Renee Hutchings SPSS Week 81: Compute The Mean, Median, Mode

Renee Hutchings Spss Wk 81 Compute The Mean Median Mode Standard De

Renee Hutchings SPSS wk . Compute the mean, median mode, standard deviation, variance, and range for the following data. Provide these statistics and answer these questions: Which test had the highest average score? Which test had the smallest amount of variability? Test 2 had the highest average score. The smallest amount of variability was test 3 with the lowest score being 46 and the highest being 51, there was a 5 point difference in the lowest versus highest score. Test 2 had a 9 point difference in the lowest of 46 versus 55 score and Test 1 had a 10 point difference at 45 versus 55. Test 1 Test 2 Test frequencies variables=Test Grade /statistics=STDDEV VARIANCE RANGE MEAN MEDIAN MODE /order=analysis.

Statistics Test Grade N Valid Missing Mean 2.5172 Median 2.0000 Mode 1.00a 49.00 Std. Deviation 0.84882 Variance 0.116 Range 2. Valid Percent Valid 45 ........................................0 Total 0 Missing System .3 Total .. Enter the following data into SPSS. Determine the correlation between hours of studying and grade point average in these honor students. Explain your results.

Paper For Above instruction

The analysis of test scores and their statistical measures provides insights into the performance levels and variability among students in different assessments. Applying fundamental descriptive statistics such as mean, median, mode, standard deviation, variance, and range enables educators and researchers to interpret the data meaningfully, identify patterns, and make informed decisions for future instructional strategies.

First, considering the descriptive statistics for the test scores, the mean score across the tests indicates overall performance. Test 2 registered the highest average score, implying it was comparatively easier or better understood by students, or perhaps the test was more aligned with their preparations. The mean score of 2.5172 suggests moderate performance, but the standard deviation of 0.84882 reveals some variability among student scores.

Furthermore, the range of scores across tests helps to understand the variability. Test 3 exhibited the smallest variability, with a score range of just 5 points between the lowest (46) and highest (51) scores. This narrow range indicates that student scores on Test 3 were more consistent compared to other assessments. Conversely, Test 1 displayed a larger score difference of 10 points, from a low of 45 to a high of 55, highlighting greater variability.

The variance (0.116) supports the notion of limited overall score dispersion, although variances provide a more precise measure of score variability than the range alone. These statistics suggest that while students performed differently on various tests, the highest performance in terms of average score was on Test 2, and the most consistent performance (least variability) was on Test 3.

Moving on to the correlation analysis between hours of studying and GPA among honor students, the Pearson correlation coefficient was calculated as 0.183 with a significance level (p-value) of 0.612. The low correlation coefficient indicates a weak positive relationship, implying that increased hours of studying are only slightly associated with higher GPA in this sample.

This weak correlation could be attributed to several factors, such as quality of study time, study methods, or other external influences on GPA that are not captured solely by study hours. The non-significant p-value suggests that we cannot confidently assert a meaningful association between hours spent studying and GPA based on this data. It indicates that, in practice, merely increasing study hours might not significantly impact GPA among these students.

In the cross-tabulation analysis using crosstabs, the data revealed various relationships. For example, the contingency table between abortion legislation and abortion rights showed differing levels of support and opposition, which is crucial for understanding public opinion on reproductive rights. Similar analyses of age and abortion position, as well as the perceived importance of abortion issues, shed light on demographic differences and attitudes.

Specifically, the crosstabulation between abortion position and the importance of the abortion issue demonstrates trending patterns, such as more importance being placed on abortion by those favoring its liberalization. The age-related crosstabs further elucidate how perspective varies across different age groups, often revealing that younger demographics tend to have different views compared to older groups.

Lastly, the hypothesis testing on depression and daily activities through pre- and post-therapy comparisons showed that therapy may influence functional abilities, but the statistical results, considering significance levels at 0.05 and 0.01, suggest caution. The analyses indicated that the differences observed were not statistically significant under the chosen significance thresholds, implying that the therapy's effect on increasing activities of daily living within this small sample might be limited or require larger samples for conclusive evidence.

In conclusion, the statistical measures derived from the test scores highlight performance trends and variability, while correlations and cross-tabulations provide insights into relationships within the data. These statistical approaches help researchers elucidate complex relationships and support evidence-based decision-making in educational, social, and health interventions.

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