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 weekâ€™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, â€œMeasures of Central Tendencyâ€ (pp. 75-111)

Chapter 4, â€œMeasures of Variabilityâ€ (pp. 113-15)

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

Using IBMÂ® SPSSÂ® statistics for research methods and social science statistics

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

Chapter 4, â€œOrganization and Presentation of Informationâ€

Chapter 11, â€œEditing Outputâ€

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

Studentâ€™s Name

Professorâ€™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 IBMÂ® SPSSÂ® statistics for research methods and social science

statistics (7th ed.). SAGE.

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