Rethinking the Measurement of Adversity

Research on adverse childhood experiences (ACEs) has unified the study of interrelated risks and generated insights into the origins of disorder and disease. Ten indicators of child maltreatment and household dysfunction are widely accepted as ACEs, but further progress requires a more systematic approach to conceptualizing and measuring ACEs. Using data from a diverse, low-income sample of women who received home visiting services in Wisconsin ( N = 1,241), this study assessed the prevalence of and interrelations among 10 conventional ACEs and 7 potential ACEs: family financial problems, food insecurity, homelessness, parental absence, parent/sibling death, bullying, and violent crime. Associations between ACEs and two outcomes, perceived stress and smoking, were examined. The factor structure and test-retest reliability of ACEs was also explored. As expected, prevalence rates were high compared to studies of more representative samples. Except for parent/sibling death, all ACEs were intercorrelated and associated at the bivariate level with perceived stress and smoking. Exploratory factor analysis confirmed that conventional ACEs loaded on two factors, child maltreatment and household dysfunction, though a more complex four-factor solution emerged once new ACEs were introduced. All ACEs demonstrated acceptable test-retest reliability. Implications and future directions toward a second generation of ACE research are discussed.

The Daily Heterosexist Experiences Questionnaire: Measuring Minority Stress Among Lesbian, Gay, Bisexual, and Transgender Adults

The authors conducted a three-phase, mixed-methods study to develop a self-report measure assessing the unique aspects of minority stress for lesbian, gay, bisexual, and transgender adults. The Daily Heterosexist Experiences Questionnaire has 50 items and nine subscales with acceptable internal reliability, and construct and concurrent validity. Mean sexual orientation and gender differences were found.

A Short Boredom Proneness Scale

It has been evident for some time that the Boredom Proneness Scale (BPS), a commonly used measure of trait boredom, does not constitute a single scale. Factor analytic studies have identified anything from two to seven factors, prompting Vodanovich and colleagues to propose an alternative two factor, short form version Boredom Proneness Scale-Short Form (BPS-SR). The present study further investigates the factor structure and validity of both the BPS and the BPS-SR. The two-factor solution obtained for the BPS-SR appears to be an artifact of item wording of reverse-scored items. These same items may also have contributed to the earlier complexity and inconsistency of results for the full BPS. An eight-item scale of only consistently worded items (i.e., those not requiring reverse scoring) was developed. This new scale demonstrated unidimensionality and the scale score had good internal consistency and construct validity comparable to the original BPS score.

Latent structure of facets of alcohol reinforcement from a behavioral economic demand curve

RATIONALE

Behavioral economic demand curves are quantitative representations of the relationship between consumption of a drug and its cost. Demand curves provide a multidimensional assessment of reinforcement, but the relationships among the various indices of reinforcement have been largely unstudied.

OBJECTIVES

The objective of the study is to use exploratory factor analysis to examine the underlying factor structure of the facets of alcohol reinforcement generated from an alcohol demand curve.

MATERIALS AND METHODS

Participants were 267 weekly drinkers [76% female; age M = 20.11 (SD = .1.51); drinks/week M = 14.33 (SD = 11.82)] who underwent a single group assessment session. Alcohol demand curves were generated via an alcohol purchase task, which assessed consumption at 14 levels of prices from $0 to $9. Five facets of demand were generated from the measure [intensity, elasticity, P (max) (maximum inelastic price), O (max) (maximum alcohol expenditure), and breakpoint], using both observed and derived calculations. Principal components analysis was used to examine the latent structure among the variables.

RESULTS

The results revealed a clear two-factor solution, which were interpreted as "Persistence," reflecting sensitivity to escalating price, and "Amplitude," reflecting the amount consumed and spent. The two factors were generally quantitatively distinct, although O (max) loaded on both.

CONCLUSIONS

These findings suggest that alcohol reinforcement as measured via a demand curve is binary in nature, with separate dimensions of price-sensitivity and volumetric consumption. If supported, these findings may contribute theoretically and experimentally to a reinforcement-based approach to alcohol use and misuse.

Exploratory factor analysis of the Oral Health Impact Profile

Although oral health-related quality of life (OHRQoL) as measured by the Oral Health Impact Profile (OHIP) is thought to be multidimensional, the nature of these dimensions is not known. The aim of this report was to explore the dimensionality of the OHIP using the Dimensions of OHRQoL (DOQ) Project, an international study of general population subjects and prosthodontic patients. Using the project's Learning Sample (n = 5173), we conducted an exploratory factor analysis on the 46 OHIP items not specifically referring to dentures for 5146 subjects with sufficiently complete data. The first eigenvalue (27·0) of the polychoric correlation matrix was more than ten times larger than the second eigenvalue (2·6), suggesting the presence of a dominant, higher-order general factor. Follow-up analyses with Horn's parallel analysis revealed a viable second-order, four-factor solution. An oblique rotation of this solution revealed four highly correlated factors that we named Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact. These four dimensions and the strong general factor are two viable hypotheses for the factor structure of the OHIP.

Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables.

Cross-cultural Validation of the 5-Factor Structure of Negative Symptoms in Schizophrenia

OBJECTIVE

Negative symptoms are currently viewed as having a 2-dimensional structure, with factors reflecting diminished expression (EXP) and motivation and pleasure (MAP). However, several factor-analytic studies suggest that the consensus around a 2-dimensional model is premature. The current study investigated and cross-culturally validated the factorial structure of BNSS-rated negative symptoms across a range of cultures and languages.

METHOD

Participants included individuals diagnosed with a psychotic disorder who had been rated on the Brief Negative Symptom Scale (BNSS) from 5 cross-cultural samples, with a total N = 1691. First, exploratory factor analysis was used to extract up to 6 factors from the data. Next, confirmatory factor analysis evaluated the fit of 5 models: (1) a 1-factor model, 2) a 2-factor model with factors of MAP and EXP, 3) a 3-factor model with inner world, external, and alogia factors; 4) a 5-factor model with separate factors for blunted affect, alogia, anhedonia, avolition, and asociality, and 5) a hierarchical model with 2 second-order factors reflecting EXP and MAP, as well as 5 first-order factors reflecting the 5 aforementioned domains.

RESULTS

Models with 4 factors or less were mediocre fits to the data. The 5-factor, 6-factor, and the hierarchical second-order 5-factor models provided excellent fit with an edge to the 5-factor model. The 5-factor structure demonstrated invariance across study samples.

CONCLUSIONS

Findings support the validity of the 5-factor structure of BNSS-rated negative symptoms across diverse cultures and languages. These findings have important implications for the diagnosis, assessment, and treatment of negative symptoms.

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.

Measuring how people view biomedical research: Reliability and validity analysis of the Research Attitudes Questionnaire

With increasing numbers of studies on research ethics and a need to improve the recruitment of research subjects, the ability to measure attitudes toward biomedical research has become important. The Research Attitudes Questionnaire is a significant predictor of the public's attitudes toward and willingness to participate in research, yet limited data are available on its psychometric properties. This study establishes the scale's internal consistency and dimensionality using a large Internet-based sample from the United States. One item was removed due to a poor item-total correlation, and three additional items were removed which formed a reverse-wording measurement artifact factor. With improved internal consistency and dimensionality, the seven-item version has the advantages of shorter administration time and improved psychometric properties.

Measuring enfranchisement: importance of and control over participation by people with disabilities

OBJECTIVE

To evaluate the psychometric properties and validity of an expanded set of community enfranchisement items that are suitable for computer adaptive testing.

DESIGN

Survey.

SETTING

Community setting.

PARTICIPANTS

Individuals with disabilities (N=1163) were recruited from an online panel generation company (51%), former rehabilitation inpatients (18%), disability community organizations (13%), a registry of rehabilitation patients (10%), and Traumatic Brain Injury and Spinal Cord Injury Model System facilities (8%). Inclusion criteria were a self-identified disability, aged ≥18 years, and the ability to read and speak English.

INTERVENTIONS

None.

MAIN OUTCOME MEASURE

Community enfranchisement.

RESULTS

Exploratory and confirmatory factor analyses of the 48 enfranchisement items suggested 2 distinct subsets of items: (1) importance of participation and (2) control over participation. Principal components analysis of the residuals suggested that the 2 item sets are unidimensional. Rating scale analysis provided evidence that the 2 item sets fit the Rasch model. Importance and control were moderately correlated with each other and with disability severity.

CONCLUSIONS

Importance of participation and control over participation define 2 distinct sets of participation enfranchisement. Preliminary evidence supports their validity.

Examining the factor structure of the Connor-Davidson Resilience Scale (CD-RISC) in a post-9/11 U.S. military veteran sample

The present study examined the structural validity of the 25-item Connor-Davidson Resilience Scale (CD-RISC) in a large sample of U.S. veterans with military service since September 11, 2001. Participants (N = 1,981) completed the 25-item CD-RISC, a structured clinical interview and a self-report questionnaire assessing psychiatric symptoms. The study sample was randomly divided into two subsamples: an initial sample (Sample 1: n = 990) and a replication sample (Sample 2: n = 991). Findings derived from exploratory factor analysis (EFA) did not support the five-factor analytic structure as initially suggested in Connor and Davidson's instrument validation study. Although parallel analyses indicated a two-factor structural model, we tested one to six factor solutions for best model fit using confirmatory factor analysis. Results supported a two-factor model of resilience, composed of adaptability- (8 items) and self-efficacy-themed (6 items) items; however, only the adaptability-themed factor was found to be consistent with our view of resilience-a factor of protection against the development of psychopathology following trauma exposure. The adaptability-themed factor may be a useful measure of resilience for post-9/11 U.S. military veterans.

Measuring Women's Empowerment in Sub-Saharan Africa: Exploratory and Confirmatory Factor Analyses of the Demographic and Health Surveys

Background: Women's status and empowerment influence health, nutrition, and socioeconomic status of women and their children. Despite its benefits, however, research on women's empowerment in Sub-Saharan Africa (SSA) is limited in scope and geography. Empowerment is variably defined and data for comparison across regions is often limited. The objective of the current study was to identify domains of empowerment from a widely available data source, Demographic and Health Surveys, across multiple regions in SSA.

Methods: Demographic and Health Surveys from nineteen countries representing four African regions were used for the analysis. A total of 26 indicators across different dimensions (economic, socio-cultural, education, and health) were used to characterize women's empowerment. Pooled data from all countries were randomly divided into two datasets—one for exploratory factor analysis (EFA) and the other for Confirmatory Factor Analysis (CFA)—to verify the factor structure hypothesized during EFA.

Results: Four factors including attitudes toward violence, labor force participation, education, and access to healthcare were found to define women's empowerment in Central, Southern, and West Africa. However, in East Africa, only three factors were relevant: attitudes toward violence, access to healthcare ranking, and labor force participation. There was limited evidence to support household decision-making, life course, or legal status domains as components of women's empowerment.

Conclusion: This foremost study advances scholarship on women's empowerment by providing a validated measure of women's empowerment for researchers and other stakeholders in health and development.

SOLOMON: a method for splitting a sample into equivalent subsamples in factor analysis

Nowadays, exploratory and confirmatory factor analyses are two important consecutive steps in an overall analysis process. The overall analysis should start with an exploratory factor analysis that explores the data and establishes a hypothesis for the factor model in the population. Then, the analysis process should be continued with a confirmatory factor analysis to assess whether the hypothesis proposed in the exploratory step is plausible in the population. To carry out the analysis, researchers usually collect a single sample, and then split it into two halves. As no specific splitting methods have been proposed to date in the context of factor analysis, researchers use a random split approach. In this paper we propose a method to split samples into equivalent subsamples similar to one that has already been proposed in the context of multivariate regression analysis. The method was tested in simulation studies and in real datasets.

New astroglial injury-defined biomarkers for neurotrauma assessment

Traumatic brain injury (TBI) is an expanding public health epidemic with pathophysiology that is difficult to diagnose and thus treat. TBI biomarkers should assess patients across severities and reveal pathophysiology, but currently, their kinetics and specificity are unclear. No single ideal TBI biomarker exists. We identified new candidates from a TBI CSF proteome by selecting trauma-released, astrocyte-enriched proteins including aldolase C (ALDOC), its 38kD breakdown product (BDP), brain lipid binding protein (BLBP), astrocytic phosphoprotein (PEA15), glutamine synthetase (GS) and new 18-25kD-GFAP-BDPs. Their levels increased over four orders of magnitude in severe TBI CSF. First post-injury week, ALDOC levels were markedly high and stable. Short-lived BLBP and PEA15 related to injury progression. ALDOC, BLBP and PEA15 appeared hyper-acutely and were similarly robust in severe and mild TBI blood; 25kD-GFAP-BDP appeared overnight after TBI and was rarely present after mild TBI. Using a human culture trauma model, we investigated biomarker kinetics. Wounded (mechanoporated) astrocytes released ALDOC, BLBP and PEA15 acutely. Delayed cell death corresponded with GFAP release and proteolysis into small GFAP-BDPs. Associating biomarkers with cellular injury stages produced astroglial injury-defined (AID) biomarkers that facilitate TBI assessment, as neurological deficits are rooted not only in death of CNS cells, but also in their functional compromise.

Stability of Symptom Clusters in Patients With Lung Cancer Receiving Chemotherapy

CONTEXT

Patients with lung cancer who undergo chemotherapy (CTX) experience multiple symptoms. Evaluation of how these symptoms cluster together and how these symptom clusters change over time are salient questions in symptom clusters research.

OBJECTIVES

The purposes of this analysis, in a sample of patients with lung cancer (n = 145) who were receiving CTX, were to 1) evaluate for differences in the number and types of symptom clusters at three time points (i.e., before their next cycle of CTX, the week after CTX, and two weeks after CTX) using ratings of symptom occurrence and severity and 2) evaluate for changes in these symptom clusters over time.

METHODS

At each assessment, a modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence and severity of 38 symptoms. Exploratory factor analyses were used to extract the symptom clusters.

RESULTS

Across the two symptom dimensions (i.e., occurrence and severity) and the three assessments, six distinct symptom clusters were identified; however, only three of these clusters (i.e., lung cancer specific, psychological, nutritional) were relatively stable across both dimensions and across time. Two additional clusters varied by time but not by symptom dimension (i.e., epithelial/gastrointestinal and epithelial). A sickness behavior cluster was identified at each assessment with the exception of the week before CTX using only the severity dimension.

CONCLUSION

Findings provide insights into the most common symptom clusters in patients with lung cancer undergoing CTX. Most common symptoms within each cluster appear to be relatively stable across the two dimensions, as well as across time.

The structure of mental disorders re-examined: is it developmentally stable and robust against additions?

Previous research suggests that patterns of comorbidity of a limited number of anxiety, depressive, substance use and antisocial personality (ASP) disorders among adults are best reflected by a hierarchical three-factor structure with two correlated factors ('anxious-misery' and 'fear') that are summarized in a second-order 'internalizing' factor and one 'externalizing' factor. It has not been examined whether this structure is developmentally stable and robust against additions of more diagnoses. Using data from a prospective-longitudinal community study of adolescents and young adults we re-evaluate the three-factor model originally proposed by Krueger (Archives of General Psychiatry, 1999; 56, 921-926). Using confirmatory factor analysis with identical conventions as in Krueger's original work we found that the three factor model did not fit robustly across age or a wider range of diagnoses. Using explanatory factor analysis we examined alternative structures. We found various clinically meaningful patterns with good fit that go substantially beyond the original three-factor structure. However, again, there is little consistency in findings when different age groups or different diagnoses are considered. Our findings suggest that psychopathology cannot be reduced to any simple structure.

Translation and Validation of the Nomophobia Questionnaire in the Italian Language: Exploratory Factor Analysis

BACKGROUND

Nomophobia, which is a neologism derived from the combination of "no mobile," "phone," and "phobia" is considered to be a modern situational phobia and indicates a fear of feeling disconnected.

OBJECTIVE

No psychometric scales are available in Italian for investigating such a construct. We therefore planned a translation and validation study of the Nomophobia Questionnaire (NMP-Q), which is an instrument developed by Yildirim and Correia. Subjects were recruited via an online survey using a snowball approach.

METHODS

The NMP-Q was translated from English into Italian using a classical "backwards and forwards" procedure. In order to explore the underlying factor structure of the translated questionnaire, an exploratory factor analysis was carried out. A principal component analysis approach with varimax rotation was performed. Multivariate regression analyses were computed to shed light on the psychological predictors of nomophobia.

RESULTS

A sample of 403 subjects volunteered to take part in the study. The average age of participants was 27.91 years (standard deviation 8.63) and the sample was comprised of 160 males (160/403, 39.7%) and 243 females (243/403, 60.3%). Forty-five subjects spent less than 1 hour on their mobile phone per day (45/403, 11.2%), 94 spent between 1 and 2 hours (94/403, 23.3%), 69 spent between 2 and 3 hours (69/403, 17.1%), 58 spent between 3 and 4 hours (58/403, 14.4%), 48 spent between 4 and 5 hours (48/403, 11.9%), 29 spent between 5 and 7 hours (29/403, 7.2%), 36 spent between 7 and 9 hours (36/403, 8.9%), and 24 spent more than 10 hours (24/403, 6.0%). The eigenvalues and scree plot supported a 3-factorial nature of the translated questionnaire. The NMP-Q showed an overall Cronbach alpha coefficient of 0.95 (0.94, 0.89, and 0.88 for the three factors). The first factor explained up to 23.32% of the total variance, while the second and third factors explained up to 23.91% and 18.67% of the variance, respectively. The total NMP-Q score correlated with the number of hours spent on a mobile phone.

CONCLUSIONS

The Italian version of the NMP-Q proved to be reliable.

Psychotic-Like Experiences at the Healthy End of the Psychosis Continuum

There is increasing evidence pointing toward a continuous distribution of psychotic symptoms and accompanying factors between subclinical and clinical populations. However, for the construction of continuum models, a more detailed knowledge of different types of psychotic-like experiences (PLE) and their associations with distress, functional impairment, and demographic variables is needed. We investigated PLE in a sample of healthy adults (N = 206) incorporating the recently developed revised Exceptional Experiences Questionnaire (PAGE-R). For the first time, the PAGE-R was cross validated with PLE, disorganized-, and negative-like symptoms [Schizotypal Personality Questionnaire (SPQ), Physical Anhedonia Scale (PAS)]. We subjected the PAGE-R to exploratory factor analyses and examined the resulting subtypes of EE for specific associations with contextual factors, valence ratings, socio-demographic variables, and general psychological burden (Revised Symptom-Checklist-90). Correlational cross-validation suggested that the PAGE-R measures facets of PLE. Importantly, we (1) identified three types of exceptional experiences (EE): Odd beliefs, dissociative anomalous perceptions, and hallucinatory anomalous perceptions. Further, the results suggested that even in healthy individuals (2) PLE and EE are indicative of reduced functioning, as reflected by increased psychological burden and lower educational achievement. Moreover, (3) similar sex-differences might exist as in psychotic patients with women reporting more positive-like symptoms and EE but less disorganized-like symptoms than men. Importantly, (4) EE might be differentially implicated in psychological functioning. We suggest that the PAGE-R holds the potential to complement the current assessment of sub-clinical psychosis. However, whereas our results might point toward a continuity of psychotic symptoms with EE and normal experiences, they require replication in larger samples as well as equivalence testing across the psychosis continuum. Future analyses incorporating the PAGE-R might shed more light onto mechanisms that are implicated in the progress or resilience toward clinical illness.

Remodelling of biological parameters during human ageing: evidence for complex regulation in longevity and in type 2 diabetes

Factor structure analyses have revealed the presence of specific biological system markers in healthy humans and diseases. However, this type of approach in very old persons and in type 2 diabetes (T2DM) is lacking. A total sample of 2,137 Italians consisted of two groups: 1,604 healthy and 533 with T2DM. Age (years) was categorized as adults (≤65), old (66-85), oldest old (>85-98) and centenarians (≥99). Specific biomarkers of routine haematological and biochemical testing were tested across each age group. Exploratory factorial analysis (EFA) by principal component method with Varimax rotation was used to identify factors including related variables. Structural equation modelling (SEM) was applied to confirm factor solutions for each age group. EFA and SEM identified specific factor structures according to age in both groups. An age-associated reduction of factor structure was observed from adults to oldest old in the healthy group (explained variance 60.4% vs 50.3%) and from adults to old in the T2DM group (explained variance 57.4% vs 44.2%). Centenarians showed three-factor structure similar to those of adults (explained variance 58.4%). The inflammatory component became the major factor in old group and was the first one in T2DM. SEM analysis in healthy subjects suggested that the glucose levels had an important role in the oldest old. Factorial structure change during healthy ageing was associated with a decrease in complexity but showed an increase in variability and inflammation. Structural relationship changes observed in healthy subjects appeared earlier in diabetic patients and later in centenarians.

Social Reward Questionnaire (SRQ): development and validation

Human beings seek out social interactions as a source of reward. To date, there have been limited attempts to identify different forms of social reward, and little is known about how the value of social rewards might vary between individuals. This study aimed to address both these issues by developing the Social Reward Questionnaire (SRQ), a measure of individual differences in the value of different social rewards. Exploratory factor analysis (EFA) was run on an initial set of 75 items (N = 305). Based on this analysis, confirmatory factor analysis (CFA) was then conducted on a second sample (N = 505) with a refined 23-item scale. This analysis was used to test a six-factor structure, which resulted in good model fit (CFI = 0.96, RSMEA = 0.07). The factors represent six subscales of social reward defined as follows: Admiration; Negative Social Potency; Passivity; Prosocial Interactions; Sexual Reward; and Sociability. All subscales demonstrated good test-retest reliability and internal consistency. Each subscale also showed a distinct pattern of associations with external correlates measuring personality traits, attitudes, and goals, thus demonstrating construct validity. Taken together, the findings suggest that the SRQ is a reliable, valid measure that can be used to assess individual differences in the value experienced from different social rewards.

Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

Excitability and irritability in preschoolers predicts later psychopathology: The importance of positive and negative emotion dysregulation

Emotion dysregulation is a risk factor for the development of a variety of psychopathologic outcomes. In children, irritability, or dysregulated negative affect, has been the primary focus, as it predicts later negative outcomes even in very young children. However, dysregulation of positive emotion is increasingly recognized as a contributor to psychopathology. Here we used an exploratory factor analysis and defined four factors of emotion dysregulation: irritability, excitability, sadness, and anhedonia, in the preschool-age psychiatric assessment collected in a sample of 302 children ages 3-5 years enriched for early onset depression. The irritability and excitability factor scores defined in preschoolers predicted later diagnosis of mood and externalizing disorders when controlling for other factor scores, social adversity, maternal history of mood disorders, and externalizing diagnoses at baseline. The preschool excitability factor score predicted emotion lability in late childhood and early adolescence when controlling for other factor scores, social adversity, and maternal history. Both excitability and irritability factor scores in preschoolers predicted global functioning into the teen years and early adolescence, respectively. These findings underscore the importance of positive, as well as negative, affect dysregulation as early as the preschool years in predicting later psychopathology, which deserves both further study and clinical consideration.

The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.

Refinement and Validation of the Student Perceptions of Physician-Pharmacist Interprofessional Clinical Education Instrument

Objective. To refine the Student Perceptions of Physician-Pharmacist Interprofessional Clinical Education (SPICE) instrument to address deficiencies observed in previous studies and to demonstrate external validity and reliability of the refined instrument in a broad population of medical and pharmacy students. Methods. The original SPICE instrument plus four pilot items generated via cognitive interviewing of students was administered to 1708 medical and pharmacy students at five academic institutions. Exploratory factor analysis was used to identify candidate model structures and evaluate their psychometric properties. Results. An improved version of the instrument was created (SPICE 2) by incorporating three pilot items and removing three original items. Validity and reliability were demonstrated. Conclusion. The SPICE 2 instrument addresses the limitations observed to date in model structure while increasing its utility. The authors recommend use of the SPICE 2 instrument moving forward.

The Arabic Version of the Cohen Perceived Stress Scale: Factorial Validity and Measurement Invariance

University students experience high levels of stress due to university transition, academic commitments, and financial matters. Higher stress perceptions along with limited coping resources endanger mental health for a considerable number of students and may ruin their performance. The current study evaluated the psychometric properties of the Cohen Perceived Stress Scale (10 items), PSS-10, in a sample of 379 female Emeriti students. Exploratory factor analysis resulted in two factors with eigenvalues of 3.88 and 1.19, which explained 60.6% of the variance. Confirmatory factor analysis revealed good model fits of two correlated factors (Comparative Fit Index (CFI) = 0.962, Tucker–Lewis Index (TLI) = 0.950, standardized root-mean-square residual (SRMR) = 0.0479, and root mean-square error of approximation (RMSEA) = 0.067). Internal consistency of the PSS-10 and its positive and negative subscales was acceptable (coefficient α = 0.67, 0.79, and 0.86, respectively). Multigroup analysis revealed that the PSS-10 holds invariance across different groups of age, marital status, and financial status (average monthly expenditure). Convergent and concurrent validity tests signify the importance of considering scores of subscales of the PSS-10 along with its total score.