Robust estimation in Education MCQs
Robust estimation in Education MCQs
Try to answer these Robust estimation in Education MCQs and check your understanding of the Robust estimation in Education subject.
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1: Which of the following transformations is most useful for correcting skewed data?
A. Tangent transformation
B. Arcsine transformation
C. Cosine transformation
D. Log transformation
2: The assumption of homogeneity of variance is met when:
A. The variances in different groups are significantly different.
B. The variances in different groups are approximately equal.
C. The variance across groups is proportional to the means of those groups.
D. The variance is the same as the interquartile range.
3: Imagine you conduct a t-test using IBM SPSS and the output reveals that Levene’s test for equality of variance is significant. What should you do? (Hint: Levene’s test tests the assumption that variances in
A. Different groups are approximately equal.)
B. Interpret the figures in the row labelled ‘equal variances assumed’.
C. Conduct a Kruskal–Wallis test instead.
D. Interpret the figures in the row labelled ‘equal variances not assumed’.
E. Collect more data.
4: What is robust estimation in the context of education?
A. Estimating the educational outcomes with a high level of precision
B. Using statistical methods that are not influenced by extreme values or outliers
C. Estimating the average performance of students in a specific subject
D. Using advanced machine learning techniques to predict educational outcomes
5: Why is robust estimation important in educational research?
A. It allows for more accurate measurement of students' abilities
B. It reduces the impact of outliers and extreme values on the results
C. It helps in comparing educational systems across different countries
D. It enables researchers to conduct experiments with large sample sizes
6: Which statistical technique is commonly used for robust estimation in educational research?
A. Ordinary Least Squares (OLS) regression
B. Hierarchical Linear Modeling (HLM)
C. Robust regression
D. Factor analysis
7: What is the advantage of using robust estimation techniques?
A. They provide unbiased estimates of parameters in the presence of outliers
B. They require less computational power compared to traditional methods
C. They can handle missing data more effectively
D. They ensure normal distribution of the data for accurate analysis
8: When is robust estimation particularly useful in educational research?
A. When the sample size is small
B. When the data contain extreme values or outliers
C. When there is a need to conduct complex factorial designs
D. When the research involves qualitative data analysis
9: Which of the following is a common robust estimation technique used in educational measurement?
A. Maximum Likelihood Estimation (MLE)
B. Weighted Least Squares (WLS)
C. The Huber M-estimator
D. Analysis of Variance (ANOVA)
10: How does robust estimation handle outliers in the data?
A. It assigns lower weights to outliers, reducing their influence on the estimates
B. It removes outliers from the dataset entirely
C. It imputes missing values for outliers using multiple imputation methods
D. It adjusts the sample size to exclude outliers from the analysis
11: What is the purpose of robust estimation in educational assessment?
A. To identify students who are underperforming in a particular subject
B. To provide accurate estimates of student achievement while accounting for measurement errors and outliers
C. To determine the effectiveness of educational interventions
D. To rank schools based on their overall performance
12: Which of the following scenarios would benefit from robust estimation techniques in educational research?
A. Analyzing the impact of a teaching intervention on students' test scores
B. Evaluating the relationship between socioeconomic status and academic achievement
C. Conducting a survey on students' attitudes and perceptions
D. Analyzing student performance in the presence of extreme score outliers
13: How does robust estimation contribute to the reliability and validity of educational research findings?
A. It ensures that measurement errors do not bias the results
B. It provides more accurate estimates of population parameters by accounting for extreme values
C. It improves the internal consistency of survey instruments
D. It enhances the generalizability of research findings to different populations
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Robust estimation in Education MCQs
