1,720,969 research outputs found
VanBovenSupplementalMaterial_rev – Supplemental material for Attention Drives Emotion: Voluntary Visual Attention Increases Perceived Emotional Intensity
No full textSupplemental material, VanBovenSupplementalMaterial_rev for Attention Drives Emotion: Voluntary Visual Attention Increases Perceived Emotional Intensity by Kellen Mrkva, Jacob Westfall and Leaf Van Boven in Psychological Science</p
VanBoven_OpenPracticesDisclosure_rev – Supplemental material for Attention Drives Emotion: Voluntary Visual Attention Increases Perceived Emotional Intensity
No full textSupplemental material, VanBoven_OpenPracticesDisclosure_rev for Attention Drives Emotion: Voluntary Visual Attention Increases Perceived Emotional Intensity by Kellen Mrkva, Jacob Westfall and Leaf Van Boven in Psychological Science</p
Plot of subjective heat ratings on a 7-point Likert scale against the “true” underlying daily temperatures.
No full textPlot of subjective heat ratings on a 7-point Likert scale against the “true” underlying daily temperatures.</p
Incremental validity in multiple regression vs. SEM.
No full textThe SEM results are from a simulation using 300,000 iterations. The multiple regression results are computed analytically. The SEM line in the left panel is a smoothed curve derived from fitting a generalized additive model with a binomial response to the simulation results tracking whether the null hypothesis was rejected. In the right panel, the SEM line and shaded region are based on first applying rolling medians of width 101 to the simulated regression coefficients and standard errors (to reduce the distorting influence of extreme outlying parameter estimates occurring particularly at low reliability values), and then fitting a generalized additive model to these rolling medians. SEM = Structural Equation Model.</p
Contour plots of Type 1 error probabilities for the argument for predictive utility.
No full textThe null hypothesis is that T1 has no partial relationship with Y after controlling for T2 (i.e., ρ1.2 = 0). The size of the true indirect effect of T1 on Y via T2 varies from small (panel A) to medium (panel B) to large (panel C).</p
Incremental validity is a statistically problematic concept
No full text<p>Psychologists often seek to demonstrate that a construct has incremental validity over and above</p>
<p>other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that two widespread strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by discussing appropriate statistical methods for establishing incremental validity.</p
Derivation of statistical properties of incremental validity.
No full textWe derive the probabilities of rejecting different combinations of regression coefficients as a function of (1) the simple or partial correlations among the outcome and the latent predictors, (2) the reliabilities, and (3) the (DOCX)</p
Choosing prediction over explanation in psychology: Lessons from machine learning
No full textAbstract: Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-exclusive emphasis on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism, but that have little (or unknown) ability to actually predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior
Illustration of residual confounding.
No full text(A) Simple relationship between daily swimming pool deaths and number of ice cream cones sold. (B) Relationship between daily swimming pool deaths and number of ice cream cones sold after controlling for subjective heat Likert ratings. (C) Relationship between daily swimming pool deaths and number of ice cream cones sold after controlling for recorded daily temperatures.</p
Test statistics as a function of assumed reliability.
No full textThe shaded region gives the range within which the test statistics are nonsignificant. In each model, assuming reliabilities below a certain value invariably caused the model to fail to converge or to yield an inadmissible solution (i.e., impossible correlation matrices for the latent variables); we only plot the results for reliability values that successfully converge on stable estimates.</p
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