Traditional Confirmatory Factor Analysis Research Articles

The Forced‐To‐Penetrate Myth Acceptance Scale (FTP‐MAS): A New Attitudinal Tool for Assessing Myths That Surround Female Perpetrated Sexual Violence Against Men

ABSTRACTThe purpose of this study was to develop and validate a new measurement tool designed to capture endorsement of myths surrounding female perpetrated sexual violence against men, specifically in ‘forced‐to‐penetrate’ cases. Data were collected among a sample of 4152 UK adults aged 18–55+ (52% female). Dimensionality and construct validity of the Forced‐to‐Penetrate Myth Acceptance Scale (FTP‐MAS) was investigated using traditional Confirmatory Factor Analysis (CFA) techniques separately for the complete sample, males only, and females only. CFA results indicated that FTP‐MAS scores are best captured by a three‐factor model (1. Distorted Sex and Gender Roles; 2. Harm Minimisation; 3. Offence Denial) across all samples tested. Excellent composite reliability and differential predictive validity were observed for all three subscales. The validated 22‐item FTP‐MAS constitutes the first measurement tool which allows for the assessment and evaluation of public attitudes towards female perpetrators who force men to penetrate them without consent. As such, this tool enables researchers to better understand the multi‐faceted nature of these myths, assess prevalence in different contexts, and can also be used as an outcome measure in research seeking to evaluate the effectiveness of interventions that aim to debunk endorsement of such myths and stereotypes.

Introduction and validation of the Modern Adolescent Dating Violence Attitude (MADVA) scale: A contemporary tool for assessing adolescent attitudes towards dating violence in offline and online environments

The study developed and validated the Modern Adolescent Dating Violence Attitude (MADVA) scale, examining young people's attitudes towards online and offline variants of adolescent dating violence and abuse (ADVA). Data were collected among 2011 adolescents from England, aged 10–25 (M 15.72 years). Dimensionality and construct validity of the MADVA was investigated using traditional Confirmatory Factor Analysis. Results indicate a six-factor model (Attitudes towards: Sexual Abuse-Online; Sexual Abuse-Offline; Psychological Abuse-Online; Psychological Abuse-Offline; Physical Abuse-Offline; Controlling Behaviour-Offline). Excellent composite reliability and differential predictive validity were observed for all six subscales. The MADVA scale enables users to better evaluate ADVA prevention-programmes.

Comparison of latent variable and psychological network models in PROMIS data: output metrics and factor structure.

Much research is still needed to compare traditional latent variable models such as confirmatory factor analysis (CFA) to emerging psychometric models such as the Gaussian graphical model (GGM). Previous comparisons of GGM centrality indices with factor loadings from CFA have discovered redundancies, and investigations into how well a GGM-based alternative to exploratory factor analysis (i.e., exploratory graph analysis, or EGA) is able to recover the hypothesized factor structure show mixed results. Importantly, such comparisons have not typically been examined in real mental and physical health symptom data, despite such data being an excellent candidate for the GGM. Our goal was to extend previous work by comparing the GGM and CFA using data from Wave 1 of the Patient Reported Outcomes Measurement Information System (PROMIS). Models were fit to PROMIS data based on 16 test forms designed to measure 9 mental and physical health domains. Our analyses borrowed a two-stage approach for handling missing data from the structural equation modeling literature. We found weaker correspondence between centrality indices and factor loadings than found by previous research, but in a similar pattern of correspondence. EGA recommended a factor structure discrepant with PROMIS domains in most cases yet may be taken to provide substantive insight into the dimensionality of PROMIS domains. In real mental and physical health data, theGGM and EGA may provide complementary information to traditional CFA metrics.

Development and Validation of Responses to Personal Mortality Measure

The aim of this study was to develop and validate the Responses to Personal Mortality Measure (RPMM), a new self-report tool to assess existential skills in relating to personal mortality. Items reflecting responses to personal mortality were generated and then selected through a rating process. They were further refined using Three-Step Test Interviews (TSTI). The psychometric properties of 91 items were explored with an online sample of 869 respondents, randomly divided into an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) subsamples. Through EFA, six factors were obtained: Emotional Response, Self-Oriented Acceptance, Transcendental Acceptance, Relational Acceptance, Relief Acceptance, and Avoidance. Internal consistency coefficients ranged between .90 and .97 ( M = .94). The six-factor model showed good fit statistics in traditional and bifactor CFAs. The construct and incremental validity were supported by relationships with the Psychological Well-Being Scale (PWB-42); the Meaning in Life Questionnaire (MLQ); the Depression, Anxiety and Stress Scale-21 (DASS-21); the Existential Death Anxiety Scale (EDAS); and the Existential Anxiety Questionnaire (EAQ). The RPMM is a self-report tool of responses to personal mortality that can be used to evaluate the relationship between existential skills and psychological wellbeing. Further research is needed to examine the psychometric properties of the RPMM in a clinical sample.

How do students’ achievement goals relate to learning from well-designed instructional videos and subsequent exam performance?

Well-designed instructional videos are powerful tools for helping students learn and prompting students to use generative strategies while learning from videos further bolsters their effectiveness. However, little is known about how individual differences in motivational factors, such as achievement goals, relate to how students learn within multimedia environments that include instructional videos and generative strategies. Therefore, in this study, we explored how achievement goals predicted undergraduate students’ behaviors when learning with instructional videos that required students to answer practice questions between videos, as well as how those activities predicted subsequent unit exam performance one week later. Additionally, we tested the best measurement models for modeling achievement goals between traditional confirmatory factor analysis and bifactor confirmatory factor analysis. The bifactor model fit our data best and was used for all subsequent analyses. Results indicated that stronger mastery goal endorsement predicted performance on the practice questions in the multimedia learning environment, which in turn positively predicted unit exam performance. In addition, students’ time spent watching videos positively predicted practice question performance. Taken together, this research emphasizes the availing role of adaptive motivations, like mastery goals, in learning from instructional videos that prompt the use of generative learning strategies.

Investigating the Applicability of Alignment-A Monte Carlo Simulation Study.

Traditional multiple-group confirmatory factor analysis (multiple-group CFA) is usually criticized for having too restrictive model assumption, namely the scalar measurement invariance. The new multiple-group analysis methodology, alignment, has become an effective alternative. The alignment evaluates measurement invariance and more importantly, permits factor mean comparisons without requiring scalar invariance which is usually required in traditional multiple-group CFA. Some simulation studies and empirical studies have investigated the applicability of alignment under different conditions, but some areas remain unexplored. Based on the simulation studies of Asparouhov and Muthén and of Flake and McCoach, this current simulation study is broken into two sections. The first study investigates the minimal group sizes required for alignment in three-factor models. The second study compares the performance of multiple-group CFA, multiple-group exploratory structural equation model (multiple-group ESEM), and alignment by including different proportions and magnitudes of cross-loadings in the models. Study 1 shows that when the model has no noninvariant parameters, the alignment requires relatively lower group sizes. Explicitly, the minimal group size required for alignment was 250 when the amount of groups was three, the minimal group size was 150 when the amount of groups was nine, and 200 when the amount of groups was 15. When there are noninvariant parameters in the model and the amount of groups is low, a group size of 350 is a safe rule of thumb. When there are noninvariant parameters in the model and the amount of groups is high, a group size of 250 is required for trustworthy results. The magnitude of noninvariance and the noninvariance rate do not affect the minimal group size required for alignment. Study 2 shows that multiple-group CFA provides accurate factor mean estimates when each factor had 20% factor loading (1 factor loading) with small-sized cross-loading. Multiple-group ESEM provides accurate factor mean estimates when the magnitude of cross-loading is small or when each factor had 20% factor loading (1 factor loading) with medium-sized cross-loading. Alignment provides accurate factor mean estimates when there are only small-sized cross-loadings in the model. The parameter estimates, coverage rates and ratios of average standard error to standard deviation for each methodology are not influenced by the amount of groups. Recommendations are concluded for using multiple-group CFA, multiple-group ESEM, traditional alignment and aligned ESEM (AESEM) based on the results. Multiple-group CFA is more suitable for use when scalar invariance is established. Multiple-group ESEM works best when there are small-sized or only a few medium-sized cross-loadings in the model. Traditional alignment allows for small-sized cross-loadings and a few noninvariant parameters in the model. AESEM integrates the advantages of alignment and ESEM, can provide accurate estimates when noninvariant parameters and cross-loadings both exist in the model. Compared to multiple-group CFA, multiple-group ESEM, the alignment methodology performs well in more situations.

One Common Factor, Four Resources, Both, or Neither: A Network Model of Career Adaptability Resources

Career adaptability is better described as interconnected resources than as manifestations of a common factor. Using a sample of 1053 responses to the Career Adapt-Abilities Scale, we compared traditional confirmatory factor analysis models (unidimensional, bifactor, hierarchical) with a confirmatory network model, which is found to outperform the others.

Identifying strategies to improve PANSS based dimensional models in schizophrenia: Accounting for multilevel structure, Bayesian model and clinical staging

BackgroundDimensional approaches can decompose a construct in a set of continuous variables, improving the characterization of complex phenotypes, such as schizophrenia. However, the five-factor model of the Positive and Negative Syndrome Scale (PANSS), the most used instrument in schizophrenia research, yielded poor fits in most confirmatory factor analysis (CFA) studies, raising concerns about its applications. Thus, we aimed to identify dimensional PANSS CFA models with good psychometric properties by comparing the traditional CFA with three methodological approaches: Bayesian CFA, multilevel modeling, and Multiple Indicators Multiple Causes (MIMIC) modeling. MethodsClinical data of 700 schizophrenia patients from four centers were analyzed. We first performed a traditional CFA. Next, we tested the three techniques: 1) a Bayesian CFA; 2) a multilevel analysis using the centers as level; and 3) a MIMIC modeling to evaluate the impact of clinical staging on PANSS factors and items. ResultsCFA and Bayesian CFA produced poor fit models. However, when adding a multilevel structure to the CFA model, a good fit model emerged. MIMIC modeling yielded significant differences in the factor structure between the clinical stages of schizophrenia. Sex, age, age of onset, and duration of illness did not significantly affect the model fit. ConclusionOur comparison of different CFA methods highlights the need for multilevel structure to achieve a good fit model and the potential utility of staging models (rather than the duration of illness) to deal with clinical heterogeneity in schizophrenia. Large prospective samples with biological data should help to understand the interplay between psychometrics concerns and neurobiology research.

Parental Attitudes Towards Inclusiveness Instrument (PATII): Psychometric evaluation of a new instrument measuring parental beliefs about gender and sexuality diversity inclusions in schools

Despite ad hoc claims that parents often are in opposition to a schooling curriculum that is inclusive of gender and sexuality diversity, there exists no research to date that has canvassed the reasons why parents may oppose or support such educational policy via a psychometrically sound instrument. The aim of the present study was to address this gap by developing and testing a new, multidimensional measure of the theorized nature of parental attitudes towards inclusiveness, the Parental Attitudes Towards Inclusiveness Instrument (PATII). The pilot sample of 998 parents who had a child attending school in any grade from Kindergarten to Year 12 were drawn from the United Kingdom (UK) and the United States (U.S.) via the online recruitment platform, Prolific. The PATII was evaluated for its reliability using McDonald's omega, construct and criterion validity, and measurement invariance utilizing exploratory structural equation modelling (ESEM), with initial ESEM analyses also compared to traditional confirmatory factor analysis (CFA) methods. Scores derived from this measure and inferences based upon those scores were reliable, valid, and also invariant across sex, religiosity, and nationality groups within this sample. Parental sex, religiosity, and nationality group membership were differentially correlated with support for and opposition to an inclusive curriculum. Lastly, the criterion validity of the PATII was supported, with the instrument's factors differentially correlated to parents' desired providers of inclusive education as predicted. Future national and international use of the PATII offers a critical first step to informing school and curriculum policy on inclusivity.

Evidence for a Bifactor Structure of the Caregiving Questionnaire with Individuals Involved in Different and Same-Sex Couple Relationships.

Despite the Caregiving Questionnaire (CQ) being a widely used measure for the study of caregiving behavior in the context of romantic relationships, to date, few studies have focused on empirically evaluating its underlying theoretical structure. The aim of this study was to examine the factorial structure and equivalence across sex and sexual orientation of this instrument. A sample of 912 Chilean individuals currently involved in a couple relationship completed the Caregiving Questionnaire and the Experiences in Close Relationship Scale. After comparing various traditional Confirmatory Factor Analysis (CFA)models, the results provide support for a multidimensional and hierarchical nature of a brief 16-items version of the CQ. More specifically, the analyses supported a bifactor-CFA solution composed of two global factors and four specific factors, suggesting that they add information to the caregiving construct in the context of couple relationships. Additionally, the scale showed measurement invariance across sex and sexual orientation. Finally, significant associations were found between CQ scores with measures of romantic attachment in the expected directions. Theoretical implications about the nature of the caregiving system are discussed.

Psychometric Properties of the Adult Self-Report: Data from over 11,000 American Adults

The first purpose of this study was to examine the factor structure of the Adult Self-Report (ASR) via traditional confirmatory factor analysis (CFA) and contemporary exploratory structural equation modeling (ESEM). The second purpose was to examine the measurement invariance of the ASR subscales across age groups. We used baseline data from the Adolescent Brain Cognitive Development study. ASR data from 11,773 participants were used to conduct the CFA and ESEM analyses and data from 11,678 participants were used to conduct measurement invariance testing. Fit indices supported both the CFA and ESEM solutions, with the ESEM solution yielding better fit indices. However, several items in the ESEM solution did not sufficiently load on their intended factors and/or cross-loaded on unintended factors. Results from the measurement invariance analysis suggested that the ASR subscales are robust and fully invariant across subgroups of adults formed on the basis of age (18–35 years vs. 36–59 years). Future research should seek to both CFA and ESEM to provide a more comprehensive assessment of the ASR.

Ant Colony Optimization and Local Weighted Structural Equation Modeling. A Tutorial on Novel Item and Person Sampling Procedures for Personality Research

Measurement in personality development faces many psychometric problems. First, theory–based measurement models do not fit the empirical data in terms of traditional confirmatory factor analysis. Second, measurement invariance across age, which is necessary for a meaningful interpretation of age–associated personality differences, is rarely accomplished. Finally, continuous moderator variables, such as age, are often artificially categorized. This categorization leads to bias when interpreting differences in personality across age. In this tutorial, we introduce methods to remedy these problems. We illustrate how Ant Colony Optimization can be used to sample indicators that meet prespecified demands such as model fit. Further, we use Local Structural Equation Modeling to resample and weight subjects to study differences in the measurement model across age as a continuous moderator variable. We also provide a detailed illustration for both tools with the Neuroticism scale of the openly available International Personality Item Pool – NEO inventory using data from the UK sample ( N = 15 827). Combined, both tools can remedy persistent problems in research on personality and its development. In addition to a step–by–step illustration, we provide commented syntax for both tools. © 2019 European Association of Personality Psychology

The Development and Validation of a New Measure of Adolescent Purpose

This goal of this study was to develop a quantitative measure that captures the dimensions of youth purpose (intention, engagement, and prosocial reasoning; Damon, Menon, and Bronk, 2003), focusing on students’ life goals and aspirations. We tested the construct validity of our new measure using bifactor exploratory structural equation modeling (B ESEM), assuming that the construct of youth purpose may be best measured as a G factor that allows items to freely cross-load in the model (Morin, Arens, & Marsh, 2016). The G factor model was better fitting than a traditional confirmatory factor analysis or ESEM. Additionally the G factor was correlated with aspects of students’ civic engagement and mastery components of academic achievement motivation. Suggestions for future research are discussed.

Psychometric evaluation of the Malay Filial Piety Scale (FPS-M) for adolescents in Malaysia

The 16-item Filial Piety Scale (FPS) has been used in different cultural groups to measure individuals’ loyalty to their family and traditions. Nevertheless, the language could be a barrier for Malay-speaking populations to fully comprehend the items. Thus, the present study translated the FPS into the Malay language (FPS-M) and examined psychometric qualities of the scale in a sample of 621 secondary school students in Malaysia, which was randomly split into an exploration sample and a validation sample. Traditional confirmatory factor analysis and exploratory structural equation modeling were applied to the exploration sample to identify the best-fit model. Results indicated that the hypothetical two-correlated-factor model was superior to the single-factor model. The model was then verified using the validation sample. The FPS-M reported good internal consistency and positive correlation with self-reported life satisfaction. However, the measurement invariance test revealed that the factor structure of the FPS-M was not equivalent across gender groups. In contrast, the factor loadings, but not intercepts, were found equivalent across the Chinese and Indian students. Overall, the results support that the FPS-M is a useful tool for measuring filial piety attitude in Malaysian adolescents. More studies are needed to identify cultural differences and modify the items to ensure that the FPS-M is applicable for all ethnicity groups.

What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups.

Scalar invariance is an unachievable ideal that in practice can only be approximated; often using potentially questionable approaches such as partial invariance based on a stepwise selection of parameter estimates with large modification indices. Study 1 demonstrates an extension of the power and flexibility of the alignment approach for comparing latent factor means in large-scale studies (30 OECD countries, 8 factors, 44 items, N = 249,840), for which scalar invariance is typically not supported in the traditional confirmatory factor analysis approach to measurement invariance (CFA-MI). Importantly, we introduce an alignment-within-CFA (AwC) approach, transforming alignment from a largely exploratory tool into a confirmatory tool, and enabling analyses that previously have not been possible with alignment (testing the invariance of uniquenesses and factor variances/covariances; multiple-group MIMIC models; contrasts on latent means) and structural equation models more generally. Specifically, it also allowed a comparison of gender differences in a 30-country MIMIC AwC (i.e., a SEM with gender as a covariate) and a 60-group AwC CFA (i.e., 30 countries × 2 genders) analysis. Study 2, a simulation study following up issues raised in Study 1, showed that latent means were more accurately estimated with alignment than with the scalar CFA-MI, and particularly with partial invariance scalar models based on the heavily criticized stepwise selection strategy. In summary, alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data. (PsycINFO Database Record

Introduction and validation of the Juror Decision Scale (JDS): An empirical investigation of the Story Model

PurposeTo develop and validate a self-report measure of individual juror decision making within criminal trials, based on theoretical features set out in the Story Model of juror decision making. MethodsThe Juror Decision Scale (JDS) and Acceptance of Modern Myths about Sexual Aggression (AMMSA) measure were completed by 324 jury-eligible participants split across 27 jury panels, after observing a rape trial re-enactment high in ecological validity. Dimensionality and construct validity of the JDS was investigated using traditional confirmatory factor analysis (CFA) techniques alongside confirmatory bifactor analysis at two time points (individual juror verdict decisions pre- and post-deliberation). Three competing models of the JDS were specified and tested using Mplus with maximum likelihood robust estimation. ResultsBifactor model with three meaningful factors (complainant believability, defendant believability, decision confidence) was the best fit for the data at both decision points. Good composite reliability and differential predictive validity were observed for the three JDS subscales. ConclusionAlongside demonstrating its multidimensional conceptualisation, the JDS development permits future empirical testing of the Story Model theoretical assertions surrounding juror decision making. Present findings also provide early evidence of a certainty principle assessment process governing individual verdict decision formation. Theoretical and practical applications are discussed.

Evidence for a Bifactor Structure of the Scales of Psychological Well-Being Using Exploratory Structural Equation Modeling

This research investigates the much-debated factor structure of the 54-item version of Ryff’s (1989) Scales of Psychological Well-being (SPWB). Using two samples (n1 = 573; n2 = 449) of undergraduate university students, we apply confirmatory factor analysis (CFA) along with recently developed exploratory structural equation modeling (ESEM) techniques to evaluate several unidimensional and multidimensional models identified in previous research, as well as a new bifactor model. In a bifactor model, items load directly on both a global and a specific factor; when tested using ESEM, cross-loadings on other specific factors are also permitted and are targeted to be as close to zero as possible. After comparing various ESEM and traditional CFA models, the results indicate that a bifactor model estimated using ESEM provided the best fit to the data. Most items were found to reflect the global factor, but some items failed to reflect the intended specific factor. Thus, the 54-item version of the SPWB appears to be a good measure of overall psychological well-being, but may need refinement as a measure of the intended specific factors, at least among young adults. The benefits of applying ESEM to investigate the factor structure of the SPWB in other populations are discussed.

Beyond factor analysis: Multidimensionality and the Parkinson's Disease Sleep Scale-Revised.

Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson’s disease (PD). The Parkinson’s Disease Sleep Scale (PDSS) and its variants (the Parkinson’s disease Sleep Scale-Revised; PDSS-R, and the Parkinson’s Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.

Exploring the Multidimensional Structure of Sensory Processing Sensitivity in Turkish Samples

We compared alternative models to explore the multidimensionality of the Highly Sensitive Person Scale (HSPS; Aron and Aron 1997) on two Turkish samples. Using exploratory factor analysis, first study (N = 412) yielded four factors representing diverse domains of sensory processing sensitivity (SPS). Using both traditional confirmatory factor analysis and exploratory structural equation modeling (ESEM), a second study (N = 341) confirmed the multidimensionality of the HSPS, and demonstrated that the four-factor ESEM solution (sensitivity to external stimuli, aesthetic sensitivity, harm avoidance, and sensitivity to overstimulation) was superior to the previously reported alternative models. Four factors were also systematically associated with the external validators including big five personality traits. Findings suggested that a multifaceted approach to SPS representing domain-specific sensitivities is needed.

Factor Structure of the Affective Style Questionnaire in Flemish Adolescents

Emotion regulation plays an important role in both healthy and problematic adolescent psychological functioning. Emotion regulation tendencies can be assessed with the Affective Style Questionnaire (ASQ; Hofmann & Kashdan, 2010), but its validity in Dutch speaking adolescents has not been investigated so far. Two methods, namely traditional confirmatory factor analysis (CFA) and the recently developed exploratory structural equations modeling (ESEM), were compared to examine the dimensional structure of the ASQ in a Flemish adolescent sample (N = 1,601). Although, as expected, the ESEM-model fit the data better than the CFA-model, the fit indices indicated that both models did not have an acceptable fit. With a shortened version of the ASQ, model fit improved substantially, but only the ESEM solution provided a good fit. The ESEM results support the use of the adapted ASQ to effectively assess the affective styles of concealing, adjusting and tolerating in Dutch-speaking adolescents.