Construct validity of global life-satisfaction judgments: A look into the black box of self–informant agreement

Life-satisfaction judgments are ubiquitously used as indicators of wellbeing. The construct validity of these judgments relies heavily on self–informant agreement. However, agreement is necessary, but not sufficient to claim that life-satisfaction judgments are valid. In addition, self–informant agreement should be based on the use of valid information about satisfaction with important life domains. An alternative possibility is that agreement is based on impressions about personality traits. We tested these two hypotheses in a model that predicted self-ratings and informant ratings of life satisfaction from shared perceptions of personality and satisfaction with life domains. In a round-robin study of families, we found that life-domains were the key predictor of self–informant agreement. However, the Depressiveness facet of Neuroticism had a small direct effect. In addition, it was indirectly related to self–informant agreement because it predicts lower satisfaction in important life-domains that were used to form life-satisfaction judgments.     

Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform Information

The standard error (SE) stopping rule, which terminates a computer adaptive test (CAT) when the SE is less than a threshold, is effective when there are informative questions for all trait levels. However, in domains such as patient-reported outcomes, the items in a bank might all target one end of the trait continuum (e.g., negative symptoms), and the bank may lack depth for many individuals. In such cases, the predicted standard error reduction (PSER) stopping rule will stop the CAT even if the SE threshold has not been reached and can avoid administering excessive questions that provide little additional information. By tuning the parameters of the PSER algorithm, a practitioner can specify a desired tradeoff between accuracy and efficiency. Using simulated data for the Patient-Reported Outcomes Measurement Information System Anxiety and Physical Function banks, we demonstrate that these parameters can substantially impact CAT performance. When the parameters were optimally tuned, the PSER stopping rule was found to outperform the SE stopping rule overall, particularly for individuals not targeted by the bank, and presented roughly the same number of items across the trait continuum. Therefore, the PSER stopping rule provides an effective method for balancing the precision and efficiency of a CAT.     

‘Grandpa,Do You Like Roller Coasters?’: Identifying Age‐Appropriate Personality Indicators

Personality development research heavily relies on the comparison of scale means across age. This approach implicitly assumes that the scales are strictly measurement invariant across age. We questioned this assumption by examining whether appropriate personality indicators change over the lifespan. Moreover, we identified which types of items (e.g. dispositions, behaviours, and interests) are particularly prone to age effects. We reanalyzed the German Revised NEO Personality Inventory normative sample (N = 11,724) and applied a genetic algorithm to select short scales that yield acceptable model fit and reliability across locally weighted samples ranging from 16 to 66 years of age. We then examined how the item selection changes across age points and item types. Emotion‐type items seemed to be interchangeable and generally applicable to people of all ages. Specific interests, attitudes, and social effect items—most prevalent within the domains of Extraversion, Agreeableness, and Openness—seemed to be more prone to measurement variations over age. A large proportion of items were systematically discarded by the item‐selection procedure, indicating that, independent of age, many items are problematic measures of the underlying traits. The implications for personality assessment and personality development research are discussed. © 2019 European Association of Personality Psychology     

Factor structure of the CNS Vital Signs computerized cognitive battery in youth with neurological diagnoses

Computerized cognitive batteries, such as CNS Vital Signs (CNSVS), can provide valuable information in clinical and research settings. However, psychometric properties, especially in children and adolescents, remain relatively understudied. The aim of this study was to investigate the factor structure of CNSVS in children and adolescents with neurological diagnoses.

Participants with neurological diagnoses (N = 280) age 7–19 years were assessed as part of their clinical care at a tertiary hospital. All participants received the full CNSVS computerized cognitive battery, which contains seven subtests designed to measure attention, executive functioning, psychomotor speed, and memory. Principal components analyses were used to examine factor structure.

Scores from CNSVS subtests loaded onto a three-component solution and accounted for 46% of the variance. The three components were deemed to best represent (1) speed, (2) memory, and (3) inhibition, with subtest scores loading differently than the original 11 primary and secondary domain scores would have suggested.

Although the CNSVS program generates numerous primary and secondary domain scores, a three-component solution represents a more parsimonious approach to interpreting performance on the CNSVS in youth with neurological diagnoses. Confirmation of this factor solution in other samples is warranted.     

A teleonomic explanatory scheme

Abstract

The paper takes the form of a dialogue between an advocate of conventional causal modelling (A) and an advocate of an expanded conception of forecasting modelling that unifies causal and teleonomic explanations (B).