A Comparison of Regularized Maximum-Likelihood, Regularized 2-Stage Least Squares, and Maximum-Likelihood Estimation with Misspecified Models, Small Samples, and Weak Factor Structure.

Several structural equation modeling estimation methods have recently been developed to alleviate problems associated with model misspecification. Two of the more popular such approaches are 2-stage least squares and regularization methods. Prior work examining the performance of these estimators has generally focused on problems with adequately sized samples and relatively large factor loadings. In contrast, relatively little research has been conducted comparing these estimation techniques with small samples and weak loadings, though both conditions are not uncommon in the multivariate modeling. The current simulation study focused on comparing these relatively new structural estimation methods for misspecified models (e.g., misspecified interactions and cross-loadings) with small samples and relatively weak factor loadings. Results indicated that regularized 2-stage least squares estimation performed better compared to the regularized structural equation modeling framework for small samples and with weak factor loadings. Implications and guidelines for applied researchers are presented.

Detecting Feigning in Adolescents on the Personality Assessment Inventory-Adolescent Form.

Much of the research on identifying feigning in psychological assessment has focused on adults with less attention to adolescents. The purpose of the present study is to expand the limited literature on detecting feigning in adolescents using the Personality Assessment Inventory - Adolescent. The study included 114 nonclinical adolescents (ages 15 to 18) recruited from high schools in the Midwest who were randomly assigned to experimental groups: honest nonclinical, uncoached feigning, and coached feigning. 50 randomly selected individuals with depression from the PAI-A clinical standardization sample were included as the honest clinical group. Sample demographics included a mean age of 16.64 years; 51.2% young men, 48.2% young women; 85.4% Caucasian, 6.7% African American, 5.5% Hispanic, and 2.4% Asian. 80% of feigning profiles reported clinical levels of depression. MANOVA results showed strong support for the Rogers Discriminant Function (RDF; d range = 1.85-2.05). The Negative Impression Management (NIM) scale also demonstrated promise (d range = 0.77-1.08), while the smallest effects for detecting feigning were found for the Malingering Index (d range = 0.58-0.70). The negative distortion indices showed good utility in differentiating between groups. Cut-scores and pragmatic implications are presented.

Quantifying Item Invariance for the Selection of the Least Biased Assessment.

An important aspect of educational and psychological measurement and evaluation of individuals is the selection of scales with appropriate evidence of reliability and validity for inferences and uses of the scores for the population of interest. One aspect of validity is the degree to which a scale fairly assesses the construct(s) of interest for members of different subgroups within the population. Typically, this issue is addressed statistically through assessment of differential item functioning (DIF) of individual items, or differential bundle functioning (DBF) of sets of items. When selecting an assessment to use for a given application (e.g., measuring intelligence), or which form of an assessment to use in a given instance, researchers need to consider the extent to which the scales work with all members of the population. Little research has examined methods for comparing the amount or magnitude of DIF/DBF present in two assessments when deciding which assessment to use. The current simulation study examines 6 different statistics for this purpose. Results show that a method based on the random effects item response theory model may be optimal for instrument comparisons, particularly when the assessments being compared are not of the same length.

Methods for the Comparison of Differential Item Functioning across Assessments.

An important aspect of the educational and psychological evaluation of individuals is the selection of scales with appropriate evidence of reliability and validity for inferences and uses of the scores for the population of interest. One key aspect of validity is the degree to which a scale fairly assesses the construct(s) of interest for members of different subgroups within the population. Typically, this issue is addressed statistically through assessment of differential item functioning (DIF) of individual items, or differential test functioning (DTF) of sets of items within the same measure. When selecting an assessment to use for a given application (e.g., measuring intelligence), or which form of an assessment to use for a test administration, researchers need to consider the extent to which the scales work with all members of the population. Little research has examined methods for comparing the amount or magnitude of DIF/DTF present in two or more assessments when deciding which assessment to use. The current study made use of 7 different statistics for this purpose, in the context of intelligence testing. Results demonstrate that by using a variety of effect sizes, the researcher can gain insights into not only which scales may contain the least amount of DTF, but also how they differ with regard to the way in which the DTF manifests itself.

Differential item functioning analysis using a multilevel Rasch mixture model: investigating the impact of disability status and receipt of testing accommodations.

The assessment of differential item functioning (DIF) remains an area of active research in psychometrics and educational measurement. In recent years, methodological innovations involving mixture Rasch models have provided researchers with an additional set of tools for more deeply understanding the root causes of DIF, while at the same time increased interest in the role of disabilities and accommodations has also made itself felt in the measurement community. The current study furthered work in both areas by using the newly described multilevel mixture Rasch model to investigate the presence of DIF associated with disability and accommodation status at both examinee and school levels for a 3rd grade language assessment. Results of the study found that indeed DIF was present at both levels of analysis, and that it was associated with the presence of disabilities and the receipt of accommodations. Implications of these results for both practitioners and researchers are discussed.

Dominance Analysis for Latent Variable Models: A Comparison of Methods With Categorical Indicators and Misspecified Models.

Dominance analysis (DA) is a very useful tool for ordering independent variables in a regression model based on their relative importance in explaining variance in the dependent variable. This approach, which was originally described by Budescu, has recently been extended to use with structural equation models examining relationships among latent variables. Research demonstrated that this approach yields accurate results for latent variable models involving normally distributed indicator variables and correctly specified models. The purpose of the current simulation study was to compare the use of this DA approach to a method based on observed regression DA and DA when the latent variable model is estimated using two-stage least squares for latent variable models with categorical indicators and/or model misspecification. Results indicated that the DA approach for latent variable models can provide accurate ordering of the variables and correct hypothesis selection when indicators are categorical and models are misspecified. A discussion of implications from this study is provided.

NASA SPRINT exercise program efficacy for vastus lateralis and soleus skeletal muscle health during 70 days of simulated microgravity.

The efficacy of the NASA SPRINT exercise countermeasures program for quadriceps (vastus lateralis) and triceps surae (soleus) skeletal muscle health was investigated during 70 days of simulated microgravity. Individuals completed 6° head-down-tilt bedrest (BR, n = 9), bedrest with resistance and aerobic exercise (BRE, n = 9), or bedrest with resistance and aerobic exercise and low-dose testosterone (BRE + T, n = 8). All groups were periodically tested for muscle (n = 9 times) and aerobic (n = 4 times) power during bedrest. In BR, surprisingly, the typical bedrest-induced decrements in vastus lateralis myofiber size and power were either blunted (myosin heavy chain, MHC I) or eliminated (MHC IIa), along with no change (P > 0.05) in %MHC distribution and blunted quadriceps atrophy. In BRE, MHC I (vastus lateralis and soleus) and IIa (vastus lateralis) contractile performance was maintained (P > 0.05) or increased (P < 0.05). Vastus lateralis hybrid fiber percentage was reduced (P < 0.05) and energy metabolism enzymes and capillarization were generally maintained (P > 0.05), while not all of these positive responses were observed in the soleus. Exercise offsets 100% of quadriceps and approximately two-thirds of soleus whole muscle mass loss. Testosterone (BRE + T) did not provide any benefit over exercise alone for either muscle and for some myocellular parameters appeared detrimental. In summary, the periodic testing likely provided a partial exercise countermeasure for the quadriceps in the bedrest group, which is a novel finding given the extremely low exercise dose. The SPRINT exercise program appears to be viable for the quadriceps; however, refinement is needed to completely protect triceps surae myocellular and whole muscle health for astronauts on long-duration spaceflights.NEW & NOTEWORTHY This study provides unique exercise countermeasures development information for astronauts on long-duration spaceflights. The NASA SPRINT program was protective for quadriceps myocellular and whole muscle health, whereas the triceps surae (soleus) was only partially protected as has been shown with other programs. The bedrest control group data may provide beneficial information for overall exercise dose and targeting fast-twitch muscle fibers. Other unique approaches for the triceps surae are needed to supplement existing exercise programs.

The Impact and Detection of Uniform Differential Item Functioning for Continuous Item Response Models.

Psychometricians have devoted much research and attention to categorical item responses, leading to the development and widespread use of item response theory for the estimation of model parameters and identification of items that do not perform in the same way for examinees from different population subgroups (e.g., differential item functioning [DIF]). With the increasing use of computer-based measurement, use of items with a continuous response modality is becoming more common. Models for use with these items have been developed and refined in recent years, but less attention has been devoted to investigating DIF for these continuous response models (CRMs). Therefore, the purpose of this simulation study was to compare the performance of three potential methods for assessing DIF for CRMs, including regression, the MIMIC model, and factor invariance testing. Study results revealed that the MIMIC model provided a combination of Type I error control and relatively high power for detecting DIF. Implications of these findings are discussed.

Fixed Effects or Mixed Effects Classifiers? Evidence From Simulated and Archival Data.

This study seeks to compare fixed and mixed effects models for the purposes of predictive classification in the presence of multilevel data. The first part of the study utilizes a Monte Carlo simulation to compare fixed and mixed effects logistic regression and random forests. An applied examination of the prediction of student retention in the public-use U.S. PISA data set was considered to verify the simulation findings. Results of this study indicate fixed effects models performed comparably with mixed effects models across both the simulation and PISA examinations. Results broadly suggest that researchers should be cognizant of the type of predictors and data structure being used, as these factors carried more weight than did the model type.

A Comparison of Confirmatory Factor Analysis and Network Models for Measurement Invariance Assessment When Indicator Residuals are Correlated.

Social science research is heavily dependent on the use of standardized assessments of a variety of phenomena, such as mood, executive functioning, and cognitive ability. An important assumption when using these instruments is that they perform similarly for all members of the population. When this assumption is violated, the validity evidence of the scores is called into question. The standard approach for assessing the factorial invariance of the measures across subgroups within the population involves multiple groups confirmatory factor analysis (MGCFA). CFA models typically, but not always, assume that once the latent structure of the model is accounted for, the residual terms for the observed indicators are uncorrelated (local independence). Commonly, correlated residuals are introduced after a baseline model shows inadequate fit and inspection of modification indices ensues to remedy fit. An alternative procedure for fitting latent variable models that may be useful when local independence does not hold is based on network models. In particular, the residual network model (RNM) offers promise with respect to fitting latent variable models in the absence of local independence via an alternative search procedure. This simulation study compared the performances of MGCFA and RNM for measurement invariance assessment when local independence is violated, and residual covariances are themselves not invariant. Results revealed that RNM had better Type I error control and higher power compared to MGCFA when local independence was absent. Implications of the results for statistical practice are discussed.

Muscle group-specific skeletal muscle aging: a 5-yr longitudinal study in septuagenarians.

There is some evidence that the age-associated change in skeletal muscle mass is muscle specific, yet the number of specific muscles that have been studied to form our understanding in this area is limited. In addition, few aging investigations have examined multiple muscles in the same individuals. This longitudinal investigation compared changes in skeletal muscle size via computed tomography of the quadriceps (rectus femoris, vastus lateralis, vastus medialis, and vastus intermedius), hamstrings (biceps femoris short and long heads, semitendinosus, and semimembranosus), psoas, rectus abdominis, lateral abdominals (obliques and transversus abdominis), and paraspinal muscles (erector spinae and multifidi) of older individuals from the Health, Aging, and Body Composition (Health ABC) study at baseline and 5.0 ± 0.1 years later (n = 469, 73 ± 3 yr and 78 ± 3 yr, 49% women, 33% black). Skeletal muscle size decreased (P < 0.05) in quadriceps (-3.3%), hamstrings (-5.9%), psoas (-0.4%), and rectus abdominis (-7.0%). The hamstrings and rectus abdominis atrophied approximately twice as much as the quadriceps (P < 0.05), whereas the quadriceps atrophied substantially more than the psoas (P < 0.05). The lateral abdominals (+5.9%) and paraspinals (+4.3%) hypertrophied (P < 0.05) to a similar degree (P > 0.05) over the 5 years. These data suggest that older individuals experience skeletal muscle atrophy and hypertrophy in a muscle group-specific fashion in the eighth decade, a critical time period in the aging process. A broader understanding of muscle group-specific skeletal muscle aging is needed to better guide exercise programs and other interventions that mitigate decrements in physical function with aging.NEW & NOTEWORTHY These longitudinal analyses of six muscle groups in septuagenarians provide novel information on the muscle group-specific aging process. Although the quadriceps, hamstrings, psoas, and rectus abdominis atrophied with different magnitudes, the lateral abdominals and paraspinals hypertrophied over the 5 years. These findings contribute to a better understanding of the skeletal muscle aging process and highlight the need to complete studies in this area with a muscle-specific focus.

Influence of aspirin on aging skeletal muscle: Insights from a cross-sectional cohort of septuagenarians.

Aspirin is one of the most commonly consumed cyclooxygenase (COX)-inhibitors and anti-inflammatory drugs and has been shown to block COX-produced regulators of inflammation and aging skeletal muscle size. We used propensity score matching to compare skeletal muscle characteristics of individuals from the Health ABC study that did not consume aspirin or any other COX-inhibiting drugs (non-consumers, n = 497, 74 ± 3 year, 168 ± 9 cm, 75.1 ± 13.8 kg, 33.1 ± 7.4% body fat, 37% women, 34% black) to those that consumed aspirin daily (and not any other COX-inhibiting drugs) and for at least 1 year (aspirin consumers, n = 515, 74 ± 3 year, 168 ± 9 cm, 76.2 ± 13.6 kg, 33.8 ± 7.1% body fat, 39% women, 30% black, average aspirin consumption: 6 year). Subjects were matched (p > 0.05) based on age, height, weight, % body fat, sex, and race (propensity scores: 0.33 ± 0.09 vs. 0.33 ± 0.09, p > 0.05). There was no difference between non-consumers and aspirin consumers for computed tomography-determined muscle size of the quadriceps (103.5 ± 0.9 vs. 104.9 ± 0.8 cm2 , p > 0.05) or hamstrings (54.6 ± 0.5 vs. 54.9 ± 0.5 cm2 , p > 0.05), or quadriceps muscle strength (111.1 ± 2.0 vs. 111.7 ± 2.0 Nm, p > 0.05). However, muscle attenuation (i.e., density) was higher in the aspirin consumers in the quadriceps (40.9 ± 0.3 vs. 44.4 ± 0.3 Hounsfield unit [HU], p < 0.05) and hamstrings (27.7 ± 0.4 vs. 33.2 ± 0.4 HU, p < 0.05). These cross sectional data suggest that chronic aspirin consumption does not influence age-related skeletal muscle atrophy, but does influence skeletal muscle composition in septuagenarians. Prospective longitudinal investigations remain necessary to better understand the influence of chronic COX regulation on aging skeletal muscle health.

Effects of fatigue on golf performance.

The purpose of this study was to determine if body position, weight transfer, and/or pelvis/trunk rotations changed as a result of a golf specific fatiguing protocol and whether these changes affected resultant club head velocity at impact and shot consistency. Six male golfers and one female golfer participated in the study, who had a mean age, height, and body mass of 23.9 +/- 3.9 years, 177.4 +/- 4.9 cm, and 75.3 +/- 9.9 kg, respectively. Path analysis was used to determine the relationships between fatigue, biomechanical variables, and resultant club head velocity at impact and shot consistency. In the statistical models representing the effects of biomechanical variables calculated at the top of the swing and ball contact, golf specific fatigue was associated with a 2.0% and 2.5% reduction in the club head velocity and a 7.1% and 9.4% improvement in the shot consistency, respectively. These data suggest that golf specific fatigue was not related to the initial lower body sagittal plane angles at address nor was simulated golf specific fatigue related to peak transverse plane pelvis and trunk rotational velocities (or their timings) in a manner that indicates a relationship to resultant club head velocity and shot consistency.

A canonical correlation analysis of intelligence and executive functioning.

Executive functioning is one of the most researched and debated topics in neuropsychology. Although neuropsychologists routinely consider executive functioning and intelligence in their assessment process, more information is needed regarding the relationship between these constructs. This study reports the results of a canonical correlation study between the most widely used measure of adult intelligence, the Wechsler Adult Intelligence Scale, 3rd edition (WAIS-III; Wechsler, 1997), and the Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001). The results suggest that, despite considerable shared variability, the measures of executive functioning maintain unique variance that is not encapsulated in the construct of global intelligence.

The prediction of intelligence in preschool children using alternative models to regression.

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Prediction With Mixed Effects Models: A Monte Carlo Simulation Study.

Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms across several varied data conditions for the purpose of prediction. Among these models, the panel neural network and Bayesian generalized mixed effects model (multilevel Bayes) consistently yielded the highest prediction accuracy in test data across nearly all data conditions.

Associations Between Active Commuting and Cardiovascular Disease in the United States.

BACKGROUND: Active commuting is inversely related with cardiovascular disease (CVD) risk factors yet associations with CVD prevalence in the US population are unknown. METHODS: Aggregate data from national surveys conducted in 2017 provided state-level percentages of adults who have/had coronary heart disease, myocardial infarction, and stroke, and who actively commuted to work. Associations between active commuting and CVD prevalence rates were assessed using Pearson correlations and generalized additive models controlling for covariates. RESULTS: Significant correlations were observed between active commuting and all CVD rates (r range = -.31 to -.47; P < .05). The generalized additive model analyses for active commuting (walking, cycling, or public transport) in all adults found no relationships with CVD rates; however, a significant curvilinear association was observed for stroke within men. The generalized additive model curves when examining commuting via walking or cycling in all adults demonstrated nuanced, generally negative linear or curvilinear associations between coronary heart disease, myocardial infarction, and stroke. CONCLUSION: Significant negative correlations were observed between active commuting and prevalence rates of coronary heart disease, myocardial infarction, and stroke. Controlling for covariates influenced these associations and highlights the need for future research to explore the potential of active commuting modes to reduce CVD in the United States.

Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis.

Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools designed to address this question, with none being universally optimal across applications. Recently, researchers have investigated the use of model fit indices that are commonly used in the conduct of confirmatory factor analysis to determine the number of factors to retain in EFA. These results have yielded mixed results, appearing to be effective when used in conjunction with normally distributed indicators, but not being as effective for categorical indicators. The purpose of this simulation study was to compare the performance of difference values for several fit indices as a method for identifying the optimal number of factors to retain in an EFA, with parallel analysis, which is one of the most reliable such extant methods. Results of the simulation demonstrated that the use of fit index difference values outperformed parallel analysis for categorical indicators, and for normally distributed indicators when factor loadings were small. Implications of these findings are discussed.

Revealing Nuanced Relationships Among Cognitive Test Anxiety, Motivation, and Self-Regulation Through Curvilinear Analyses.

Student motivational profiles and self-regulated learning strategies are significant influences on overall academic success in university settings. Test anxiety has been repeatedly linked to maladaptive learning strategies and ineffective motivational frameworks. However, the results in the field have been inconsistent with respect to the precise interactions among these variables. This study employed anonymous responses from a group of volunteer students in a mid-sized Midwestern United States university, serving a primarily Caucasian and female population with an average age of 20 years. Adopting a curvilinear analytic design, this study attempted to examine the relationships among these common domains of inquiry into student thriving. The results of this study provide insights examining under which conditions cognitive test anxiety is most likely to be heightened or diminished. The results demonstrated that levels of test anxiety were greatest when (a) learners adopted primarily extrinsic or primarily intrinsic goal orientations, (b) academic tasks where the outcome was uncertain, (c) learners adopted passive learning strategies, and (d) learning strategies were more personally involved (as opposed to externalized study behaviors). Our results add to the field by identifying curvilinear models provide greater utility in identifying the relationships among these critical emotional and cognitive factors in academic settings. Furthermore, we advocate for employing identification and intervention strategies that recognize individually specific profiles of interactions among test anxiety, motivation, and self-regulation to promote more optimal success in supporting learners in university settings.

Poverty and Covid-19: Rates of Incidence and Deaths in the United States During the First 10 Weeks of the Pandemic.

The Covid-19 pandemic in the winter and spring of 2020 represents a major challenge to the world health care system that has not been seen perhaps since the influenza pandemic in 1918. The virus has spread across the world, claiming lives on all continents with the exception of Antarctica. Since its arrival in the United States, attention has been paid to how Covid-19 cases and deaths have been distributed across varying socioeconomic and ethnic groups. The goal of this study was to examine this issue during the early weeks of the pandemic, with the hope of shedding some light on how the number of cases and the number of deaths were, or were not related to poverty. Results of this study revealed that during the early weeks of the pandemic more disadvantaged counties in the United States had a larger number of confirmed Covid-19 cases, but that over time this trend changed so that by the beginning of April, 2020 more affluent counties had more confirmed cases of the virus. The number of deaths due to Covid-19 were associated with poorer and more urban counties. Discussion of these results focuses on the possibility that testing for the virus was less available in more disadvantaged counties later in the pandemic than was the case earlier, as the result of an overall lack of adequate testing resources across the nation.