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This study explores digital and paper reading processes and outcomes for 371 fifth to eighth graders completing a reading task similar to standardized testing. Results showed students highlighted and annotated more when reading the paper versus digital text. Also, reading on paper versus digitally was slightly supportive of reading comprehension for the longer section of text. For behaviors, digital highlighting and looking back at the paper text were supportive of reading comprehension, whereas paper highlighting was negatively related, likely because paper highlighting tended to occur often outside of important areas of the text. Paper and digital annotating, online dictionary use, and digital look-back did not link to comprehension, when controlling for other covariates. Links to theory, research, and practice are discussed.

Purpose: Growing evidence suggests that fatigue associated with listening difficulties is particularly problematic for children with hearing loss (CHL). However, sensitive, reliable, and valid measures of listening-related fatigue do not exist. To address this gap, this article describes the development, psychometric evaluation, and preliminary validation of a suite of scales designed to assess listening-related fatigue in CHL: the pediatric versions of the Vanderbilt Fatigue Scale (VFS-Peds). Method: Test development employed best practices, including operationalizing the construct of listening-related fatigue from the perspective of target respondents (i.e., children, their parents, and teachers). Test items were developed based on input from these groups. Dimensionality was evaluated using exploratory factor analyses (EFAs). Item response theory (IRT) and differential item functioning (DIF) analyses were used to identify high-quality items, which were further evaluated and refined to create the final versions of the VFS-Peds. Results: The VFS-Peds is appropriate for use with children aged 6--17 years and consists of child self-report (VFS-C), parent proxy--report (VFS-P), and teacher proxy--report (VFS-T) scales. EFA of child self-report and teacher proxy data suggested that listening-related fatigue was unidimensional in nature. In contrast, parent data suggested a multidimensional construct, composed of mental (cognitive, social, and emotional) and physical domains. IRT analyses suggested that items were of good quality, with high information and good discriminability. DIF analyses revealed the scales provided a comparable measure of fatigue regardless of the child's gender, age, or hearing status. Test information was acceptable over a wide range of fatigue severities and all scales yielded acceptable reliability and validity. Conclusions: This article describes the development, psychometric evaluation, and validation of the VFS-Peds. Results suggest that the VFS-Peds provide a sensitive, reliable, and valid measure of listening-related fatigue in children that may be appropriate for clinical use. Such scales could be used to identify those children most affected by listening-related fatigue, and given their apparent sensitivity, the scales may also be useful for examining the effectiveness of potential interventions targeting listening-related fatigue in children.

This article presents a multilevel longitudinal nested logit model for analyzing correct response and error types in multilevel longitudinal intervention data collected under a pretest-posttest, cluster randomized trial design. The use of the model is illustrated with a real data analysis, including a model comparison study regarding model complexity and cluster bias. Two substantive research questions regarding the intervention effect on correct response probability and error patterns are investigated using the proposed model. The recovery of item parameters for the proposed model using two sample size conditions is examined via a simulation study. The accuracy of the parameter estimates is comparable with those found in previous studies for the same family of models, except for the intercept parameters of correct responses. Finally, the impact of ignoring cluster membership in the model on the parameter estimation is also studied by fitting a single-level model to multilevel data. Ignoring cluster membership in the model adversely affects the estimation of intercept parameters in correct and error responses.

Purpose: The primary purposes of this study were to examine the effects of hearing loss and respondent type (self- vs. parent-proxy report) on subjective fatigue in children. We also examined associations between child-specific factors and fatigue ratings. Method: Subjective fatigue was assessed using the Pediatric Quality of Life Inventory Multidimensional Fatigue Scale (PedsQL-MFS; Varni, Burwinkle, Katz, Meeske, & Dickinson, 2002). We compared self- and parent-proxy ratings from 60 children with hearing loss (CHL) and 43 children with normal hearing (CNH). The children ranged in age from 6 to 12 years. Results: School-age CHL experienced more overall and cognitive fatigue than CNH, although the differences were smaller than previously reported. Parent-proxy report was not strongly associated with child self-report, and parents tended to underestimate their child's fatigue, particularly sleep/rest fatigue. Language ability was also associated with subjective fatigue. For CHL and CNH, as language abilities increased, cognitive fatigue decreased. Conclusions: School-age CHL experience more subjective fatigue than CNH. The poor association between parent-proxy and child reports suggests that the parent-proxy version of the PedsQL-MFS should not be used in isolation when assessing fatigue in school-age children. Future research should examine how language abilities may modulate fatigue and its potential academic consequences in CHL.

The current study reports on a large-scale quantitative analysis of classroom talk practices and links to different measures of reading achievement within upper elementary classrooms. Data involving 745 fourth- and fifth-grade teachers and 18,844 students from the Measures of Effective Teaching (MET) study were used. Talk was quantified via various talk-related indicators from 2 observation protocols and a student survey. Dimensionality analyses suggest these indicators represent 4 factors consisting of teacher explaining, questioning, encouraging of student talk, and big-picture communicating. Links to 2 different standardized reading achievement measures were also modeled with improved ratings of teacher explanations and questioning predicting higher standardized reading scores. Relationships varied, though, by different measures of classroom talk (i.e., observational protocols vs. student surveys) and levels of analysis (i.e., the student, class period, or school level). Students' but not observers' ratings of talk practices linked to standardized reading at the class period level, whereas observers' ratings related to standardized reading performance at the school level. Interpretations, implications for future research, and connections to educational practice are conveyed.

In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the test scores are used for a response variable and a covariate, respectively, to estimate the intervention effect. However, these test scores are frequently subject to measurement error and statistical inferences based on the model ignoring measurement error can yield a biased estimate of the intervention effect. When multiple domains exist in test data, it is sometimes more informative to detect the intervention effect for each domain than for the entire test. This paper presents applications of the multilevel multidimensional item response model with measurement error adjustments in a response variable and a covariate to estimate the intervention effect for each domain.

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences.

Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error-driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation-based accounts (Pickering & Branigan, 1999; Reitter, Keller, & Moore, 2011). Both theories predict that speakers should be primed by the syntactic choices of others, but only activation-based accounts predict that speakers should be able to prime themselves. Here we test whether speakers can be primed by their own productions in three behavioral experiments and find evidence of structural persistence following both comprehension and speakers' own productions. We also find that comprehension-based priming effects are larger for rarer syntactic structures than for more common ones, which is most consistent with error-driven accounts. Because neither error-driven accounts nor activation-based accounts fully explain the data, we propose a hybrid model.

Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are linear. However, this linearity assumption obscures the differential effects of covariates over their range in the presence of nonlinearity. Therefore, this paper presents exploratory plots that describe the potential nonlinear effects of person and item covariates on binary outcome variables. This paper also illustrates the use of EIRMs with smooth functions to model these nonlinear effects. The smooth functions examined in this study include univariate smooths of continuous person or item covariates, tensor product smooths of continuous person and item covariates, and by‐variable smooths between a continuous person covariate and a binary item covariate. Parameter estimation was performed using the mgcv R package through the maximum penalized likelihood estimation method. In the empirical study, we identified a nonlinear effect of the person‐by‐item covariate interaction and discussed its practical implications. Furthermore, the parameter recovery and the model comparison method and hypothesis testing procedures presented were evaluated via simulation studies under the same conditions observed in the empirical study. [ABSTRACT FROM AUTHOR]

A multilevel latent transition analysis (LTA) with a mixture IRT measurement model (MixIRTM) is described for investigating the effectiveness of an intervention. The addition of a MixIRTM to the multilevel LTA permits consideration of both potential heterogeneity in students' response to instructional intervention as well as a methodology for assessing stage sequential change over time at both student and teacher levels. Results from an LTA-MixIRTM and multilevel LTA-MixIRTM were compared in thecontext of an educational intervention study. Both models were able to describe homogeneities in problem solving and transition patterns. However, ignoring a multilevel structure in LTA-MixIRTM led to different results in group membership assignment in empirical results. Results for the multilevel LTA-MixIRTM indicated that there were distinct individual differences in the different transition patterns. The students receiving the intervention treatment outscored their business as usual (i.e., control group) counterparts on the curriculum-based Fractions Computation test. In addition, 27.4 % of the students in the sample moved from the low ability student-level latent class to the high ability student-level latent class. Students were characterized differently depending on the teacher-level latent class.

Popular statistical software provides the Bayesian information criterion (BIC) for multi‐level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi‐level model with respect to level‐specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi‐level models. In this study, we derive the BIC's penalty term for level‐specific fixed‐ and random‐effect selection in a two‐level nested design. In this new version of BIC, called BICE1, this penalty term is decomposed into two parts if the random‐effect variance–covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called BICE2, in the presence of redundant random effects. We show that the derived formulae, BICE1 and BICE2, adhere to empirical values via numerical demonstration and that BICE (E indicating either E1 or E2) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi‐level conditions through a simulation study. In addition, the use of BICE1 is illustrated with a textbook example dataset. [ABSTRACT FROM AUTHOR]

Utilizing a longitudinal item response model, this study investigated the effect of item parameter drift (IPD) on item parameters and person scores via a Monte Carlo study. Item parameter recovery was investigated for various IPD patterns in terms of bias and root mean-square error (RMSE), and percentage of time the 95% confidence interval covered the true parameter. The simulation results suggest that item parameters were not recovered well when IPD was ignored, especially if there was a larger number of IPD conditions. In addition, coverage was not accurate in all IPD conditions when IPD is ignored. Also, the results suggest that the accuracy of person scores (measured by bias) is potentially problematic when the larger number of IPD items is ignored. However, the overall accuracy (measured by RMSE) and coverage were unexpectedly acceptable in the presence of IPD as defined in this study.

Cross-level invariance in a multilevel item response model can be investigated by testing whether the within-level item discriminations are equal to the between-level item discriminations. Testing the cross-level invariance assumption is important to understand constructs in multilevel data. However, in most multilevel item response model applications, the cross-level invariance is assumed without testing of the cross-level invariance assumption. In this study, the detection methods of differential item discrimination (DID) over levels and the consequences of ignoring DID are illustrated and discussed with the use of multilevel item response models. Simulation results showed that the likelihood ratio test (LRT) performed well in detecting global DID at the test level when some portion of the items exhibited DID. At the item level, the Akaike information criterion (AIC), the sample-size adjusted Bayesian information criterion (saBIC), LRT, and Wald test showed a satisfactory rejection rate (>0.8) when some portion of the items exhibited DID and the items had lower intraclass correlations (or higher DID magnitudes). When DID was ignored, the accuracy of the item discrimination estimates and standard errors was mainly problematic. Implications of the findings and limitations are discussed.

The purpose of the current study is to examine the performance of four information criteria (Akaike's information criterion [AIC], corrected AIC [AICC] Bayesian information criterion [BIC], sample-size adjusted BIC [SABIC]) for detecting the correct number of latent classes in the mixture Rasch model through simulations. The simulation study manipulated various class-distinction features (percentages of class-variant items, magnitudes, and patterns of item difficulty differences) and mixing proportions, assuming that a mixture Rasch model with two latent classes was the true model. Unlike previous studies that showed BIC's superiority to other indices, our findings from this study suggested that the four information criteria had differential performance depending on the percentage of class-variant items and the magnitude and pattern of item difficulty differences under a two-class structure. Furthermore, the present study revealed that AICC and SABIC generally performed as good as or better than their counterparts, AIC and BIC, respectively, for the class-class structure with a sample of 3,000.

Heart rate (HR) and blood pressure (BP) measured by a patient can be used to monitor response to pharmacologic therapies. Continuously measured HR and BP are multivariate time series as sequences of values at regularly spaced intervals over time and changes in pharmacologic therapies interrupt the multivariate time series and create phases. In the multivariate interrupted time series, there are change processes in multiple phases, such as level changes, linear or non-linear trend changes, and time-varying serial dependence. This paper presents an application of a multivariate dynamic additive model to account for these change processes. In addition, a simulation study is conducted to evaluate the model's parameter recovery and to demonstrate the consequences of ignoring the time-varying serial dependence in HR and BP when detecting level changes and trend changes. The results of the simulation study show that the accuracy and precision of parameter estimates are satisfactory. Furthermore, the simulation results present that ignoring time-varying serial dependence in the same conditions as those found in the application results in biased estimates and standard errors for the level changes and trend changes. [ABSTRACT FROM AUTHOR]

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and posttest scores that are most often used in MLM are total scores. In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. In this article, using ordinary least squares and an attenuation formula, we derive the measurement error correction formula for cluster-level group difference estimates from MLM in the presence of measurement error in the outcome, the covariate, or both. Examples are provided to illustrate the correction formula in cluster randomized and observational studies using between-cluster reliability coefficients recently developed.

The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called latent DIF analysis, is provided and its applications in the literature are surveyed. Then, the methodological issues pertaining to latent DIF analysis are described, including mixture item response models, parameter estimation, and latent DIF detection methods. Finally, recommended steps for latent DIF analysis are illustrated using empirical data.

This teaching tip identifies ways to "WIN" at vocabulary learning. Specifically, the approach conveys three morphological strategies in the mnemonic "WIN." These three strategies remind students to find smaller units of meaning within bigger words, look for those units in other words that they know, and notice the context. Each strategy is described with an example. Also, ways to integrate "WIN" within comprehension instruction and adapt instruction based on word difficulty are included. Overall, this teaching tip provides a framework for educators to use when designing and implementing effective word instruction.

This study explores reader, word, and learning activity characteristics related to vocabulary learning for 202 fifth and sixth graders (N = 118 and 84, respectively) learning 16 words. Three measures of word knowledge were used: multiple-choice definition knowledge, self-report of meaning knowledge, and production of morphologically related words. Results indicate that significant vocabulary learning occurred on each measure and that certain words were easier to learn for certain types of readers. Controlling for other predictors in the model, reader characteristics like morphological awareness, reading comprehension, and language background were significant predictors of vocabulary learning, but not word reading fluency. Also, word characteristics like morphological family size and opaqueness were significant predictors of word difficulty but not number of morphemes or frequency of the word or root-word or affix. Controlling for other predictors in the model, morphological learning activities supported vocabulary learning for all 3 aspects of word knowledge. Implications for theory and instruction are discussed.

A pretest-posttest cluster-randomized trial involving 31 middle schools and 335 students with disabilities tested the effects of combining explicit and anchored instruction on fraction computation and problem solving. Results of standardized and researcher-developed tests showed that students who were taught with the blended units outscored students in Business As Usual classes. Students made the largest gains in computing with fractions and on problems related to ratios, proportions, and geometry. The findings suggest important implications for the way curriculum is designed for middle school students with disabilities who exhibit low performance in math.

The current study models reader, item, and word contributions to the lexical representations of 39 morphologically complex words for 172 middle school students using a crossed random-effects item response model with multiple outcomes. We report 3 findings. First, results suggest that lexical representations can be characterized by separate but correlated dimensions of derived-word reading, spelling, and meaning. Second, a reader's morphological knowledge of a root word (e.g., "isolate") contributes to that reader's lexical representations (i.e., mental representation of form and meaning) of a related derived word (e.g., "isolation"). Specifically, when controlling for reader and word characteristics, knowledge of root-word reading and morphological relatives and self-reported knowledge of meaning contributed to derived-word reading. Similarly, knowledge of root-word spelling and morphological relatives and self-reported knowledge of meaning contributed to derived-word spelling. Also, knowledge of root-word morphological relatives and self-reported knowledge of meaning contributed to self-reported knowledge of derived-word meaning. Our third finding is that after controlling for root-word knowledge, part of the remaining variability in lexical representations can be explained by reader and word characteristics, with different patterns of results for each lexical representation dimension. The significant reader characteristics include reading comprehension, morphological awareness, and vocabulary knowledge, whereas the significant word characteristics include frequency of the derived and root word, orthographic--phonological opaqueness, and phonological opaqueness. A model of what constitutes and contributes to lexical representations for adolescent readers is suggested.

This study compared how students with learning difficulties in math (MLD) who were randomly assigned to two instructional conditions answered items on problem solving tests aligned to the Common Core State Standards Initiative for Mathematics. Posttest scores showed improvement in the math performance of students receiving Enhanced Anchored Instruction (EAI) and typical instruction but the improvement of students in the EAI condition was greater. Much of the total variance in latent ability scores was explained at the teacher level. As we have observed in previous studies, teachers' use of interactive technology tools combined with engaging hands-on applications can make important differences in what students with MLD achieve in mathematics, particularly in problem solving. (Contains 1 note, 4 tables, and 4 figures.)

The current study uses a crossed random-effects item response model to simultaneously examine both reader and word characteristics and interactions between them that predict the reading of 39 morphologically complex words for 221 middle school students. Results suggest that a reader's ability to read a root word (e.g., "isolate") predicts that reader's ability to read a related derived word (e.g., "isolation"). After controlling for root-word reading, results also suggest that the remaining variability in derived-word reading can be explained by word and reader characteristics. The significant word characteristics include derived-word frequency and root-word frequency but not morpheme neighborhood size, average family frequency, number of morphemes, or semantic opaqueness. The significant reader characteristics include morphological awareness and vocabulary knowledge but not reading comprehension. Only phonological and orthographic-phonological opaqueness interacted with the effect of root-word reading, suggesting that students were less able to apply root-word knowledge when the root word changed phonologically (with or without an orthographic change) in the larger derived word. Discussion is included regarding how findings from this study inform the development of models of word reading for adolescents. (Contains 3 tables, 3 figures, and 2 notes.)

In the presence of test speededness, the parameter estimates of item response theory models can be poorly estimated due to conditional dependencies among items, particularly for end-of-test items (i.e., speeded items). This article conducted a systematic comparison of five-item calibration procedures--a two-parameter logistic (2PL) model, a one-dimensional mixture model, a two-step strategy (a combination of the one-dimensional mixture and the 2PL), a two-dimensional mixture model, and a hybrid model--by examining how sample size, percentage of speeded examinees, percentage of missing responses, and way of scoring missing responses (incorrect vs. omitted) affect the item parameter estimation in speeded tests. For nonspeeded items, all five procedures showed similar results in recovering item parameters. For speeded items, the one-dimensional mixture model, the two-step strategy, and the two-dimensional mixture model provided largely similar results and performed better than the 2PL model and the hybrid model in calibrating slope parameters. However, those three procedures performed similarly to the hybrid model in estimating intercept parameters. As expected, the 2PL model did not appear to be as accurate as the other models in recovering item parameters, especially when there were large numbers of examinees showing speededness and a high percentage of missing responses with incorrect scoring. Real data analysis further described the similarities and differences between the five procedures. (Contains 8 notes, 1 figure, and 7 tables.)

The models used in this article are secondary dimension mixture models with the potential to explain differential item functioning (DIF) between latent classes, called latent DIF. The focus is on models with a secondary dimension that is at the same time specific to the DIF latent class and linked to an item property. A description of the models is provided along with a means of estimating model parameters using easily available software and a description of how the models behave in two applications. One application concerns a test that is sensitive to speededness and the other is based on an arithmetic operations test where the division items show latent DIF. (Contains 4 tables and 1 figure.)

Current methods for detecting growth of students' problem-solving skills in math focus mainly on analyzing changes in test scores. Score-level analysis, however, may fail to reflect subtle changes that might be evident at the item level. This article demonstrates a method for studying item-level changes using data from a multiwave experiment with a teaching method called enhanced anchored instruction (EAI). The analysis combines a mixture Rasch model for detecting individual differences within latent groups with a latent transition analysis model for tracking changes in latent group membership over the course of EAI. The analysis clearly indicates the effects of EAI and how they differ for members of each latent class. Comparisons are provided with a standard analysis of changes in test scores. Implications of the new approach are discussed for detecting subtle transformations in the math performance of students across a range of ability levels. (Contains 4 tables and 3 figures.)

A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation study indicated that model recovery using the LTA-MRM was good except for small sample size-short test conditions. A real data application of a mathematics intervention with middle school students indicated that the LTA-MRM clearly detected the intervention effect and also provided a means of helping to better understand the effects compared to a standard multiwave analysis of variance. (Contains 9 tables and 2 figures.)