Exploring teacher popularity: associations with teacher characteristics and student outcomes in primary school
TL;DR: In this article, conditions and consequences of teacher popularity in primary schools were investigated, and teacher popularity was embedded in a theoretical framework that describes relationships between teacher competence, teaching quality, and student outcomes.
Abstract: In this study, we investigated conditions and consequences of teacher popularity in primary schools. Teacher popularity is embedded in a theoretical framework that describes relationships between teacher competence, teaching quality, and student outcomes. In the empirical analyses, we used multilevel modeling to distinguish between individual students’ liking of the teacher and a teacher’s popularity as rated by the whole class (N = 1070 students, 54 teachers). The classroom level composite of the extent to which students liked their teacher was a reliable indicator of teacher popularity. Teacher popularity was associated with teacher self-reports of self-efficacy and teaching enthusiasm and with external observers’ ratings of teaching quality. The grades students received were not related to the popularity ratings. In a longitudinal study, teacher popularity predicted students’ learning gains and interest development over and above the effects of teaching quality. These results suggest that teacher popularity can be a useful and informative indicator in research on students’ academic development and teacher effectiveness.
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TL;DR: Teachers' teaching-related competence beliefs such as perceived teaching ability and self-efficacy have been linked to their occupational well-being and external evaluations of instructional qualit....
Abstract: Teachers’ teaching-related competence beliefs such as perceived teaching ability and self-efficacy have been linked to their occupational well-being and external evaluations of instructional qualit...
19 citations
TL;DR: Wang et al. as discussed by the authors investigated the relationship between resilience, student-teacher relationship, and parent-child separation-PTSS (PCS-pTSS) in rural left-behind children in Anhui province of China.
5 citations
TL;DR: The authors found that people tend to like others more if they are similar rather than dissimilar to themselves, and that students tend to prefer teachers with whom they share similar personal characteristics and interests.
Abstract: Research suggests that people tend to like others more if they are similar rather than dissimilar to themselves Likewise, students may tend to prefer teachers with whom they share similar personal
2 citations
TL;DR: In this paper , the authors experimentally varied the addressee in teaching-quality items capturing six dimensions of teacher support in two school subjects and investigated differences between the two versions in mean levels, level of agreement, associations between dimensions using the same versus different addressees, and correlations with a variety of student outcome variables.
2 citations
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TL;DR: In this article, the authors present guidelines for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges, and the confidence intervals for each of the forms are reviewed.
Abstract: Reliability coefficients often take the form of intraclass correlation coefficients. In this article, guidelines are given for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges. Relevant to the choice of the coefficient are the appropriate statistical model for the reliability and the application to be made of the reliability results. Confidence intervals for each of the forms are reviewed.
21,185 citations
TL;DR: Self-Determination Theory (SDT) as mentioned in this paper maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being.
Abstract: Self-determination theory (SDT) maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness. We discuss the SDT concept of needs as it relates to previous need theories, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being. This concept of needs leads to the hypotheses that different regulatory processes underlying goal pursuits are differentially associated with effective functioning and well-being and also that different goal contents have different relations to the quality of behavior and mental health, specifically because different regulatory processes and different goal contents are associated with differing degrees of need satisfaction. Social contexts and individual differences that support satisfaction of the basic needs facilitate natural growth processes including intrinsically motivated behavior and integration of extrinsic motivations, whereas those that forestall autonomy, competence, or relatedness are associated with poorer motivation, performance, and well-being. We also discuss the relation of the psychological needs to cultural values, evolutionary processes, and other contemporary motivation theories.
20,832 citations
TL;DR: The expectancy-value theory of motivation is discussed, focusing on an expectancy- value model developed and researched by Eccles, Wigfield, and their colleagues, and its components are compared to those of related constructs, including self-efficacy, intrinsic and extrinsic motivation, and interest.
5,389 citations
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23 Apr 2010TL;DR: This chapter discusses how to improve the accuracy of Maximum Likelihood Analyses by extending EM to Multivariate Data, and the role of First Derivatives in this process.
Abstract: Part 1. An Introduction to Missing Data. 1.1 Introduction. 1.2 Chapter Overview. 1.3 Missing Data Patterns. 1.4 A Conceptual Overview of Missing Data heory. 1.5 A More Formal Description of Missing Data Theory. 1.6 Why Is the Missing Data Mechanism Important? 1.7 How Plausible Is the Missing at Random Mechanism? 1.8 An Inclusive Analysis Strategy. 1.9 Testing the Missing Completely at Random Mechanism. 1.10 Planned Missing Data Designs. 1.11 The Three-Form Design. 1.12 Planned Missing Data for Longitudinal Designs. 1.13 Conducting Power Analyses for Planned Missing Data Designs. 1.14 Data Analysis Example. 1.15 Summary. 1.16 Recommended Readings. Part 2. Traditional Methods for Dealing with Missing Data. 2.1 Chapter Overview. 2.2 An Overview of Deletion Methods. 2.3 Listwise Deletion. 2.4 Pairwise Deletion. 2.5 An Overview of Single Imputation Techniques. 2.6 Arithmetic Mean Imputation. 2.7 Regression Imputation. 2.8 Stochastic Regression Imputation. 2.9 Hot-Deck Imputation. 2.10 Similar Response Pattern Imputation. 2.11 Averaging the Available Items. 2.12 Last Observation Carried Forward. 2.13 An Illustrative Simulation Study. 2.14 Summary. 2.15 Recommended Readings. Part 3. An Introduction to Maximum Likelihood Estimation. 3.1 Chapter Overview. 3.2 The Univariate Normal Distribution. 3.3 The Sample Likelihood. 3.4 The Log-Likelihood. 3.5 Estimating Unknown Parameters. 3.6 The Role of First Derivatives. 3.7 Estimating Standard Errors. 3.8 Maximum Likelihood Estimation with Multivariate Normal Data. 3.9 A Bivariate Analysis Example. 3.10 Iterative Optimization Algorithms. 3.11 Significance Testing Using the Wald Statistic. 3.12 The Likelihood Ratio Test Statistic. 3.13 Should I Use the Wald Test or the Likelihood Ratio Statistic? 3.14 Data Analysis Example 1. 3.15 Data Analysis Example 2. 3.16 Summary. 3.17 Recommended Readings. Part 4. Maximum Likelihood Missing Data Handling. 4.1 Chapter Overview. 4.2 The Missing Data Log-Likelihood. 4.3 How Do the Incomplete Data Records Improve Estimation? 4.4 An Illustrative Computer Simulation Study. 4.5 Estimating Standard Errors with Missing Data. 4.6 Observed Versus Expected Information. 4.7 A Bivariate Analysis Example. 4.8 An Illustrative Computer Simulation Study. 4.9 An Overview of the EM Algorithm. 4.10 A Detailed Description of the EM Algorithm. 4.11 A Bivariate Analysis Example. 4.12 Extending EM to Multivariate Data. 4.13 Maximum Likelihood Software Options. 4.14 Data Analysis Example 1. 4.15 Data Analysis Example 2. 4.16 Data Analysis Example 3. 4.17 Data Analysis Example 4. 4.18 Data Analysis Example 5. 4.19 Summary. 4.20 Recommended Readings. Part 5. Improving the Accuracy of Maximum Likelihood Analyses. 5.1 Chapter Overview. 5.2 The Rationale for an Inclusive Analysis Strategy. 5.3 An Illustrative Computer Simulation Study. 5.4 Identifying a Set of Auxiliary Variables. 5.5 Incorporating Auxiliary Variables Into a Maximum Likelihood Analysis. 5.6 The Saturated Correlates Model. 5.7 The Impact of Non-Normal Data. 5.8 Robust Standard Errors. 5.9 Bootstrap Standard Errors. 5.10 The Rescaled Likelihood Ratio Test. 5.11 Bootstrapping the Likelihood Ratio Statistic. 5.12 Data Analysis Example 1. 5.13 Data Analysis Example 2. 5.14 Data Analysis Example 3. 5.15 Summary. 5.16 Recommended Readings. Part 6. An Introduction to Bayesian Estimation. 6.1 Chapter Overview. 6.2 What Makes Bayesian Statistics Different? 6.3 A Conceptual Overview of Bayesian Estimation. 6.4 Bayes' Theorem. 6.5 An Analysis Example. 6.6 How Does Bayesian Estimation Apply to Multiple Imputation? 6.7 The Posterior Distribution of the Mean. 6.8 The Posterior Distribution of the Variance. 6.9 The Posterior Distribution of a Covariance Matrix. 6.10 Summary. 6.11 Recommended Readings. Part 7. The Imputation Phase of Multiple Imputation. 7.1 Chapter Overview. 7.2 A Conceptual Description of the Imputation Phase. 7.3 A Bayesian Description of the Imputation Phase. 7.4 A Bivariate Analysis Example. 7.5 Data Augmentation with Multivariate Data. 7.6 Selecting Variables for Imputation. 7.7 The Meaning of Convergence. 7.8 Convergence Diagnostics. 7.9 Time-Series Plots. 7.10 Autocorrelation Function Plots. 7.11 Assessing Convergence from Alternate Starting Values. 7.12 Convergence Problems. 7.13 Generating the Final Set of Imputations. 7.14 How Many Data Sets Are Needed? 7.15 Summary. 7.16 Recommended Readings. Part 8. The Analysis and Pooling Phases of Multiple Imputation. 8.1 Chapter Overview. 8.2 The Analysis Phase. 8.3 Combining Parameter Estimates in the Pooling Phase. 8.4 Transforming Parameter Estimates Prior to Combining. 8.5 Pooling Standard Errors. 8.6 The Fraction of Missing Information and the Relative Increase in Variance. 8.7 When Is Multiple Imputation Comparable to Maximum Likelihood? 8.8 An Illustrative Computer Simulation Study. 8.9 Significance Testing Using the t Statistic. 8.10 An Overview of Multiparameter Significance Tests. 8.11 Testing Multiple Parameters Using the D1 Statistic. 8.12 Testing Multiple Parameters by Combining Wald Tests. 8.13 Testing Multiple Parameters by Combining Likelihood Ratio Statistics. 8.14 Data Analysis Example 1. 8.15 Data Analysis Example 2. 8.16 Data Analysis Example 3. 8.17 Summary. 8.18 Recommended Readings. Part 9. Practical Issues in Multiple Imputation. 9.1 Chapter Overview. 9.2 Dealing with Convergence Problems. 9.3 Dealing with Non-Normal Data. 9.4 To Round or Not to Round? 9.5 Preserving Interaction Effects. 9.6 Imputing Multiple-Item Questionnaires. 9.7 Alternate Imputation Algorithms. 9.8 Multiple Imputation Software Options. 9.9 Data Analysis Example 1. 9.10 Data Analysis Example 2. 9.11 Summary. 9.12 Recommended Readings. Part 10. Models for Missing Not at Random Data. 10.1 Chapter Overview. 10.2 An Ad Hoc Approach to Dealing with MNAR Data. 10.3 The Theoretical Rationale for MNAR Models. 10.4 The Classic Selection Model. 10.5 Estimating the Selection Model. 10.6 Limitations of the Selection Model. 10.7 An Illustrative Analysis. 10.8 The Pattern Mixture Model. 10.9 Limitations of the Pattern Mixture Model. 10.10 An Overview of the Longitudinal Growth Model. 10.11 A Longitudinal Selection Model. 10.12 Random Coefficient Selection Models. 10.13 Pattern Mixture Models for Longitudinal Analyses. 10.14 Identification Strategies for Longitudinal Pattern Mixture Models. 10.15 Delta Method Standard Errors. 10.16 Overview of the Data Analysis Examples. 10.17 Data Analysis Example 1. 10.18 Data Analysis Example 2. 10.19 Data Analysis Example 3. 10.20 Data Analysis Example 4. 10.21 Summary. 10.22 Recommended Readings. Part 11. Wrapping Things Up: Some Final Practical Considerations. 11.1 Chapter Overview. 11.2 Maximum Likelihood Software Options. 11.3 Multiple Imputation Software Options. 11.4 Choosing between Maximum Likelihood and Multiple Imputation. 11.5 Reporting the Results from a Missing Data Analysis. 11.6 Final Thoughts. 11.7 Recommended Readings.
3,910 citations
"Exploring teacher popularity: assoc..." refers background in this paper
...The issue of missing values requires careful consideration (Enders 2010)....
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01 Jan 2000
3,879 citations
"Exploring teacher popularity: assoc..." refers methods in this paper
...This aggregation follows a fuzzy composition process as described in the framework from Bliese (2000), meaning that the constructs at the two levels of analysis (students’ individual liking of the teacher and teacher popularity) are related to each other but not the same construct....
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...We computed the ICC2 index (Bliese 2000; Lüdtke et al. 2009) to examine whether teacher popularity could be reliably assessed at the classroom level (Research Question 1)....
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...We computed the ICC2 index (Bliese 2000; Lüdtke et al....
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