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Multivariate Behavioral Research

Peter H Westfall, Andrea L Arias, Larry V Fulton
Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the "variance explained" interpretation. Third, the unit length and uncorrelatedness constraints of the standard motivation do not allow re-scaling or oblique rotations, which are common in practice...
July 17, 2017: Multivariate Behavioral Research
Daniel McNeish
Studies on small sample properties of multilevel models have become increasingly prominent in the methodological literature in response to the frequency with which small sample data appear in empirical studies. Simulation results generally recommend that empirical researchers employ restricted maximum likelihood estimation (REML) with a Kenward-Roger correction with small samples in frequentist contexts to minimize small sample bias in estimation and to prevent inflation of Type-I error rates. However, simulation studies focus on recommendations for best practice, and there is little to no explanation of why traditional maximum likelihood (ML) breaks down with smaller samples, what differentiates REML from ML, or how the Kenward-Roger correction remedies lingering small sample issues...
July 17, 2017: Multivariate Behavioral Research
Sierra A Bainter
Psychometric models for item-level data are broadly useful in psychology. A recurring issue for estimating item factor analysis (IFA) models is low-item endorsement (item sparseness), due to limited sample sizes or extreme items such as rare symptoms or behaviors. In this paper, I demonstrate that under conditions characterized by sparseness, currently available estimation methods, including maximum likelihood (ML), are likely to fail to converge or lead to extreme estimates and low empirical power. Bayesian estimation incorporating prior information is a promising alternative to ML estimation for IFA models with item sparseness...
July 17, 2017: Multivariate Behavioral Research
Youn Seon Lim, Fritz Drasgow
A nonparametric technique based on the Hamming distance is proposed in this research by recognizing that once the attribute vector is known, or correctly estimated with high probability, one can determine the item-by-attribute vectors for new items undergoing calibration. We consider the setting where Q is known for a large item bank, and the q-vectors of additional items are estimated. The method is studied in simulation under a wide variety of conditions, and is illustrated with the Tatsuoka fraction subtraction data...
July 17, 2017: Multivariate Behavioral Research
John J Dziak, Kimberly L Henry
Researchers often build regression models to relate a response to a set of predictor variables. In some cases, there are predictors that apply to some participants, or to some measurement occasions, but not others. For example, a romantic partner's substance use may be a key predictor of one's own substance use. However, not all participants have a partner, and in a longitudinal study, participants may have a partner during only some occasions. This could be viewed as missing data, but of a very distinctive type: the values are not just unknown but also undefined...
June 16, 2017: Multivariate Behavioral Research
R Philip Chalmers, Jolynn Pek, Yang Liu
Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs)...
June 8, 2017: Multivariate Behavioral Research
Janne K Adolf, Manuel C Voelkle, Annette Brose, Florian Schmiedek
Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models...
May 22, 2017: Multivariate Behavioral Research
Yu Liu, Stephen G West, Roy Levy, Leona S Aiken
In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional variance expression obtained when the values of X and Z are fixed. A simulation study using randomly sampled predictors compared four approaches: (a) the Aiken and West ( 1991 ) test of simple slopes at fixed population values of Z, (b) the Aiken and West test at sample-estimated values of Z, (c) a 95% percentile bootstrap confidence interval approach, and (d) a fully Bayesian approach with diffuse priors...
May 2, 2017: Multivariate Behavioral Research
Danielle O Dean, Daniel J Bauer, Mitchell J Prinstein
A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common-as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network...
May 2017: Multivariate Behavioral Research
Yu Liu, Craig K Enders
In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences...
May 2017: Multivariate Behavioral Research
Kuan-Yu Jin, Wen-Chung Wang
Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity...
May 2017: Multivariate Behavioral Research
Niels G Waller, Leah Feuerstahler
In this study, we explored item and person parameter recovery of the four-parameter model (4PM) in over 24,000 real, realistic, and idealized data sets. In the first analyses, we fit the 4PM and three alternative models to data from three Minnesota Multiphasic Personality Inventory-Adolescent form factor scales using Bayesian modal estimation (BME). Our results indicated that the 4PM fits these scales better than simpler item Response Theory (IRT) models. Next, using the parameter estimates from these real data analyses, we estimated 4PM item parameters in 6,000 realistic data sets to establish minimum sample size requirements for accurate item and person parameter recovery...
May 2017: Multivariate Behavioral Research
Natalie A Koziol, James A Bovaird, Sonia Suarez
Sampling designs of large-scale survey studies are typically complex, involving multiple design features such as clustering and unequal probabilities of selection. Single-level (i.e., population-averaged) methods that use adjusted variance estimators and multilevel (i.e., cluster-specific) methods provide two alternatives for modeling clustered data. Although the literature comparing these methods is vast, comparisons have been limited to the context in which all sampling units are selected with equal probabilities (thus circumventing the need for sampling weights)...
May 2017: Multivariate Behavioral Research
Kyle M Lang, Wei Wu
Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison ( 2002 ); and a joint modeling approach based on the general location model...
May 2017: Multivariate Behavioral Research
Samantha F Anderson, Scott E Maxwell
Psychology is undergoing a replication crisis. The discussion surrounding this crisis has centered on mistrust of previous findings. Researchers planning replication studies often use the original study sample effect size as the basis for sample size planning. However, this strategy ignores uncertainty and publication bias in estimated effect sizes, resulting in overly optimistic calculations. A psychologist who intends to obtain power of .80 in the replication study, and performs calculations accordingly, may have an actual power lower than ...
May 2017: Multivariate Behavioral Research
Dexin Shi, Hairong Song, Xiaolan Liao, Robert Terry, Lori A Snyder
Specification search problems refer to two important but under-addressed issues in testing for factorial invariance: how to select proper reference indicators and how to locate specific non-invariant parameters. In this study, we propose a two-step procedure to solve these issues. Step 1 is to identify a proper reference indicator using the Bayesian structural equation modeling approach. An item is selected if it is associated with the highest likelihood to be invariant across groups. Step 2 is to locate specific non-invariant parameters, given that a proper reference indicator has already been selected in Step 1...
April 21, 2017: Multivariate Behavioral Research
Leslie A Brick, Colleen A Redding, Andrea L Paiva, Lisa L Harlow, Wayne F Velicer
The transition from childhood to adolescence is a crucial period for the development of healthy behaviors to be sustained later in life. With obesity a leading public health problem, the promotion of healthy behaviors has the potential to make a huge impact. The current study evaluated Stage of Change progression in a large (N = 4158) computer-delivered, Transtheoretical Model-tailored intervention focusing on physical activity and fruit and vegetable consumption (FV). Markov models were used to explore stage transitions and patterns of discrete change from sixth to ninth grade...
April 20, 2017: Multivariate Behavioral Research
Wes Bonifay, Li Cai
Complexity in item response theory (IRT) has traditionally been quantified by simply counting the number of freely estimated parameters in the model. However, complexity is also contingent upon the functional form of the model. We examined four popular IRT models-exploratory factor analytic, bifactor, DINA, and DINO-with different functional forms but the same number of free parameters. In comparison, a simpler (unidimensional 3PL) model was specified such that it had 1 more parameter than the previous models...
April 20, 2017: Multivariate Behavioral Research
Francisco J Abad, Eduardo Garcia-Garzon, Luis E Garrido, Juan R Barrada
The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-loadings, near-zero loadings, or both. A Monte Carlo simulation was carried out to test the performance of SLi, SL, SLt, and the two analytic bi-factor rotations, bi-quartimin and bi-geomin...
April 4, 2017: Multivariate Behavioral Research
J Jongerling, H Hoijtink
The total variance of a first-order autoregressive AR(1) time series is well known in time series literature. However, despite the increased use and interest in two-level AR(1) models, an equation for the total variance of these models does not exist. This paper presents an approximation of this total variance. It will be used to compute the unexplained and explained variance at each level of the model, the proportion of explained variance, and the intraclass correlation (ICC). The use of these variances and the ICC will be illustrated using an example concerning structured diary data about the positive affect of 96 married women...
April 4, 2017: Multivariate Behavioral Research
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