Types of descriptive and inferential statistics to analyze data

Types of descriptive and inferential statistics to analyze data

Various types of descriptive and inferential statistics can be used to assess the data
analyzed in the case study titled, "Social Work Research: Measuring group success." Descriptive
entails collecting data through graphs, tables, or summary measures organized to make sense of
data. Therefore, to manage and describe the information provided by the eight women in the
twelve-week program. In the case study, the frequency distribution model is appropriate to assess
the total scores on the pretest questionnaire issued (Simonsohn et al.,2019). Also, the frequency
model is suitable for them to determine the data on the questionnaire as it displays the number of
times a particular data occurs.
In collecting and organizing data, I would design a column for stress, depression, and
anxiety, which are the significant factors in the Depression Anxiety Stress Scale (DASS).
Through the creation of pie charts to make a comparison of cumulative percentages. Similarly, I
would use measures of dispersion which is characterized by the range and standard deviation.
Thus, applying variability criteria is appropriate for creating a box plot that shows the
interquartile contents of data from each of the eight women who participated in the study.
Secondly, the use of inferential statistics is also appropriate since bivariate tests would be
better assessed from the data provided in the 12-week study. As such, a T-test would be suitable
for each dependent sample that would be compared with findings from the pre and post-test
outcomes for every woman in the support group (Simonsohn et al.,2019). Ultimately, I would

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find it meaningful to seek correlations in symptoms that each woman exhibits, such as
depression, anxiety, and stress. For instance, my prediction would point to a linkage/ correlation
in symptoms when anxiety, stress, and depression increase.