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their overall satisfaction with CSH, but more likely that their influence is correlated with the
other dimensions of overall satisfaction that are in the model. Each customer satisfaction
covariate is scored by customers using a 7-point Likert scale (where '1' indicates the
customer is “extremely dissatisfied” and '7' indicates “extremely satisfied”), and thus each
utilizes 7 dummy variables in the coding scheme. We denote these dummy variables as
7
7
7
7
7
{79
ii
q
,
{82a
ii
q
,
{82b
ii
q
,
{82d
ii
q
, and
{82f
ii
q
, respectively, and they are
1
1
1
1
1
defined as follows:
1
if customer response to q79 is
i
q
79
i
0
otherwise ,
1
if customer response to q82a is
i
q
82a
i
0
otherwise ,
1
if customer response to q92b is
i
q
82b
i
0
otherwise ,
1
if customer response to q82d is
i
q
82d
i
0
otherwise ,
1
if customer response to q82f is
i
q
82f
i
0
otherwise .
Assembling all of the covariates together, we then have a total of 77 covariates in
x
. Thus,
the vector of slopes
in the link equations has dimension 77
. Combined with the 4
4
intercept parameters
{
i
, the model we have developed has a total of 81 parameters. We
note it is conceivable that interactions between the defined covariates could be important
contributors to the model. However, interaction effects based on the current data set were
difficult to assess because of confounding issues. As the data set gets larger over time, it is
conceivable the confounding issues could be resolved and interaction effects could be tested
for statistical significance.
1
3.2 Model fitting and interpretation
The SAS code for obtaining maximum likelihood estimates (MLEs) for the model
parameters
4
is shown in Appendix A. Lines 1-4 are used to read in the data
that is stored as a space delimited text file 'indata.txt' that is located in the indicated
directory. All of the input variables on the file are coded as integer values. The PROC
LOGISTIC section of the code (lines 5-10) directs the fitting of the multinomial logistic
regression model. The class statement is used to specify that all of the covariate variables are
categorical in nature, and the param=glm option specifies to use the dummy variable coding
scheme that was defined in the previous section. Table 2 summarizes the portion of the SAS
output that reports the maximum likelihood estimates for
{
i
and
1
4
. Note that the zero
for the slope of the last level of each covariate is a structural zero resulting from the non-full
rank dummy variable coding used when fitting the model.
{
i
and
1
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