When a person takes alternative forms of the same test across replications of the testing procedure, the test taker's observed scores on the alternative forms are rarely identical. In educational and psychological measurement, inconsistencies in a test taker's scores that are irrelevant to the construct being measured are attributed to errors of measurement. Typically, errors of measurement are summarized as the standard deviation of a test taker's observed scores over replication of the same testing procedure. Assuming that errors of measurement follow a multinomial distribution (i.e., multinomial error model), the main goal of this study was to propose two interval estimation procedures, which are referred to as the score-like and Perks procedures, for true scores of a test with polytomous items. The performance of the score-like and Perks procedures was compared with that of two normal approximation procedures under the multinomial error model and a procedure based on item response theory (IRT) through simulation. In general, the score-like and Perks procedures outperformed the other three procedures when data were generated under the multinomial error theory framework and showed reasonable results when data were generated under the IRT framework. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Numerous tutorial publications are available to researchers seeking the procedures needed to analyze longitudinal count response variable data. However, most of the available tutorial publications have drawbacks that limit their usefulness to applied researchers, and to the best of our knowledge, very few publications make both the sample data and the data analysis syntax scripts available to readers to allow an interactive replication of analyses. The purpose of this article is to provide readers a systematic tutorial for analyzing longitudinal count data that involves a discontinuity, or an intervening event that alters the count change trajectory, using multilevel generalized linear mixed models. The longitudinal count data analysis model options and their assumptions, how the linear model equations for each can be used to correctly specify and analyze each model using Mplus or R, how to select the best-fitting longitudinal count model, and how to interpret and present results, are all described. The example data, analysis syntax scripts, and additional files are all available to readers as online supplemental materials. (PsycInfo Database Record (c) 2020 APA, all rights reserved).A norm-referenced score expresses the position of an individual test taker in the reference population, thereby enabling a proper interpretation of the test score. Such normed scores are derived from test scores obtained from a sample of the reference population. Typically, multiple reference populations exist for a test, namely when the norm-referenced scores depend on individual characteristic(s), as age (and sex). To derive normed scores, regression-based norming has gained large popularity. The advantages of this method over traditional norming are its flexible nature, yielding potentially more realistic norms, and its efficiency, requiring potentially smaller sample sizes to achieve the same precision. In this tutorial, we introduce the reader to regression-based norming, using the generalized additive models for location, scale, and shape (GAMLSS). This approach has been useful in norm estimation of various psychological tests. We discuss the rationale of regression-based norming, theoretical properties of GAMLSS and their relationships to other regression-based norming models. Based on 6 steps, we describe how to (a) design a normative study to gather proper normative sample data; (b) select a proper GAMLSS model for an empirical scale; (c) derive the desired normed scores for the scale from the fitted model, including those for a composite scale; and (d) visualize the results to achieve insight into the properties of the scale. Following these steps yields regression-based norms with GAMLSS for a psychological test, as we illustrate with normative data of the intelligence test IDS-2. The complete R code and data set is provided as online supplemental material. (PsycInfo Database Record (c) 2020 APA, all rights reserved).The purpose of this article is to examine the statistical characteristics of binary sequences with the aim of uncovering the implicit cues that people use when making forecasts of what comes next. Information theory was used to quantify these statistical characteristics. In 2 experiments people were presented with 100 intact sequences of 20 Xs and Os and simply asked to forecast whether the 21st event in each sequence will be an X or an O. Multilevel logistic regression models were used to estimate the odds associated with these forecasts under different experimental manipulations. In a third experiment people judged the forecastability of sequences in a paired-comparison task. The results from the first 2 experiments showed that third-order redundancy (i.e., information provided by knowledge of the preceding pairs of events) was the most salient cue influencing forecasts. Ferroptosis inhibitor Experiment 3 showed that judgments of forecastability were based on this cue as well. When examining intact sequences with the goal of forecasting what comes next, people are more sensitive to higher-order transitional probabilities than has been previously suggested. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Most music is temporally organized within a metrical hierarchy, having nested periodic patterns that give rise to the experience of stronger (downbeat) and weaker (upbeat) events. Musical meter presumably makes it possible to dance, sing, and play instruments in synchrony with others. It is nevertheless unclear whether or not listeners perceive multiple levels of periodicity simultaneously, and if they do, when and how they learn to do this. We tested children, adolescents, and musically trained and untrained adults with a new meter perception task. We presented excerpts of human-performed music paired with metronomes that matched or mismatched the metrical structure of the music at 2 hierarchical levels (beat and measure), and asked listeners to provide a rating of fit of metronome and music. Fit ratings suggested that adults with and without musical training were sensitive to both levels of meter simultaneously, but ratings were more strongly influenced by beat-level than by measure-level synchrony. Sensitivity to two simultaneous levels of meter was not evident in children or adolescents.