Ingrid Wurpts - Academia.edu (original) (raw)

Papers by Ingrid Wurpts

Using Science-Based Innovations to Transform Practice, 2014

Multivariate Behavioral Research, 2015

Introduction: In psychology research, many of the statistical analyses used describe or predict r... more Introduction: In psychology research, many of the statistical analyses used describe or predict relationships that exist among psychological variables. However, these variable-oriented methods use data that has been aggregated across individuals. Such methods often do not account for how the relationships among variables may vary for different individuals or groups. Thus it is important to use person-oriented methods that detect individual differences, along with the popular variable-oriented methods in order to create a more comprehensive understanding of the similarities and differences among human behavior. This is especially important in intervention research, where research conclusions can affect clinical practice and government policy. This study describes and tests the accuracy of a person-oriented measure of mediation: the percent of participants that have data consistent with mediation. Given complete mediation where the mediated effect is positive and the treatment (X), th...

Frontiers in psychology, 2014

The purpose of this study was to examine in which way adding more indicators or a covariate influ... more The purpose of this study was to examine in which way adding more indicators or a covariate influences the performance of latent class analysis (LCA). We varied the sample size (100 ≤ N ≤ 2000), number, and quality of binary indicators (between 4 and 12 indicators with conditional response probabilities of [0.3, 0.7], [0.2, 0.8], or [0.1, 0.9]), and the strength of covariate effects (zero, small, medium, large) in a Monte Carlo simulation study of 2- and 3-class models. The results suggested that in general, a larger sample size, more indicators, a higher quality of indicators, and a larger covariate effect lead to more converged and proper replications, as well as fewer boundary parameter estimates and less parameter bias. Furthermore, interactions among these study factors demonstrated how using more or higher quality indicators, as well as larger covariate effect size, could sometimes compensate for small sample size. Including a covariate appeared to be generally beneficial, alt...

Handbook of Psychology, Second Edition, 2012

Introduction: Prevention researchers are often interested in understanding the presence and magni... more Introduction: Prevention researchers are often interested in understanding the presence and magnitude of the effects that mediate the relationship between prevention programs and their outcomes. Also, many researchers are now calculating confidence intervals for estimated effects, as confidence intervals provide more detailed information about the effect than the binary outcome of a significance test. Although estimation of these mediated effects is usually straightforward, estimation of standard errors and confidence intervals is more complicated, as the mediated effect does not always follow a normal distribution. More accurate methods can be used that accommodate the distribution of the product for confidence interval estimation. There are a now a variety of methods available for two-path mediated effects. For more complicated chains of mediation with three or more paths, there are fewer options. This study describes and applies a SAS program that calculates Monte Carlo confidenc...

Using Science-Based Innovations to Transform Practice, 2014

Multivariate Behavioral Research, 2015

Introduction: In psychology research, many of the statistical analyses used describe or predict r... more Introduction: In psychology research, many of the statistical analyses used describe or predict relationships that exist among psychological variables. However, these variable-oriented methods use data that has been aggregated across individuals. Such methods often do not account for how the relationships among variables may vary for different individuals or groups. Thus it is important to use person-oriented methods that detect individual differences, along with the popular variable-oriented methods in order to create a more comprehensive understanding of the similarities and differences among human behavior. This is especially important in intervention research, where research conclusions can affect clinical practice and government policy. This study describes and tests the accuracy of a person-oriented measure of mediation: the percent of participants that have data consistent with mediation. Given complete mediation where the mediated effect is positive and the treatment (X), th...

Frontiers in psychology, 2014

The purpose of this study was to examine in which way adding more indicators or a covariate influ... more The purpose of this study was to examine in which way adding more indicators or a covariate influences the performance of latent class analysis (LCA). We varied the sample size (100 ≤ N ≤ 2000), number, and quality of binary indicators (between 4 and 12 indicators with conditional response probabilities of [0.3, 0.7], [0.2, 0.8], or [0.1, 0.9]), and the strength of covariate effects (zero, small, medium, large) in a Monte Carlo simulation study of 2- and 3-class models. The results suggested that in general, a larger sample size, more indicators, a higher quality of indicators, and a larger covariate effect lead to more converged and proper replications, as well as fewer boundary parameter estimates and less parameter bias. Furthermore, interactions among these study factors demonstrated how using more or higher quality indicators, as well as larger covariate effect size, could sometimes compensate for small sample size. Including a covariate appeared to be generally beneficial, alt...

Handbook of Psychology, Second Edition, 2012

Introduction: Prevention researchers are often interested in understanding the presence and magni... more Introduction: Prevention researchers are often interested in understanding the presence and magnitude of the effects that mediate the relationship between prevention programs and their outcomes. Also, many researchers are now calculating confidence intervals for estimated effects, as confidence intervals provide more detailed information about the effect than the binary outcome of a significance test. Although estimation of these mediated effects is usually straightforward, estimation of standard errors and confidence intervals is more complicated, as the mediated effect does not always follow a normal distribution. More accurate methods can be used that accommodate the distribution of the product for confidence interval estimation. There are a now a variety of methods available for two-path mediated effects. For more complicated chains of mediation with three or more paths, there are fewer options. This study describes and applies a SAS program that calculates Monte Carlo confidenc...