Assignment: Introduction to Quantitative Analysis: Confidence Intervals

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.