What is included in the data analysis plan?

A critical component to theses' and dissertations' method section is the data analysis section or data analysis plan. The data analysis plan clearly identifies the specific statistical techniques (i.e., statistical tests) used to examine the research questions. Often, students get stuck in data analysis, or data analysis plan, because selecting the statistics can vary between parametric and non-parametric statistics. the data analysis plan describes not only the statistics, but the assumptions associated with these statistics. The data analysis plan can be a bit tricky: the specific statistics have to take into consideration the research questions and the type of data that the researcher is collecting(e.g., nominal data, interval data, etc.). A portion of the data analysis plan is to justify why these statistics selected are appropriate for a researcher's hypotheses and research questions. To give you an example for a data analysis plan, a researcher examining differences on an interval level variable (e.g., depression symptom) by gender, an independent sample t-test would be the appropriate statistical technique.

If you have any questions about the data analysis plan or justifying the appropriate statistics in the data analysis, feel free to visit us at www.StatisticsSolutions.com or call us at 877-437-8622. We are experts in writing data analysis and editing data analysis sections.

Chi Square

One of the most common statistical tests we are asked to run at Statistics Solutions is the chi-square, aka Pearson chi-square, cross-tabulation/cross-tab, ect... It seems like there is a lot of confusion about when to use this test and how to use this test. Let’s start out with the “when”.

Chi-square statistical analysis is used when we want to know if there is a relationship between 2 categorical or nominal variables. For example, say I want to know if there is a relationship between males and their level of education. Really, we are looking at a relationship between the variable gender, which is dichotomous (two levels or groups in the variable) with respondents or participants being either male or female, and the variable education, which we’ll say is also dichotomous (high school or below and above high school).

What is the relationship here? We might have hypothesized that there would be a significant relationship between males and education, the nature of which would be men tending to be less educated than women. If our chi-square test is significant - we’ll talk about what makes it significant later – we’ll see some pattern of relationship between these two groups.

Gender * Education Crosstabulation

Count

		Education		Total
		High School or Below	Above High School
Gender	Male	31	25	56
	Female	14	30	44
Total		45	55	100

This is the actual output table we would get if we ran this test. There is no real wrong way to look at the the numbers, since the chi-square is really telling us if the rows are significantly related to the columns.

You can see from the table that 31 participants were male and had an education level of High School or Below and looking at just that column we can see that far more males than females had an education level of High School or Below. There is another number that jumps out at me, and that is the Female row. Notice the 30. Within the Female row or group we could say, 30 had an education level Above High School compared to only 14 with an education level of High School or Below. This is fairly clear, but even more easily seen if we look at the percentages. Let’s look at percentages first within each of the education groups.

Again this is the exact table:

Gender * Education Crosstabulation

			Education		Total
			High School or Below	Above High School
Gender	Male	Count	31	25	56
		% within Education	68.9%	45.5%	56.0%
		% of Total	31.0%	25.0%	56.0%
	Female	Count	14	30	44
		% within Education	31.1%	54.5%	44.0%
		% of Total	14.0%	30.0%	44.0%
Total		Count	45	55	100
		% within Education	100.0%	100.0%	100.0%
		% of Total	45.0%	55.0%	100.0%

This table looks a little confusing, but look a closer look at the names and we can decipher what this means. The numbers of interest are bolded in red. The table shows that 68.9% of the participants/respondents are male and have an education level of High School or Below. You can see that the percentage of males in this education level is much higher than the percentage of females, which is 31.1%. In fact, there are more than twice as many males as females in the High School or Below education level.