The dynamic interplay between sleep and mood: an intensive longitudinal study of individuals with bipolar disorder.

BACKGROUND: Sleep disturbances are important symptoms to monitor in people with bipolar disorder (BD) but the precise longitudinal relationships between sleep and mood remain unclear. We aimed to examine associations between stable and dynamic aspects of sleep and mood in people with BD, and assess individual differences in the strength of these associations. METHODS: Participants (N = 649) with BD-I (N = 400) and BD-II (N = 249) provided weekly self-reports of insomnia, depression and (hypo)mania symptoms using the True Colours online monitoring tool for 21 months. Dynamic structural equation models were used to examine the interplay between weekly reports of insomnia and mood. The effects of clinical and demographic characteristics on associations were also assessed. RESULTS: Increased variability in insomnia symptoms was associated with increased mood variability. In the sample as a whole, we found strong evidence of bidirectional relationships between insomnia and depressive symptoms but only weak support for bidirectional relationships between insomnia and (hypo)manic symptoms. We found substantial variability between participants in the strength of prospective associations between insomnia and mood, which depended on age, gender, bipolar subtype, and a history of rapid cycling. CONCLUSIONS: Our results highlight the importance of monitoring sleep in people with BD. However, researchers and clinicians investigating the association between sleep and mood should consider subgroup differences in this relationship. Advances in digital technology mean that intensive longitudinal data on sleep and mood are becoming increasingly available. Novel methods to analyse these data present an exciting opportunity for furthering our understanding of BD.

Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

Confirmatory Factor Analysis (CFA) has been a popular yet limited approach to assessing latent factor structures. Despite items rarely loading exclusively on one latent factor in multifactorial scales, CFA assumes all indicators/items should load uniquely on their allocated latent dimensions. To address this weakness, Exploratory Structural Equation Modeling (ESEM) combines exploratory factor analyses (EFA) and CFA procedures, allowing cross-loadings to occur when assessing hypothesized models. Although such advantages have enhanced ESEM popularity, its adoption is often limited by software rigidity and complex coding difficulties. To address these obstacles, the current tutorial presents a streamlined, step-by-step approach using the open-source software R while providing both R and Mplus ESEM syntax. The tutorial demonstrates the sequence of the ESEM stages by examining the frequently debated Strengths and Difficulties Questionnaire (SDQ) factor structure, using openly accessible data from the Longitudinal Study of Australian Children (LSAC). As ESEM may allow a better understanding of the complex associations in multidimensional scales, this tutorial may optimize the epidemiological and clinical assessment of common yet multifaceted psychiatric presentations.

Comparing multiple statistical software for multiple-indicator, multiple-cause modeling: an application of gender disparity in adult cognitive functioning using MIDUS II dataset.

BACKGROUND: The multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis. It is a special case of structural equation modeling (SEM), which is modeled under latent variable framework. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software packages SAS CALIS procedure, R lavaan package, and Mplus version 8.0. METHODS: In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N = 4109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented. RESULTS: In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men. CONCLUSIONS: Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

Equidistant Response Options on Likert-Type Instruments: Testing the Interval Scaling Assumption Using Mplus.

The purpose of the present study was to provide the means to evaluate the "interval-scaling" assumption that governs the use of parametric statistics and continuous data estimators in self-report instruments that utilize Likert-type scaling. Using simulated and real data, the methodology to test for this important assumption is evaluated using the popular software Mplus 8.8. Evidence on meeting the assumption is provided using the Wald test and the equidistant index. It is suggested that routine evaluations of self-report instruments engage the present methodology so that the most appropriate estimator will be implemented when testing the construct validity of self-report instruments.

The item position effects in international examinations: the roles of gender.

The goal of the present study was to evaluate the roles of item position in terms of item difficulty levels in the assessment of aptitude. Using data from a National Examination in Saudi Arabia, the item position effect was evaluated as a teacher licensure test (GTLT) was administered using five different forms with the same items appearing in a different order. Results indicated minuscule in magnitude position effects estimates, overall, with initially 11.1% of the tests being significant but all of them failing to reach significance using the Holm-Bonferroni's and Sidak corrective procedures. With regard to gender, item position effects emerged in 47.6% of the tests after adjusting the level of significance using the Sidak correction. Interestingly, the direction of effect was consistent so that in 87% of the significant gender comparisons, item position effects were in the direction where females were spending more time on items when they appeared in later positions on the test compared to males. Assuming that items appearing later on the test are likely more difficult, the present findings suggest a profile of deep processing and active engagement in females when facing achievement tests.

Identifying Ability and Nonability Groups: Incorporating Response Times Using Mixture Modeling.

The goal of the present study was to address the analytical complexity of incorporating responses and response times through applying the Jeon and De Boeck mixture item response theory model in Mplus 8.7. Using both simulated and real data, we attempt to identify subgroups of responders that are rapid guessers or engage knowledge retrieval strategies. When applying the mixture model to a measure of contextual error in linguistics results pointed to the presence of a knowledge retrieval strategy. That is, a participant either knows the content (morphology, grammar rules) and can identify the error, or lacks the requisite knowledge and cannot benefit from spending more time on an item. In contrast, as item difficulty progressed, the high-ability group utilized the additional time to make informed guesses. The methodology is illustrated using annotated code in Mplus 8.7.

Required sample size to detect mediation in 3-level implementation studies.

BACKGROUND: Statistical tests of mediation are important for advancing implementation science; however, little research has examined the sample sizes needed to detect mediation in 3-level designs (e.g., organization, provider, patient) that are common in implementation research. Using a generalizable Monte Carlo simulation method, this paper examines the sample sizes required to detect mediation in 3-level designs under a range of conditions plausible for implementation studies. METHOD: Statistical power was estimated for 17,496 3-level mediation designs in which the independent variable (X) resided at the highest cluster level (e.g., organization), the mediator (M) resided at the intermediate nested level (e.g., provider), and the outcome (Y) resided at the lowest nested level (e.g., patient). Designs varied by sample size per level, intraclass correlation coefficients of M and Y, effect sizes of the two paths constituting the indirect (mediation) effect (i.e., X→M and M→Y), and size of the direct effect. Power estimates were generated for all designs using two statistical models-conventional linear multilevel modeling of manifest variables (MVM) and multilevel structural equation modeling (MSEM)-for both 1- and 2-sided hypothesis tests. RESULTS: For 2-sided tests, statistical power to detect mediation was sufficient (≥0.8) in only 463 designs (2.6%) estimated using MVM and 228 designs (1.3%) estimated using MSEM; the minimum number of highest-level units needed to achieve adequate power was 40; the minimum total sample size was 900 observations. For 1-sided tests, 808 designs (4.6%) estimated using MVM and 369 designs (2.1%) estimated using MSEM had adequate power; the minimum number of highest-level units was 20; the minimum total sample was 600. At least one large effect size for either the X→M or M→Y path was necessary to achieve adequate power across all conditions. CONCLUSIONS: While our analysis has important limitations, results suggest many of the 3-level mediation designs that can realistically be conducted in implementation research lack statistical power to detect mediation of highest-level independent variables unless effect sizes are large and 40 or more highest-level units are enrolled. We suggest strategies to increase statistical power for multilevel mediation designs and innovations to improve the feasibility of mediation tests in implementation research.

Transnational Solidarity Within the EU: Public Support for Risk-Sharing and Redistribution.

This paper aims to discover underlying, as yet theoretically and empirically unexplored, distinctions in citizens' views of transnational solidarity within the European Union (EU). Building on literature regarding national welfare states, the paper presents an original concept of transnational solidarity consisting of two latent, not directly measurable, dimensions: first, citizens' preferences for risk-sharing among EU states and, second, their preferences for intra-EU redistribution. The diverse types of transnational solidarity examined in previous research should be assignable to either one or the other dimension. Moreover, previous research is based on the idea that the concept of transnational solidarity is comparable across EU countries; however, this premise has not been empirically examined so far. To test both these assumptions, I analyze data collected in Austria, Germany, and Greece in 2019 or 2020. The study runs multi-group confirmatory factor analysis to test whether the presented concept of transnational solidarity (a) applies and (b) is comparable between these nations. The empirical analysis supports both these assumptions. The populations of the three countries share the same understanding of transnational solidarity even though the willingness to express cross-country risk-sharing and redistribution varies significantly between the states. The study contributes to current research in the fields of European integration, political sociology, and survey methodology.

A Critical Appraisal of the Evidence Supporting the Factor Structure of Extant Coping Instruments.

This paper evaluated the evidence supporting the factor structure of extant coping instruments based on modern psychometric standards. Our literature search identified nine coping instruments that are routinely used to measure coping strategies in adult populations. While nearly 10 thousand papers have been published using these instruments, only 39 studies have investigated their psychometric validity. Our findings revealed that the majority of these studies did not follow current psychometric recommendations for establishing internal validity in part because they did not account for the ordinal nature of the data. Further, studies employing exploratory factor analysis used methods for identifying the number of factors to retain that have been found to have a low accuracy in a simulation study while those employing confirmatory factor analysis reported model fit statistics that did not meet widely accepted benchmarks. Hence, conflicting results were found within and across the nine coping instruments. Recommendations are made for improving future validation studies.

Changes in Relationship Commitment Across the Transition to Parenthood: Pre-pregnancy Happiness as a Protective Resource.

The transition to parenthood is both a joyous and a challenging event in a relationship. Studies to date have found mostly negative effects of the birth of the first child on the parental relationship. We propose that partners' pre-pregnancy individual happiness may serve as a buffer against these negative effects. We predicted that parents who are happy prior to pregnancy fare better in terms of relationship commitment after childbirth than unhappy parents. To test our prediction, we used data of a 5-wave longitudinal study among 109 Dutch newlywed couples who had their first child during the study and a comparison group of 55 couples who remained childless. We found that the relationship commitment of fathers with higher pre-pregnancy happiness and fathers with a partner with higher pre-pregnancy happiness increased slightly in the years after childbirth, whereas the relationship commitment of fathers with lower pre-pregnancy happiness and fathers with a partner with lower pre-pregnancy happiness decreased. In addition, the relationship commitment of mothers with a happier partner prior to pregnancy decreased only slightly across the transition to parenthood but showed a steeper decline for mothers with a partner with average or lower pre-pregnancy happiness. In line with the idea that happiness acts as a resource when partners have to deal with relationship challenges, individual happiness predicted changes in relationship commitment for parents, but not for partners who remained childless.

On the Statistical and Practical Limitations of Thurstonian IRT Models.

Forced-choice questionnaires have been proposed to avoid common response biases typically associated with rating scale questionnaires. To overcome ipsativity issues of trait scores obtained from classical scoring approaches of forced-choice items, advanced methods from item response theory (IRT) such as the Thurstonian IRT model have been proposed. For convenient model specification, we introduce the thurstonianIRT R package, which uses Mplus, lavaan, and Stan for model estimation. Based on practical considerations, we establish that items within one block need to be equally keyed to achieve similar social desirability, which is essential for creating forced-choice questionnaires that have the potential to resist faking intentions. According to extensive simulations, measuring up to five traits using blocks of only equally keyed items does not yield sufficiently accurate trait scores and inter-trait correlation estimates, neither for frequentist nor for Bayesian estimation methods. As a result, persons' trait scores remain partially ipsative and, thus, do not allow for valid comparisons between persons. However, we demonstrate that trait scores based on only equally keyed blocks can be improved substantially by measuring a sizable number of traits. More specifically, in our simulations of 30 traits, scores based on only equally keyed blocks were non-ipsative and highly accurate. We conclude that in high-stakes situations where persons are motivated to give fake answers, Thurstonian IRT models should only be applied to tests measuring a sizable number of traits.

Estimation of an IRT Model by Mplus for Dichotomously Scored Responses Under Different Estimation Methods.

The purpose of this article is twofold. The first is to provide evaluative information on the recovery of model parameters and their standard errors for the two-parameter item response theory (IRT) model using different estimation methods by Mplus. The second is to provide easily accessible information for practitioners, instructors, and students about the relationships between IRT and item factor analysis (FA) parameterizations. Specifically, this is done using the "Theta" and "Delta" parameterizations in Mplus for unidimensional and multidimensional modeling with dichotomous and polytomous responses with and without the scaling constant D. The first objective aims at investigating differences that may occur when using different estimation methods in Mplus for binary response modeling. The second objective was motivated by practical interest observed among graduate students and applied researchers. The relations between IRT and Mplus FA "Theta" and "Delta" parameterizations are described using expressions without the use of matrices, which can be understood efficiently by applied researchers and students.

MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.

MplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish three objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of MplusAutomation is to support methodological developments in structural equation modeling using Mplus.

Using iMCFA to Perform the CFA, Multilevel CFA, and Maximum Model for Analyzing Complex Survey Data.

To construct CFA, MCFA, and maximum MCFA with LISREL v.8 and below, we provide iMCFA (integrated Multilevel Confirmatory Analysis) to examine the potential multilevel factorial structure in the complex survey data. Modeling multilevel structure for complex survey data is complicated because building a multilevel model is not an infallible statistical strategy unless the hypothesized model is close to the real data structure. Methodologists have suggested using different modeling techniques to investigate potential multilevel structure of survey data. Using iMCFA, researchers can visually set the between- and within-level factorial structure to fit MCFA, CFA and/or MAX MCFA models for complex survey data. iMCFA can then yield between- and within-level variance-covariance matrices, calculate intraclass correlations, perform the analyses and generate the outputs for respective models. The summary of the analytical outputs from LISREL is gathered and tabulated for further model comparison and interpretation. iMCFA also provides LISREL syntax of different models for researchers' future use. An empirical and a simulated multilevel dataset with complex and simple structures in the within or between level was used to illustrate the usability and the effectiveness of the iMCFA procedure on analyzing complex survey data. The analytic results of iMCFA using Muthen's limited information estimator were compared with those of Mplus using Full Information Maximum Likelihood regarding the effectiveness of different estimation methods.

[The latent variable growth curve model of longitudinal data and its implementation in Mplus].

To discuss the latent variable growth curve model of longitudinal data and give its implementation method in Mplus. The application of Mplus software has been used to deal with the longitudinal data of mental health status of college students in an university. Results show that the model can process the longitudinal data with latent variables, which can compare the differences of the overall development trend and individual development, also taking a covariate into the model to improve the effect of model fitting. Using Mplus software to process the longitudinal data with latent variables, the program is simple and easy to operate. This study provides the latent variable growth curve model of longitudinal data and its procedure of implementation in Mplus, and the statistical methodology guidance and reference for practical applications of epidemiological cohort study.

Introduction to bifactor polytomous item response theory analysis.

A bifactor item response theory model can be used to aid in the interpretation of the dimensionality of a multifaceted questionnaire that assumes continuous latent variables underlying the propensity to respond to items. This model can be used to describe the locations of people on a general continuous latent variable as well as on continuous orthogonal specific traits that characterize responses to groups of items. The bifactor graded response (bifac-GR) model is presented in contrast to a correlated traits (or multidimensional GR model) and unidimensional GR model. Bifac-GR model specification, assumptions, estimation, and interpretation are demonstrated with a reanalysis of data (Campbell, 2008) on the Shared Activities Questionnaire. We also show the importance of marginalizing the slopes for interpretation purposes and we extend the concept to the interpretation of the information function. To go along with the illustrative example analyses, we have made available supplementary files that include command file (syntax) examples and outputs from flexMIRT, IRTPRO, R, Mplus, and STATA. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jsp.2016.11.001. Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article.

Response style analysis with threshold and multi-process IRT models: A review and tutorial.

Two different item response theory model frameworks have been proposed for the assessment and control of response styles in rating data. According to one framework, response styles can be assessed by analysing threshold parameters in Rasch models for ordinal data and in mixture-distribution extensions of such models. A different framework is provided by multi-process item response tree models, which can be used to disentangle response processes that are related to the substantive traits and response tendencies elicited by the response scale. In this tutorial, the two approaches are reviewed, illustrated with an empirical data set of the two-dimensional 'Personal Need for Structure' construct, and compared in terms of multiple criteria. Mplus is used as a software framework for (mixed) polytomous Rasch models and item response tree models as well as for demonstrating how parsimonious model variants can be specified to test assumptions on the structure of response styles and attitude strength. Although both frameworks are shown to account for response styles, they differ on the quantitative criteria of model selection, practical aspects of model estimation, and conceptual issues of representing response styles as continuous and multidimensional sources of individual differences in psychological assessment.

The refractive outcome of Toric Lentis Mplus implant in cataract surgery.

AIM: To evaluate the refractive outcome of Toric Lentis Mplus intraocular lens (IOL) implant. METHODS: This is a retrospective case series. Consecutive patients with corneal astigmatism of at least 1.5 D had Toric Lentis Mplus IOL implant during cataract surgery. The exclusion criteria included irregular astigmatism on corneal topography, large scotopic pupil diameter (>6 mm), poor visual potential and significant ocular comorbidity. Postoperative manifest refraction, uncorrected distance visual acuity (UDVA), best-corrected distance visual acuity (BCVA), uncorrected intermediate visual acuity (UIVA) at 3/4 m and uncorrected near visual acuity (UNVA) were obtained. RESULTS: There were 70 eyes from 49 patients in this study. Patients were refracted at a median of 8.9wk (range 4.0 to 15.5) from the operation date. Sixty-five percent of eyes had 6/7.5 (0.10 logMAR) or better, and 99% 6/12 (0.30 logMAR) or better postoperative UDVA. Eighty-nine percent could read Jaeger (J) 3 (0.28 logMAR) and 95% J5 (0.37 logMAR) at 40 cm. The median magnitude of astigmatism decreased from 1.91 D to 0.49 D (Wilcoxon, P<0.001) after the operation. The range of the cylindrical error was reduced from 1.5-3.95 D (keratometric) preoperatively to 0.00-1.46 D (subjective refraction transposed to corneal plane) postoperatively. CONCLUSION: Toric Lentis Mplus IOL has good predictability in reducing preexisting corneal astigmatism.

The Reduced RUM as a Logit Model: Parameterization and Constraints.

Cognitive diagnosis models (CDMs) for educational assessment are constrained latent class models. Examinees are assigned to classes of intellectual proficiency defined in terms of cognitive skills called attributes, which an examinee may or may not have mastered. The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among psychometricians. Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) are typically used for estimating the Reduced RUM. Commercial implementations of the EM algorithm are available in the latent class analysis (LCA) routines of Latent GOLD and Mplus, for example. Fitting the Reduced RUM with an LCA routine requires that it be reparameterized as a logit model, with constraints imposed on the parameters. For models involving two attributes, these have been worked out. However, for models involving more than two attributes, the parameterization and the constraints are nontrivial and currently unknown. In this article, the general parameterization of the Reduced RUM as a logit model involving any number of attributes and the associated parameter constraints are derived. As a practical illustration, the LCA routine in Mplus is used for fitting the Reduced RUM to two synthetic data sets and to a real-world data set; for comparison, the results obtained by using the MCMC implementation in OpenBUGS are also provided.

Error induced by the estimation of the corneal power and the effective lens position with a rotationally asymmetric refractive multifocal intraocular lens.

AIM: To evaluate the prediction error in intraocular lens (IOL) power calculation for a rotationally asymmetric refractive multifocal IOL and the impact on this error of the optimization of the keratometric estimation of the corneal power and the prediction of the effective lens position (ELP). METHODS: Retrospective study including a total of 25 eyes of 13 patients (age, 50 to 83y) with previous cataract surgery with implantation of the Lentis Mplus LS-312 IOL (Oculentis GmbH, Germany). In all cases, an adjusted IOL power (PIOLadj) was calculated based on Gaussian optics using a variable keratometric index value (nkadj) for the estimation of the corneal power (Pkadj) and on a new value for ELP (ELPadj) obtained by multiple regression analysis. This PIOLadj was compared with the IOL power implanted (PIOLReal) and the value proposed by three conventional formulas (Haigis, Hoffer Q and Holladay I). RESULTS: PIOLReal was not significantly different than PIOLadj and Holladay IOL power (P>0.05). In the Bland and Altman analysis, PIOLadj showed lower mean difference (-0.07 D) and limits of agreement (of 1.47 and -1.61 D) when compared to PIOLReal than the IOL power value obtained with the Holladay formula. Furthermore, ELPadj was significantly lower than ELP calculated with other conventional formulas (P<0.01) and was found to be dependent on axial length, anterior chamber depth and Pkadj. CONCLUSION: Refractive outcomes after cataract surgery with implantation of the multifocal IOL Lentis Mplus LS-312 can be optimized by minimizing the keratometric error and by estimating ELP using a mathematical expression dependent on anatomical factors.