and their never-ending supports. This study was a one-way design where the independent variable was animation interactivity. In addition to a control group (Static Group) provided with only static materials, there were three groups with different levels of animation interactivity: 1) Animation with simple interactivity (Simple Animation Group), 2) animation with input vii manipulation (Input Group), and 3) animation with practice and feedback (Practice Group). A sample of 123 college students participated in the study and was randomly assigned into groups. They gathered in the computer lab to work with the animation program and then took online surveys and tests for evaluation. Students were expected to learn Principles of Hypothesis Testing (concepts of type I error, type II error and pvalue). The data collected in this study included 1) student learning attitudes, 2) achievement and confidence pre-test scores, 3) achievement and confidence post-test scores, and 4) program perception. Also, student manipulation of the animation program was recorded as Web log data. The data were analyzed by using multivariate analysis (MANOVA), univariate analysis (ANOVA), regression analysis, regression tree analysis and case analysis.The findings were as follows: 1) Animation interactivity impacted students' improvement in understanding (p=.006) and lower-level applying (p=.042), 2) animation interactivity did not impact student confidence and program perception, 3) the regression analysis indicated that student prior knowledge and interest were the most important predictors on student achievement post-test scores instead of program manipulation, and 4) the regression tree showed that there were interactions among student interest, prior knowledge, and program manipulation on the achievement post-test scores. The case analysis showed that not all students manipulated the interactive animation program as expected due to a lack of motivation and cognitive skills, and this could decrease the effect of the interactive animation. This study hoped to broaden theories on interactive learning and serve as a reference for future statistics curriculum designers and textbook publishers.viii He asked students to first clarify the definition of the type I and type II error, and then look for clues in the question for computation. The type I and type II error problems are difficult for most novice learners. But with the concrete examples of "bags," "boxes," List of Tablesor "envelopes," statistics was no longer as difficult for me. This particular teaching method is valuable for use in all introductory-level classes. 3I believe this struggle with learning statistics is not limited to myself, but is common to many other students. As a result, I created an online animated program based on my instructor's teaching method in order to help learners who also struggle with learning type I error, type II error and the p-value. This online animation was created to popularize this particular teaching method. To benefit learners with different ...
Purpose-The associations between characteristics of family relationships and family trends in cancer worry and the psychological adjustment of recipients of genetic testing for cancer susceptibility were investigated.Methods-Data provided by 178 individuals from 24 families with Lynch syndrome who participated in a cohort study investigating psychological and behavioral outcomes of genetic testing were used. Responses from multiple family members were aggregated to construct family trends representing norms and departure from norms in cancer worry.Results-Lower perceived family cohesion at baseline and decrease in this variable at 6-months after receipt of test results were associated with higher depression scores at 12-months. More variability in cancer worry among family members at baseline was also associated with higher depression scores at 12-months. Increase in family conflict was associated with decrease in depression scores among individuals from families with higher levels of cancer worry on average and less variability among the members.Conclusions-Family relationships and family trends in levels of cancer worry may play important roles in the psychological adjustment of genetic test recipients. The findings highlight the complexity of familial environment surrounding individuals that undergo genetic testing and suggest the benefits of considering these factors when providing genetic services.
A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel—Haenszel and logistic regression analysis. A nonparametric classification rule is examined through simulation and real data, and Type I error and power are compared with equivalent Mantel—Haenszel, logistic regression, and discriminant analyses.
The authors examined associations among 266 undergraduate men's perceptions of parental bonding, gender role conflict, affect regulation capacity, and adult attachment avoidance. Partial support for the hypothesized mediation effects was found, with results suggesting that emotion regulation suppression and reappraisal helped explain the association between men's memories of maternal bonding care and adult attachment avoidance. Furthermore, results identified potential mechanisms of influence underlying the development of men's avoidance of intimacy. Clinical implications and suggestions for further research are provided.
This study examined the performance of the maximum Fisher's information, the maximum posterior weighted information, and the minimum expected posterior variance methods for selecting items in a computerized adaptive testing system when the items were grouped in testlets. A simulation study compared the efficiency of ability estimation among the item selection techniques under varying conditions of local-item dependency when the response model was either the three-parameter-logistic item response theory or the three-parameter-logistic testlet response theory. The item selection techniques performed similarly within any particular condition, the practical implications of which are discussed within the article.Keywords computerized adaptive testing, item response theory, testlet response theory One advantage of computerized adaptive testing (CAT) is the increased measurement efficiency that is associated with tailoring items to an individual examinee's ability (y) level. The measurement efficiency increase will depend on the accuracy of the examinee's estimated theta level. Because suboptimal point estimates of y are likely to occur early in the adaptive testing process, there has been a substantial amount of research comparing the performance of different item selection techniques within CAT systems under dichotomous and polytomous response models. However, there are no published research studies comparing item selection technique performance under the testlet response theory (TRT) model. The primary objective of this study is to investigate the effects of using different item selection techniques to choose testlets that have been modeled to have varying degrees of dependency. A secondary objective is to investigate the possibility of an interaction between item selection technique and response model when the item dependency is modeled correctly under TRT versus when it is mismodeled under item response theory (IRT).Various item selection methods have been proposed to overcome inefficiency due to inaccurate estimation of y. Chang and Ying (1996) proposed a more global information approach using the Kullback-Leibler information. Veerkamp and Berger (1997) developed a procedure to select Article
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