PSY-7864 - Assessment 2 UPLOAD

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Capella University *

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7864

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Statistics

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Apr 3, 2024

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Uploaded by DeanElementCheetah38

Data Analysis and Application Tim Anderson Capella University 1

Data Analysis Plan The variables from the grades.jasp file are quiz 1, gpa, total, and final. All of the variables in this analysis are continuous. Is there a relationship between total and final? The null hypothesis is there is no relationship between these variables and the alternative hypothesis is there is some relationship between these variables. The research questions for the variables gpa and quiz 1 would be, is there a relationship between the gpa and quiz 1? The null hypothesis is there is no relationship between these variables and the alternative hypothesis is there is some relationship between these variables. Testing Assumptions Descriptive Statistics quiz1 gpa total final Skewness -0.851 -0.220 -0.757 -0.341 Std. Error of Skewness 0.236 0.236 0.236 0.236 Kurtosis 0.162 -0.688 1.146 -0.277 Std. Error of Kurtosis 0.467 0.467 0.467 0.467 The descriptive statistics table shows that the skewness for total is -0.757 and the kurtosis is 1.146. The skewness for the final is -0.341 and the kurtosis is -0.277. The total and final are approximately symmetric since the skewness for both variables is between -1 and 1. But the kurtosis of the total is not between -1 and 1 which means the normality is violated (Warner, 2020). The descriptive statistics table shows that the skewness for quiz 1 is -0.851 and the kurtosis is 0.162. The skewness for the gpa is -0.22 and the kurtosis is -0.688. The gpa and quiz 1 are approximately symmetric since the skewness and kurtosis for both variables are between -1 and 1 (Warner, 2020). The normality would not be violated. 2

Results & Interpretation Pearson's Correlations Variable quiz1 gpa total final 1. quiz1 Pearson's r — p-value — 2. gpa Pearson's r 0.152 — p-value 0.121 — 3. total Pearson's r 0.797 ** * 0.318*** — p-value < .001 < .001 — 4. final Pearson's r 0.499 ** * 0.379*** 0.875*** — p-value < .001 < .001 < .001 — * p < .05, ** p < .01, *** p < .001 There is a statistically strong positive correlation between the total and final scores, r(103) = 0.875, p <.001. P-value < .001 rejects the null hypothesis (Warner, 2020). There is a statistically negligible positive correlation between the gpa and quiz 1 scores, r(103) = 0.152, p = 0.121. P-value = 0.121 states to not reject the null hypothesis (Warner, 2020). Statistical Conclusions Based on the analysis of the data we can conclude that the alternate hypothesis is that there is a relationship between the two variables, total and final scores. The correlation between quiz 1 and gpa confirms a null hypothesis that there is no relationship between the variables. The conclusion from the correlation is that total scores and final scores have a positive correlation because your final score would reflect your overall total scores throughout the class. The conclusion from the correlation of the variables quiz 1 and gpa is that the score of the first test did not have an overall effect on the gpa because it is just one quiz during the duration of the course. The limitations with this correlation analysis is that the variables relationship does 3

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not refer to each individual meaning that the correlation between the total and final score variables may not mean anything. If quiz 1 did not have to do with specific coursework that was covered in the course it may not be a fair assessment of students’ knowledge. An alternative test that may result in useful data is to measure the relationship between final scores and the amount of time students spend studying for the final. Application The field of applied behavior analysis would find this type of analysis beneficial because it is focused on data. Professionals in this field must take data from their client’s behaviors and analyze it to determine which program should be implemented. Two variables that could be used in the analysis would be language skill scores and time spent with an applied behavior analysis professional. Using this data is important to find out if the time spent with professionals is helping clients to progress. Using histograms could be beneficial for professionals as they explain client progress to parents or guardians. 4