# Introduction to Quantitative Analysis: Confidence Intervals

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Question description

For this Assignment, you will calculate a confidence interval in SPSS for one of the variables from your Week 2 and Week 3 Assignments.

To prepare for this Assignment:

• Review the Learning Resources related to probability, sampling distributions, and confidence intervals.
• For additional support, review the Skill Builder: Confidence Intervals and the Skill Builder: Sampling Distributions, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.
• Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you chose) from Week 2.
• Choose an appropriate variable from Weeks 2 and 3 and calculate a confidence interval in SPSS.
• Once you perform your confidence interval, review Chapter 5 and 11 of the Wagner text to understand how to copy and paste your output into your Word document.

For this Assignment:

Write a 2- to 3-paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document.

Based on the results of your data in this confidence interval Assignment, provide a brief explanation of what the implications for social change might be.

Attached are weeks 2 and 3 assignments

Week 2 Assignment Visually Displaying Data Results Visual displaying of information includes the utilization of various statistical tools such as the bar charts, line graphs, tables, pie charts among other visual display tools to present a huge amount of raw data. Often, the visual displays are utilized by scholars to present statistical evaluation findings; thus, fostering the capacity of the audience to conceptualize and interpret the presented statistical information. Wickham notes that visual display of information summarizes statistical information into various subsets that are useful in different ways (2016). High school longitudinal dataset reveals various continuous, nominal and ordinal attribute, which while displayed visually could provide different useful insights. Selected Variables The selected nominal and continuous variables used in this study are obtained from the High school Longitudinal Study dataset. X1LOCALE is the selected categorical variable labeled as T1 School locale (urbanicity) on the data set. The element reveals the location of the school involved in the study categorized as city, suburb, town, or in the rural areas. On the other hand, the selected continuous variable is the X1MTHID labeled as the scale of student's mathematics identity. Notably, the student’s mathematics identity was scored on a scale with a negative score being the least identity while the highest score implying a high student’s identity with mathematics. Categorical Variable Ary, Jacobs, Irvine, and Walker (2018) define a categorical variable as an element that takes a limited possible value. The figure below reveals the attribute for the selected categorical variable the school locale (urbanicity). Figure 1: A Bar Graph for the School Locale (N, 23503) Inference Figure 1 above reveals the percentage for each attribute in the categorical variable School locale (urbanicity) in the dataset. Based on the figure, the variable attributes city, suburb, town and rural areas reveals 28.46 %, 36.03 %, 11.86 % and 23.65% respectively. This implies that the highest number of the respondent in the study came from suburb schools, followed closely by city schools students while the least representation in the study was from town schools. Continuous Variable Notably, a continuous variable is an attribute that posits infinite possible values (Ary et al., 2018). The figure below reveals the selected continuous variable the scale of student's mathematics identity. Figure 2: A Line Graph for Scale of Student's Mathematics Identity (N, 23503) Inference The figure 2 above depicts various scores for the student's mathematics identity. While the negative scale implies the least identity with mathematics, the highest positive score depicts high student's identity with mathematics subject. In this case, the score that reveals the highest frequency of 5388 respondents is 0.60 close to the highest score of 1.76. This implies that a significantly large number of students reveal an identity with mathematics subject. However, the chart reveals that there is still a high number of students that depicted low scale below 0.0 implying that they did not have mathematics identity. The implication for Social Change The visually displayed data for the continuous variable student's mathematics identity has a high implication for social change. Firstly, the school's administration could utilize the information in devising ways essential for improving the student’s identity with the mathematics subject. Besides, the displayed data could enhance the change of mathematics teachers’ approach to the subject to the students with a desire of improving the identity with the subjects. Thus, this would enhance the implementation of changes in the mathematics subject teaching approach, heightening the ability of the students to get conversant with the mathematics. Reference List Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education. Cengage Learning. Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer. Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.
Week 3 Assignment Introduction to Quantitative Analysis: Descriptive Analysis For the purpose of this assignment my variables of interest (from assignment 2) are X1LOCALE which the categorical variable. The dataset reflects the label as T1 School Locale (urbanicity), which shows the location of the school in a study that categorizes city, suburb, town or rural areas. The continuous variable that is chosen is the X1MTHID which is the scale of students’ mathematical identity. The student's mathematical identity was scored by using the negative score as the least identity, while the positive or highest score is reflective of a high student identity with Math. X1LOCALE- T1 School Locale (urbanicity) This is a categorical variable. The following bar chart was created based on this variable. The chart above displays the percentage of each element in the T1 School locale (urbanicity) in the dataset. The chart reveals these elements accordingly, they include city at 28.46%, suburb 36.03%, town 11.86%, and rural areas 23.65% respectively. In the study, this indicates that the highest number students or respondents came from schools in the suburb, followed closely by students from schools in the city and rural areas, and the students or respondents from the town schools which were least represented. X1MTHID-Scale of Student's Mathematics Identity This is a continuous variable. The following line graph was created based on this variable Line graph for Scale of Student's Mathematics identity Analysis The bar chart above displays the percentage of each element in the T1 School locale (urbanicity) in the dataset. The chart reflects these elements accordingly, they include city at 28.46%, suburb 36.03%, town 11.86%, and rural areas 23.65%. In the study, this indicates that the highest number respondents came from schools in the suburb, this is closely followed by respondents or students from schools in the city and rural areas, and the respondents or students that were least represented were from the town schools. Based on the Line graph several scores for the student's mathematical identity have been identified. Here the negative scales indicate the students least identity with Math, while the scores that are highest and positive portrays a high student identity with the math subject. The graph indicates that the highest frequency score of 5388 respondents is 0.60 close to 1.76, being the highest score. This assumes that a lot of students or a very large number of them show an identity with the Math subject. However, the chart also implies there are a large number of students that portrayed a low scale below 0.0. This implies that they did not have an identity with Math. Implication for Social Justice There is a high implication for Social change from the data or information for the continuous variable as it pertains to the student's mathematics identity. This data or information could be used by the school's administration to make strategic decisions as it pertains to improving student's identity with the Math subject. The data could also improve changes to the approach of Math teachers to the subject, to their students who have the desire to improve their identity with the subject. Mathematical teaching approach could also be improved by implementing changes that would increase the ability for students to be comfortable with the subject. References Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education. Cengage Learning. Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer. Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.

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School: Carnegie Mellon University

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