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A sample suffers range restriction (RR) when its variance is reduced comparing with its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how this problem affects different outputs of the factor analysis: multivariate normality (MVN), estimation process, goodness-of-fit, recovery of factor loadings, and reliability. In doing so, a Monte Carlo study was conducted. Data were generated following the linear selective sampling model, simulating tests varying their sample size (N = 200 and 500 cases), test size (J = 6, 12, 18, and 24 items), loading size (L = 0.50, 0.70, and 0.90), and restriction size (from R = 1, 0.90, 0.80, and so on till 0.10 selection ratio). Our results systematically suggest that an interaction between decreasing the loading size and increasing the restriction size affects the MVN assessment, obstructs the estimation process, and leads to an underestimation of the factor loadings and reliability. However, most of the MVN tests and most of the fit indices employed were nonsensitive to the RR problem. We provide some recommendations to applied researchers.

Technology-facilitated sexual violence (TFSV) includes different forms of digital violence, such as online gender-based violence, online gender- and sexuality-based violence, digital sexual harassment, online sexual coercion, and nonconsensual pornography. The aim of this study was to design and validate a measure to assess the perpetration and victimization of each dimension of TFSV. The relationships between the different dimensions and differences by gender and sexual orientation were also analyzed. The participants were a sample of 2,486 adults (69% women) from Spain, aged between 16 and 79 (M = 25.95; DT = 9.809) years. The Technology-Facilitated Sexual Violence Scales were found to be valid and reliable instruments, supporting our recommendation for the use of these scales. Network analysis and solution-based exploratory factor analyses showed that the dimensions of online sexual coercion and nonconsensual pornography clustered together. All the perpetration variables were related to sexism. Finally, cis women and nonheterosexual people reported higher victimization scores overall compared to cis men and heterosexuals, respectively, while cis men reported higher perpetration scores overall than cis women. [ABSTRACT FROM AUTHOR]

The authors analyze and compare the factor convergence and predictive power of the Revised NEO Personality Inventory (NEO-PI-R) and the Zuckerman-Kuhlman-Aluja Personality Questionnaire (ZKA-PQ/SF) with respect to the Five-Factor Personality Inventory for ICD-11 (FFiCD). A total of 803 White Spanish subjects were analyzed. All the personality domains had significant predictive power with regard to the FFiCD except NEO Openness. The explained variance of the personality domains with respect to FFiCD Negative Affectivity (71% and 77%) and Detachment (56% and 56%) were similar for NEO-PI-R and ZKA-PQ/SF, respectively, but the NEO-PI-R accounted for greater variance for FFiCD Anankastia, Dissociality, and Disinhibition. The FFiCD facets of Rashness, Thrill- Seeking (Disinhibition), and Unassertiveness (Detachment) were located in factors other than those theoretically expected. The authors conclude that normal personality measured by the NEO-PI-R and the ZKA-PQ/SF contribute, in a differential but complementary way, to knowledge of the maladaptive personality measured by the FFiCD. [ABSTRACT FROM AUTHOR]

At present, there are two models of pathological personality represented in two measurement instruments, the Personality Inventory for DSM-5 (PID-5) and the Personality Inventory for ICD-11 (PiCD). Although both instruments have shown high convergence, the PID-5 has the advantage of including facets that might offer greater predictive capacity. An alternative to the PiCD has recently been proposed to overcome this drawback, namely the Five-Factor Personality Inventory for ICD-11 (FFiCD). The present study aims to explore its internal structure and to offer additional validity evidence. Data were collected for a total of 1,409 community individuals from two populations in Spain. They responded to the FFiCD, PiCD, the PID-5/SF, and measures of personality functioning and personality disorder screening (Level of Personality Functioning Scale Self-Report [LPFS-SR] and International Personality Disorder Examination [IPDE]). The internal structure of the FFiCD was analyzed through exploratory factor analysis with oblique and bifactor rotations. The predictive capacity of the domains and facets was examined. The structure reported in the original study was replicated, as were the convergent validity data with respect to the PID-5/SF and the PiCD. Facets were grouped into four factors corresponding to the theoretical domains, including a bipolar Anankastia/Disinhibition factor. High correlations were found with the LPFS-SR, and the patterns of relationships with the IPDE shed light on which aspects of pathological personality contribute most to the 10 traditional personality disorders. In general, validity evidence is provided to support the use of the FFiCD, though four of the 20 facets merit revision so that a simpler factor structure can be obtained. Public Significance Statement: The present study investigated the internal structure of the FFiCD in a large sample and sought to provide additional validity evidence using the PiCD, the PID-5/SF, LPFS-SR, and a measure for screening personality disorders (IPDE). The results support the use of the FFiCD, although four of the 20 facets could be revised for enhanced performance. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis models (CDMs) allow classifying respondents into a set of discrete attribute profiles. The internal structure of the test is determined in a Q-matrix, whose correct specification is necessary to achieve an accurate attribute profile classification. Several empirical Q-matrix estimation and validation methods have been proposed with the aim of providing well-specified Q-matrices. However, these methods require the number of attributes to be set in advance. No systematic studies about CDMs dimensionality assessment have been conducted, which contrasts with the vast existing literature for the factor analysis framework. To address this gap, the present study evaluates the performance of several dimensionality assessment methods from the factor analysis literature in determining the number of attributes in the context of CDMs. The explored methods were parallel analysis, minimum average partial, very simple structure, DETECT, empirical Kaiser criterion, exploratory graph analysis, and a machine learning factor forest model. Additionally, a model comparison approach was considered, which consists in comparing the model-fit of empirically estimated Q-matrices. The performance of these methods was assessed by means of a comprehensive simulation study that included different generating number of attributes, item qualities, sample sizes, ratios of the number of items to attribute, correlations among the attributes, attributes thresholds, and generating CDM. Results showed that parallel analysis (with Pearson correlations and mean eigenvalue criterion), factor forest model, and model comparison (with AIC) are suitable alternatives to determine the number of attributes in CDM applications, with an overall percentage of correct estimates above 76% of the conditions. The accuracy increased to 97% when these three methods agreed on the number of attributes. In short, the present study supports the use of three methods in assessing the dimensionality of CDMs. This will allow to test the assumption of correct dimensionality present in the Q-matrix estimation and validation methods, as well as to gather evidence of validity to support the use of the scores obtained with these models. The findings of this study are illustrated using real data from an intelligence test to provide guidelines for assessing the dimensionality of CDM data in applied settings. [ABSTRACT FROM AUTHOR]

Background: Need for affect (NA) refers to individual differences in the motivation to approach or avoid emotion-inducing situations and activities. Prior research has demonstrated that NA is a relevant construct for understanding psychological processes related to affect. The present study aimed to adapt and validate the English version of the Need for Affect Questionnaire (NAQ), as well as the short version (i.e., the NAQ-S), to Spanish. We examined evidence of validity and reliability in the NAQ and NAQ-S scores. Method: Study 1 (N = 416) analyzed the psychometric properties of scores from the Spanish adaptation of the NAQ, as well as the relationships with measures of other relevant psychological constructs. Study 2 (N = 118) tested the validity and reliability of scores from the Spanish adaptation of the NAQ-S. Results: We found two main factors (i.e., Emotion Approach and Emotion Avoidance), and the internal consistency of both the NAQ and the NAQ-S scores, as well as their test-retest reliability, were adequate. Relationships with other measures were in line with prior research, providing positive evidence of concurrent validity. Conclusions: Scores from both instruments showed validity and reliability, so should be considered in future research related to NA. [ABSTRACT FROM AUTHOR]