Development of the Perinatal Depression Inventory (PDI)-14 using item response theory: a comparison of the BDI-II, EPDS, PDI, and PHQ-9

The objective of this study is to develop a simple, brief, self-report perinatal depression inventory that accurately measures severity in a number of populations. Our team developed 159 Likert-scale perinatal depression items using simple sentences with a fifth-grade reading level. Based on iterative cognitive interviewing (CI), an expert panel improved and winnowed the item pool based on pre-determined criteria. The resulting 67 items were administered to a sample of 628 pregnant and 251 postpartum women with different levels of depression at private and public sector obstetrics clinics, together with the Beck Depression Inventory (BDI-II), Edinburg Postpartum Depression Scale (EPDS), and the Patient Health Questionnaire (PHQ-9), as well as Module A of the Structured Clinical Interview for DSM-IV Diagnoses (SCID). Responses were evaluated using Item Response Theory (IRT). The Perinatal Depression Inventory (PDI)-14 items are highly informative regarding depression severity and function similarly and informatively across pregnant/postpartum, white/non-white, and private-clinic/public-clinic populations. PDI-14 scores correlate well with the PHQ-9, EPDS, and BDI-II, but the PDI-14 provides a more precise measure of severity using far fewer words. The PDI-14 is a brief depression assessment that excels at accurately measuring depression severity across a wide range of severity and perinatal populations.Electronic supplementary materialThe online version of this article (doi:10.1007/s00737-015-0553-9) contains supplementary material, which is available to authorized users

Person level analysis in latent growth curve models

Latent growth curve modeling is an increasingly popular approach for evaluating longitudinal data. Researchers tend to focus on overall model fit information or component model fit information when evaluating a latent growth curve model (LGCM). However, there is also an interest in understanding a given individual's level and pattern of change over time, specifically an interest in identifying observations with aberrant patterns of change. Thus it is also important to examine model fit at the level of the individual. Currently there are several proposed approaches for evaluating person level fit information from a LGCM including factor score based approaches (Bollen & Curran, 2006; Coffman & Millsap, 2006) and person log-likelihood based approaches (Coffman & Millsap, 2006; McArdle, 1997). Even with multiple methods for evaluating person-level information, it is unusual for researchers to report any examination of the person level fit information. Researchers may be hesitant to use person level fit indices because there are very few studies that evaluate how effective these person level fit indices are at identifying aberrant observations, or what criteria to use with the indices. In order to better understand which approaches for evaluating person level information will perform best for LGCMs, this research uses simulation studies to examine the application of several person level fit indices to the detection of three types of aberrant observations including: extreme trajectory aberrance, extreme variability aberrance, and functional form aberrance. Results indicate that examining factor score estimates directly can help to identify extreme trajectory aberrance, while approaches examining factor score residuals or examining a person log-likelihood are better at identifying extreme variability aberrance. The performance of these approaches improved with more observation times and higher communality. All of the factor score estimate approaches were able to identify functional form aberrance, as long as there were a sufficient number of observation times and either higher communality or a greater difference between the functional forms of interest

A trifactor model for integrating ratings across multiple informants

Psychologists often obtain ratings for target individuals from multiple informants such as parents or peers. In this article we propose a trifactor model for multiple informant data that separates target-level variability from informant-level variability and item-level variability. By leveraging item-level data, the trifactor model allows for examination of a single trait rated on a single target. In contrast to many psychometric models developed for multitrait-multimethod data, the trifactor model is predominantly a measurement model. It is used to evaluate item quality in scale development, test hypotheses about sources of target variability (e.g., sources of trait differences) versus informant variability (e.g., sources of rater bias), and generate integrative scores that are purged of the subjective biases of single informants

Development of the Perinatal Depression Inventory (PDI)-14 using item response theory: a comparison of the BDI-II, EPDS, PDI, and PHQ-9

The objective of this study is to develop a simple, brief, self-report perinatal depression inventory that accurately measures severity in a number of populations. Our team developed 159 Likert-scale perinatal depression items using simple sentences with a fifth-grade reading level. Based on iterative cognitive interviewing (CI), an expert panel improved and winnowed the item pool based on pre-determined criteria. The resulting 67 items were administered to a sample of 628 pregnant and 251 postpartum women with different levels of depression at private and public sector obstetrics clinics, together with the Beck Depression Inventory (BDI-II), Edinburg Postpartum Depression Scale (EPDS), and the Patient Health Questionnaire (PHQ-9), as well as Module A of the Structured Clinical Interview for DSM-IV Diagnoses (SCID). Responses were evaluated using Item Response Theory (IRT). The Perinatal Depression Inventory (PDI)-14 items are highly informative regarding depression severity and function similarly and informatively across pregnant/postpartum, white/non-white, and private-clinic/public-clinic populations. PDI-14 scores correlate well with the PHQ-9, EPDS, and BDI-II, but the PDI-14 provides a more precise measure of severity using far fewer words. The PDI-14 is a brief depression assessment that excels at accurately measuring depression severity across a wide range of severity and perinatal populations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00737-015-0553-9) contains supplementary material, which is available to authorized users