Show MoreIn this paper, the author will delineate the characteristics between qualitative and quantitative research, as well as their methodologies. The purpose of this paper is to give the reader a brief glimpse behind each research approach, by determining the strengths and weaknesses of both. The terms “subjective” and “objective” will be viewed in accordance with each research paradigm by considering the role of the observer and addressing how the researcher conducts his or her analysis using these approaches. The author will also describe the preferred approach given to each modality and how each model can be utilized in a study of aggression.
Qualitative and Quantitative are two differentiated paradigms of research, which operate…show more content…
Researchers collect and study various empirical materials; case studies, direct observations, personal experiences, which describes the participant’s problematic and routine behavior in their daily lives (Denzien & Lincoln, 2008). The objective of qualitative research is to define certain aspects in phenomenology, with the intentions to clarify the subject in the research (Patton, 1990). It is a measurement requirement for research design and analysis and is more inductive (Denzien & Lincoln, 2000). In contrast, Qualitative approach is used when developing a theory and utilizes words, texts, through direct observation. There is more flexibility in research because it allows the researcher to interact more with the participants. Qualitative research is used in many disciplines including, history, philosophy, sociology, anthropology, and psychology. It is a subjective process and is based on problems found in research through in depth reasoning and solutions. Qualitative researchers are concerned primary with process rather than the outcome or product (Patton, 1990). The researcher is the primary instrument for data collection, and analysis, by using human instruments, rather than inventories, questionnaires, and machines (Firestone, 1987).
The reliability of qualitative research is a weakness because the process is under-standardized and is heavenly relied on insights and the abilities of the observer; however, making the initial
The controlled crosstabular analysis is also referred to by the phrase "the elaboration method". While we will have gone over this in class, you may want to look that phrase up in a couple of methods texts for a more in depth discussion.
The first thing you have to do is choose which of the two hypotheses you tested is your primary hypothesis (HINT: it is most likely the hypothesis tested in crosstab 1.
You are then going to control the relationship between the variables in your primary hypothesis by looking at the relationship between your independent variable and your dependent variable at every level of your control variable. What this means is that the computer builds a crosstab table to examine the relationship between your IV and DB for each responce category of the control variable. For example, if I were interested in the relationship between political party (PARTYID) and frequency of sexual relations (SEXFREQ) and I controlled that relationship by sex. SPSS would build a table crossing PARTYID and SEXFREQ for males and another table crossing PARTYID and SEXFREQ for females. If I had controlled by AGE instead, SPSS would have built a table crossing PARTYID and SEXFREQ for each age category. Each of these separate tables will have its own chi-square statistics and its own lambda and/or gamma statistics (if you asked SPSS to calculate statistics).
Now, for the write up there are just about 5 different variations for the controlled crosstab write-up. You will need to see which one fits your situation. One of the major factors in deciding which variation you use will be the relationship you originally observed between your IV and DV in your earlier crosstabular analysis. Here we go:
The first two cases occur when your initial crosstabular analysis weren't significant.
If the original crosstabular analysis relating your independent variable and dependent variable WAS NOT SIGNIFICANT and you look at each crosstab table for every level of your control variable and they are still not significant, you can then say: "My original relationship was not significant and when controlled by my control variable, Z, the relationship remained non-significant.".
If the original crosstabular analysis relating your independent variable and dependent variable WAS NOT SIGNIFICANT and you look at each crosstab table for every level of your control variable and one or more of the tables IS SIGNIFICANT, then you can say: "My original relationship was not significant; however, controlling by Z revealed a suppressed relationship between X and Y".
The next three cases occur when your initial crosstabular relationship was significant.
If the original crosstabular analysis relating your independent variable to your dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and ALL of the tables STILL SHOW A SIGNIFICANT RELATIONSHIP, then you can say: "My original relationship was significant and when controlled by Z remains significant. The relationship between X and Y is not caused by the influence of Z".
If the original crosstabular analysis relating your independent variable and dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and ALL of the crosstab tables ARE NOT SIGNIFICANT, then you can say: "My original relationship was significant, but controlling for Z, the relationship now appears to be spurious. Z appears to be responsible for the observed relationships between X and Y."
Lastly, we have the tricky one--the mixed case. This case is, of course, what most of you are likely to see when you look at your controlled crosstabular analysis. IF the original crosstab comparing your independent variable and dependent variable WAS SIGNIFICANT and you look at each crosstab table for every level of your control variable and see that SOME of the tables ARE SIGNIFICANT and SOME ARE NOT SIGNIFICANT, then you will need to make a judgment call. Here's the judgment:
Were there enough respondents in each of the controlled crosstab tables?
WHY IS THIS THE IMPORTANT JUDGMENT CALL? We know that as your N in a crosstab table increases that smaller differences are more likely to be considered statistically significant. It is possible that your data still exhibits the same patterns (in the percentages) that you saw in your earlier crosstab , but since your sample is divided across several tables it won't be statistically significant.
IF you believe that the table does show the same pattern, but fails to be significant due to a small number of respondents. You may argue that. If you can argue that for all the controlled crosstab tables that aren't significant (if there aren't too many), then you could state that "It appears that the relationship between X and Y persists when one looks at the patterns in the column percentages; however, some of the controlled crosstab tables are not statistically significant. Still, I would argue against calling this a spurious relationship. My reading is that the relationship between X and Y is not truly caused by Z."
OTHERWISE, you will need to argue that the control variable mediates the relationship. That is, the control variable really helps delineate in which situations the relationship holds. For instance, you might find that your relationship between X and Y holds for whites but not for blacks or holds for males but not for females. This can be very important information. In this case you will need to report the significant relationships like you did in Crosstab 1.