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The Questionnaire Development Process
Cross-Tabs with Chi-Square
Chi-square in general studies causal relationship and thus the hypotheses would be created for each of them at 95% significance level. By conducting the test and interpreting the results through the p-value, we can either accept or not accept the null hypothesis.
Cross-Tabs with Chi-Square statistics would be used to test all the six hypotheses described in the “Hypothesis” section.
The questionnaire designed specific to these proposed hypothesis are:
- Do you own a credit card?
- How frequently do you purchase products/services online?
- What is your age?
- What is your gender?
- On an average, how much time (per week) do you spend while surfing the Net?
- What is your annual family income?
- Do you use E-banking?
Regression Analysis
The Regression Analysis would be performed between the dependent variable “Average Amount spent per purchase made online” and the independent variables such as Frequency of Purchase of products and services online, Family Income, owning a Credit Card, Marital Status, Gender, Occupation, Education and Age.
Along with the questionnaire listed above for CROSS-TABS WITH CHI-SQUARE, following additional questionnaire are applicable to regression analysis:
- What is the highest level of education you have completed?
- What is your current primary occupation?
- What is your marital status?
Anova
The null hypothesis for this is also created at a 95% significant variable and then depending on the significant value from the results, the hypothesis is accepted or not accepted.
Questionnaire listed above for CROSS-TABS WITH CHI-SQUARE can also be used to test the hypothesis of ANOVA.
Factor Analysis
To find the major factors on which customers can be loaded, Factor Analysis would be done based on the following questionnaire and the attributes:
Q: Recall your earlier online buying/shopping experience and indicate your agreement with the following statements:
- I prefer making a purchase from internet than using local malls or stores
- I can get the latest information from the Internet regarding different products/services that is not available in the market
- Online shopping is more convenient than in-store shopping
- Online shopping saves time over in-store shopping
- It is safe to use a credit card while shopping on the Internet
- Online shopping allows me to shop anywhere and at anytime
- I trust the delivery process of the shopping websites
- Products purchased through Internet are of guaranteed quality
- Internet provides regular discounts and promotional offers to me
- Cash on Delivery is a better way to pay while shopping on the Internet
- Sometimes, I can find products online which I may not find in-stores
- I have faced problems while shopping online
- I continue shopping online despite facing problems on some occasions
- I do not shop online only because I do not own a credit card
The above attributes need to be answered on a 5-poing Likert scale (1= Strongly Agree and 5= Strongly Disagree)
Cluster analysis
Depending on the reasons for a person to be online, consumers can be clustered into homogeneous groups. The corresponding questionnaire and factors are listed below:
Q: I usually look on the internet (please indicate the frequency):
- News or Information
- Websites of company regarding product
- Travel and leisure
- Spent time in social media sites like Facebook
- Online shopping sites such as Flipkart
- Education related sites
- Official works, email
Once the consumers are online, they can further be clustered on the basis of factors which influence them while making an online purchase. The corresponding questionnaire and factors are listed below:
Q: Mark the importance of the factors which influence you while making an online purchase?
- Brand Name
- Service delivery time
- Website Content
- Recommendation by friends
- Online Ads – posters/banners
- Online reviews by users of product
- Ease of payment and security
Discriminant Analysis
The Discriminant Analysis would be performed between the dependent variable “online buyer or none buyer” and the independent variables such as Education, Gender, Monthly Income, owning a Credit Card, E-banking, use of social media sites and Age.
The questionnaires used for Discriminant Analysis have already been listed down as part of the other statistical techniques explained above.
Experimental Technique
Experimental or data analysis technique used for understanding buyer behaviors:-
Cluster analysis can be used to identify homogeneous groups of buyers. Then the buying behavior of each group may be examined separately.
Cluster analysis is a class of techniques used to classify objects or cases into relatively homogeneous groups called clusters. Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters.Cluster analysis is also called classification analysis. In cluster analysis there is no prior knowledge about the group or cluster membership is required for any of the objects, which is in contrast with the discriminant analysis.
Cross-Tabs with Chi-Square
A cross-tabulation is a joint frequency distribution of cases based on two or more categorical variables. Displaying a distribution of cases by their values on two or more variables is known as contingency table analysis and is one of the more commonly used analytic methods in the social sciences.
The Chi-square statistic is the primary statistic used for figuring out the significance of the cross-tabulation table. It is used to test for independence between the variables. If the variables are independent of each other (or in other words they have no relation), then the Chi-Square test will be non-significant. If the variables are found to be related, then the results of the statistical test will be “significant” and we can state that there is some relationship between the variables.
Regression Analysis
Regression analysis is a statistical technique for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.By conducting the tests and interpreting the results, we can determine the adjusted R2 value which tells us how good the regression model fits to the data. If the value is high, then the model fits well to the data and that there is a high correlation between the variables. On the other hand, if the value is low, then the model does not fit very well to the data and there is no significant correlation between the variables.
Anova
Analysis of variance, better known as ANOVA, helps us to group the data into various population samples and then check their relationship with an independent variable, which we consider to be significant depending on the responses from the questionnaire.
Factor Analysis
Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in fewer unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors, plus “error” terms. The information gained about the inter-dependencies between observed variables can be used later to reduce the set of variables in a data-set This procedure helps gaining insight into psycho-graphic variables.
Discriminant Analysis
It is a regression based statistical technique used in determining which particular classification or groups (such as ‘ill’ or ‘healthy’) an item of data or an object (such as a patient) belongs to on the basis of its characteristics or essential features. It differs from group building techniques such as cluster analysis in that the classifications or groups to choose from must be known in advance.
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