in the psychological, organizational behavior, medic…
Common language effect size for correlations
The Pearson correlation coefficient can be translated to a common language effect size, which shows the probability of obtaining a certain value on one variable, given the value on the other variable. This common language effect size makes the size of a correlation coefficient understandable to laypeople. Three examples are provided to demonstrate the application of the common language effect size in interpreting Pearson correlation coefficients …
A novel measure of effect size for mediation analysis.
Mediation analysis has become one of the most popular statistical methods in the social sciences. However, many currently available effect size measures for mediation have limitations that restrict their use to specific mediation models. In this article, we develop a measure of effect size that addresses these limitations. We show how modification of a currently existing effect size measure results in a novel effect size measure with many desirab…
Big data in psychology: A framework for research advancement
The potential for big data to provide value for psychology is significant. However, the pursuit of big data remains an uncertain and risky undertaking for the average psychological researcher. In this article, we address some of this uncertainty by discussing the potential impact of big data on the type of data available for psychological research, addressing the benefits and most significant challenges that emerge from these data, and organizing…
Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty
The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typicall…
Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures.
A composite score is the sum of a set of components. For example, a total test score can be defined as the sum of the individual items. The reliability of composite scores is of interest in a wide variety of contexts due to their widespread use and applicability to many disciplines. The psychometric literature has devoted considerable time to discussing how to best estimate the population reliability value. However, all point estimates of a relia…
Multilevel Modeling Using R
Multilevel Modelling using R provides a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent vari…
Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost
The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize b…
Assessing Omitted Confounder Bias in Multilevel Mediation Models
To draw valid inference about an indirect effect in a mediation model, there must be no omitted confounders. No omitted confounders means that there are no common causes of hypothesized causal relationships. When the no-omitted-confounder assumption is violated, inference about indirect effects can be severely biased and the results potentially misleading. Despite the increasing attention to address confounder bias in single-level mediation, this…
On effect size.
The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qua…
Effect size measures for mediation models: Quantitative strategies for communicating indirect effects.
The statistical analysis of mediation effects has become an indispensable tool for helping scientists investigate processes thought to be causal. Yet, in spite of many recent advances in the estimation and testing of mediation effects, little attention has been given to methods for communicating effect size and the practical importance of those effect sizes. Our goals in this article are to (a) outline some general desiderata for effect size meas…
A Comparison of Two-Group Classification Methods
The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics…
Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation: Sample Size Planning for Narrow Confidence Intervals
The root mean square error of approximation (RMSEA) is one of the most widely reported measures of misfit/fit in applications of structural equation modeling. When the RMSEA is of interest, so too should be the accompanying confidence interval. A narrow confidence interval reveals that the plausible parameter values are confined to a relatively small range at the specified level of confidence. The accuracy in parameter estimation approach to samp…
Longitudinal gender and age bias in a prominent amateur new media community
Despite early hopes that the internet would facilitate more socially equitable communication, many age-old forms of discrimination appear to have been preserved. Men are routinely aggressive towards women, experienced users harass newcomers, and young people dominate new social and entertainment media. The current study statistically examines peer scoring and reviewing behavior by over 300,000 users of a prominent new media website over a seven-y…
The Effects of Initially Misclassified Data on the Effectiveness of Discriminant Function Analysis and Finite Mixture Modeling
Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if available, knowledge of group status to estimate paramete…
Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation
This review examines recent advances in sample size planning, not only from the perspective of an individual researcher, but also with regard to the goal of developing cumulative knowledge. Psychologists have traditionally thought of sample size planning in terms of power analysis. Although we review recent advances in power analysis, our main focus is the desirability of achieving accurate parameter estimates, either instead of or in addition to…
Sample Size Planning for the Squared Multiple Correlation Coefficient: Accuracy in Parameter Estimation via Narrow Confidence Intervals
Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size so that the expected width of the confidence interval will be sufficiently narrow. Modifications of t…
The Effects of Nonnormal Distributions on Confidence Intervals Around the Standardized Mean Difference: Bootstrap and Parametric Confidence Intervals
The standardized group mean difference, Cohen’s d, is among the most commonly used and intuitively appealing effect sizes for group comparisons. However, reporting this point estimate alone does not reflect the extent to which sampling error may have led to an obtained value. A confidence interval expresses the uncertainty that exists between d and the population value, δ, it represents. A set of Monte Carlo simulations was conducted to examine t…
Big data in psychology: A framework for research advancement
The potential for big data to provide value for psychology is significant. However, the pursuit of big data remains an uncertain and risky undertaking for the average psychological researcher. In this article, we address some of this uncertainty by discussing the potential impact of big data on the type of data available for psychological research, addressing the benefits and most significant challenges that emerge from these data, and organizing…
Who says there’s a problem? Preferences on the sending and receiving of prohibitive voice
Which employees are likely to warn leaders about threats to the workplace? When employees do speak up, will these messages gain the leader’s interest? In this article, we rely on theories of power to predict how employee characteristics (work prevention regulatory focus, closeness to the leader (leader-member exchange) and rank) influence whether employees send messages about threats (prohibitive voice). We also explore whether employee character…
Longitudinal gender and age bias in a prominent amateur new media community
Despite early hopes that the internet would facilitate more socially equitable communication, many age-old forms of discrimination appear to have been preserved. Men are routinely aggressive towards women, experienced users harass newcomers, and young people dominate new social and entertainment media. The current study statistically examines peer scoring and reviewing behavior by over 300,000 users of a prominent new media website over a seven-y…
The Effects of Nonnormal Distributions on Confidence Intervals Around the Standardized Mean Difference: Bootstrap and Parametric Confidence Intervals
The standardized group mean difference, Cohen’s d, is among the most commonly used and intuitively appealing effect sizes for group comparisons. However, reporting this point estimate alone does not reflect the extent to which sampling error may have led to an obtained value. A confidence interval expresses the uncertainty that exists between d and the population value, δ, it represents. A set of Monte Carlo simulations was conducted to examine t…
Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation
This review examines recent advances in sample size planning, not only from the perspective of an individual researcher, but also with regard to the goal of developing cumulative knowledge. Psychologists have traditionally thought of sample size planning in terms of power analysis. Although we review recent advances in power analysis, our main focus is the desirability of achieving accurate parameter estimates, either instead of or in addition to…
Sample Size Planning for the Squared Multiple Correlation Coefficient: Accuracy in Parameter Estimation via Narrow Confidence Intervals
Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size so that the expected width of the confidence interval will be sufficiently narrow. Modifications of t…
The Effects of Initially Misclassified Data on the Effectiveness of Discriminant Function Analysis and Finite Mixture Modeling
Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if available, knowledge of group status to estimate paramete…
Effect size measures for mediation models: Quantitative strategies for communicating indirect effects.
The statistical analysis of mediation effects has become an indispensable tool for helping scientists investigate processes thought to be causal. Yet, in spite of many recent advances in the estimation and testing of mediation effects, little attention has been given to methods for communicating effect size and the practical importance of those effect sizes. Our goals in this article are to (a) outline some general desiderata for effect size meas…
A Comparison of Two-Group Classification Methods
The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics…
Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation: Sample Size Planning for Narrow Confidence Intervals
The root mean square error of approximation (RMSEA) is one of the most widely reported measures of misfit/fit in applications of structural equation modeling. When the RMSEA is of interest, so too should be the accompanying confidence interval. A narrow confidence interval reveals that the plausible parameter values are confined to a relatively small range at the specified level of confidence. The accuracy in parameter estimation approach to samp…
Longitudinal gender and age bias in a prominent amateur new media community
Despite early hopes that the internet would facilitate more socially equitable communication, many age-old forms of discrimination appear to have been preserved. Men are routinely aggressive towards women, experienced users harass newcomers, and young people dominate new social and entertainment media. The current study statistically examines peer scoring and reviewing behavior by over 300,000 users of a prominent new media website over a seven-y…
On effect size.
The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qua…
Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures.
A composite score is the sum of a set of components. For example, a total test score can be defined as the sum of the individual items. The reliability of composite scores is of interest in a wide variety of contexts due to their widespread use and applicability to many disciplines. The psychometric literature has devoted considerable time to discussing how to best estimate the population reliability value. However, all point estimates of a relia…
Multilevel Modeling Using R
Multilevel Modelling using R provides a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent vari…
Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost
The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize b…
Assessing Omitted Confounder Bias in Multilevel Mediation Models
To draw valid inference about an indirect effect in a mediation model, there must be no omitted confounders. No omitted confounders means that there are no common causes of hypothesized causal relationships. When the no-omitted-confounder assumption is violated, inference about indirect effects can be severely biased and the results potentially misleading. Despite the increasing attention to address confounder bias in single-level mediation, this…
Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty
The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typicall…
A novel measure of effect size for mediation analysis.
Mediation analysis has become one of the most popular statistical methods in the social sciences. However, many currently available effect size measures for mediation have limitations that restrict their use to specific mediation models. In this article, we develop a measure of effect size that addresses these limitations. We show how modification of a currently existing effect size measure results in a novel effect size measure with many desirab…
Big data in psychology: A framework for research advancement
The potential for big data to provide value for psychology is significant. However, the pursuit of big data remains an uncertain and risky undertaking for the average psychological researcher. In this article, we address some of this uncertainty by discussing the potential impact of big data on the type of data available for psychological research, addressing the benefits and most significant challenges that emerge from these data, and organizing…
Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations.
Clustered data are common in many fields. Some prominent examples of clustering are employees clustered within supervisors, students within classrooms, and clients within therapists. Many methods exist that explicitly consider the dependency introduced by a clustered data structure, but the multitude of available options has resulted in rigid disciplinary preferences. For example, those working in the psychological, organizational behavior, medic…
Common language effect size for correlations
The Pearson correlation coefficient can be translated to a common language effect size, which shows the probability of obtaining a certain value on one variable, given the value on the other variable. This common language effect size makes the size of a correlation coefficient understandable to laypeople. Three examples are provided to demonstrate the application of the common language effect size in interpreting Pearson correlation coefficients …
Indirect Effects in Sequential Mediation Models: Evaluating Methods for Hypothesis Testing and Confidence Interval Formation
Complex mediation models, such as a two-mediator sequential model, have become more prevalent in the literature. To test an indirect effect in a two-mediator model, we conducted a large-scale Monte Carlo simulation study of the Type I error, statistical power, and confidence interval coverage rates of 10 frequentist and Bayesian confidence/credible intervals (CIs) for normally and nonnormally distributed data. The simulation included never-studie…
Who says there’s a problem? Preferences on the sending and receiving of prohibitive voice
Which employees are likely to warn leaders about threats to the workplace? When employees do speak up, will these messages gain the leader’s interest? In this article, we rely on theories of power to predict how employee characteristics (work prevention regulatory focus, closeness to the leader (leader-member exchange) and rank) influence whether employees send messages about threats (prohibitive voice). We also explore whether employee character…
Multilevel Modeling Using R
Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in …
Computer Science (17 works) · Mathematics (16 works) · Statistics (15 works) · Psychology (10 works) · Econometrics (9 works) · Sample size determination (9 works) · Artificial Intelligence (7 works) · Statistical Methods and Bayesian Inference (7 works) · Population (6 works) · Confidence interval (5 works)