Discuss the below:
1: According to the text, "summarization of data is performed with percentages and averages (Burns & Bush, 2012)." In the case of the "Women At Work" survey done by the bureau of labor statistics, there are actually several instances of this type of data. But the first is usually the best so for this example I will use the following pulled right from the report:
Average Annual Expenditures by Single Women
In 2008-09, single women spent, on average, 25.4 percent of their annual expenditures on shelter. Those women earning an annual pretax income of less than $5,000-which represented the lowest income group-spent , on average, a higher proportional amount (49.3 percent of total spending) on food, shelter, and apparel and services than women in all other income groups (Bureau of Labor Statistics, 2018).
This example shows not only the percentage but the average as well.
Generalizing
According to the text, "generalization is the act of estimating a population fact from a sample finding (Burns & Bush, 2012)." There is not very many instances in the report where a population is specifically estimated from the sample, however there is a projection from the finding in the following text.
Labor Force Projections
From 2008 to 2018, the women's civilian labor force is projected to increase by 9.0 percent, or 6,462,000. The number of women aged 65to 74 in the civilian labor force is projected to increase more than the number of women in any other age group-increasing by 89.8 percent, or 2,030,000 (Bureau of Labor Statistics, 2018).
Difference
Again, according to the text, differences compare "averages or percent in the sample data to see if there are meaningful differences with percentages and averages (Burns & Bush, 2012)." This is highlighted by the following text in the report:
Educational Attainment of Young Women
In the October when they were 23 years old, 23.4 percent of young women held a bachelor's degree (or higher), compared with 14.3 percent of young men(Bureau of Labor Statistics, 2018).
Relationships
Finally, according to the text, "Relating variables to each other in a meaningful way with, cross tabulations, correlations or regression analysis. The following data from the report shows the comparison between men's and women's occupation based on their job:
Ratio of Women's to Men's Earnings by Occupation
The ratio of women's to men's earnings, for all occupations, was 81.2 percent in 2010. The ratio varies by occupation. In occupations such as personal financial advisors, retail salespersons, insurance sales agents, and lawyers, for example, the earnings ratios are lower than the overall ratio of women's to men's earnings. In occupations such as stock clerks and order fillers, bill and account collectors, and combined food preparation and serving workers, women earn more than men(Bureau of Labor Statistics, 2018).
References
Bureau of Labor Statistics. (2018). Women aAt Work. US Bureau of Labor Statistics . Washington DC: US Government.
Burns, A. C., & Bush, R. F. (2012). Basic Marketing Research: Using Microsoft Excel Data Analysis(Vol. 3rd Ed). New York: Prentice Hall.
2: An important part of data analysis is determining what your objective is with the data, to describe, generalize, find differences, or find relationships (Burns 2012). If your objective is to describe, you will summarize the data in which you "describe the most typical response to a question" often in percentages or averages (Burns 2012). The BLS Spotlight on Statistics: Women at Work provides an example of this when they say "In 2008-09, single women spent, on average, 25.4 percent of their annual expenditures on shelter" (2011). Generalizations are similar, but they take the sample results and apply them to the "population that the sample represents" (Burns 2012). "From 2008 to 2018, the women's civilian labor force is projected to increase by 9.0 percent, or 6,462,000" (BLS 2011) is an example of a generalization. Differences can be found when results are compared, "In 2009, a total of 319 fatal work injuries involved women, compared with a total of 4,021 for men" (BLS 2011) is an example of finding a difference. Relationships are found when some data correlates with other data, such as in the statement "Overall, young women were more likely to have graduated from high school and to have attended college" (BLS 2011) which finds a relationship between gender and high school and college graduation rates.