Assignment: Introduction to Quantitative Analysis: Descriptive Analysis
Reeb79
WK2Assgn.docxVISUALLY DATA 3
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.
