建构反应题中能力估计准确性的影响因素:评分者人数和项目个数的交互作用

采用康春花、孙小坚和曾平飞(2016)提出的等级反应多水平侧面模型探讨了评分者人数和项目个数对被试能力估计准确性的影响。模拟研究的结果表明:(1)随着项目个数的增加,估计值与真值之间的相关也不断增加;(2)评分者人数和项目个数在平均绝对偏差(MAB)和误差均方根(RMSE)上的主效应均显著,两者间的交互效应也显著;(3)简单效应分析发现,当项目较少时,3个评分者条件下的能力估计准确性最好; 随着项目个数的增加,4个评分者的估计误差迅速下降,且表现变为最好。     

Discounting: A practical guide to multilevel analysis of choice data

Multilevel modeling provides the ability to simultaneously evaluate the discounting of individuals and groups by examining choices between smaller sooner and larger later rewards. A multilevel logistic regression approach is advocated in which sensitivity to relative reward magnitude and relative delay are considered as separate contributors to choice. Examples of how to fit choice data using multilevel logistic models are provided to help researchers in the adoption of these methods.     

Polytomous multilevel testlet models for testlet‐based assessments with complex sampling designs

A pplications of standard item response theory models assume local independence of items and persons. This paper presents polytomous multilevel testlet models for dual dependence due to item and person clustering in testlet‐based assessments with clustered samples. Simulation and survey data were analysed with a multilevel partial credit testlet model. This model was compared with three alternative models – a testlet partial credit model (PCM), multilevel PCM, and PCM – in terms of model parameter estimation. The results indicated that the deviance information criterion was the fit index that always correctly identified the true multilevel testlet model based on the quantified evidence in model selection, while the Akaike and Bayesian information criteria could not identify the true model. In general, the estimation model and the magnitude of item and person clustering impacted the estimation accuracy of ability parameters, while only the estimation model and the magnitude of item clustering affected the item parameter estimation accuracy. Furthermore, ignoring item clustering effects produced higher total errors in item parameter estimates but did not have much impact on the accuracy of ability parameter estimates, while ignoring person clustering effects yielded higher total errors in ability parameter estimates but did not have much effect on the accuracy of item parameter estimates. When both clustering effects were ignored in the PCM, item and ability parameter estimation accuracy was reduced.     

Development and Initial Validation of a Scale Measuring the Beliefs of Educators Regarding Response to Intervention

This article presents information on the development and initial validation of the 16-item Response to Intervention (RTI) Beliefs Scale. The scale is designed to measure the extent to which educators working in schools hold beliefs consistent with the tenets of RTI. The authors administered the instrument to 2,430 educators in 62 elementary schools in the fall of 2007 and 2,443 educators in 68 elementary schools in the spring of 2008. Exploratory, single-level confirmatory, and multilevel confirmatory factor analysis procedures were used to examine construct validity. Results supported a correlated 3-factor model (Academic Abilities and Performance of Students with Disabilities, Data-Based Decision Making, and Functions of Core and Supplemental Instruction) at both the school and educator levels of analysis. Furthermore, the factor scores derived from the model demonstrated significant, positive relations to RTI implementation. Reliability estimates for two of the three factor scores exceeded.70. Implications for research on educator beliefs and implementation of RTI as well as implications for school psychologists supporting RTI implementation are discussed.     

Younger and older adults weigh multiple cues in a similar manner to generate judgments of learning

One’s memory for past test performance (MPT) is a key piece of information individuals use when deciding how to restudy material. We used a multi-trial recognition memory task to examine adult age differences in the influence of MPT (measured by actual Trial 1 memory accuracy and subjective confidence judgments, CJs) along with Trial 1 judgments of learning (JOLs), objective and participant-estimated recognition fluencies, and Trial 2 study time on Trial 2 JOLs. We found evidence of simultaneous and independent influences of multiple objective and subjective (i.e., metacognitive) cues on Trial 2 JOLs, and these relationships were highly similar for younger and older adults. Individual differences in Trial 1 recognition accuracy and CJs on Trial 2 JOLs indicate that individuals may vary in the degree to which they rely on each MPT cue when assessing subsequent memory confidence. Aging appears to spare the ability to access multiple cues when making JOLs.     

Constituent-morpheme priming: Implications from the morphology of two-kanji compound words

Abstract: The diversity in the morphological structure of two‐kanji compound words is a matter of special concern for models of the Japanese mental lexicon. This study discusses two proposals for models of the Japanese mental lexicon – Hirose’s (1992, 1994, 1996 ) hypotheses and a Japanese lemma‐unit version of the multilevel interactive‐activation framework – in terms of their ability to cope with this diversity. As the proposals make different predictions concerning constituent‐morpheme priming, patterns of facilitation were examined in two experiments with five word‐formation principles as experimental conditions. Experiment 1, using the long stimulus onset asynchrony (SOA) of 3000 ms employed by Hirose (1992 ), only found significant differences between the first‐ and second‐element conditions in one of the word‐formation conditions. Experiment 2, using a short SOA of 250 ms, confirmed the pattern of priming obtained in Experiment 1. These results are more consistent with the prediction from the Japanese lemma‐unit model.     

Heterogeneous factor analysis models: A bayesian approach

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.