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DESCRIPTIVE STATISTICS 1 DESCRIPTIVE STATISTICS 2 Introduction to Quantitative Analysis: Descriptive Analysis Sarieta Bryant Walden University RSCH 8210/7210/6210: Quantitative Reasoning and Analysis Dr. William Tetu 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 non-formal, primary, secondary, and post-secondary 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.

Non-formal: 20.1 % Primary: 31.9 % Secondary: 35.0% Post-Secondary: 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 post-secondary education 12.71% compared to 66.9 % of the total number of secondary and primary levels. 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 (Frankfort-Nachmias, Leon-Guerrero, & Davis, 2020). The number then drop significantly to12.17% of those with post-secondary 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. Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications. 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 Post-secondary. Few of the respondents are under the post-secondary 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. Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020).

Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.

Paper for above instructions

Descriptive Statistics: Exploring Quantitative Analysis

Introduction

Descriptive statistics serve as a foundational pillar in the realm of quantitative analysis, facilitating the summarization and organization of data to unveil underlying patterns, distributions, and insights. The primary objective of this article is to demonstrate the applications and implications of descriptive statistics through an analysis of categorical and continuous variables using a dataset from Afrobarometer. Utilizing the Statistical Package for the Social Sciences (SPSS) software will simplify and enhance the accuracy of our analysis.

Overview of Descriptive Statistics

Descriptive statistics quantitatively describe characteristics of a dataset. Broadly categorized into measures of central tendency (mean, median, and mode) and measures of variability (variance and standard deviation), descriptive statistics allow researchers to characterize a set of data effectively. According to Moore, McCabe, and Craig (2021), descriptive statistics have a critical role in the initial stages of data analysis; they help in understanding the distributions of variables, identifying outliers, and informing further statistical analyses.

Analyzing Continuous Variables: Lived Poverty Index

Central Tendency Analysis

Using SPSS, we examined the lived poverty index—a continuous variable representing respondents’ subjective experiences of poverty. The calculated measures are as follows:
- Mean: 1.245
- Median: 1.1728
- Mode: 0.0
Among these three measures, the mean is a preferred metric because it incorporates all data points, providing a balanced representation (Wagner, 2020). The mean suggests that respondents, on average, experience a moderate level of poverty.

Variability Analysis

To gauge variability, we computed:
- Standard Deviation: 0.9456
- Variance: 0.874
These metrics indicate that the data have a moderate spread around the mean. Specifically, the standard deviation of 0.9456 indicates that individual responses tend to deviate from the average. This insight into the lived poverty index suggests that while many respondents experience poverty, there exists a significant range of experiences among individuals.

Contextualizing the Findings in Social Change

The analysis of the lived poverty index reveals important implications for social change. The majority of respondents reporting below-average poverty levels indicates systemic issues that may necessitate targeted interventions to uplift low-income populations. The prevalence of values clustering around the mean suggests that while some individuals face extreme poverty, others have slightly better economic conditions. It implies the dual challenge of addressing both extreme and moderate poverty in society, a perspective echoed in the research of Baird, McIntosh, and Özler (2017), which emphasizes tailoring poverty alleviation strategies to varied needs.

Analyzing Categorical Variables: Education Level

Frequency Distribution Analysis

Shifting our focus to the education category—a categorical variable—we analyzed the governmental survey responses, which encompasses the following educational levels:
- Non-formal education: 20.1%
- Primary education: 31.9%
- Secondary education: 35.0%
- Post-secondary education: 12.71%
The data reveal that a majority of respondents (66.9%) completed at least primary education, reflecting a potentially educated population. However, the drop to 12.71% for post-secondary education underscores underlying barriers to higher education access.

Implications for Social Change

The distribution of educational attainment is crucial for societal progress. A significant proportion of respondents having at least primary education signifies a foundation upon which further educational improvements can be built. However, the low percentage of individuals with post-secondary education raises questions about access to higher education and social equity (Luke, 2019). Addressing educational inequalities can serve as a catalyst for broader societal change, as higher education is closely tied to improved job prospects and enhanced quality of life, as noted by Kahn and Wicks (2019).

Visualizing Data

SPSS also allows for the visual representation of data, which can enhance comprehension. For the categorical variable of education, bar charts or pie charts effectively communicate the proportions of respondents in each educational category. For continuous variables like the lived poverty index, histograms can display the distribution clearly (Wagner, 2020). Visualization aids in highlighting key findings and drawing attention to significant trends within the data.

Conclusion

Descriptive statistics are indispensable for summarizing and elucidating complex datasets. Analyzing the Afrobarometer dataset reveals critical insights into the lived poverty index and education levels across respondents. The use of SPSS streamlines this process, enabling researchers to draw meaningful conclusions and implications for social change effectively.
As shown through the analysis, both the lived poverty index and education levels reveal underlying societal issues that require attention. Continuous monitoring and analysis using descriptive statistics can guide policymakers in crafting evidence-based initiatives aimed at poverty alleviation and educational access.

References

1. Baird, S., McIntosh, C., & Özler, B. (2017). The effects of cash transfers on schooling outcomes in developing countries: A meta-analysis. World Bank Research Observer, 32(2), 259-281.
2. Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
3. Kahn, V., & Wicks, P. (2019). The effect of education on income and social outcomes: New evidence from the UK. Journal of Policy Analysis and Management, 38(2), 273-299.
4. Moore, D. S., McCabe, G. P., & Craig, B. A. (2021). Introduction to the practice of statistics (7th ed.). New York, NY: W. H. Freeman and Company.
5. Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
6. Creswell, J. W. & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage Publications.
7. U.S. Census Bureau. (2021). Statistical abstract of the United States: 2021. Washington, DC: U.S. Government Printing Office.
8. Hartt, M. (2018). Visualizing data: A guide to visual data analysis. New York, NY: SAGE Publications.
9. Williams, D. R. et al. (2019). Estimating economic and social impacts of social determinants in education: Applying intentional frameworks and innovative practices. Journal of Education and Social Change, 2(1), 5-22.
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