Introduction

Introduction: 6/5/2011 From what I've read so far (and I am anxious to finish chapter four) I feel like Statistics can be compared to treading lightly through a bowl of spaghetti. Yes, there are distinct ingredients. Yes, you can identify the parts. But, to analyze what is going on in that bowl of spaghetti AND //accurately// represent it to someone who can not see the bowl directly is the challenge. Beyond that challenge, to parcel out what YOU would like someone to see in that bowl of spaghetti is an even more fine-grained challenge. Then you have the great divide between qualitative and quantitative data analysis. I get the impression that the vocabulary and language to be used for each of these is quite different. Take, for example, bar chart and histogram. Hmm. Kind of look the same, BUT one is representing qualitative data and one is representing quantitative data. When I started my research class two semesters ago, I found it curious that other students were declaring themselves pragmatists right off the get go. I kind of took that as a cop-out. So, they want to strattle the qual/quan divide. But, as I continued through the course, I became more informed, of course, but also I started to understand the climate, the journey if you will, that educational research has made. Pragmatics will consider both methods. But as I read about the statistical differences, I can see where someone would want to stick to one "language" or the other. It is tough talking about categorical data and then jumping into quantitative. I really came out of my research class thinking I'd like to conduct a mixed-method research study. I could see the complimentarity of using both methods in the study that I critiqued for my assignment. I would like to, at some point, go back and look at that study to understand the qual and quantitative methods that they used. Re-evaluate my critique from a more informed view point, once I complete statistics. My take-away for this weeks readings is that I want to proceed cautiously and carefully. I want to really understand as I go what I'm doing. It is extremely easy to be misled and misleading unless you really understand what you are doing. The good news is that this isn't math. It's quite logical, Dr. Spock.

Lisa: bar charts and histograms both illustrate quantitative data. Bar charts are used in categorical or ordinal data and histograms require continuous data. Categorical data IS quantitative data.

The primary difference between qualitative and quantitative analyses is determining the initial approach to the data. Quantitative assumes a hypothesis and then tests that hypothesis. Qualitative (especially grounded theory) collects data, then looks for an explanation for that data. Typically quantitative analysis is used when you want to have a nomethetic explanation (identify the 2 or 3 key factors for a particular event, decision, etc.). Qualitative analyses is frequently used when you want an idiographic explanation (one the hones in on the details).

The difference in perspectives and purpose can sometimes make a mixed methods approach a challenge. In addition, there are many researchers who hold to one type of analyses and is unfamiliar/dismissive of the other approach. When you begin work on your dissertation, make sure that you know your committee members' philosophy prior to deciding a method.