Overview

This document contains the information and requirements for Assessment 3 as well as the             workshop guidelines. By following the workshop instructions, you are building towards your final assignment!

Only one team member needs to submit the final report as an MS PowerPoint file with your team  surnames and student ID numbers. Please ensure all team member names and IDs are on the front slide of the report.

In Assessment 3, you will put into practice what you have learnt in the course. You are required to  analyse and interpret a case study data set by using graphs and summary statistics from your SPSS  outputs. The data set allows you to use Crosstabs to look for associations between 2 non-metric      (nominal and ordinal) variables, t-tests to compare the difference between 2 groups and ANOVA to compare the difference between 3 or more groups.

In sum, this assessment gives you an opportunity to apply your skills in:

•    Getting familiar with SPSS and practice some simple coding

•     Descriptive Statistics (e.g. graphs, frequency distribution, means, standard deviation)

•    Crosstabs (e.g. Chi-square analysis)

•    Comparing Means (e.g. one-way ANOVA, different types of t-tests)

Please read the following case. Based on this case, you are required to produce a marketing research report (created in power point) using your market analysis and statistical knowledge.

Case Description

Please note that this case is fictitious.

Thompson – Market Expansion Strategy for New Zealand

IT product and services firms are growing fast in New Zealand. This growth has attracted                       international companies seeking new opportunities to expand the market. One of those companies   is Thompson, an Australia based IT company. Thompson offers a varied range of IT products such as  laptops, desktop PCs, and printers. The management team of Thompson is considering expanding      from Australia to New Zealand and aims to target all New Zealand (NZ) regions. Thompson intends    to first launch laptops. Thomson believe consumers have different needs, wants, lifestyles, and           interests that affect what they look for in a laptop. They feel it is important to identify whether there are distinct groups of consumers (i.e. market segments) relevant to their business offering. However, they need the help of market researchers to find this out.

Their primary marketing objective is to identify and describe these distinct market segments in order to target suitable prospects with greater profit potential. Thompson may need to develop a                 differentiated strategy for marketing communications, advertising and servicing that fits the needs    and characteristics of each target segment.

Given the intense competition IT companies now face from alternative devices such as smart-phones and tablets, Thompson will need to think carefully about a viable strategy in terms of who to target   and how to reach/service each target provided there appears to be sufficient interest in their              business offering.

Thompson recently hired a consulting team to design and conduct an online survey. Between June    and August 2022 they emailed the survey link to 1500 consumers who live throughout New Zealand. The questionnaire and an SPSS data set of the responses to these questions can be downloaded         from the Stream site.  Assume the sample is representative of New Zealand consumers. Thompson is unsure of how to analyse this data set in order to determine whether they can achieve their                marketing objectives. This is where you and your team come in.

Based on this case the following marketing management decision problems are apparent:

1.    Should Thompson enter the New Zealand market with laptop products?

2.    If so, who should they target and how?

To investigate these marketing management decision problems, we have formulated six research questions.

1.   What are the demographics of the target market (i.e. the laptop buyers)?

2.   What is the level of interest in laptops in the New Zealand market?

3.   What selection criteria are important in choosing a laptop?

4.   What information sources (i.e. Newspaper versus the Internet) are more important?

5.   What laptop information sources are important to which target market group (i.e. males versus females)?

6.   What laptop selection criteria are important to which target market group (i.e. different Ethnicities)?

Your job is to give answers to each research question by analysing the data and subsequently providing Thompson with useful information to make their decisions:

1. Should Thompson enter the New Zealand market with laptop products?

2. If so, who should they target and how?

General Instructions

The weekly labs (or online workshops if we are in lockdown) will walk you step-by-step through the analysis for each research question to generate the results for your report (that ultimately should    help answer the decision problem). Try to conduct each type of analysis in the lab session held after the lecture, where the technique is covered. Use a USB stick to save your work as the SPSS outputs will not save, and you will end up having to rerun the analysis. All relevant outputs produced by following this sheet should be redesigned and re-formatted into a practical and “management  friendly” reader style. Students should attend one lab session per week and then work                independently within their groups to complete any work that is not finished in the lab sessions.

You will present your findings on the case study discussed in this document in the form of a report (created in PowerPoint) directed to Thompson, the Australia-based IT company. The case study    described above contains a set of marketing research questions.  Your report should outline the     potential for answering those research questions based on your analysis of the data. Your reports should contain ALL of the following sections:

•    Title Page (Group name, member names & IDs, Course number and name, assignment description) (1 slide)

•    Table of Contents with page numbers (1 slide)

•     Brief Introduction section – Survey facts, Project Management (1 slide)

•    Summary of Research Questions

•     Description of Sample

•    Summary of Main Findings (Organised by Research Questions)

•     Recommendations section

•    Appendices

Your report reader expects to see BOTH brief technical explanations of the procedures you followed (should be put into the Appendix), written in your own words, and a translation of the results into   meaningful, practical marketing implications for the business discussed in the case described. Any  figures, tables and graphs included in the report must be formatted so they are easy to read and       must be discussed where they are presented in the report.

Up to 30% of the marks may be deducted for reports that are not concise, poorly formatted or which contain excessive spelling and grammatical errors.  Use the MS Word spelling and grammar check      and have someone proofread your report. Use the active voice, not passive style writing to achieve   conciseness.

Exercises/Questions for Assignment 3

Workshop 1-Editing and Coding:

Open the questionnaire and dataset

1.    Download the sample questionnaire called ‘Laptop Questionnaire’ from Stream and save it on your H-drive.

2.    Download the data from the 488 survey respondents in the SPSS file called “Laptop Survey Date” on Stream. After saving the SPSS file to your h-drive, please open the file.

The fictitious data set represents the responses that survey participants gave to the survey               questions.  Read each question one at a time, then check/compare the question with the SPSS data file to see how it is recorded in SPSS. This will help you understand your data.

SPSS opens two screens, one with an output file and one with the dataset. We first focus on the dataset. On the dataset screen, you can see two tabs on the bottom left corner: Data view and Variable view.

Data view: This is where you put all your data. (Responses from people who have filled out a questionnaire/survey)

Variable view: Here, you can give (and change) names to variables corresponding to questions in the questionnaire. You can also define properties here (more about that later).

We first have a look at the Variable view. In total, you should have 31 columns (variables) in your    data view tab. Each variable relates to one question from your questionnaire. For instance, the first variable is PNO, the Participants Questionnaire Number (a code for each respondent).

The second variable is called Q1, which represents the first question, “Do you plan to buy a laptop in the next 12 months?” The Variable name tends to be very short. Since variable names are very short, it is also a good idea to add slightly longer labels to all variables. Making the variable understandable but not too long allows you to remember its meaning without having to go back to the                          questionnaire. In this case “Plan to buy in 12 months” makes sense and is easily read in the window   without having to scroll the whole question.

The Name and the Label of a variable in each column also have Values. Values assign numbers to the respondents’ answers that we also call coding answers. The main reason we code answers (by giving them numbers) is that most statistical packages like SPSS will only do mathematical analysis with       numbers, not words. One very useful property of SPSS is that it allows you to assign a description for each numeric value a variable can take. This makes it easier to work with the data without having to go back and check what the value of each variable represents. Most of the answers in our data set    are already coded. However, the last 3 variables still need coding. To code the last 3 Variables, carry out the following actions:

Coding Instructions

1.    Go to the Variable View and click on the cell defined by the row “(Q12) Gender” . Under the “Value” column add the following Value Labels: 0 = Male; 1 = Female.

2.    Next, give names to the levels of “Age Group”, using the names given below.

1 = 18-29

2 = 30-39

3 = 40-49

4 = 50-59

5 = 60 and above

3.     Give names to the levels of “Ethnic background”, using the terms below.

1 = Asian

2 = NZ European or European

3 = Maori

4 = Other Ethnicity

Do you see what happens if you go to the Data View tab and then to the View menu (top-left) and uncheck “Value Labels”? What happens if you check it again?

At this stage, it is useful to again save your data to your H-drive and USB. Please do so using the file name Laptop Survey Data.sav. In general, it is a good habit to save the data frequently, to avoid re- doing work in case of a computer problem.

Measurement levels

In the variable view tab, you can see a few remaining columns we have not explained. Most of these are easily interpreted, but there are two that need some extra attention.

Type: This gives the type of the variable; in our case, these should all be numeric.

Measure: Gives the scale level of the variable, Nominal (i.e., the number is just a label), Ordinal (i.e., the number is more than just a label and implies a certain order, such as higher means more) and Scale (i.e., the number implies a certain order and the difference between values is meaningful). SPSS does not distinguish between Ratio or Interval scales and labels them both as Scale.

Often SPSS does the thinking for you, but with “Type” and “Measure”, it is wise to double check as SPSS does make some mistakes here. Now all variables in your data set are Nominal. Do you think all those levels are correct? Further, what scale level should each variable be? Check out every variable and correct the scale level if it is wrong (hint: 25 variables have the wrong scale level defined).

Workshop 2- Descriptive Statistics:

Descriptive Statistics:

So now we have checked and entered, if necessary, all the data. Next, we want to analyse what the data means. One way of analysing data is to obtain descriptive statistics.

For assessment 3 (final report), you will be able to use the results of your descriptive statistics to

describe your sample.

First, we want to describe the sample: Have a look in the drop-down menus in your SPSS for              "Analyse", "Descriptive Statistics", and next ", Frequencies". Describe (i.e. profile) respondents by    running “frequencies” on Q8-Q14 in the data set. You can make pie charts and bar charts by clicking on “Charts” followed by “Bar Chart” or “Pie Chart” .

If you did it correctly, SPSS has now added these tables and graphs to your output file Output1.spo. Please save this file to your H-drive and USB using the name Survey Laptop Results.spo. By saving    this file after every new output is added, all output of this session should be stored in this file. Go    back to your output file. Look at your bar graphs. For the appropriate variables (keep the scale level in mind), order the answer categories in the graph from high to low. To do this, double click on the graph. Within the Chart Editor, select X. You can now sort the categories by a statistic.

Look at all the tables and graphs in the output. What do the tables and graphs tell you? Who is in      your sample? With the outputs of this analysis, you can describe your sample. However, keep in        mind that the scale level determines how you should summarise and present each variable (e.g., the use of a table, pie chart, bar chart depend on the scale level- nominal, ordinal, scale). Pick the tables and graphs that are the most appropriate way to summarise each variable. If you can’t remember,   go back to the lecture material- Descriptive Statistics).

Copy the appropriate tables and graphs into your PowerPoint (which starts the document for your    final report). The Lecture Material on Data Analysis -Descriptive Statistics will show examples of how you can present your graphs and tables.

Workshop 3- Inferential Statistics:

Cross-tabs using Chi-Square analysis:

We are now ready to embark on slightly more advanced analyses. The first research question aims   at profiling the target market (i.e. the laptop buyers) in terms of their demographics. To answer      this research question, we conduct and interpret some Chi-square analyses. Please go to "Analyze",  "Descriptive Statistics", and "Crosstabs". Create 4 cross tables by putting Q1 in the row box and Q11- Q14 in the columns box. Click ok. Go back to your output file. Do you see any associations?

Now go back to crosstabs "Analyse" → "Descriptive Statistics" → "Crosstabs" and click on                   "Statistics,” check the "Chi-Square" box, and click "Continue". Also, click on "Cells" and check both    the "Counts Observed" and the "Counts Expected" box. Also, under Percentage, click “Column” . Run the cross tables again. When you look at the output, how would you interpret it? Please keep in        mind that we should only interpret outputs that show significant associations! Think about why Chi- square is an appropriate method to analyse the associations for these variables? You should include the rationale for using Chi-Square in your appendix.

Copy the tables/graphs that show significant associations into your PowerPoint document (the           document you will submit as assignment 3). Keep in mind that this table belongs to the first research question. Start answering research question two based on your tables.  The Lecture Material in          Week Data Analysis -Inference Statistics will show examples of how you can present your results.

Workshop 4- Testing for differences 1 :

The second research question assesses the level of interest in laptops in the in the New Zealand       market. In order to answer this research question, we first want to look at some descriptive statistics

(If you forgot how to do descriptive statistics go back to workshop 1 that explains how to make           frequency tables and charts). For our second research question we need Q1 and Q4. Make a                frequency table and a pie chart of consumers who plan to buy a laptop and those who do not plan to buy a laptop (Q1) and run another frequency table and bar charts on Q4 (likelihood to buy a                customised laptop made by a new brand). Look at all the tables and graphs in the output. What do    the tables and graphs tell you? How would you answer research question one based on the tables     and/or graphs?  Since we want to make sure that we can infer that our results are also true for the    population of interest (and not just our sample), in the next step, we calculate a one sample t-test     that will tell us whether our results are significant or not.

One sample t-test

In the first step, we look at the value for the likelihood to buy a customised laptop made by a new   brand (Q4). Go to question 4 and check the values. What value represents at least some likelihood?

Now try to formally test whether respondents are interested in a new customised laptop via a one- sample t-test. Go to "Analyse", "Compare means", "One Sample T-test" and select the variables. Put "3" as the test value. Think about why 3 is that an appropriate value to test against? What does the output tell you?

You need the output of this analysis for your report to answer research objective two. Copy and        paste the graphs/tables into your PowerPoint document. Try to answer this research objective using the result of the analysis. The Lecture Material in the Week on Data Analysis -Testing for Differences 1 will provide examples of how you can present your results.

The third research question assesses and the importance of selection criteria in choosing a laptop. To answer this research question, we first look at the values for the importance scale for the              selection criteria (Q6a-Q6i). What value represents no importance, and what value represents at      least some importance? Remember, you can see the values under ‘Variable View’ . Next, to get an     initial feel for the answer, we first create an Error Bar for the selection criteria. Go to "Graphs,"         "Legacy Dialogs," "Error Bar," and select "Summaries of separate variables", click on "Define", select all the selection criteria (Q6a-Q6i) and click OK. In the output, double-click on the error bar graph     and transpose it by clicking “Options” followed by “Transpose Chart”, i.e., this ensures that the         variable names are on the y-axis. Next, we want to order the mean values from high to low. We do   this by clicking on the y-axis. If you did this successfully another window will open. You can now sort the variables by selecting ‘statistic’ . Leave the direction as “ascending” . Click ‘Apply’ . Have a look at  your output. What do you see? What are the most important selection criteria? What are the least  important selection criteria?

Now try to formally test which one of the selection criteria are very important. We can do this again by doing a one sample t-test. Go again to "Analyze", "Compare means", "One Sample T test" and select the variables (Q6a- Q6i). Put "3" as the test value. What do you see? Which of the selection criteria are very important?

Copy the error bar and the tables from the analysis into your power point document. Start                answering research objective 3 using the graphs and tables. The Lecture Material in the Week on    Data Analysis -Testing for Differences 1 will provide examples on how you can present your results.

Paired Sample t-test:

The fourth research question assesses whether the Internet or Newspaper is more important as information sources when buying a new laptop.

Now try to formally test whether the Newspaper or the Internet as information sources are more  important. We do this using a paired sample t-test. Go to "Analyze", "Compare means", "Paired-    Samples T-test", select the selection criteria variables (Q7a and Q7c) you want to test against each other, and click OK. What does the output tell you?

Copy the tables from the analysis into your PowerPoint document. Start answering research question 4 using the tables. The Lecture Material in the Week on Data Analysis -Testing for  Differences 1 will provide examples of how you can present your results

Workshop 5-testing for differences 2

Independent samples t-test:

The fifth research question assesses what laptop information sources are important to which          target market group (males versus females). In order to give answers to this question, we calculate an independent sample t-test.

In order to know whether males and females differ significantly in terms of the importance they give to different information sources (Q7a-Q7f), Go to "Analyse", "Compare means", "Independent           sample T-test". The test variables are Q7a to Q7f, and the grouping variable is Gender. Click also on  "Define Groups" and type for group 1 a "0" and for group 2 a "1" (why?). What do we learn from the output?

Copy the results of this analysis into your PowerPoint under the fourth research objective. Start writing answers to research question 5. The Lecture Material on the Data Analysis -Testing for   Differences 2 will provide examples of how you can present your results.

One-way ANOVA

In order to give answers to the last research question (Q6), Assess what laptop selection criteria are important to which target market group (i.e., different Ethnicities), we calculate an ANOVA. Why    would we use ANOVA for this?

In order to know how people from a different Ethnic background differ in terms of the selection          criteria, go to "Analyse", "Compare means", "One-Way ANOVA". The dependent variables are the      selection criteria (Q6a-Q6i), and the "Factor" is the variable for ethnicity. Click also on "Post-hoc         multiple comparisons" and select the Scheffe test (click continue). Next, click on "Options" and select

"Descriptive Statistics" and "Means plot". In the output, double-click on the Means plots (at the end) and make the y-axes comparable across the plots by choosing the range from 1 (minimum) to 4         (maximum). You can achieve that by double clicking on each graph/means plot. A separate window  will open. Click on the Y-Axis. Select Minimum 1 and Maximum 4.  Click “Apply” . Repeat this for each Means plot graph.

Go back and have a look at your ANOVA output. What do we learn from the output?

Copy the results of this analysis into your PowerPoint under research question six. Start writing     answers to the last research question. The Lecture Material in the Week on Data Analysis -Testing for Differences 2) will provide examples of how you can present your results.