Variables Measurement in SPSS

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Variables Measurement in SPSS

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The two variables that are of interest to me from the dataset provided in the Afrobarometer are the age and gender. The two variables are significant in the Afrobarometer survey as each play a role in the explanation of public attitudes towards various issues such as democracy, governance and economic conditions in the African countries where the survey was conducted (Afrobarometer, n.d.). The age variable can indicate the age group of the participants of the survey and their response towards their attitude in matters relating to governance, democracy and even on the economic conditions. in the specific country. The main reason for introducing the age variable is to identify how various age groups in the specific country think about the issue of democracy, governance and even on other conditions such as economic conditions.

The other variable of gender helps to explain the male and female attitudes towards the economic conditions, governance and even democracy (Afrobarometer, n.d.). The Afrobarometer survey helped to determine if different gender have different opinions when it concerns democracy and economic conditions as well as the governance.

The unit of analysis of the two variables is individuals. The unit of analysis in any research is the major entity that is analyzed in the research or the study. In the Aforbarometer survey, the unit of analysis is the individual attitude towards the economic conditions, the governance, the democracy (Afrobarometer, n.d.). The unit of analysis gives the what or who is being focused on in the research (“Unit of observation versus unit of analysis,” 2015). Therefore, in the Afrobarometer research, the unit of analysis happens to be the individuals or the public which in other words refer to the citizens of the specific country where the survey was conducted.

The two variables have different levels of measurement. The levels of measurements are the scales or the way that the variables identified in the study are grouped or classified. The level of measurement is mainly determined by the values that are assigned to the variables. There are four levels of measurement, and they include the nominal, ordinal, interval and then the ratio level (Watson, Atkinson, & Egerton, 2006). The various level of measurement has different attributes that enable measure variables differently. The two variables of age and gender have differing measurements. In the case of the age variable which in the Afrobarometer merged data set is condensed age, it is measured using the ordinal level. The reason for measuring the age in the ordinal level is because the age can be arranged in a certain order. The gender variable is measured at the nominal level of measurement. The gender is either you are a male or a female and nothing more. Due to the nature of the gender variable, it is therefore measured at the nominal level of measurement.

The gender and age variable can be used to answer the question of social change in that they will explain the way gender views political power if the social change is about power in the case of the gender variable. Similarly, the age variable can be used to answer social change questions in that, the youths, the middle-aged and even the old aged can say about the political powers. Social change implies that the gender especially the women might want to push for more political powers for themselves as social change (Yunus, 2010). The other implications are that in the case of the age, the youth might want to push for the political power to be given to young, energetic men and women who can serve the nations better.

References

Afrobarometer. (n.d.). Afrobarometer. Retrieved from http://afrobarometer.org/sites/default/files/publications/Working%20paper/AfropaperNo81

Unit of observation versus unit of analysis. (2015). BMJ, h5534. doi:10.1136/bmj.h5534

Watson, R., Atkinson, I., & Egerton, P. (2006). Levels of Measurement. Successful Statistics for Nursing and Healthcare, 7-24. doi:10.1007/978-0-230-21159-9_2

Yunus, F. (2010). Statistics Using SPSS: An Integrative Approach, second edition. Journal of Applied Statistics, 37(12), 2119-2120. doi:10.1080/02664760903075515




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