AllCare Journal Feeds

Psychological Methods - Vol 15, Iss 3

Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues.

A general multilevel SEM framework for assessing multilevel mediation.
Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.
There is considerable interest in using propensity score (PS) statistical techniques to address questions of causal inference in psychological research. Many PS techniques exist, yet few guidelines are available to aid applied researchers in their understanding, use, and evaluation. In this study, the authors give an overview of available techniques for PS estimation and PS application. They also provide a way to help compare PS techniques, using the resulting measured covariate balance as the criterion for selecting between techniques. The empirical example for this study involves the potential causal relationship linking early-onset cannabis problems and subsequent negative mental health outcomes and uses data from a prospective cohort study. PS techniques are described and evaluated on the basis of their ability to balance the distributions of measured potentially confounding covariates for individuals with and without early-onset cannabis problems. This article identifies the PS techniques that yield good statistical balance of the chosen measured covariates within the context of this particular research question and cohort. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

The importance of covariate selection in controlling for selection bias in observational studies.
The assumption of strongly ignorable treatment assignment is required for eliminating selection bias in observational studies. To meet this assumption, researchers often rely on a strategy of selecting covariates that they think will control for selection bias. Theory indicates that the most important covariates are those highly correlated with both the real selection process and the potential outcomes. However, when planning a study, it is rarely possible to identify such covariates with certainty. In this article, we report on an extensive reanalysis of a within-study comparison that contrasts a randomized experiment and a quasi-experiment. Various covariate sets were used to adjust for initial group differences in the quasi-experiment that was characterized by self-selection into treatment. The adjusted effect sizes were then compared with the experimental ones to identify which individual covariates, and which conceptually grouped sets of covariates, were responsible for the high degree of bias reduction achieved in the adjusted quasi-experiment. Such results provide strong clues about preferred strategies for identifying the covariates most likely to reduce bias when planning a study and when the true selection process is not known. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

Testing multiple outcomes in repeated measures designs.
This study investigates procedures for controlling the familywise error rate (FWR) when testing hypotheses about multiple, correlated outcome variables in repeated measures (RM) designs. A content analysis of RM research articles published in 4 psychology journals revealed that 3 quarters of studies tested hypotheses about 2 or more outcome variables. Several procedures originally proposed for testing multiple outcomes in 2-group designs are extended to 2-group RM designs. The investigated procedures include 2 modified Bonferroni procedures that adjust the level of significance, ?, for the effective number of outcomes and a permutation step-down (PSD) procedure. The FWR, any-variable power, and all-variable power are investigated in a Monte Carlo study. One modified Bonferroni procedure frequently resulted in inflated FWRs, whereas the PSD procedure controlled the FWR. The PSD procedure could be substantially more powerful than the conventional Bonferroni procedure, which does not account for dependencies among the outcome variables. However, the difference in power between the PSD procedure, which does account for these dependencies, and Hochberg's step-up procedure, which does not, were negligible. A numeric example illustrates implementation of these multiple-testing procedures. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

Bayesian evaluation of inequality and equality constrained hypotheses for contingency tables.
In this article, a Bayesian model selection approach is introduced that can select the best of a set of inequality and equality constrained hypotheses for contingency tables. The hypotheses are presented in terms of cell probabilities allowing researchers to test (in)equality constrained hypotheses in a format that is directly related to the data. The proposed method is investigated by several simulation studies and shows good performance. Software that allows researchers to apply the Bayesian approach to their own data is also provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

How often is prep close to the true replication probability?
Largely due to dissatisfaction with the standard null hypothesis significance testing procedure, researchers have begun to consider alternatives. For example, Killeen (2005a) has argued that researchers should calculate prep that is purported to indicate the probability that, if the experiment in question were replicated, the obtained finding would be in the same direction as the original finding. However, Killeen also seems to indicate that rather than being the probability of replication, prep is actually the probability of obtaining a finding whereby the experimental group mean exceeds the control group mean. Our goal was to determine the relative frequency with which obtained prep statistics are close to true replication probabilities. Regardless of which way prep is defined, our simulations show that it is unlikely to be close to the true value unless both the population effect magnitude and the sample size are uncommonly large. The definitional problem in combination with the inaccuracy under either interpretation, constitutes an important challenge for those who espouse the routine computation of prep statistics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

AllCare Behavioral Health Services, Inc.

AllCare Journal Feeds

APA: Applied & Practice

APA: Basic Research

APA: General Topics

APA: Health/Mental Health

APA: Professional Issues, Public Policy

APA: Social/Personality, Development, Education

APA: Theory, Reviews, Methodology

BMC: Health Services

BMC: Informatics and Digital Libraries

BMC: Neuroscience

BMC: Nutrition

BMC: Psychiatry