Part of the problem is that experimental psychology and inter-individual differences psychology focus on two different kinds of reliability: Experimental psychology aims to provide effects that occur in all (or almost all) individuals and are of similar size in all individuals, and therefore are replicable in group-level analyses across different samples. Moreover, when the same effect is measured as response time (RT) difference score and error difference score, even these two measures of the same effect often do not correlate (e.g., Hedge et al., 2018). One problem is the so-called “reliability paradox” (Hedge et al., 2017): It has repeatedly been observed that standard experimental effects such as the Stroop effect, the Simon effect, or the task-switch cost-effects that have been replicated in thousands of studies-have surprisingly low split-half and retest reliability. We suggest that care should be taken when using evidence-accumulation model difference scores for correlational approaches because the parameter difference scores can correlate in the absence of any true inter-individual differences at the population level. In the simulations, we only observed this spurious negative correlation when either (a) there was no true difference in model parameters between simulated experimental conditions, or (b) only drift rate was manipulated between simulated experimental conditions when a true difference existed in boundary separation, non-decision time, or all three main parameters, the correlation disappeared. The most pronounced spurious effect is a negative correlation between boundary difference and non-decision difference, which amounts to r = –. In the present paper, we report spurious correlations between such model parameter difference scores, both in empirical data and in computer simulations. Researchers often compute experimental effects as simple difference scores between two within-subject conditions and such difference scores can also be computed on model parameters.
In their simplest form, evidence-accumulation models include three parameters: The average rate of evidence accumulation over time (drift rate) and the amount of evidence that needs to be accumulated before a response becomes selected (boundary) both characterise the response-selection process a third parameter summarises all processes before and after the response-selection process (non-decision time).
the high numerical values of one variable relate to the low numerical values of the other.Evidence-accumulation models are a useful tool for investigating the cognitive processes that give rise to behavioural data patterns in reaction times (RTs) and error rates. Negative correlation exists if one variable decreases when the other increases, i.e.the high numerical values of one variable relate to the high numerical values of the other. Positive correlation exists if one variable increases simultaneously with the other, i.e.If there is correlation found, depending upon the numerical values measured, this can be either positive or negative. If correlation is found between two variables it means that when there is a systematic change in one variable, there is also a systematic change in the other the variables alter together over a certain period of time. It is often misunderstood that correlation analysis determines cause and effect however, this is not the case because other variables that are not present in the research may have impacted on the results. This particular type of analysis is useful when a researcher wants to establish if there are possible connections between variables. Computer Assisted Personal Interviews (CAPI)Ĭorrelation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables.Computer Assisted Web Interviews (CAWI).Computer Assisted Telephonic Interviews (CATI).
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