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Response time data have a positively skewed distribution. The challenge with this is that a measure of central tendency and dispersion does not adequately describe a skewed distribution. A researcher relying on only response time mean and standard deviation could make incorrect conclusions about response time. The best way to analyze response time data is with a distribution analysis. One reason that response time distribution analyses are atypical is that at least 100 trials are recommended per participant and condition. In the current tutorial, we demonstrate a distribution analysis technique that requires as few as 40 participants with 40 trials per condition. This technique involves geometric quantile averaging (GQA) and the quantile maximum probability estimator (QMPE). Each step of the analysis is detailed with a MATLAB script, flexible MATLAB functions, and experimental response time data. Our goal was to lower the barriers to entry for response time distribution analysis so that more researchers will choose to thoroughly examine response time data.

Time series and electroencephalographic data are often noisy sources of data. In addition, the samples are often small or medium so that confidence intervals for a given time point taken in isolation may be large. Decorrelation techniques were shown to be adequate and exact for repeated-measure designs where correlation is assumed constant across pairs of measurements. This assumption cannot be assumed in time series and electroencephalographic data where correlations are most-likely vanishing with temporal distance between pairs of points. Herein, we present a decorrelation technique based on an assumption of local correlation. This technique is illustrated with fMRI data from 14 participants and from EEG data from 24 participants.

Over the past decades, many researchers have identified ways of reasoning in the domain of statistics and probabilities that do not match statistics and probabilities results. Some of these inadequate conceptualizations are reviewed herein. They include, among others, the gambler’s fallacy, the law of small numbers, the misunderstanding of randomness, and they touch various aspects of statistics reasoning (sampling procedures, probability estimation, mean estimation, variance estimation, and inference). A classification is put forward.

Le 9 juin 2023 s’est tenu le 12e colloque annuel Méthodes quantitatives en sciences humaines à l’Université TÉLUQ, Montréal. Sept conférenciers ont présenté leurs résultats de recherche. Sébastien Béland a évalué l'efficacité de différents coefficients populaires sur des items unidimensionnels à réponse dichotomique. Félix Laliberté a présenté StepMix, un package Python et R pour le clustering basé sur des modèles de mélanges généralisés (classes latentes, profils latents). Eric Frenette a fait part des défis méthodologiques et statistiques rencontrés lors de l'évaluation du programme longitudinal \emph {Gagnant pour la vie} (version 1.0) implanté dans une école à concentration sport au Québec. André Achim a présenté une nouvelle technique de symétrisation de distributions asymétriques. Pier-Olivier Caron a présenté une comparaison des propriétés statistiques (puissance, erreur de type I, biais) des modèles de médiations longitudinaux et transversaux. Denis Cousineau a présenté deux techniques analogues à l'ANOVA : l'analyse des proportions utilisant la transformation arcsine (ANOPA) et l'analyse des fréquences de données (ANOFA). Louis Laurencelle propose un test complet et vraisemblable sur le plan paramétrique de la différence entre deux proportions jumelées. \\ The 12th annual meeting Quantitative Methods in the Humanities (Méthodes quantitatives en sciences humaines, MQSH) was held on June 9, 2023 at Université TÉLUQ, Montreal. Seven speakers presented their research findings. Sébastien Béland assessed the effectiveness of various popular coefficients on unidimensional items with dichotomous responses. Félix Laliberté presented StepMix, a Python and R package for model-based clustering and mixture (latent classes, latent profiles). Eric Frenette presented the methodological and statistical challenges encountered during the evaluation of the longitudinal program \emph {Gagnant pour la vie} (version 1.0 ; Eng. Win for life) implemented in a sports-oriented school in Quebec. André Achim presented a new technique for symmetrizing asymmetrical distributions. Pier-Olivier Caron presented a comparison of the statistical properties of longitudinal and cross-sectional mediation models. Denis Cousineau presented two techniques analogous to ANOVA: analysis of proportions using the arcsine transformation (ANOPA) and analysis of data frequencies (ANOFA). Louis Laurencelle proposed a complete and parametrically plausible test of the difference between two paired proportions.

Analyses of frequencies are commonly done using a chi-square test. This test, derived from a normal approximation, is deemed generally efficient (controlling type-I error rates fairly well and having good statistical power). However, in the case of factorial designs, it is difficult to decompose a total test statistic into additive interaction effects and main effects. Herein, we present an alternative test based on the $G$ statistic. The test has similar type-I error rates and power as the former one. However, it is based on a total statistic that is naturally decomposed additively into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVAs. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. We also examine how to render plots of frequencies along with confidence intervals. Finally, quantifying effect sizes and planning statistical power are described under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models.

Statistics plays a crucial role in our daily lives, enabling us to make informed decisions and understand trends. Quantitative methods courses are particularly known to generate more anxiety than other mainstream courses (Baloglu, 2003). This editorial introduces a special issue on statistical vignettes.

There exist many variants of confidence intervals for Cohen's d_p in within-subject designs. Herein, we review three past proposals (Morris, 2000; Algina & Keselman, 2003, Goulet-Pelletier & Cousineau, 2018) and examine five new ones, four of which are based on the recently discovered distribution of d_p in such design. We examine each method according to their accuracy in coverage rate (desired coverage is 95% in this study), symmetry (i. e., equal rejection rates from the left and from the right), and width of the interval. It is found that the past three proposals are pseudo confidence intervals, being too liberal under some circumstances (fortunately uncommon for the methods of Morris and Algina & Keselman). Additionally, they are not asymptotically accurate. Finally, they do not have symmetrical rejection rates on the left and on the right. Four of the five new techniques are asymptotically accurate but three of these are liberal for small samples. Finally, the relation of confidence intervals with inferential statistics testing is considered.

In this document, I demonstrate an approximate distribution of Cohen's d_p in within-subject designs. The distribution follows a noncentral t distribution with degrees of freedom depending on the correlation between the measures. The result generalizes the distribution of Cohen's d_p to both between-subject and single-group designs, yielding a flexible and integrative measure for comparing effect sizes across different study designs.

The diffusion model is useful for analyzing data from decision making experiments as it gives information about a dataset that regular statistical tests cannot, including: the rate of processing, the encoding and motor response times, and decision thresholds. The EZ diffusion model is a restricted version of the diffusion model with some parameter variability set to zero, allowing for quicker analyses. Here we describe the EZ diffusion model --including how it was derived mathematically-- the measurement units of the parameters, and how it can be generalized to starting points other than the mid-point. We also show how its parameters can be estimated using computer software (the model is available with many software programs such as R and Excel, to which we add SPSS and a Mathematica code). Finally, an EZ analysis was run on one dataset obtained from a 'Same'-'Different' experiment.

In within-subject designs, the multiple scores of a given participant are correlated. This correlation implies that the observed variance can be partitioned into between-subject variance and between-measure variance. The basic confidence interval about the mean does not separate these two sources and is therefore of little use in within-subject designs. Two solutions have been proposed, one (Loftus and Masson) requires the computation of the interaction terms including the subject and all within-subject factors, the other (Cousineau and Morey) requires a two-step transformation of the data. As shown, these two methods are nearly equivalent. Herein, I present a correlation-adjusted method which requires the mean correlation across all pairs of measurements. This solution is shown to be similar to the other two for data satisfying the compound symmetry assumption. It is found to be too liberal for data having homogeneous correlations and heterogeneous variances but a Welch correction for heterogeneous variances can be used. Finally, it is inadequate for data that do not satisfy the compound symmetry assumption but satisfy the sphericity assumption. A statistical test of compound symmetry is discussed.

GRD is a popular tool to genenrate random data on the fly. It is most useful in statistic classes where the students can generate with a single short syntax, or using a graphical interface, random data that differs on every run but yet can implement effect sizes, outliers, etc. With the new versions of SPSS (version 27 and above) which is now using a new version of Python, it was necessary to upgrade the extension. Here, GRD 2.1 is presented which works with SPSS 27 and above.

This vignette presents an in-class demonstration to introduce the notion of standard error. The students must be able to compute means using SPSS. They must also be able to eye-ball standard deviation from a list of numbers. In doing the activity, they will be exposed to sampling. The demonstration uses a random number generator so that each student faces a different dataset simulating IQs from a single group of participants. Factors affecting standard errors (sample size and population variance) are explored. Finally, the actual formula for standard error is then compared to the roughly estimated standard deviation of the sample means..

Effect sizes and confidence intervals are important statistics to assess the magnitude and the precision of an effect. The various standardized effect sizes can be grouped in three categories depending on the experimental design: measures of the difference between two means (the $d$ family), measures of strength of association (e.$g$., $r$, $R^2$, $\eta ^2$, $\omega ^2$), and risk estimates (e.g., odds ratio, relative risk, phi; Kirk, 1996). Part I of this study reviews the $d$ family, with a special focus on Cohen's $d$ and Hedges' $g$ for two-independent groups and two-repeated measures (or paired samples) designs. The present paper answers questions concerning the $d$ family via Monte Carlo simulations. First, four different denominators are often proposed to standardize the mean difference in a repeated measures design. Which one should be used? Second, the literature proposes several approximations to estimate the standard error. Which one most closely estimates the true standard deviation of the distribution? Lastly, central and noncentral methods have been proposed to construct a confidence interval around $d$. Which method leads to more precise coverage, and how to calculate it? Results suggest that the best way to standardize the effect in both designs is by using the pooled standard deviation in conjunction with a correction factor to unbias $d$. Likewise, the best standard error approximation is given by substituting the gamma function from the true formula by its approximation. Lastly, results from the confidence interval simulations show that, under the normality assumption, the noncentral method is always superior, especially with small sample sizes. However, the central method is equivalent to the noncentral method when $n$ is greater than 20 in each group for a between-group design and when $n$ is greater than 24 pairs of observations for a repeated measures design. A practical guide to apply the findings of this study can be found after the general discussion.

Teaching tools are necessary to promote learning and encourage the understanding of course content. Among those tools, televoting is one by which students can answer anonymously on a question presented by an instructor. Here, we present ivote, a small application that can be used to that end. ivote is free and does not require registration from the professor nor the students, and does not present advertisements. This software can be used in conjunction with PowerPoint with minimal programming; the votes can also be exported to SPSS, Excel, Mathematica or R easily. Students can express their vote as long as they have an internet access either by entering a URL or by scanning a QR code.

The results of the method for estimating confidence intervals according to Steiger and Fouladi (1997) were not displayed correctly in our published paper (Figure 7 in Appendix C). We correct the figure here. The new figure changes our interpretation of the method to a more favorable appreciation.

This editorial introduces the special issue dedicated to sampling models.

This study investigates the correlates of statistics anxiety. Considering that statistics anxiety and spatial anxiety have been separately correlated with related constructs (e.g., mathematics anxiety, academic performance, etc.), the possibility that spatial anxiety plays a role in statistics anxiety is explored. When facing statistics or mathematics operations, people may imagine or visualize the task operations they must do to obtain the result. To examine this hypothesis, 778 students in a Social or Health Sciences program, enrolled in a--often mandatory--statistics course from Canadian, French and Belgian universities completed an online survey. The results show moderate to strong positive correlations between all three types of anxiety (spatial, mathematics, and statistics). In addition, a mediation analysis reveals the intermediate role played by mathematics anxiety in the relationship between spatial and statistics anxieties. Nonetheless, the direct link from spatial anxiety to statistics anxiety is non-negligible in the model. Finally, the results also indicate that women report higher levels of statistics anxiety, which may be partly explained by their higher level of spatial anxiety.

Statistical analyses have grown immensely since the inception of computational methods. However, many quantitative methods classes teach sampling and sub-sampling at a very abstract level despite the fact that, with the faster computers of today, these notions could be demonstrated live to the students. For this reason, we have created a simple extension module for SPSS that can sub-sample and Bootstrap data, GSD (Generator of Sub-sampled Data). In this paper, we describe and show how to use the GSD module as well as provide short descriptions of both the sub-sampling and Bootstrap methods. In addition, as this article aims to inspire instructors to introduce these concepts in their statistics classes of all levels, we provide three short exercises that are ready for curriculum implementation.

The GRD extension command for SPSS (Harding & Cousineau, 2014) has been used in a variety of applications since its inception. Ranging from a teaching tool to demonstrate statistical analyses, to an inferential tool used to find critical values instead of looking into a z-table, GRD has been very well received. However, some users have requested other data generation components that would make GRD a more complete extension command: the possibility to add contaminants to the generated dataset as well as the ability to generate correlated variables. Another component we added is a graphical user interface (or GUI) that makes GRD accessible through the drop-down menus in the SPSS Data Editor window. This GUI allows users to generate a simple dataset by entering parameters in dedicated fields rather than writing out the full script. Finally, we devised a small series of exercises to help users get acquainted with the new subcommands and GUI.

Two errors have been found in the published article that are corrected here. All the simulations reported were error-free.

Companion articles are a new category of articles published in the Quantitative Methods for Psychology. They are meant to provide a detailed, step-by-step, explanation of how to run a specific analysis used in a previously published article but described with fewer details.

In 1998/1999 three participants trained for up to 74 hour long sessions to find a target in visual displays of 1, 2, or 4 objects. There were four targets and four foils that never changed. Displays occurred simultaneously, or the objects occurred successively, or the features of each object occurred successively. When successive, the SOAs were short (17, 33, or 50 ms) so the displays appeared simultaneous, making it likely that the search strategy was the same in all conditions. A 2004 publication examined only the simultaneous condition and found evidence for serial search as well as some small amount of automatic attention to targets, and occasional early or late search termination. A 2021 publication examined only the displays with single objects, obtaining evidence for dynamic perception of features. Building on these results we present a simple dynamic model that explains the main processes operating in all the experimental conditions.

The same-different task is a classic paradigm that requires participants to judge whether two successively presented stimuli are the same or different. While this task is simple, with results that have been replicated many times, response times (RTs) and accuracy for both same and different decisions remain difficult to model. The biggest obstacle in modeling the task lies within its effect referred to as the fast-same phenomenon whereby participants are much faster at responding "same" than "different," while most standard cognitive models predict the opposite. In this study, we investigated whether this effect is the result of identity priming activated by the first stimulus. We ran four variants of the same-different task in which identity priming is intended to be attenuated or cancelled in half of the trials. Results for all four variants show that a complete visual match between both stimuli is necessary to observe a fast-same effect and that hampering this relation attenuates same RTs while different RTs remained relatively unchanged.

A vast majority of social science students experience statistics anxiety in their statistics class, a course often perceived as the most difficult one of their academic paths. The present study examines the role of attitudes towards statistics, cognitive emotion regulation strategies, and satisfaction of psychological needs in the prediction of statistics anxiety as well as the contribution of gender onto statistics anxiety. Two hundred forty-two undergraduate social sciences students in Canada completed the study. Positive attitude towards statistics, fewer maladaptive emotion regulation strategies, and satisfaction of psychological needs were related to less statistics anxiety; adaptive emotion regulation strategies, however, were not. Furthermore, women experienced more statistics anxiety than men. Results provide insight about individual differences that may impact experiences of statistics anxiety and overall learning in the context of a statistics course.

Semantic organization of knowledge has a long history in theories of creativity. Flexibility of thinking and distant connec-tions are indispensable elements of a creative network. Simultaneously, convergence of thoughts and evaluation of ideas areessential at many stages of the creative process. The current study evaluates these complementary aspects through the lensof an exploratory concept known as mental boundaries. Correlation analyses are used to compare flexible and rigid ten-dencies of organizing the world, the concepts of intellect, schizotypy, perfectionism, divergent thinking and self-perceivedcreativity. Results (n = 316) reveal an interesting contrasting pattern where divergent thinking is significantly related toflexible internal and external organizations, whereas self-perceived creativity is significantly related to rigid external andnon-significantly related to rigid internal organizations. The present findings have implications for the measurement ofcreativity and the identification of factors that facilitate the creative process.

The purpose of this study was to test three pathways from achievement goals to academic performance in statistics classes. Participants were 247 undergraduate students in psychology taking an introductory course on statistics. They completed questionnaires shortly after the midterm, and their final grades were provided by their professors at the end of the semester. Structural equation modeling results reveal three distinct paths from achievement goals to academic performance. Results suggest that the more participants adopted mastery goals in the context of their statistics course, the less they experienced anxiety and the better they performed in the course at the end of the semester.

The goal of the current study was to measure the processing demands on the stages of information processing with internal and external foci of attention. Participants completed simple and two-choice reaction time tasks with internal and external foci of attention. Donders' subtraction method was used to isolate the cumulative duration of stages unique to simple and choice reaction time tasks. Mean reaction time was comparable with internal and external foci of attention in simple and two-choice reaction time tasks. These results suggest that processing demands were comparable with internal and external foci of attention. We hypothesize that there was not a processing advantage for an external focus in simple reaction time because the required movements had low movement complexity. [ABSTRACT FROM AUTHOR]