MKC2500 Assignment 2: Marketing Research Analysis Sem 1 | Monash

MKC2500 Assignment 2 - Guide 

Like assignment 1, assignment 2 also addresses the issue of the public's support for the social media ban for children under 16. Suppose that another research team has conducted a research study on this issue and collected some survey data. Your task as a marketing research analyst is to draft a brief research report on the basis of this questionnaire and data (henceforth, "the social media ban survey" and "the social media ban data." Both files are available on Moodle). A research report communicates the results and key findings of a study to clients. The notes below give you detailed instruction on the assignment. 

1. Definition of the research problem 

(a). Define the market research problem (MRP) 

Based on the questionnaire provided to you, define a marketing research problem (MRP) with components. The overarching statement of the MRP should be "to identify and better understand the key drivers/predictors of the Australian public's support for banning social media use among the under 16s" You need to formulate at least twocomponents for your MRP. 

Note that your components for assignment 2 may or may not be the same as the components for your assignment 1. In either case, the MRP must be able to be addressed with the social media ban dataset. In approaching this task, I would suggest that you start by carefully reading through the accompanying questionnaireand familiarize yourself with the SPSS dataset. Ask yourself the following questions: what information has been collected from the target population? What are the variables that are measured? How can your client, the Minister of Education (the Hon Jason Clare MP), make use of the information to improve their decision-making? 

(b). provide a brief justification for your MRP 

Provide a brief explanation on how this research can help your client. This should be on the basis of your components and consistent with section 3 below where you discuss the managerial implication of your findings.

2. Research approach and hypotheses 

Come up with at least 6 comparative or relational research questions (RQ). While it is OK if some of your RQs are the same as that of your assignment 1 submission, you should try to develop your own original RQs. Collectively, your RQs should cover ALL the components of your marketing research problem proposed in the previous section. Note that because these RQs need to be "answerable" by the social media ban dataset,they have to involve (and only involve) variables measured in the social media ban survey. This requires you to be very familiar with each question in the questionnaire. 

For each research question, 

• Clearly identify which component the RQ corresponds to. 
• Clearly state both the null and alternative hypotheses, which are to be tested in the data analysis section 
• Clearly identify ALL the variablesthat you use to answer the research question and the question in the questionnaire/dataset that measures this variable (to illustrate with a hypothetical example, if the variable you use is "age of the respondent", and it is measured by question 15 in the accompanying questionnaire, you should include the information in your report). If you use a recoded variable, describe the recoding (for example., "young customers": age <=30; "mature customers": age > 30, etc) 
• Name the statistical testyou use to test the hypothesis (for example, "an independent- samples t-test of the difference in loyalty between male and female"). If a multiple regression is used to test several hypotheses simultaneously, name the dependent and independent variables that will be included in the regression.

For this section, usage of bullet points and tables is required. 

A list of the main statistical tests discussed in the lecture is provided below. Please note that you are NOT required to use all the techniques (choose only what is appropriate for your hypotheses). That being said, appropriate use of a variety of techniques or the usage of more advanced techniques such as multiple regressions is a necessity for high marks for this section. 

You need to consider a number of issues in deciding which technique to choose for a particular test. For example, certain techniques are only appropriate for interval-scaled data, while others can be used for both interval- and ordinal-scaled data. Similarly, some techniques only allow for comparison between two groups, while others allow you to compare the differences between multiple groups.

List of the main statistical techniques 

1. T tests (dependent/independent samples) can be used to test for differences between means of subgroups.  
2. Chi square test can be used to test the association between two categorically scaled variables. It can also be called a test of independence.
3. Analysis of variance (ANOVA) can be used to see whether there are any differences across the categories of the non‐metric variables with respect to any of the metric variables.
4. Correlation analysis measures the degree to which there is a linear association between two interval or ratio scaled variables.
5. Multiple regression can be used to explain the variation in dependent variables (outcome or effect variables) using other metric variables as independent variables (predictors).

3. Data analysis, findings and interpretation 

(a). Data description 

First, briefly describe the data based on the information you have. Consider the following questions as you work on this section. 

1. What is the target population? 
2. What is the sample size? 
3. Broadly speaking, what kind of information has been gathered with the questionnaire (e.g., target population's attitudes and behaviour towards what? What demographic information is collected) 
4. Descriptive characteristics of your main variables: for EACH of the variables used in your first 3 research questions (RQ 1 to RQ 3, as formulated by you in section 2), show its frequency distribution (one-way tabulation). 

• You only need to produce a bar chart for each variable (i.e., the SPSS frequency table is NOT required) (see week 7 lecture slides for details) 
• Please do this for both metric and categorical variables 
• For the re-codedvariables in these RQs, provide this information for the re-coded version (i.e., not the source variable from which the re-coded variable is derived)
• This information is to be put inthe appendix 

(b). Reporting of the findings 

You are required to report the results of your statistical tests. Include the SPSS output for each set of hypotheses tested. Make sure that the output tables are properly labelledand clearly indicate which set of hypotheses it tests.