Data Analysis Plans
Plan for Data Analysis for Demographic Variables
As soon as the data is gathered, it is necessary to clarify the basic descriptive statistical tests that could help to understand the nature of the information and analyze the demographic variables (Lazar, Feng, & Hochheiser, 2010). The essence of the demographic analysis is to compare the existing demographic variables, describe the nature of their relationships, and clarify if there are deviations that should be mentioned in regards to continuous variables.
This type of statistical test is frequently used when it is necessary to analyze the data obtained from questionnaires and introduce a clear picture of what should be studied. It is suggested to focus on such methods as charts, tables, and graphs within the frames of which it is possible to introduce a summary of the discussions. These tests are based on the measures of central tendency i.e. mean, median, and mode, and measures of spread i.e. variance, range, and different types of deviations (Lazar, Feng, & Hochheiser, 2010).
The total number should be counted, then all scores should be added up, and, finally, the total number should be divided according to the number of cases. In general, the quantitative description in a properly managed form will be offered to create a solid basis for further analyses and investigations.
Plan for Data Analysis of Study Variables
The analysis of study variables could be based on both descriptive and inferential statistical tests. Therefore, it is crucial to understand the differences between such types of tests and make the right choices in regards to the methods, instruments, and samples offered in the current project. Descriptive statistics deals with the analysis of the information with the help of which it is possible to describe and summarize the facts.
However, the main disadvantage of this method is the impossibility to make conclusions and generalize the results. It could be defined as the way to describe the data only. In comparison to descriptive statistics, inferential statistical tests provide information about corresponding values and make conclusions about the parameters of the chosen population (Lodico, Spaulding, & Voegtle, 2010).
Several descriptive and inferential statistical tests should be used to succeed in this kind of analysis. A Chi-square significance test could be offered to show whether some important differences between the populations should be tested. This test is significant in case the association between variables have been proved and non-significant in case no association has been discovered. It is also planned to use t-tests and one-way ANOVA to identify and explain the worth of variables in the chosen experimental study. These tests should help investigate how the independent variable that is a community nursing intervention could influence the childhood obesity rates in different states.
The associations between certain categorical and numerical variables could be tested with the help of the chosen methods. T-test helps to analyze the null hypothesis and explain if the difference between the variables is statistically significant or not. The one-way ANOVA is another type of test that helps evaluate the statistical significance of the difference between the variables. The peculiar feature of this test is the possibility to consider the equality of three or more variables at the same time and introduce a clear picture of what answers help to determine the main difference and what answers remain to be insignificant for the study.
Lazar, J., Feng, J.H., & Hochheiser, H. (2010). Research methods in human-computer interaction. West Sussex, UK: John Wiley & Sons.
Lodico, M.G., Spaulding, D.T., & Voegtle, K.H. (2010). Methods in educational research: From theory to practice. West Sussex, UK: John Wiley & Sons.