SOMDEEP SEN; Business Analyst: Trimax Analytics (e) [email protected] ; (p): 09748229123 LinkedIn: http://linkd.in/1ifqs3x
Jan 27, 2015
SOMDEEP SEN; Business Analyst: Trimax Analytics
(e) [email protected]; (p): 09748229123
LinkedIn: http://linkd.in/1ifqs3x
Plot
In 2007, the State Service Commission (SSC) New Zealand commissioned a survey
The survey was known as Driver's survey
Purpose: To the determine the key drivers that influence satisfaction with service delivery
SSC identified that more State Services are becoming available online
So, the research was done to spot factors driving satisfaction for services delivered online
Objective
To identify the key drivers of satisfaction of online services for obtaining information
Sample Size: 1243; total sample size has been considered for the analysis
Sampling error: 2.78%
Demographic info: Age, Gender, Income (Household income has been taken)
10 point rating scale has been used to get the response for satisfaction
10 point scale is an interval scale
But it can be treated as continuous
This is helpful in conducting the regression analysis
Respondent demographic data: Categorical in nature
51.5% of the respondents were female
Mean Age: 47; Mean Household income : 285000.50
Only 0.3% of the respondents reported no income
.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
1.3
5.0
9.3
6.5
12.1
13.2
8.5
12.1
10.9
6.8
3.74.7
5.8%
Age
4.1
8.68.1
8.4
18.3
21.1
23.4
7.7
.0
5.0
10.0
15.0
20.0
25.0
%
Income
The respondents were asked about 22 public service websites
18% of the respondents had visited at least one website
Top 3 mostly visited websites:
Inland Revenue (61.3%), Local council (39.3%), Local library (38.1%)
Top 3 most recently visited website:
Inland Revenue (22%), Local Library (15.8%) & Local council (9.2%)
Interestingly similar patterns have emerged in mostly visited & most frequently visited sites
A very small % of the people (0.3%) were unsure about the purpose of the visit
31.9%
67.8%
For Work For Presonal
The respondents were asked to rate the overall experience on a ten point rating scale
79.5% of the respondents showed more than average satisfaction level(>5)
50.2% respondents showed very satisfaction level (>=8)
.0
5.0
10.0
15.0
20.0
25.0
0 1 2 3 4 5 6 7 8 9 10
.6 .82.0
3.2 4.2
9.711.1
18.2
22.3
12.5
15.4
%
Ratings
P value(0.893)>α(0.05)
Overall experience is not dependent upon age group
P value(0.06)>α(0.05)
Overall experience is not dependent upon household income
Dependent Variable
Overall satisfaction of service delivery while obtaining information (q12a_1)
Justification:
10 point rating scale; can be treated as continuous
Talks about ‘overall’ satisfaction
Provides better correlation with independent variables as compared to 7a & 12_a_2
Independent variables:
q8a_1 – q8a_1 to q8a_4 - q8a_4 (Total no: 33)
Justification:
10 point rating scale; can be treated as continuous
Statements are randomized for statistical accuracy
Only statements specifically tested in the cognitive tests remain next to each other
Respondents can tell the difference between these statements
Treatment
10 point rating scale was used to get the responses for the predictor & the predicted
But there was also an option of 11 and(or) 12
11 and (or) 12 stood for Unsure/Not applicable
Hence those responses were replaced by mode of the responses
Justification:
Dropping the responses having 11 & 12 would reduce the sample size
Considering 11 and(or) 12 may disrupt the continuous nature of the scale
Replacing with zero may lead to biasedness as it would mean very dissatisfied &
Replacing with mean is difficult as mean of the responses may not be a whole number
Hence mode seems to be appropriate choice as it would be a whole number
Replacing with mode may minimize the biasedness as it is a response of the majority
Before conducting the bi-variate analysis certain variables were dropped
As per the questionnaire independent variables- 11& 12; 23, 24 & 25 were to be kept together as findings
Among these variable 11 & 25 were retained as they had stronger correlation with the dependent variable
Total number of independent variables left: 30Note:•I feel confident that my privacy was fully protected on this website (11)•I feel confident that my information was not shared with anyone else (12)•The tone of the language on the site was appropriate (23)•The language on this site was easy to understand (24)•The information on the website was easy to understand (25)
The bi-variate analysis
Correlations (and p-values) between the predicted and each predictor
Correlations (and p-values) of predictors with each other
Findings of the bi-variate analysis:
Each of the predictor had significant relationship with the predicted (p value<α for each)
Note: Please refer to the excel sheet Cor_1 to view the detail results
Missing value Treatment
‘Proc means’ in SAS was used to check the missing values
Neither the predicted nor the predictors had any missing values
Outlier Treatment
‘Proc univariate’ in SAS was used to check the outliers
The variables didn’t have any outliers
None of the variables had any values less than zero or more than 10
Independent variables were removed one as per the following steps:
a. Checking for the variable with highest VIF(Variation Inflation Factor), Xi
b. Finding the highest value of Xi in the Co-linearity Diagnostic Table
c. Finding the corresponding highest value in that row, to give us the most co-linear
variable with Xi , say Xj
d. Comparing the p-values of Xi and Xj to remove the variable with higher p-value
e. The R-sq value for the model was checked after removal of each variable
R-sq value was obtained
when there were 10 in-
dependant variables in
total
Some of the p-
values at this state
were still quite high.
Thus they were
removed from our
model
Finally, we
arrived at a
model with
R-sq of
0.653 with 6
independent
variables, b
ut the R-sq
value was
low
The
previous
regression
model was
re-run by
taking
q12a_1
and
removing
q8a_4_27
which
showed a
better R-sq
Regression Equation:
q12_a_1 = 0.053 + 0.223*q8a_1_2 + 0.221*q8a_2_13 + 0.264*q8a_2_16 + 0.143*q8a_3_20 +0.200 * q8a_4_32
Where,
q12_a_1 -: Overall satisfaction of service delivery while obtaining information
Intercept :- = 0.053
q8a_1_2 -: It was easy to find what I was looking for
q8a_2_13 -: I was able to do everything I needed to do online
q8a_2_16 -: Amount of time it took to get the overall service was acceptable
q8a_3_20 -: The information needed on the site was up-to-date
q8a_4_32 -: It’s an example of good value for tax dollars spent
Refining the model
Preparation of Dashboard
Preparation of the story board
Drawing Conclusion
Making Recommendations