scholarly journals Just a Mirage: On the Incremental Predictive Validity of Subjective Age

2018 ◽  
Vol 5 (2) ◽  
pp. 141-162 ◽  
Author(s):  
Hannes Zacher ◽  
Cort W Rudolph
Keyword(s):  
Predictive Validity ◽  
Subjective Age ◽  
Incremental Predictive Validity

The Role of Personality in Predicting (Change in) Students’ Academic Success Across Four Years of Secondary School

2016 ◽  
Vol 32 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Marion Spengler ◽  
Martin Brunner ◽  
Romain Martin ◽  
Oliver Lüdtke
Keyword(s):  
Academic Success ◽  
Predictive Validity ◽  
Big Five ◽  
Educational Outcomes ◽  
International Student ◽  
Longitudinal Research ◽  
Student Assessment ◽  
Big Five Personality ◽  
International Student Assessment ◽  
Incremental Predictive Validity

Abstract. Little longitudinal research has addressed the question of whether the Big Five personality traits (i.e., O, C, E, A, N) predict educational outcomes and whether the Big Five provide incremental predictive validity for educational outcomes when prior achievement, intelligence, and academic self-concept are controlled for. Also, little is known about whether noncognitive factors are related to change in academic success, especially grades, after controlling for its stability. To address these research questions, we used data from the Luxembourg longitudinal extension of the 2009 cycle of the Program for International Student Assessment (PISA). We included data from two student cohorts that were followed across 4 years: The first cohort was in ninth grade when they participated in PISA (N = 240); the second cohort in 10th grade (N = 276). Correlational results showed that Conscientiousness and Openness were substantially related to subject-specific grades in Mathematics, French, and German across several school years. There was evidence for incremental predictive validity beyond intelligence and academic self-concepts. When controlling for the stability of grades, there were only small effects of all predictors on later grades. In summary, students’ personality (but also intelligence and academic self-concepts) predicted the stable part of grades rather than change.

Risk Tolerance Profiling Measure: Testing Its Reliability and Validities

2020 ◽  
pp. JFCP-19-00033
Author(s):  
Wee Kang Chung ◽  
Wing Tung Au
Keyword(s):  
Predictive Validity ◽  
Financial Institutions ◽  
Financial Risk ◽  
Risk Tolerance ◽  
Risk Profiling ◽  
Loss Data ◽  
Loss Tolerance ◽  
Investment Experience ◽  
Financial Risk Tolerance ◽  
Incremental Predictive Validity

This study examines the degree to which the customer risk profiling measure (CRPM), commonly used by financial institutions to determine loss tolerance of investors, is psychometrically valid in assessing risk tolerance and predicting anxiety after experiencing a significant investment loss. Data were collected online from 91 respondents with various investment experience, Results suggest that CPRM is significantly correlated with the Grable and Lytton's Financial Risk Tolerance Scale (G/L-RTS), a validated financial risk tolerance measure. CPRM is also able to predict anxiety after experiencing a significant investment loss. Furthermore, CRPM also demonstrates incremental predictive validity above and beyond G/L-RTS in predicting anxiety after investment loss.

scholarly journals Re-assessing the incremental predictive validity of Implicit Association Tests

2019 ◽  
Author(s):  
Jordan Axt ◽  
Nick Buttrick ◽  
Charles R. Ebersole ◽  
Jacalyn Huband
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Predictive Validity ◽  
Linear Regression Analysis ◽  
Self Report ◽  
Equation Modeling ◽  
Measurement Reliability ◽  
Implicit Association ◽  
The Impact ◽  
Incremental Predictive Validity

Indirect measures of attitudes or stereotypes, such as the Implicit Association Test (IAT), assess associations that are relatively automatic, unintentional, or uncontrollable. A primary argument for the IAT’s use is that it can predict relevant outcomes beyond parallel direct measures, such as self-report (a claim referred to as demonstrating incremental predictive validity). Prior work on this issue relied primarily on least squares linear regression analyses, which are unable to correct for measurement (un)reliability and may then seriously inflate false positive rates in claims of incremental predictive validity. Properly accounting for the impact of measurement reliability requires using Structural Equation Modeling (SEM). In a pre-registered analysis, we investigated 10 IATs and 250 outcomes variables ( N > 14,000), and found that 69.6% of outcomes were reliably correlated with the IAT. Among outcomes that were associated with both the IAT and self-report, the IAT showed incremental predictive validity in 58.6% of cases using least squares linear regression analysis and 59.2% of cases when using SEM, with the two analytic approaches reaching the same conclusion 91.4% of the time. Though the two analysis strategies largely converged, discrepancies were large enough to suggest a non trivial percentage of conclusions drawn from least squares linear regression will be erroneous. As only SEM properly accounts for measurement reliability, it should be adopted in future analyses. To facilitate that goal, we provide tools for researchers to complete SEM analyses on tests concerning the incremental predictive validity of the IAT.

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