Logit Analysis/Multinomial Logit Analysis

[pretty_jana]

Can Logit Analysis/Multinomial Logit determine brand preference drivers? If yes, what should the data format be and how does the analysis go?

Thanks in advance.

[Statman]

Logistic Regression can but recall that the DV is dichotomous. Below is a quote from the SPSS Help:

"Logistic regression is useful for situations in which you want to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous. Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables in the model. Logistic regression is applicable to a broader range of research situations than discriminant analysis."

As far as the data, I add the following, also from the SPSS Help:

"Data. The dependent variable should be dichotomous. Independent variables can be interval level or categorical; if categorical, they should be dummy or indicator coded.

Assumptions. Logistic regression does not rely on distributional assumptions in the same sense that discriminant analysis does. However, your solution may be more stable if your predictors have a multivariate normal distribution. Additionally, as with other forms of regression, multicollinearity among the predictors can lead to biased estimates and inflated standard errors. The procedure is most effective when group membership is a truly categorical variable; if group membership is based on values of a continuous variable (for example, "high IQ" versus "low IQ"), you should consider using linear regression to take advantage of the richer information offered by the continuous variable itself."

Hope this helps,

[pretty_jana]

Hi Statman,

Thanks for your quick reply.

Is Logit Analysis and Logistic Regression the same? From what I recall, they have the same assumptions but for the logit analysis we get the log of the dependent variable. I am not sure. Please correct me if I'm wrong.

Another question regarding the data format, what if my dependent variable is not dichotomous (it is actually a 10-point rating scale) and all my independent variable is dichotmous, which analysis is more suitable?

Many thanks.

[Statman]

They are treated as the same.

If the DV is scale then you need to use an ordinal regression technique.

The following comes from the SPSS Help:

"Ordinal Regression allows you to model the dependence of a polytomous ordinal response on a set of predictors, which can be factors or covariates. The design of Ordinal Regression is based on the methodology of McCullagh (1980, 1998), and the procedure is referred to as PLUM in the syntax.

Standard linear regression analysis involves minimizing the sum-of-squared differences between a response (dependent) variable and a weighted combination of predictor (independent) variables. The estimated coefficients reflect how changes in the predictors affect the response. The response is assumed to be numerical, in the sense that changes in the level of the response are equivalent throughout the range of the response. For example, the difference in height between a person who is 150 cm tall and a person who is 140 cm tall is 10 cm, which has the same meaning as the difference in height between a person who is 210 cm tall and a person who is 200 cm tall. These relationships do not necessarily hold for ordinal variables, in which the choice and number of response categories can be quite arbitrary.

Example. Ordinal Regression could be used to study patient reaction to drug dosage. The possible reactions may be classified as "none," "mild," "moderate," or "severe." The difference between a mild and moderate reaction is difficult or impossible to quantify and is based on perception. Moreover, the difference between a mild and moderate response may be greater or less than the difference between a moderate and severe response.

Data. The dependent variable is assumed to be ordinal and can be numeric or string. The ordering is determined by sorting the values of the dependent variable in ascending order. The lowest value defines the first category. Factor variables are assumed to be categorical. Covariate variables must be numeric. Note that using more than one continuous covariate can easily result in the creation of a very large cell probabilities table."

Hope this helps

Do contact me directly if you need

[tezanbildik]

I have a sample consisted of 49 depressed adolescents. These patients treated with a drug for 12 weeks.

We administered three scales: 1. HAMD, 2. WHOQOL-100, 3. Visuel Analog scale.

Variables are:
HAMD baseline score,
HAMD endpoint score,
WHOQOL-100 baseline score
WHOQOL-100 week 1
Visuel Analog scale baseline score
Visuel Analog scale week 1
Treatment response (1=remission; 2=no remission)


I want to use the logistic regression for determining whether early changes, defined as those observed between baseline and week 1, in WHOQOL-100 and Visuel Analog scale items are related to treatment outcome for patients who receive treatment.
In thia analyses, change in the WHOQOL-100 and Visuel Analog scale from baseline to week 1 and HAMD baseline score are independent variables.
Treatment response is the dependent variable.

How does SPSS use to determine the change in the scale from baseline to week 1.
Thank you

Tezan Bildik

[Statman]

If I understand your description then I believe it will fall under a 'repeated measures' model and, yes, SPSS can handle this.

S