Assignment: Introduction to Quantitative Analysis: Confidence Intervals
Assignment: Introduction to Quantitative Analysis: Confidence Intervals
In your Week 2 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.
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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 3paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
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.
FrankfortNachmias, C., LeonGuerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
 Chapter 5, “The Normal Distribution” (pp. 151177)
 Chapter 6, “Sampling and Sampling Distributions” (pp. 179209)
 Chapter 7, “Estimation” (pp. 211240)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
 Chapter 3, “Selecting and Sampling Cases”
 Chapter 5, “Charts and Graphs”
 Chapter 11, “Editing Output”
https://rpsychologist.com/
https://academicguides.waldenu.edu/rsch8210

WK2AssgnBryantS1.docx
VISUALLY DATA 1
Introduction to Quantitative Analysis: Visually Displaying Data Results
Walden University
Sarieta Bryant
RSCH 8210/7210/6210: Quantitative Reasoning and Analysis
March 9, 2021
Introduction to Quantitative Analysis: Visually Displaying Data Results
Introduction
Data is not that useful in its raw form; though some experts can still observe the data and generate inferences, it is still cryptic for novices and individuals with minimal data skills to obtain information from such data. Therefore, it is important to transform it into a form that would be easy to get information.
There are different software products used for visually displaying data from enormous data that analysis could be done easily. Statistical Package for Social Science (SPSS) is one of the best examples of these software products used for managing and analyzing quantitative data.
There are different forms in that SPSS allows us to display the data visually. First, the user can use tables where a subset of the data from a large data set is presented for analysis.
Second, the charts/graphs can also be used to display data visually. These charts include bar graphs, line graphs, histograms, and pie charts.
The display in the visuals can show different types of data variables which can be continuous or categorical.
Continuous type of variable consists of “data that take an infinite number of variables between any two variables” (Wagner, 2020). On the other hand, “group the data into groups” (Wagner, 2020). For instance, race, sex, age group and educational level.
Categorical
Figure 1 shows an instance of categorical data. We can observe that we are given four distinct education levels – No formal education, Primary, Secondary, and Postsecondary. Few of the respondents are under the postsecondary educational category. From the graph, we can see that majority of the response are secondary school respondents.
Continuous
Figure 2 shows an instance of the continuous variable representing the respondent lived poverty index. It is the distribution of the respondent poverty index in a continuous form. The distribution of the lived poverty index starts from .0 to 4.0.
The implication for Social Change
From the two visual representations, we can find the implication on the social change. First, we can see that most of the respondents had completed the secondary school education level from the education category. These are the majority of the respondent who respondent to the questionnaire. Their average age was 37.19.
For the continuous variable, we considered the lived poverty index of the respondent. We can see that the majority of them had a poverty index of 0. Few of them had a poverty index of 3.8. others had their index within the range of 0.2 and 3.6.
Conclusion
Visually displaying data is one way in which data is simplified for quick comprehension. It is hard to obtain information from this data without the use of this technique. The use of SPSS aids in generating the required visual designs depending on the type of data variable related to the given piece of data. The two types of data variables are categorical variables and continuous variables. In the discussion, we have discussed the education level and lived poverty index to show each data category, respectively.
References
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
FrankfortNachmias, C., LeonGuerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.

WK3AssgnBryantS.docx
DESCRIPTIVE STATISTICS 1
DESCRIPTIVE STATISTICS 2
Introduction to Quantitative Analysis: Descriptive Analysis
Sarieta Bryant
Walden University
RSCH 8210/7210/6210: Quantitative Reasoning and Analysis
March 17, 2021
Introduction to Quantitative Analysis: Descriptive Analysis
Introduction
The statistical analyses involve the scrutiny of data to understand their aspects. This activity is what is known as descriptive analysis, and it gives an idea of the distribution of data, and it helps in detecting outliers and typos and the association among variables. This paper will discuss descriptive statistics related to the categorical and continuous variables of the data set.
The Statistical Packages for the Social Sciences (SPSS) software allow us to perform a descriptive analysis of enormous data sets in a simplified manner. We will use the Afro barometer dataset to discuss how descriptive analyses apply to the categorical and continuous variables.
Selected continuous: Lived Poverty Index
Through the SPSS we analyze the lived poverty index data set and we found out different aspects of the measure of central tendency. The following are the result of the measurements.
· Mean = 1.245
· Media = 1.1728
· Mode = .0
Among these three central tendency measures, the mean is the best because it is calculated, and it uses all values in the dataset. Therefore, mean has substantial chances of accuracy as compared to the other two.
Standard deviation and variance are also the best descriptive statistics used to describe the data behavior with their mean. We found the following results from the SPSS software.
· Standard Deviation = 0.9456
· Variance = 0.874
From these values obtained from our data set, we can observe that the standard deviation slightly diverges from its mean. They are slightly lower than the mean; the standard deviation deviates from 0.2994 and variance 0.371.
Variable in context of social change
Relating the finding with the data set, we can observe that most of the people who answered the survey are below the mean as observed from the standard deviation and variance. They show the dispersion of the data sets from the mean of the entire data (Wagner, 2020)., in some instances, the dispersion might be higher than the mean and lower in others, as for our case.
Categorical Variable: Education Category
Our variables show the number of respondents under the education category. The categories under this variable include nonformal, primary, secondary, and postsecondary levels. Figure 1 shows the graphical visualization of these levels.
The SPSS shows the frequency distribution of these variables in terms of percentiles, besides the visualization. The following are the frequency distribution as observed from the software.
Nonformal: 20.1 %
Primary: 31.9 %
Secondary: 35.0%
PostSecondary: 12.71%
From the observation and the calculated percentiles, we can find out the variability of the provided variables. Most of the respondents attained secondary verifiable. 20.1 % of the respondent have attained no formal education; this shows that over 79% of the respondent are educated. Those with postsecondary education 12.71% compared to 66.9 % of the total number of secondary and primary levels. Assignment: Introduction to Quantitative Analysis: Confidence Intervals
Variable in context of social change
These variables in the context of social change show that majority of the population of consideration have attained secondary education. The finding from the sample reflects the entire population of study (FrankfortNachmias, LeonGuerrero, & Davis, 2020). The number then drop significantly to12.17% of those with postsecondary education.
References
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
FrankfortNachmias, C., LeonGuerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.