Quantitative methods involve the process of collecting, analyzing, interpreting, and writing the result of a quantitative data. Specific methods exist in both survey and experimental research (i.e., statistics, the process of measurement, testing a theory) that advance the study of a phenomena. Essentially, the researcher is hoping the numbers will yield quantitative findings that can be generalized to describe a population and/or identified issue.
Quantitative research examines the relationship between variables and subsequently, advances discourse regarding the implications of quantitative findings. Note that the final research report has a set structure and includes the following: introduction, literature and theory, methods, results, and discussion. Like qualitative researchers, those who engage in this form of inquiry also have assumptions about testing theories deductively and quantitative procedures exist to control bias and alternative explanations. Quantitative methodologies are conducted in a way so that other researchers are able to generalize and replicate the findings.
Download the introduction to quantitative research below to learn more about quantitative research design:
Quantitative Methodys by Russell K Schutt
Schutt, R. K. (2010). Introduction to quantitative research. 16(36), 1-10.
Schutt introduces the reader to what quantitative research. In doing so, he goes into the history of quantitative research the differences between this research and qualitative research. Schutt defines quantitative research as the process to explaining phenomena by collecting quantitative data, which are analyzed by mathematically based methods. The reader should note that many researchers take a pragmatic approach and will use the appropriate research methods to conduct their experiment, and in many case, mixed-methods approaches are appropriate. After that section, Schutt then gives four types of research questions and situations in which quantitative research should be used. Schutt then introduces the reader to some terms that they should know. The introduction ends with some common misconceptions about quantitative research, questions to test what the reader just read, and suggested readings to further the reader’s knowledge about this specific type of research.
Quantitative Research Tips
The following is a guideline to analyzing qualitative data, to best understand how one research step is related to another fro a complete discussion of the data analysis procedures. For the purposes of this example, assume that the data collected is quantitative survey data:
Step 1. Report information about the number of members of the sample who did and did not return the survey. A table with numbers and percentage describing respondents and nonrespondents is a useful tool to present this information.
Step 2. Discuss the method by which response bias will be determined. Response bias is the effect of noresponse on survey estimates (Fowler, 2002). Bias means that if nonrespondents had responded, their responses would have substantially changed the overall results. Mention the procedures used to check for response bias, such as wave analysis or a respondent/nonrespondent analysis.
Step 3. Discuss a plan to provide a descriptive analysis of data for all independent and dependent variables in the study. This analysis should indicate the means, standard deviations, and range of scores for these variables.
Step 4. If the proposed contains an instrument with scales or a plan to develop scales (combining items on scales), identify the statistical procedure (i.e., factor analysis) for accomplishing this. Also, netion reliability checks for the internal consistency of the scales.
Step 5. Identify the statistics and the statistical computer program for testing the major inferential research questions or hypotheses in the proposed study. The inferential questions or hypothesis relate to variables or compare groups in terms of variables so that inferences can be drawn from the sample to a population. Provide a rationale for the choice of statistical test and mention the assumptions associated with the statistic.
Step 6. A final step in the data analysis is to present the results in tables or figures and interpret the results from the statistical test. An interpretation of the results means that the researcher draws conclusions from the results for the research questions, hypothesis, and the larger meaning of the results. This interpretive analysis involves several steps.
A Checklist of Questions for Designing an Experimental Procedure
Readers need to know about the selection, assignment, and number of participants who will take part in the experiment. Consider the following suggestions when writing the method section for an experiement:
Who are the participants in the study?
What is the population to which the results of the participants will be generalized?
How were the participants selected? Was random selection method used?
How will the participants be randomly assigned? Will they be matched? How?
How many participants will be in the experiment and control group(s)?
What is the dependent variable or variables (i.e., outcome variable) in the study? How will it be measured? Will it be measured before or after the experiment?
What is the treatment condition(s)? How was it operationalized?
Will variables be covaried in the experiment? How will the be measured?
What experimental research design will be used? What would a visual model of this design look like?
What instrument(s) will be used to measure the outcome in the study? Why was it chosen? Who developed it? Does it have established validity and reliability? Has permission been sought to use it?
What the steps in the procedure (e.g., random assignment of participants to groups, collection of demographics information, administration of treatment(s), administration of posttest)?
What are potential threats to internal and external validit for the experimental design and procedure? How will they be addressed?
Will a pilot test be conducted?
What statistics will be used to analyze the data (e.g., descriptive and inferential)?
How will the results be interpreted?