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1 Afrobarometer Dataset Analysis DrPH, Walden University COURSE RSCH- 8210R: Quantitative Reasoning and Analysis Dr. Nancy Rea June , 2020

2 Afrobarometer Dataset Analysis Quantitative data in research provides the answers to what and how many when attempting to quantify a problem. The data gives clues to what and how many, which allows individuals to understand the needs and resources lacking in a community and country. Quantitative research also provides essential data for disease outcomes, patients care models, and contributing to the field. The Afrobarometer data set is produced by a pan African research institution that surveys what Africans think with the influence of a particular political group (Afrobarometer, n.d.). This paper will review three variables and their implication for social change. Variables Q8c. How often gone without medical care The variable of medical care is vital in access to care. The number of participants in Africa that has gone without medical care was answered from a range of choices of either never to always. The unit of analysis was individuals, and the measurement was ordinal. Ordinal measures the ranking of reporting, which would rank from never gone without medical care to always. In this study, approximately 53.2 % have gone without some level of medical care (SPSS Inc., Chicago, Ill., USA) . The social ramification of health care access continues to be a social issue for those in various countries and socioeconomic status. Understanding this data can bring about the need for global change to health care system access. Q1. Age The Afrobarometer data set was utilized to assess the age variable. The age variable provides the age range of individuals responding to the survey. The unit of analysis for age would be individual and the level of measurement for the age variable is a scale based on the

3 data set. The scale measurement is comparable to interval and ratio. However, age would be aligned to the ratio measurement because zero does not exist and there is no age of zero (Wagner, 2020). Aging in society has implication of social change related to policies and programs. Aging in Africa has further implications of long-standing traditions and influence over the younger generations (Schatz & Seeley, 2015). The mean for the age ratio is 37.39 the output from the SPSS software (SPSS Inc., Chicago, Ill., USA). Table 1: Descriptive Statistics of Age Mean N Range Minimum Maximum Mean Std. Deviation Q1. Age 10232 87 18 105 37.39 14.863 Valid N (listwise) 10232 From “Afrobarometer: Dataset B,” IBM SPSS v25, 2017. Q8c. How often gone without medical care The variable of medical care is vital in access to care. The number of participants in Africa that have gone without medical care was answered from a range of either never to always. The unit of analysis is individual, and the measurement is ordinal. Ordinal is the measurement of the reporting ranking, which would rank from never gone without medical care to always. In this study, approximately 53.2 % have gone without some level of medical care (IBM SPSS, 2017). The social ramification of health care access continues to be a social issue for those in various countries and socioeconomic status. Understanding this data can bring about the need for global change to health care system access. Employment status The variable represents the employment status of the individuals participating in a survey. The unit of analysis is individual with a nominal measurement, which helps classify data. The data reveals employment status at 32.9% (IBM SPSS, 2017). The data shows high

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4 unemployment, which has a social implication of financial uncertainty, may strain familial relationships, and a country's stability. Conclusion Obtaining the knowledge that data can provide to a researcher assists in mitigating problems by finding viable solutions. The Afrobarometer dataset offers a glimpse into possible social issues and concerns in Africa. The need to access health care and decrease unemployment are two elements that will give constancy and work on creating better infrastructure for countries. Continuing to gather data, and correlating variables to predict outcomes can increase awareness to community and country. Knowledge is the first step in social change.

5 References Afrobarometer. (n.d.). Our history. Retrieved https://www.afrobarometer.org/about IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp. Schatz, E., & Seeley, J. (2015). Gender, ageing and carework in East and Southern Africa: A review. Global public health, 10(10), 1185–1200. https://doi.org/10.1080/17441692.2015.1035664 Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.).

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