Discovering statistics using IBM SPSS statistics

Discovering statistics using IBM SPSS statistics

Reply must be at least 200-300 words. For each thread, you must support your assertions with at least 2 citations from sources such as your textbook, peer-reviewed journal articles, and the Bible.

Field, A. P. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Los Angeles, CA: Sage.

Justin

Mixed methods research attempts to merge quantitative and qualitative research design in order to complete a more comprehensive view and analysis of issues or problems. According to Johnson, Onwuegbuzie, and Turner (2007), “mixed methods research is, generally speaking, an approach to knowledge (theory and practice) that attempts to consider multiple viewpoints, perspectives, positions, and standpoints (always including the standpoints of qualitative and quantitative research)” (as cited by Wilson, 2013, p. 275). The methods imposed by this particular research encompasses questionnaires, interviews, observations, or surveys. However, data can be collected and used in quantitative research in order to represent findings with numerical values and statistics (Field, 2018). Qualitative and quantitative research both have purpose and importance; however, the two combined can provide analytical and statistical data to fully explain theories and hypotheses. According to Rohrer, Brummer, Schmukle, Goebel, & Wagner (2017), “While these analytical strategies might aim to fulfill quality criteria that are well known to quantitative researchers, such as reliability and validity their utility is limited when considering the types of answers submitted as answers to open-ended questions on surveys” (p. 1). However, sometimes numerical studies or quantitative research can be or should be converted into mixed methods research in order to fully explain, dissect, analyze, and publish more definitive findings.

Conversion to Mixed Methods Research: 16.2
In order to utilize a mixed-method, design the researcher would first need to explore the data and decide if the information fits the model or if the checks can be made to identify outliers, normality, homogeneity, etc. (Field, 2018). When the data is explored researchers can use boxplots, histograms, and descriptive statistics in order to plug the data into graph form and follow-through with conducting a Mauchly and Levene’s test (Field, 2018). Furthermore, If the data explored fits the model, then follow-up tests can and should be conducted in order to calculate the effect sizes. If the data fits the model, then correct outliers and normality problems can be assessed by using ANOVA through the SPSS systems. These systems use multilevel models and multivariate tests to ensure correct outliers and normality problems are assessed and identified (Field, 2018). Follow-up tests include testing specific hypotheses or determining if no hypotheses are present. Specific hypotheses identified will allow researchers to determine that planned comparisons can be made and if no hypotheses are detected then post hoc tests need to be performed.

Using Mixed Methods in the Example

In order to use qualitative and quantitative data in the provided example, a researcher could impose that the quantitative data is already collected; however, survey research could be utilized using questionnaires or observational research to complete the transition of the study to provide a more in-depth picture. In this case, survey research questionnaires could be compiled and handed out at the beginning and the end of the speed dating session in order to gauge the participants’ initial and end perceptions. Questions could also relate to the implicit bias or expectations versus the actual opinions of the process and include questions that relate to how participants felt during the evolution. Also, these questionnaires can provide a more personal and human interaction within the study by incorporating qualitative research that is supported by quantitative data regarding the subjects’ date.

Conclusion

Mixed methods research is critical to scientific and academic studies. This form or research allows more intimate and detailed research, through qualitative measures, that is backed by statistical quantitative methods. According to Rohrer, Brummer, Schmukle, Goebel, & Wagner (2017), “While these analytical strategies might aim to fulfill quality criteria that are well known to quantitative researchers, such as reliability and validity their utility is limited when considering the types of answers submitted as answers to open-ended questions on surveys” (p. 1). Not only can more comprehensive and inclusive research be used to find the truth and the pursuit or knowledge be applied in academia but can also serve individuals in their personal lives. John 7:18 states, “Whoever speaks on their own does so to gain personal glory, but he who seeks the glory of the one who sent him is a man of truth; there is nothing false about him” (New International Version). Therefore, researchers and Christians alike should reflect clarity and truth in professional and social lives.

References

Field, A. (2018). Discovering statistics using IBM SPSS statistics: North American Edition. London, UK: Sage.

Rohrer, J., Brummer, M., Schmukle, S., Goebel, J., & Wagner, G. (2017). What else are you worried about? Integrating textual responses into quantitative social science research. PloS One, 12(7), 1-34.