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This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits. Our simulation study results demonstrated the superiority of the Latent Moderated Structural Equations (LMS) approach over the Unconstrained Product Indicator (UPI) method in power and providing more accurate estimates of differences in latent interactions between groups. Utilizing multiple-group analysis on real-world data, we evaluated its efficacy in identifying latent interaction differences between groups. Empirical data analysis did not reveal discrepancies between LMS and UPI in terms of detecting differences in latent interactions between boys and girls, although they indicated varying sizes of differences in interaction effects. The study concludes with recommendations for examining differences in latent interactions and suggests avenues for future research. Our findings aim to deepen the understanding of intricate relationships among variables, group differences, and the moderating effects of latent variables, ultimately facilitating the development of more accurate theoretical models. [ABSTRACT FROM AUTHOR]

Rigorous scholarship is essential to the continued growth of group work, yet the unique nature of this counseling specialty poses challenges for quantitative researchers. The purpose of this proposal is to overview unique challenges to quantitative research with groups in the counseling field, including difficulty in obtaining large sample sizes and the violation of the independence assumption. Current practice is illustrated by referencing recent quantitative research in "The Journal for Specialists in Group Work," and recommendations are provided for best practices in designing, analyzing, and reporting quantitative research.

Latent growth modeling (LGM) is a popular and flexible technique that may be used when data are collected across several different measurement occasions. Modeling the appropriate growth trajectory has important implications with respect to the accurate interpretation of parameter estimates of interest in a latent growth model that may impact educational policy decisions. A Monte Carlo simulation study was conducted to examine the accuracy of six information-based criteria (i.e., AIC, CAIC, AICC, BIC, nBIC, and HQIC) when selecting among various growth trajectories modeled using LGM under different sample size, number of time points, and growth trajectory scenarios. The accuracy of the information criteria generally improved as sample size increased. The cubic and linear growth models were distinguished most accurately by the information criteria. All of the nonlinear models were more easily distinguished as the number of time points increased. The comparative performance of the six information criteria was dependent upon the manipulated conditions. Implications of the findings are discussed.

The authors conducted a content analysis on quantitative studies published in "The Journal for Specialists in Group Work" ("JSGW") between 2012 and 2015. This brief report provides a general overview of the current practices of quantitative group research in counseling. The following study characteristics are reported and discussed: study design, sample size (total number of participants, group sizes, and number of groups), statistical tests and/or techniques used, and how/whether the violation of independence assumption was addressed. The authors make recommendations for best practices in quantitative group work research.

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in intervention studies and analysis models that can simultaneously include such outcomes are available, we found very limited use of these models in intervention research. To encourage greater use of multivariate analysis for mixed outcomes, this article highlights the benefits and describes important features of models that can incorporate a mix of normal and binary outcomes. Models for intervention research are then fit using Mplus and results interpreted using data from an evaluation of the Early Head Start program, a randomized trial designed to improve child outcomes for an at-risk population. The models illustrated estimate treatment effects for mixed responses in standard and multilevel experimental designs.

This article demonstrates an associative latent transition-based model for analyzing the association between two discrete-time survival analysis (DTSA) models. DTSA allows social and behavioral researchers to investigate the qualitative change in event occurrence. Typically, DTSA is applied to a single survival process, where the hazard of an event occurrence is estimated across time. However, when researchers have hypotheses concerning the relationship between two survival processes, DTSA needs to be extended to associative DTSA. The study describes the latent transition based DTSA and its extension to explore the association between two event occurrences. M + software is used to illustrate the model parameterizations that are applicable to social and behavioral research topics. The illustrations use data related to juvenile delinquent and criminal behaviors. We close with a discussion of the capabilities of latent transition based DTSA. [ABSTRACT FROM AUTHOR]

Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low variance of the discrimination parameters used to generate the data. This simulation study reproduced the Whittaker et al. study by incorporating more variability in the discrimination parameter estimates used to generate the data. The results indicated that the majority of the criteria performed more accurately when selecting the more parameterized models. Differences among the criteria performance under certain conditions and implications for model selection practice are discussed. (Contains 2 tables and 8 figures.)

Latent means methods such as multiple-indicator multiple-cause (MIMIC) and structured means modeling (SMM) allow researchers to determine whether or not a significant difference exists between groups' factor means. Strong invariance is typically recommended when interpreting latent mean differences. The extent of the impact of noninvariant intercepts on conclusions made when implementing both MIMIC and SMM methods was the main purpose of this study. The impact of intercept noninvariance on Type I error rates, power, and two model fit indices when using MIMIC and SMM approaches under various conditions were examined. Type I error and power were adversely affected by intercept noninvariance. Although the fit indices did not detect small misspecifications in the form of noninvariant intercepts, one did perform more optimally. (Contains 4 tables and 3 footnotes.)

When tests consist of multiple-choice and constructed-response items, researchers are confronted with the question of which item response theory (IRT) model combination will appropriately represent the data collected from these mixed-format tests. This simulation study examined the performance of six model selection criteria, including the likelihood ratio test, Akaike's information criterion (AIC), corrected AIC, Bayesian information criterion, Hannon and Quinn's information criterion, and consistent AIC, with respect to correct model selection among a set of three competing mixed-format IRT models (i.e., one-parameter logistic/partial credit [1PL/PC], two-parameter logistic/generalized partial credit [2PL/GPC], and three-parameter logistic/generalized partial credit [3PL/GPC]). The criteria were able to correctly select less parameterized IRT models, including the PC, 1PL, and 1PL/PC models. In contrast, the criteria were less able to correctly select more parameterized IRT models, including the GPC, 3PL, and 3PL/GPC models. Implications of the findings and recommendations are discussed. (Contains 9 figures, 3 tables, and 4 notes.)

Model modification is oftentimes conducted after discovering a badly fitting structural equation model. During the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are 2 statistics that may be used to aid in the selection of parameters to add to a model to improve the fit. The purpose of this study was to extend the literature by examining the performance of the MI and the SEPC used independently and in conjunction with one another in terms of arriving at the correct confirmatory factor model. The results indicated that, in general, the SEPC outperformed the MI when arriving at the correct confirmatory factor model. However, they performed more similarly as factor loading size, sample size, and misspecified parameter size increased. The author provides recommendations on when the MI and SEPC perform more optimally. (Contains 4 tables and 1 figure.)

This study assessed the interpretability of scaled scores based on either number correct (NC) scoring for a paper-and-pencil test or one of two methods of scoring computer-based tests: an item pattern (IP) scoring method and a method based on equated NC scoring. The equated NC scoring method for computer-based tests was proposed as an alternative to item response theory IP scoring to improve consumers' understanding of reported scores. Results indicated that a test taker's performance level and the method used to report scores may adversely affect participants' correct interpretation of test scores. The IP score report made it difficult for participants to interpret the reported scores. The equated NC scoring method did not yield score reports that are any easier to understand than the IP scoring method. (Contains 9 tables.)

The importance of effective instruction on student academic and social achievement has been well documented. Strong classroom management and the use of high rates of opportunities to respond (OTR) have been two advocated classroom practices to positively impact student performance. This article presents an analysis of data collected across 35 general education classrooms in four elementary schools, assessing instructional variables associated with OTR. The relationship among OTR, measures of classroom management, and student work products was analyzed across Title and non-Title schools. Results indicate that teachers in the present study used components of OTR at rates similar to past research, but there were clear differences among Title I and non-Title schools. In addition, as teacher use of key instructional variables increased or decreased, other key variables posited as necessary by the literature often suffered. Implications for future research are discussed for students in high- and low-risk general education classrooms. (Contains 6 tables and 2 figures.)

We investigated motivation for taking low stakes tests. Based on expectancy-value theory, we expected that the effect of student perceptions of three task values (interest, usefulness, and importance) on low stakes test performance would be mediated by the student's reported effort. We hypothesized that all three task value components would play a significant role in predicting test-taking effort, and that effort would significantly predict test performance. Participants were 1005 undergraduate students enrolled at four midsize public universities. After students took all four subtests of CBASE, a standardized general education exam, they immediately filled out a motivation survey. Path analyses showed that the task value variables "usefulness" and "importance" significantly predicted test-taking effort and performance for all four tests. These results provide evidence that students who report trying hard on low stakes tests score higher than those who do not. The results indicate that if students do not perceive importance or usefulness of an exam, their effort suffers and so does their test score. While the data are correlational, they suggest that it might be useful for test administrators and school staff to communicate to students the importance and usefulness of the test that they are being asked to complete. (Contains 2 tables and 5 figures.)

The purpose of this study was to examine the use of three early numeracy measures to monitor the mathematics progress of students across time. One hundred and seven kindergarten and Grade 1 students were administered quantity discrimination, number identification, and missing-number measures once each month for 7 months. Alternate form reliability was adequate for instructional decision making, whereas criterion validity coefficients comparing the early numeracy measures to teacher judgment of student proficiency in mathematics and students' performance on a district-administered standardized test were lower than those observed in previous research. We used hierarchical linear modeling at each grade level to examine the ability of the three measures to model growth across time. All measures produced growth rates that were significant across time, for each grade level, with linear growth observed for the Number Identification measure only. (Contains 6 tables and 3 figures.)

Because technology in the home is becoming ever more available, understanding the role of technology in early childhood development is increasingly an opportunity and a concern for educators. This report examines data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99, to provide an overview of the role of technology in the home and its impact on reading and math achievement in the early grades. The results show that access and use of technology influence children's academic achievement. Although the results show that having access to technology can contribute significantly to the achievement of young children, mere access is unlikely to be sufficient for all young children to benefit from technology in the home. Adults will need to mediate the use of these potential learning tools for children. Based on the findings of this report, policymakers should support increasing the availability of computers for low-income families and continue to discourage extensive viewing of TV, and researchers should focus on the specific methods that parents can enact to realize the learning potential of an increasingly ubiquitous resource: computers and the Internet in the home.

Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each cross-validation index was measured in terms of true model selection rate as well as consistency of model selection. The performance of the cross-validation indices tended to improve as factor loading and sample size increased but performed less well as nonnormality increased. The double cross-validated indices outperformed their simple cross-validated counterparts in certain conditions. Recommendations are provided as to which cross-validation methods would optimally perform in a given condition.

The authors conducted a content analysis on new scale development articles appearing in the "Journal of Counseling Psychology" during 10 years (1995 to 2004). The authors analyze and discuss characteristics of the exploratory and confirmatory factor analysis procedures in these scale development studies with respect to sample characteristics, factorability, extraction methods, rotation methods, item deletion or retention, factor retention, and model fit indexes. The authors uncovered a variety of specific practices that were at variance with the current literature on factor analysis or structural equation modeling. They make recommendations for best practices in scale development research in counseling psychology using exploratory and confirmatory factor analysis. (Contains 3 tables.)

In this study we examined differences between online distance education and traditional classroom learning for an introductory undergraduate statistics course. Two outcome dimensions were measured: students' final grades and student satisfaction with the course. Using independent samples t-tests, results indicated that there was no significant difference in grades between the online and traditional classroom contexts. However, students enrolled in the online course were significantly less satisfied with the course than the traditional classroom students on several dimensions. This finding is inconsistent with the "no significant difference phenomenon," described in Russell's (1999) annotated bibliography, which supports minimal outcome differences between online courses and face-to-face courses.

A Monte Carlo study extended the research of MacKinnon, Lockwood, Hoffman, West, and Sheets (2002) for single-level designs by examining the statistical performance of four methods to test for mediation in a multilevel experimental design. The design studied was a two-group experiment that was replicated across several sites, included a single intervening variable and outcome, and assumed that the effects of the treatment and mediator were constant across sites. The findings provide new evidence of the benefits of and further support for using the asymmetric confidence limits approach to test for mediation. In addition, the authors provide further support for using confidence intervals to assess if treatment effects are completely mediated, as using traditional hypothesis testing may lead to erroneous conclusions.

The current study aims to investigate mediation in regression mixture models. There has been little research that has examined the combination of mediation and regression mixture models to determine if there are latent subgroups that vary in their levels of mediation. This investigation aims to address this gap by simulating varying conditions of sample size, number of latent classes, mixing proportions, class intercept separation, direct effects, and class separation on mediating effects. Information criteria (i.e., AIC, BIC, aBIC) and likelihood ratio tests (i.e., LMR, VLMR, and BLRT) were evaluated for model selection. The results suggest that the BIC and BLRT perform best at identifying the correct number of latent classes. The class enumeration indices improved in accuracy as sample size, class intercept separation, and separation on the mediating effect increased. The current investigation identifies conditions where class enumeration is most accurate with mediation in regression mixture models. [ABSTRACT FROM AUTHOR]

A parallel process growth model is often limited to the developments of two main trends due to model complexity. Two major parallel processes are only modeled to examine how the changes in two longitudinal processes might be related without considering other growth models possibly related to the two main processes. However, the exclusion of a third LGM risks introducing bias into key associations while capturing the relations between only two growth processes, since they are not controlled. This study presents a method to address the effect of spurious relationship while minimizing convergence issues when other processes are not considered. The suggested method utilizes the factor score estimates for other relevant growth models to control the spurious effect. Results revealed the superiority of the parallel process model using the factor scores over the typical parallel process model and illustrated the importance of considering growth models relevant to the parallel process model. [ABSTRACT FROM AUTHOR]

This article presents an evaluation of the Client Management Classification System (CMC), a method for assessment and differential supervision of offenders that embodies the principle of responsivity. As in prior evaluations of the CMC, probationers whose officers were trained in CMC techniques experienced lower rates of revocation compared with regularly supervised subjects. However, the experimental group incurred similar or higher rates of rules violations and arrests. Of particular interest, the study found that supervision of experimental subjects did not conform to recommended CMC strategies. In combination, these results suggest the possibility that training in CMC successfully heightened officers' understanding of offender motivations and needs, leading them to view probationer misconduct in a more lenient and flexible context--and thereby producing the appearance of favorable outcomes. The findings have implications for the design of evaluations of efforts to implement principles of effective offender treatment in community corrections agencies. (Contains 7 tables and 13 notes.)

The authors investigated the influence of effect size and comment inclusion on readers' perceptions of research results. In three experiments, undergraduates, graduates, and faculty read a journal article that either included or did not include an effect size and commentary about the effect size. Contrary to a previous study by Robinson, Fouladi, Williams, and Bera (2002), which concluded that including effect sizes causes readers to overestimate result importance, the authors found that including a comment about the magnitude of the effect size was more important than simply including the effect size in influencing undergraduates' perceptions of research results' importance. Graduate students and faculty members were less influenced by inclusion of their effect sizes or comments. Recommendations concerning effect size and comment inclusion policies are discussed. (Contains 3 tables.)