MKGGC2420/MKG2420 Marketing Research Methods - Assignment 2 -Explanation of Analyses

Overview

Before explaining the types of analyses used for the second assignment, the variables they each used and why they are used, it is worthwhile explaining what this study aimed to achieve. Essentially, it examined peoples' attitudes towards and intention to engage in ecotourism. Using your first assignments to supplement my own research for this topic, I found common influential themes and put together a conceptual framework. This is a series of relationships, starting with independent variables that eventually lead to the dependent variables (e.g. intention to engage in ecotourism). The following is the framework being examined, which consists of variables that were represented by series of questions in the surveyyou completed via Survey Monkey:

Questions relating to gender, education, age and income were also asked. While these are not featured in the framework, they are still important to ask as it allows findings to be generalised to the broader population, to other populations and possible differences between these demographic characteristics to be identified in the research.

Mean Ranking:
Variables used:
Holiday-related activities (as themes)

Purpose:

Mean ranking is a simple way to determine the mean score of an attribute from a data set. It also provides students with the opportunity to determine the mean scores for a particular holiday-related activity themes (e.g. water-based activities, snow sports, camping etc.).

Instructions:
Step 1.
Open the excel document sent with these instructions. Using row 70 of the document for the mean scores, select the cell under the question being examined (e.g. You will put the mean score for kayaking in 70a, the mean score for surfing in 70b and so on). Once it has been selected, type in:

=AVERAGE

Excel should automatically generate the brackets at the end of =AVERAGE, and should look like the following image.

Step 2.

Select the range of answers for the question you are calculating the mean for. You can either type it in manually for highlight the range (in the example provided in the following image, the questions are in cells A2 through to A19).

Step 3.
Once the formula has been added, press enter and it will give you the mean score for that question.

Step 4.

Discuss the results. Here, students don't have to state and explain every single mean score they have calculated, just ones that appear important or relevant (whether they are much higher or lower than others in their given question series or compared to all of the other questions/attributes). Students may need to add sections of this data as an appendix if that makes it easier to discuss in the assignment.

Independent Sample T-Test:
Variables used:
Gender, holiday-related activities (as individual activities)

Purpose:

An independent sample t-test is an inferential statistical test used to determine whether there is a statistically significant difference between the means in two unrelated groups. In this instance, it is being used to see whether there are any differences in attitudes between males and females towards a number of holiday-related activities. Refer to the end of Week 8's lecture of a discussion on how this method of analysis is used and an example of how you would typically report the findings.

Pearson's Correlation Coefficient:
Variables used:
Environmental Concern, Ecotourism Activities, Attitudes towards ecotourism and intention to go on an ecotourism holiday (or, as they appear in the outputs, EnvCon, EcoTourAttr, Attitude and Intention)

Purpose:
Pearson's correlation coefficient is used to test correlations between two variables. In this instance, it's being used to test the following relationships (the first three variables represent independent variables, while the fourth variable, intention, represents the dependent variable):

Environmental Concern

• Ecotourism Attributes
•  Attitudes towards Ecotourism
• Intention to go on an ecotourism holiday

Refer to the end of Week 8's lecture of a discussion on how this method of analysis is used, an example of how you would typically report the findings and a scale that shows how to determine the strength and direction of the relationship is reported in the outputs.

ANOVA:

Variables used:
Education, Environmental Concern, Attitude towards ecotourism and social influence (or, as they appear in the outputs, Education, EnvCon, Attitude andSocialInfluence)

Purpose:
An ANOVA (or analysis of variance) is used to analyse the differences among group means. In this instance, it's being used to see if there are any significant differences between the three education levels and levels of environmental concern, attitude towards ecotourism and social norms.

Instructions:
SPSS Statistics generates quite a few tables in its one-way ANOVA analysis. The purpose of this walk-through is to show you the main tables required to understand the results from the ANOVA (and Tukey post-hoc) tests. Keep in mind the following examples used are using a different context to the assignment (as in, it uses different variables from a completely different study).

Descriptives Table:
The descriptives table (see below) provides some very useful descriptive statistics, including the mean, standard deviation and 95% confidence intervals for the dependent variable (which for this example, is Time) for each separate group (Beginners, Intermediate and Advanced), as well as when all groups are combined (Total). These figures are useful when you need to describe your data.

ANOVA Table
This is the table that shows the output of the ANOVA analysis and whether we have a statistically significant difference between our group means. We can see that the significance level is 0.021 (p = .021), which is below 0.05. and, therefore, there is a statistically significant difference in the mean length of time to complete the spreadsheet problem between the different courses taken. This is great to know, but we do not know which of the specific groups differed. Luckily, we can find this out in the Multiple Comparisons table which contains the results of post-hoc tests.

Multiple Comparisons Table
From the results so far, we know that there are significant differences between the groups as a whole. The table below, Multiple Comparisons, shows which groups differed from each other. The Tukey post-hoc test is generally the preferred test for conducting post-hoc tests on a one-way ANOVA, but there are many others. We can see from the table below that there is a significant difference in time to complete the problem between the group that took the beginner course and the intermediate course (p = 0.046), as well as between the beginner course and advanced course (p = 0.034). However, there were no differences between the groups that took the intermediate and advanced course (p = 0.989).

Reporting the output of the one-way ANOVA
Based on the results above, we could report the results of the study as follows (N.B., this does not include the results from your assumptions tests or effect size calculations):

There was a statistically significant difference between groups as determined by one-way ANOVA (F(2,27) = 4.467, p = .021). A Tukey post-hoc test revealed that the time to complete the problem was statistically significantly lower after taking the intermediate (23.6 ± 3.3 min, p = .046) and advanced (23.4 ± 3.2 min, p = .034) course compared to the beginners course (27.2 ± 3.0 min). There were no statistically significant differences between the intermediate and advanced groups (p = .989).