Đề 1 – Bài tập, đề thi trắc nghiệm online SPSS

A A. To sort cases in the dataset. B B. To choose a subset of cases based on certain criteria for analysis. C C. To merge multiple datasets. D D. To transpose the dataset.

A A. The hypothesis of no effect. B B. The hypothesis that contradicts the null hypothesis and represents the researcher's prediction. C C. The hypothesis that is always accepted. D D. The hypothesis that is tested directly by statistical tests.

A A. Pearson Correlation B B. T-test C C. Chi-Square Test D D. Regression Analysis

A A. The t-statistic value. B B. The degrees of freedom. C C. The p-value (Sig. value). D D. The mean difference.

A A. The central tendency of the data. B B. The spread or dispersion of data points around the mean. C C. The median value of the data. D D. The most frequent value in the data.

A A. Scale B B. Ordinal C C. Nominal D D. Continuous

A A. The variable that is manipulated by the researcher. B B. The variable that is predicted or explained by other variables. C C. The variable used to group cases. D D. The variable that is kept constant.

A A. Entering data directly into SPSS. B B. Viewing the output of statistical analyses. C C. Writing and executing SPSS commands for statistical analysis and data manipulation. D D. Defining variable properties.

A A. Failing to reject a false null hypothesis. B B. Rejecting a true null hypothesis. C C. Correctly rejecting a false null hypothesis. D D. Correctly failing to reject a true null hypothesis.

A A. To make inferences about a population based on a sample. B B. To summarize and describe the main features of a dataset. C C. To test hypotheses about group differences. D D. To predict the value of one variable from another.

A A. Select Cases B B. Recode into Different Variables C C. Split File D D. Compute Variable

A A. To display the raw data values. B B. To define and modify variable properties like name, type, and labels. C C. To execute statistical analyses. D D. To view the results of statistical tests.

A A. To change the values of an existing variable. B B. To create a new variable based on the values of an existing variable, leaving the original variable unchanged. C C. To delete variables from the dataset. D D. To rename existing variables.

A A. Bar chart B B. Pie chart C C. Scatter plot D D. Gantt chart

A A. The hypothesis that the researcher is trying to prove. B B. The hypothesis of no effect or no difference. C C. The hypothesis that is accepted if the p-value is significant. D D. The hypothesis based on previous research.

A A. Median B B. Mode C C. Mean D D. Range

A A. Nominal B B. Ordinal C C. Scale D D. String

A A. Paired-Samples T-Test B B. Independent-Samples T-Test C C. ANOVA D D. Correlation

A A. Support for the null hypothesis. B B. Failure to reject the null hypothesis. C C. Rejection of the null hypothesis in favor of the alternative hypothesis. D D. The result is not statistically significant.

A A. The correlation between two variables. B B. The difference between two group means. C C. The difference among means of three or more groups. D D. The frequency of categories in a single variable.

A A. *.sav B B. *.txt C C. *.xls or *.xlsx D D. *.dat

A A. Syntax is easier for beginners to learn. B B. Syntax is faster for data entry. C C. Syntax allows for better reproducibility and automation of analyses. D D. Syntax is required for all basic statistical analyses.

A A. Rejecting a true null hypothesis. B B. Correctly rejecting a false null hypothesis. C C. Failing to reject a false null hypothesis. D D. Correctly failing to reject a true null hypothesis.

A A. To compare means of two independent groups. B B. To compare means of two related groups or repeated measures on the same group. C C. To compare means of more than two groups. D D. To assess the relationship between two variables.

A A. Statistical Package for the Social Sciences B B. Software Program for Statistical Solutions C C. Systematic Processing of Statistical Surveys D D. Superior Performance in Statistical Software

A A. When data is normally distributed. B B. When sample sizes are large. C C. When the assumptions of parametric tests (like normality) are violated. D D. When analyzing interval or ratio data.

A A. A strong positive correlation. B B. A weak positive correlation. C C. A strong negative correlation. D D. A weak negative correlation.

A A. To recode existing variables. B B. To create new variables by performing calculations on existing variables. C C. To select a subset of cases. D D. To split the dataset into subgroups.

A A. To delete variables from the dataset. B B. To change the format of the output. C C. To modify variables to meet the assumptions of statistical tests or improve data interpretation. D D. To create new cases in the dataset.

A A. Output View B B. Syntax View C C. Data View D D. Variable View