Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.

OBJECTIVE: Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA). METHOD: Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors. RESULTS: ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040. CONCLUSION: Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.

Determining fall risk change throughout pregnancy: the accuracy of postpartum survey and relationship to fall efficacy.

All epidemiological studies on pregnancy fall risk to date have relied on postpartum recall. This study investigated the accuracy of postpartum recall of falls that were reported during pregnancy, including assessment of fall efficacy as a possible reason for recall inaccuracy. Twenty participants reported fall experiences weekly during pregnancy, but one participant was excluded as an outlier. A fall efficacy questionnaire was completed every six weeks during pregnancy. A postpartum survey to mimic previous studies (Dunning, Lemasters, and Bhattacharya 2010; Dunning et al. 2003) was delivered to determine recall accuracy. Postpartum recall of fall events each gestational month matches the previous study (Dunning, Lemasters, and Bhattacharya 2010). However, recall of falls is 16% underestimated and recall of all fall events is 30% overestimated in postpartum survey. There is a slight relationship between fall efficacy and true falls, but not between fall efficacy and fall recall. Our study suggests fall risk needs to be intermittently surveyed throughout pregnancy rather than assessed via postpartum survey.Practitioner summary: This study investigated the accuracy of postpartum survey of fall risk during pregnancy and the possibility of fall efficacy as a covariate. We used three corresponding surveys. We found inaccuracies in postpartum survey, not explain by fall efficacy.

Students' 2018 PISA reading self-concept: Identifying predictors and examining model generalizability for emergent bilinguals.

Decades of research have indicated that reading self-concept is an important predictor of reading achievement. During this period, the population of emergent bilinguals has continued to increase within United States' schools. However, the existing literature has tended to examine native English speakers' and emergent bilinguals' reading self-concept in the aggregate, thereby potentially obfuscating the unique pathways through which reading self-concept predicts reading achievement. Furthermore, due to the overreliance of native English speakers in samples relating to theory development, researchers attempting to examine predictors of reading achievement may a priori select variables that are more aligned with native English speakers' experiences. To address this issue, we adopted Elastic Net, which is a theoretically agnostic methodology and machine learning approach to variable selection to identify the proximal and distal predictors of reading self-concept for the entire population; in our study, participants from the United States who participated in PISA 2018 served as the baseline group to determine significant predictors of reading self-concept with the intent of identifying potential new directions for future researchers. Based on Elastic Net analysis, 20 variables at the student level, three variables at the teacher level, and 12 variables at the school level were identified as the most salient predictors of reading self-concept. We then utilized a multilevel modeling approach to test model generalizability of the identified predictors of reading self-concept for emergent bilinguals and native English speakers. We disaggregated and compared findings for both emergent bilinguals and native English speakers. Our results indicate that although some predictors were important for both groups (e.g., perceived information and communications technologies competence), other predictors were not (e.g., competitiveness). Suggestions for future directions and implications of the present study are examined.

Relationships Among English Language Proficiency, Self-efficacy, Motivation, Motivational Intensity, and Achievement in an ESP/EAP Context.

This study, the first of the kind in the field of English for specific purposes, examined direct and indirect relationships among English language proficiency, English public speaking (EPS) motivation, motivational intensity, self-efficacy, and EPS achievement. The sample consisted of 189 non-English-major students. The final structural equation model yielded an acceptable fit to the data and explained 23.4% of the variance in EPS achievement. English language proficiency and EPS self-efficacy had both direct and indirect (via, respectively, self-efficacy and motivational intensity) impacts on EPS performance. Ought-to self emerged as the strongest contributor to explaining motivation (R2 = .90), followed by learning experience (R2 = .57), and ideal self (R2 = .32). Implications are discussed.

Leader development begins at home: Overparenting harms adolescent leader emergence.

There is increasing interest in the early roots and influencing factors of leadership potential from a life span development perspective. This conceptual and empirical work extends traditional approaches focusing on adults in organizational settings. From the perspective of early influences on leader development, the goal of this study was to examine the effects of overparenting on adolescent leader emergence, influencing mechanisms, and sex differences. Students (N = 1,255) from 55 classrooms in 13 junior high schools participated, with additional responses from their parents, peers, and teachers. The results indicated that overparenting is negatively related to adolescent leader emergence as indicated by parent ratings, teacher ratings, and peer nominations in addition to leader role occupancy. The negative effects of overparenting on leader emergence (perceived and actual) were serially mediated by self-esteem and leader self-efficacy. In addition, sex difference analysis revealed that male adolescents received more overparenting and showed less leader emergence (perceived and actual) than female adolescents. Female adolescents' self-esteem was more likely to be negatively related to overparenting, and female adolescents' leader emergence (perceived and actual) was more strongly related to their leader self-efficacy when compared with male adolescents. Implications for life span leader development theory, for youth and adult leadership development practices, and for parenting practices on future generations are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Investigation of Missing Responses in Q-Matrix Validation.

Missing data can be a serious issue for practitioners and researchers who are tasked with Q-matrix validation analysis in implementation of cognitive diagnostic models. The article investigates the impact of missing responses, and four common approaches (treat as incorrect, logistic regression, listwise deletion, and expectation-maximization [EM] imputation) for dealing with them, on the performance of two major Q-matrix validation methods (the EM-based δ-method and the nonparametric Q-matrix refinement method) across multiple factors. Results of the simulation study show that both validation methods perform better when missing responses are imputed using EM imputation or logistic regression instead of being treated as incorrect and using listwise deletion. The nonparametric Q-matrix validation method outperforms the EM-based δ-method in most conditions. Higher missing rates yield poorer performance of both methods. Number of attributes and items have an impact on performance of both methods as well. Results of a real data example are also discussed in the study.

Examining DIF in the Context of CDMs When the Q-Matrix Is Misspecified.

The rise in popularity and use of cognitive diagnostic models (CDMs) in educational research are partly motivated by the models' ability to provide diagnostic information regarding students' strengths and weaknesses in a variety of content areas. An important step to ensure appropriate interpretations from CDMs is to investigate differential item functioning (DIF). To this end, the current simulation study examined the performance of three methods to detect DIF in CDMs, with particular emphasis on the impact of Q-matrix misspecification on methods' performance. Results illustrated that logistic regression and Mantel-Haenszel had better control of Type I error than the Wald test; however, high power rates were found using logistic regression and Wald methods, only. In addition to the tradeoff between Type I error control and acceptable power, our results suggested that Q-matrix complexity and item structures yield different results for different methods, presenting a more complex picture of the methods' performance. Finally, implications and future directions are discussed.

Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality.

Information about the psychometric properties of items can be highly useful in assessment development, for example, in item response theory (IRT) applications and computerized adaptive testing. Although literature on parameter recovery in unidimensional IRT abounds, less is known about parameter recovery in multidimensional IRT (MIRT), notably when tests exhibit complex structures or when latent traits are nonnormal. The current simulation study focuses on investigation of the effects of complex item structures and the shape of examinees' latent trait distributions on item parameter recovery in compensatory MIRT models for dichotomous items. Outcome variables included bias and root mean square error. Results indicated that when latent traits were skewed, item parameter recovery was generally adversely impacted. In addition, the presence of complexity contributed to decreases in the precision of parameter recovery, particularly for discrimination parameters along one dimension when at least one latent trait was generated as skewed.