scholarly journals A Reappraisal of the Threshold Hypothesis of Creativity and Intelligence

2020 ◽  
Vol 8 (4) ◽  
pp. 38
Author(s):  
Selina Weiss ◽  
Diana Steger ◽  
Ulrich Schroeders ◽  
Oliver Wilhelm
Keyword(s):  
Regression Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Continuous Variable ◽  
Multivariate Methods ◽  
Sufficient Condition ◽  
Segmented Regression ◽  
Segmented Regression Analysis ◽  
History Of ◽  
Analytical Procedures

Intelligence has been declared as a necessary but not sufficient condition for creativity, which was subsequently (erroneously) translated into the so-called threshold hypothesis. This hypothesis predicts a change in the correlation between creativity and intelligence at around 1.33 standard deviations above the population mean. A closer inspection of previous inconclusive results suggests that the heterogeneity is mostly due to the use of suboptimal data analytical procedures. Herein, we applied and compared three methods that allowed us to handle intelligence as a continuous variable. In more detail, we examined the threshold of the creativity-intelligence relation with (a) scatterplots and heteroscedasticity analysis, (b) segmented regression analysis, and (c) local structural equation models in two multivariate studies (N1 = 456; N2 = 438). We found no evidence for the threshold hypothesis of creativity across different analytical procedures in both studies. Given the problematic history of the threshold hypothesis and its unequivocal rejection with appropriate multivariate methods, we recommend the total abandonment of the threshold.

scholarly journals A Reappraisal of the Threshold Hypothesis of Creativity and Intelligence

2020 ◽  
Author(s):  
Selina Weiss ◽  
Diana Steger ◽  
Ulrich Schroeders ◽  
Oliver Wilhelm
Keyword(s):  
Regression Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Continuous Variable ◽  
Multivariate Methods ◽  
Sufficient Condition ◽  
Segmented Regression ◽  
Segmented Regression Analysis ◽  
History Of ◽  
Analytical Procedures

Intelligence has been declared as a necessary but not sufficient condition for creativity, which was subsequently (erroneously) translated into the so-called threshold hypothesis. This hypothesis predicts a change in the correlation between creativity and intelligence at around 1.33 standard deviations above the population mean. A closer inspection of previous inconclusive results suggests that the heterogeneity is mostly due to the use of suboptimal data analytical procedures. Herein, we applied and compared three methods that allowed us to handle intelligence as a continuous variable. In more detail, we examined the threshold of the creativity-intelligence relation with (a) scatterplots and heteroscedasticity analysis, (b) segmented regression analysis, and (c) local structural equation models in two multivariate studies (N1 = 456; N2 = 438). We found no evidence for the threshold hypothesis of creativity across different analytical procedures in both studies. Given the problematic history of the threshold hypothesis and its unequivocal rejection with appropriate multivariate methods, we recommend the total abandonment of the threshold.

scholarly journals Reliability of a lifetime history of major depression: implications for heritability and co-morbidity

1998 ◽  
Vol 28 (4) ◽  
pp. 857-870 ◽  
Author(s):  
D. L. FOLEY ◽  
M. C. NEALE ◽  
K. S. KENDLER
Keyword(s):  
Major Depression ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Familial Risk ◽  
Ratio Test ◽  
Psychiatric Interview ◽  
Lifetime History ◽  
Co Morbidity ◽  
Familial Influences ◽  
History Of

Background. In unselected samples, the diagnosis of major depression (MD) is not highly reliable. It is not known if occasion-specific influences on reliability index familial risk factors for MD, or how reliability is associated with risk for co-morbid anxiety disorders.Methods. An unselected sample of 847 female twin pairs was interviewed twice, 5 years apart, about their lifetime history (LTH) of MD, generalized anxiety disorder (GAD) and panic disorder (PD). Familial influences on reliability were examined using structural equation models. Logistic regression was used to identify clinical features that predict reliable diagnosis. Co-morbidity was characterized using the continuation ratio test.Results. The reliability of a LTH of MD over 5 years was fair (κ=0·43). There was no evidence for occasion-specific familial influences on reliability, and heritability of reliably diagnosed MD was estimated at 66%. Subjects with unreliably diagnosed MD reported fewer symptoms and, if diagnosed with MD only at the first interview, less impairment and help seeking, or, if diagnosed with MD only at the second interview, fewer episodes and a longer illness. A history of co-morbid GAD or PD is more prevalent among subjects with reliably diagnosed MD.Conclusions. A diagnosis of MD based on a single psychiatric interview incorporates a substantial amount of measurement error but there is no evidence that transient influences on recall and diagnosis index familial risk for MD. Quantitative indices of risk for MD based on multiple interviews should reflect both the characteristics of MD and the temporal order of positive diagnoses.

scholarly journals Segmented regression analysis of emergency departments patient visits from Septicemia in Taiwan

2018 ◽  
Vol 7 (2) ◽  
pp. 149-155
Author(s):  
I-Shiang Tzeng ◽  
Kuo-Liong Chien ◽  
Yu-Kang Tu ◽  
Jau-Yuan Chen ◽  
Chau Yee Ng ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Emergency Departments ◽  
Segmented Regression ◽  
Segmented Regression Analysis

scholarly journals A 'Weight of Evidence' approach to evaluating structural equation models

One Ecosystem ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
James Grace
Keyword(s):  
Structural Equation Modeling ◽  
Model Selection ◽  
Model Evaluation ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Weight Of Evidence ◽  
Equation Modeling ◽  
Future Studies ◽  
Current State ◽  
History Of

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

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