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Assignment: Introduction to Quantitative Analysis: Confidence Intervals

With this assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. In Week 3, you used the same variables as in Week 2 to perform a descriptive analysis of the data. 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: Using the SPSS software, open the High School Longitudinal Study dataset (whichever you chose) from Week 2. Use this link https://nces.ed.gov/surveys/hsls09 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 3- to 4-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. Use appropriate APA format. Refer to the APA manual for appropriate citations. Here are some APA references you can use to assist with the assignments. I need 4 to 6 references. References Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications. Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications. DATA FROM WEEK 2 Introduction to Quantitative Analysis: Visually Displaying Data Results Visual Display of Data 1st Variable: Is Science beneficial? 2nd Variable: Is There A Mean Difference for Science Based on Gender? Table 1: Mean Vs. Gender SCORES Gender Mean N Minimum Maximum Variance Female 24.35 14628 32 65 408.253 Male 44.70 14628 39 80 327.389 Total 37.34 29256 0 80 452.297 Analysis Results The results of figure1demonstrate that numerous students do strongly agree with science being beneficial for everyday life. From the pie chart, 61% of students are certain that science is beneficial with a small percentage of 5.67 disagreeing with the statement that was presented. The results from the second variable show mean variance in scores between the genders. The results show that males scored an average of 44.70 as their female counterpart averaged 24.35. The maximum score for male was 80% whereas for the female was 65%. Therefore, we can conclude that male are best performers in science. However, students are more likely to choose science related courses such as computer science and engineering as their career of choice hoping for better future life (Krapp& Prenzel, 2011). DATA FROM WEEK 3 Table Set 1.1: Workplace Discrimination Results Discrimination at Work in Past 5 yrs N Valid 1456 Missing 1411 Variance .234 Discrimination at Work in Past 5 Years Frequency Frequency Percent Valid Percent Cumulative Percent Yes 269 9.4 18.5 18.5 No 1094 38.2 75.1 93.6 Valid Did not work or did not seek work 93 3.2 6.4 100.0 Total 1456 50.8 100 Central Tendency and Variability Frankfort-Nachmias and Leon-Guerrero (2018) suggested that the central tendency is normally measured through three methods that comprise of mean (the average value of data), median (the middle value of an ordered dataset), and mode (the most frequent value of the data). Variability analyzes variance among distributions of a categorical data (Frankfort-Nachmias & Leon-Guerrero, 2018). The General Social Survey 2016 dataset was utilized to perform analyses of both the categorical and continuous variables (Wagner, 2016). Krishnamoorthy (2016) found that the central tendency (mean, median, and mode) were used to perform the analyses on the continuous variable. It is an appropriate methodology because it calculates a single value from a continuous data and describes the collection of data by locating the central-point from the continuous dataset (Krishnamoorthy, 2016). However, this study will specifically use mean for its overall central tendency results conclusion because the dataset is from a large sample size and does not include outliers. For the categorical variable analyses, frequency distribution and variance were used. The frequency distribution and variance are appropriate analyses methods for categorical variable (Manikandan, 2011). The frequency distribution displays different measurement groups and to compare number of observations for each group (Manikandan, 2011). Presentation of Results Categorical Variable: The categorical variable (Variable No: 178 DISWK5) was stratified into three groups 1=Yes and 2=No, and 3=Did not work or did not seek work Research Question: Does discrimination exists at your workplace? Table 2: Number of Hours Usually Worked in a Week Statistics N Valid 1646 Missing 1221 Mean 40.91 Median 40.00 Mode 40 Std. Deviation 14.406 The results of central tendency as illustrated in Table 2, indicates that the mean, median, and mode were equivalent to 40. Though, the mean score recorded a higher value of 40.91. The standard deviation, σ =14.406. Based on the dataset attributes (large sample size and absence of outliers), mean score is chosen. Therefore, the results confirm that American’s average number of working hours in a week is 40.91. That is equivalent to 41 hours a week.

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