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For this

Introduction to Quantitative Analysis: Descriptive Analysis

Assignment, you will examine the same two variables you used from your Week 2 Assignment and perform the appropriate descriptive analysis of the data given.

To prepare for this Assignment:

Review this week’s Learning Resources and the

Central Tendency and Variability

media program.

For additional support, review the

Skill Builder: Measures of Central Tendency for Continuous Variables

,

Skill Builder: Standard Deviation as a Measure of Variability for Continuous Variables

and the

Skill Builder: Measures of Central Tendency and Variability for Categorical Variables

, 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 from your Assignment in Week 2.

Choose the same two variables you chose from your Week 2 Assignment and perform the appropriate descriptive analysis of the data.

Once you perform your descriptive analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

Write a 2- to 3-paragraph analysis of your descriptive analysis results and include a copy and paste your output from your analysis into your final document.

Based on the results of your data, provide a brief explanation of what the implications for social change might be. Early in your Assignment, when you relate which dataset you analyzed, please include the mean of the following variables. 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.

Use appropriate APA format, citations and referencing. Refer to the APA manual for appropriate citation.

References:

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020).

Social statistics for a diverse society

(9th ed.). Thousand Oaks, CA: Sage Publication

Chapter 3, “Measures of Central Tendency” (pp. 75-111)

Chapter 4, “Measures of Variability” (pp. 113-15)

Wagner, III, W. E. (2020).

Using IBM® SPSS® statistics for research methods and social science statistics

(7th ed.). Thousand Oaks, CA: Sage Publications.

Chapter 4, “Organization and Presentation of Information”

Chapter 11, “Editing Output”

Walden University, LLC. (Producer). (2016d).

Descriptive statistics

[Video file]. Baltimore, MD: Author.

IBM SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study datase Access Code: c97c92daa032c18cef95

1
Introduction to Quantitative Analysis: Visually Displaying Data Results
Student’s Name
Professor’s Name
Course Name
Date
2
Introduction to Quantitative Analysis: Visually Displaying Data Results
Two factors were selected for this task to demonstrate a convincing statistical analytic
tool. The most important variable selected is the respondent’s financial condition. Financial
condition is characterized by the social status or class of a person or group. In most cases, this is
assessed as a combination of education, salary, and occupation. This variable is considered as a
metric or percentage stretch or interval ratio variable.
Table 1 provides a brief statistics of the results. The key data shows that the statistical
information in this se is the composition of variables for socio-financial conditions. The data
show 21,444 significant cases with 2,059 missing. The mean value of the samples was 0.0541,
the median is – 0.0109, while the mode is – 0.78. The standard deviation, or variance fraction, of
how widely the information is spread in the sample is 0.78030.
Based on the empirical rule, a mean of 0.0541 gives or takes a standard deviation of
0.78030 giving an asymmetry of 0.367. This distortion is almost non-existent, so this information
is insignificant. Measuring excess -.805. Positive overload indicates a sharp, heavy tail
displacement. Negative excess indicates some degree of transference. The range is 4.81, which is
the difference between the main and largest values. The base financial condition is – 1.93, and
the peak is 2.88.
Because financial status was chosen as the variable, it is difficult for the analysts to
decide exactly what is being assessed (Wagner, 2017). However, using variables such as age will
make the information more accurate. Frequency graphs are also powerful in analyzing and
presenting data in this case.
3
Table 1: Statistics of socio-economic status
N
Valid
21444
Missing
2059
Mean
0.0541

Median
0.0109
Mode
-0.78
Std.
0.7803
Deviation
Skewness
0.367
Std. Error of Skewness
0.017
Kurtosis
-0.085
Std. Error of Kurtosis
0.033
Range
4.81
Minimum
-1.93
Maximum
2.88
Figure 2 shows the pivoted chart for the data with a decent visual representation of the
information. Dissemination of information makes sense because it has similarities to ordinary
circulation (Frankfort-Nachmias et al., 2020). The histogram provides an idea of how the
frequency or speed contrasts between classes with variable proportions. The histogram shows
that most of the respondents ranged from miserable to one, with most of the respondents being in
the middle.
4
Figure 2: The pivoted chart representation of data
Total
4
3
2
1
Total
0
(blank)
-1
(blank)
(blank)
Kurtosis Maximum Mean
(blank)
(blank)
(blank)
(blank)
(blank)
Median Minimum ModeStd. Deviation
Std. Error of Skewness
-2
-3
The next variable selected is the gender of the reserve. This variable is considered as a
direct factor. The valid cases in this factor constitutes 23,497 respondents with six missing. The
frequency table of these variables is much clearer showing that 11,973 respondents were male
and 11,524 respondents were female. The figures are then broken down into percentages
showing 50.9% males and 49.0% females. Figure 3 shows a bar graph as a visual representation
of information showing that there are slightly more boys than girls. 50.9% male and 49.0%
female participated in this data analysis from the total number of 23,503.
5
Figure 3: Pie Chart showing gender participation
Female
49%
Male
51%
Male
Female
Social Change
Part of the positive social change in this conversation is assessing the socioeconomic
status. The author understands Frankfort-Nachmias et al. (2020) assertion that there should be no
difference in age, profession or level of education to bring about friendly change. These statuses
can affect a person’s work. Hence, a positive social change must continue to erase how
socioeconomic disparities can be exploited when targeting gaps in this aspect.
Conclusion
From this analysis, an improved understanding of SPSS programming this week is
reflected. The notes below the link, shared progressively, depict how to use the application in an
easy and efficient means. The video also shows that when selecting factors, it is ideal to choose
the data to be used carefully as they determine the type of analytic tool to use for given set of
data. Representing the data in pie-chart, pivoted chart, and other aspects also showcases a
progressive understanding of data analysis using various models.
6
References
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse
society (9th ed.). Sage Publications.
Wagner, W. E. (2017). Using IBM® SPSS® statistics for research methods and social science
statistics (7th ed.). SAGE.

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