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.

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.

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.

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.

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.

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.

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.

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.

Accuracy of Nonexercise Prediction Equations for Assessing Longitudinal Changes to Cardiorespiratory Fitness in Apparently Healthy Adults: BALL ST Cohort.

Background Repeated assessment of cardiorespiratory fitness (CRF) improves mortality risk predictions in apparently healthy adults. Accordingly, the American Heart Association suggests routine clinical assessment of CRF using, at a minimum, nonexercise prediction equations. However, the accuracy of nonexercise prediction equations over time is unknown. Therefore, we compared the ability of nonexercise prediction equations to detect changes in directly measured CRF. Methods and Results The sample included 987 apparently healthy adults from the BALL ST (Ball State Adult Fitness Longitudinal Lifestyle Study) cohort (33% women; average age, 43.1±10.4 years) who completed 2 cardiopulmonary exercise tests ≥3 months apart (3.2±5.4 years of follow-up). The change in estimated CRF (eCRF) from 27 distinct nonexercise prediction equations was compared with the change in directly measured CRF. Analysis included Pearson product moment correlations, SEE values, intraclass correlation coefficient values, Cohen's κ coefficients, γ coefficients, and the Benjamini-Hochberg procedure to compare eCRF with directly measured CRF. The change in eCRF from 26 of 27 equations was significantly associated to the change in directly measured CRF (P<0.001), with intraclass correlation coefficient values ranging from 0.06 to 0.63. For 16 of the 27 equations, the change in eCRF was significantly different from the change in directly measured CRF. The median percentage of participants correctly classified as having increased, decreased, or no change in CRF was 56% (range, 39%-61%). Conclusions Variability was observed in the accuracy between nonexercise prediction equations and the ability of equations to detect changes in CRF. Considering the appreciable error that prediction equations had with detecting even directional changes in CRF, these results suggest eCRF may have limited clinical utility.

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.

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.

Modeling of self-report behavior data using the generalized covariates in a uniform and shifted binomial mixture model: An empirical example and Monte Carlo simulation.

Social scientists routinely collect data using questionnaires and surveys. Items on these instruments frequently involve scales with multiple ordered options that respondents use to report intensity of feelings or behaviors. Given their popularity, a variety of statistical models have been developed for analyzing data collected using these items. A model that has been recently described for working with ordinal items is the covariates in a uniform and shifted binomial mixture (CUB). The CUB model characterizes responses to ordinal items as a function of two parameters: (a) response feeling (or intensity), and (b) response uncertainty. This model has been extended to include a third parameter measuring likelihood of respondents selecting a socially desirable or safe response, known as the shelter option. This model has been primarily used to investigate items measuring political opinions or product preferences. However, the CUB with a shelter parameter and covariates generalized covariates in a uniform and shifted ninomial mixture model (GeCUB) seems particularly well suited for characterizing self-reported behaviors, particularly those that are not considered positive (i.e., substance abuse). The purpose of this study is to apply this extension of the CUB to the modeling of self-reported substance use behavior by teenagers. Results from the GeCUB model estimation revealed that subjects used the "no use" response as a shelter option at relatively high rates for marijuana use but not for cigarettes or alcohol. In addition, females reported less use and less certainty in their responses than did males. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Skeletal muscle size, function, and adiposity with lifelong aerobic exercise.

We examined the influence of lifelong aerobic exercise on skeletal muscle size, function, and adiposity. Young exercisers [YE; n = 20, 10 women (W), 25 ± 1 yr], lifelong exercisers (LLE; n = 28, 7 W, 74 ± 2 yr), and old healthy nonexercisers (OH; n = 20, 10 W, 75 ± 1 yr) were studied. On average, LLE exercised 5 days/wk for 7 h/wk over the past 52 ± 1 yr. The LLE men were subdivided by exercise intensity [Performance (LLE-P), n = 14; Fitness (LLE-F), n = 7]. Upper and lower leg muscle size and adiposity [intermuscular adipose tissue (IMAT)] were determined via MRI, and quadriceps isotonic and isometric function was assessed. For the quadriceps, aging decreased muscle size, isotonic and isometric strength, contraction velocity (men only), and power (P < 0.05). In women, LLE did not influence muscle size or function. In men, LLE attenuated the decline in muscle size and isometric strength by ~50% (P < 0.05). LLE did not influence other aspects of muscle function, nor did training intensity influence muscle size or function. For the triceps surae, aging decreased muscle size only in the women, whereas LLE (both sexes) and training intensity (LLE men) did not influence muscle size. In both sexes, aging increased thigh and calf IMAT by ~130% (P < 0.05), whereas LLE attenuated the thigh increase by ~50% (P < 0.05). In the LLE men, higher training intensity decreased thigh and calf IMAT by ~30% (P < 0.05). In summary, aging and lifelong aerobic exercise influenced muscle size, function, and adipose tissue infiltration in a sex- and muscle-specific fashion. Higher training intensity throughout the life span provided greater protection against adipose tissue infiltration into muscle.NEW & NOTEWORTHY This is the first study to examine skeletal muscle size, function, and adiposity in women and men in their eighth decade of life that have engaged in lifelong aerobic exercise. The findings reveal sex and upper and lower leg muscle group-specific benefits related to skeletal muscle size, function, and adiposity and that exercise intensity influences intermuscular adiposity. This emerging cohort will further our understanding of the health implications of maintaining exercise throughout the life span.

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.

Single-muscle fiber contractile properties in lifelong aerobic exercising women.

The purpose of this study was to examine the effects of lifelong aerobic exercise on single-muscle fiber performance in trained women (LLE; n = 7, 72 ± 2 yr) by comparing them to old healthy nonexercisers (OH; n = 10, 75 ± 1 yr) and young exercisers (YE; n = 10, 25 ± 1 yr). On average, LLE had exercised ~5 days/wk for ~7 h/wk over the past 48 ± 2 yr. Each subject had a vastus lateralis muscle biopsy to examine myosin heavy chain (MHC) I and IIa single-muscle fiber size and function (strength, speed, power). MHC I fiber size was similar across all three cohorts (YE = 5,178 ± 157, LLE = 4,983 ± 184, OH = 4,902 ± 159 µm2). MHC IIa fiber size decreased (P < 0.05) 36% with aging (YE = 4,719 ± 164 vs. OH = 3,031 ± 153 µm2), with LLE showing a similar 31% reduction (3,253 ± 189 µm2). LLE had 17% more powerful (P < 0.05) MHC I fibers and offset the 18% decline in MHC IIa fiber power observed with aging (P < 0.05). The LLE contractile power was driven by greater strength (+11%, P = 0.056) in MHC I fibers and elevated contractile speed (+12%, P < 0.05) in MHC IIa fibers. These data indicate that lifelong exercise did not benefit MHC I or IIa muscle fiber size. However, LLE had contractile function adaptations that enhanced MHC I fiber power and preserved MHC IIa fiber power through different contractile mechanisms (strength vs. speed). The single-muscle fiber contractile properties observed with lifelong aerobic exercise are unique and provide new insights into aging skeletal muscle plasticity in women at the myocellular level.NEW & NOTEWORTHY This is the first investigation to examine the effects of lifelong exercise on single-muscle fiber physiology in women. Nearly 50 yr of moderate to vigorous aerobic exercise training resulted in enhanced slow-twitch fiber power primarily by increasing force production, whereas fast-twitch fiber power was preserved primarily by increasing contractile speed. These unique muscle fiber power profiles helped offset the effects of fast-twitch fiber atrophy and highlight the benefits of lifelong aerobic exercise for myocellular health.

The Influence of Change in Cardiorespiratory Fitness With Short-Term Exercise Training on Mortality Risk From The Ball State Adult Fitness Longitudinal Lifestyle Study.

OBJECTIVE: To assess the influence of changes in cardiorespiratory fitness (CRF) after exercise training on mortality risk in a cohort of self-referred, apparently healthy adults. PATIENTS AND METHODS: A total of 683 participants (404 men, 279 women; mean age: 42.7±11.0 y) underwent two maximal cardiopulmonary exercise tests (CPX) between March 20, 1970, and December 11, 2012, to assess CRF at baseline (CPX1) and post-exercise training (CPX2). Participants were followed for an average of 29.8±10.7 years after their CPX2. Cox proportional hazards models were performed to determine the relationship of CRF change with mortality, with change in CRF as a continuous variable, as well as a categorical variable. A Wald chi-square test was used to compare the coefficients estimating the relationship of peak oxygen consumption (VO2peak) at CPX1 with VO2peak measured at CPX2 with time until death for all-cause mortality. RESULTS: During the follow-up period there were 180 deaths. When assessed independently, there were 20% (95% CI, 10-49%) and 38% (95% CI, 7-66%) lower mortality risks per 1 metabolic equivalent improvement in CRF (P<.01) in men and women, respectively, after multivariable adjustment. Those that remained unfit had ∼2-fold higher risk for all-cause mortality compared with those that remained fit and CRF at CPX2 was a stronger predictor of all-cause mortality than at CPX1 (P=.02). CONCLUSION: Improving CRF through exercise training lowers mortality risk. Clinicians should encourage individuals to participate in exercise training to improve CRF to lower risk of mortality.

Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters.

Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the treatment of multilevel data specifically when there is random coefficient variation in small samples. The methods being compared are fixed effects modeling (the industry standard in business and managerial sciences), multilevel modeling using restricted maximum likelihood (REML) estimation (the industry standard in the social and behavioral sciences), multilevel modeling using the Kenward-Rogers correction, and Bayesian estimation using Markov Chain Monte Carlo. Results indicate that multilevel modeling does have an advantage over fixed effects modeling when Level 2 slope parameter variance exists. Bayesian estimation of multilevel effects can be advantageous over traditional multilevel modeling using REML, but only when prior probabilities are correctly specified. Results are presented in terms of Type I error, power, parameter estimation bias, empirical parameter estimate standard error, and parameter 95% coverage rates, and recommendations 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.