Research Methodology, Design, And Methods

Research Methodology, Design, and Methods

Identify the research methodology chosen for this research project and provide rationale for why it is appropriate given the problems.

Explain whether the research design is exploratory, causal, or descriptive. Provide rationale for the choice.

Describe the research methods used for this research study based on the research methodology, research design, and research questions, and provide a rationale as to why they were chosen. They might include a combination of experimentation, descriptive statistics, correlation, and causal-comparative methods.

Specify how the data were most likely collected to test the hypotheses. Data collection methods include, but are not limited to, survey, observation, and records analysis.

Briefly describe the type of sampling design that was most likely used for the data that were collected. Choices include, but are not limited to, random sample, convenience sample, etc. Explain your rationale for your sampling design selection(s).

Specify the statistical procedures used to test each set of hypotheses from among correlation, regression, t test, and ANOVA. Explain why each procedure was the most appropriate choice. For example: Correlation is the preferred procedure to use to test the RQ1 hypotheses since the interest is whether a relationship exists between an independent variable (IV) and dependent variable (DV). Correlation will indicate if there is a relationship between height (IV) and weight (DV), the strength of the relationship, and the direction of the relationship.

Paper For Above instruction

For this research project, the chosen methodology is quantitative, primarily because the focus is on measuring and analyzing relationships between variables to address specific business problems. Quantitative research allows for collection of numerical data that can be statistically analyzed to derive meaningful insights, making it appropriate for testing hypotheses and establishing causal relationships within a business context (Creswell & Creswell, 2018).

The research design selected for this study is descriptive. Descriptive research helps in understanding the characteristics of a population or phenomenon and provides valuable insights into the current state of affairs related to the business problem under investigation. This design is particularly suitable because it allows for detailed data collection on variables such as customer satisfaction levels, employee engagement, or market trends, which are vital for strategic decision-making (Maxwell, 2013). The choice of a descriptive design aligns with the need to gather comprehensive data that reflects real-world conditions without manipulating variables, setting the stage for further exploratory or causal analysis.

In terms of research methods, a combination of surveys and records analysis will be employed. Surveys are ideal for collecting primary data directly from stakeholders, such as customers, employees, or managers, enabling the researcher to gather perceptions, attitudes, and behaviors. Records analysis will provide secondary data, such as sales records, financial statements, or operational reports, that support the quantitative analysis. This mixed approach enhances data validity by triangulating findings from different sources (Neuman, 2014).

The data collection process will involve structured questionnaires distributed via online platforms or face-to-face interactions, depending on respondent accessibility. For secondary data, organizational records will be accessed with appropriate permissions. This approach ensures a broad and representative sample, facilitating robust statistical analysis.

The sampling design is likely to utilize a stratified random sampling method. This approach involves dividing the population into strata, such as different customer segments or employee groups, and then randomly selecting samples from each group. The rationale for this design is to ensure that all relevant subgroups are adequately represented, thereby increasing the generalizability of the findings and reducing sampling bias (Kish, 1965).

For data analysis, correlation and regression analyses will be primarily used to test hypotheses regarding relationships and predictive power between variables. For example, correlation analysis will be employed to determine the strength and direction of the relationship between customer satisfaction (IV) and customer loyalty (DV). Regression analysis will be used to understand how multiple independent variables collectively influence a dependent variable, assessing their relative importance (Field, 2013). T-tests and ANOVA will be conducted when comparing means across different groups, such as comparing customer satisfaction levels between different stores or demographic groups. These tests are appropriate because they can determine if observed differences are statistically significant, providing evidence for or against proposed hypotheses (Meyers et al., 2013).

References

• Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
• Kish, L. (1965). Survey sampling. John Wiley & Sons.
• Maxwell, J. A. (2013). Qualitative research design: An interactive approach. Sage Publications.
• Meyers, L. S., Gamst, G., & Guarino, A. J. (2013). Applied multivariate research: Design and interpretation. Sage Publications.
• Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches. Pearson.