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Quantitative Research Essay Example

Quantitative research involves collecting numerical data though content analysis, performance tests, personality measures, questionnaires, etc. Quantitative research allows for experimental and non-experimental research. Experimental research tests cause and effect, while non-experimental research, such as, correlation research, relates variables, and survey research describes trends. There are three basic types of quantitative research questions: descriptive research questions, correlational research questions, and causal research questions. However, research involves collecting word data though interviews, open-ended questionnaires, observations, content analysis, focus groups, etc. Qualitative research can explore common experience (Grounded Theory Research), shared culture (Ethnography), an environment (Case Study), or an individual (Narrative Research). Both type of research ask research questions, focus on type of data, and have variables.

Research Questions

Research Questions are clear, concise, and arguable questions. A qualitative research question “explores a central concept or phenomenon” (Schreiber 2012). An example of a qualitative research question is how do African American male elementary students read popular fiction written by Caucasian female authors? This is an ethnographic research question that will explore hared culture of a group of people. Differently, a quantitative research question “relates attributes or characteristics of individuals or organizations” (Schreiber 2012). There are three basic types of quantitative research questions: descriptive research questions, correlational research questions, and causal research questions. Descriptive research questions describe variables being studied.

An example of descriptive research question is does reading popular fiction increase test scores in elementary students? This research question is an experimental research question that tests the cause and effect. Another research question is the correlational research question, which relates variables. An example of correlational research question is do elementary students who read popular fiction has a higher test scores than those who do not read popular fiction? This is also a correlation research that relate variables. The final basic type of quantitative research question is the causal research question. Causal research questions test for a cause and effect relationship between variables. An example of causal research questions is all other factors being equal, do elementary students reading popular fiction achieve better test scores than students reading curriculum-required fiction? This is also an experimental research question that tests the cause and effect.

Variables

Quantitative research and qualitative research have variables; a variable is any measured characteristic or attribute that differs for different subjects (Lane 2006). Variable can be either independent (IV), or dependent (DV). An independent variable is a variable that remain the same, but the researcher manipulates a dependent variable. Additionally, there are covariate (CoV) relates variable, is related to the DV, and is also a predictor value. In the example of a qualitative research question is how do African American male elementary students read assigned popular fiction written by Caucasian female authors? the IV is gender, age, and ethnicity, the DV is the assigned popular fiction, and the CoV is socioeconomic status.

In the example of a quantitative research question is does reading assigned popular fiction increase test scores in elementary students? the IV is the age, gender, and ethnicity, the DV is assigned popular fiction, and the CoV is socioeconomic status. An example of the quantitative research question is do elementary students who read assigned popular fiction has a higher test scores than those who do not read popular fiction? the IV is age, gender, and ethnicity, the DV is assigned popular fiction, and the CoV is socioeconomic status. The final example of a quantitative research question is do elementary students reading assigned popular fiction achieve better test scores than students reading curriculum-required fiction? the IV is age, ethnicity, and gender, the DV is assigned popular fiction and curriculum-required fiction, and the CoV is socio-economic status.

Type of Data

Quantitative research uses Ordinal, Ratio, or Interval Scale, while qualitativew research uses Nominal Scale. Both nominal and ordinal data are categorical, but interval and ratio data are continuous. In the example of a qualitative research question is how do African American male elementary students read assigned popular fiction written by Caucasian female authors? That data is nominal. Nominal data is categorical. Nominal data is consists of assigning items to groups or categories that cannot be related to each other (StatSoft 2010). The nominal data is African American and male, a well as Caucasian and female. Moreover, in the example of a quantitative research question is does reading assigned popular fiction increase test scores in elementary students? that data is interval. Interval data rank order, but also quantify and compare the sizes of differences between them (StatSoft 2010).

Interval data is continuous. The interval data is the test scores, because they can be higher or lower, but it is not known how much higher. An example of the quantitative research question is do elementary students who read assigned popular fiction has a higher test scores than those who do not read popular fiction? that data is ratio data. Ratio data is continuous. Ratio data rank order, but also quantify and compare the sizes of differences between them. Ratio data has an absolute zero and allows for how many times greater. The research can find out how many times greater are the test scores of elementary student who read popular fiction than those who did not. The final example of a quantitative research question is do elementary students reading popular fiction achieve better test scores than students reading curriculum-required fiction? that data is ratio data. Like the question above, the research can discover how many times greater are the test scores of elementary student who read popular fiction than those who did not.

Conclusion

Both type of research ask research questions, focus on type of data, and have variables. Quantitative research uses an objective/unbiased approach to collecting numerical data from participants and analyzes these numbers using statistics in order to answer a specific, narrow research question. On the other hand, qualitative research uses a subjective/biased approach to collecting words from participants and analyzes/describes these words in order to answer a broad, general research question.

Bibliography

•Lane, D.M. (2006, June). Glossary. Rice Virtual Lab in Statistics Retrieved January 31, 2014, from http://davidmlane.com/hyperstat/glossary.html •Research Methods ** Available in the Presentations section. •Schreiber, Deborah Ph.D. 2012 Research Methods in Education •StatSoft, Inc. (2010). Electronic Statistics Textbook. Elementary Concepts. Retrieved January 31, 2014, from http://www.statsoft.com/textbook/elementary-concepts-in-statistics/?button=1

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.

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