The conditional logit model with XLSTAT-Conjoint
Dataset for Conditional Logit model XLS224 KB
-
Conjoint Conjoint analysis software
- View Conjoint page
- Download Trial version
- See users' feedback
-
System configuration
- Windows:
- Versions: 9x/Me/NT/2000/XP/Vista/Win 7
- Excel: 97 and later
- Processor: 32 or 64 bits
- Hard disk: 150 Mb
- Mac OS X:
- OS: OS X
- Excel: X, 2004 and 2011
- Hard disk: 150Mb.
- Windows:
Benefits
-
Easy and user-friendly
Easy and user-friendly XLSTAT is flawlessly integrated with Microsoft Excel which is the most popular spreadsheet worldwide. This integration makes it one of the simplest available tools to work with as it utilizes the same philosophy as Microsoft Excel. The program is accessible in a dedicated XLSTAT tab. The analyses are grouped into functional menus. The dialog boxes are user-friendly and setting up an analysis is straightforward.
-
Data and results shared seamlessly
Data and results shared seamlessly One of the greatest advantages of XLSTAT is the way you can share data and results seamlessly. As the results are stored in Microsoft Excel, anyone can access them. There is no need for the receiver to have an XLSTAT license or any additional viewer which makes your team-work easier and more affordable. In addition, results are easily integrable into other Microsoft Office software such as PowerPoint, so that you can create striking presentation in minutes.
-
Modular
Modular XLSTAT is a modular product. XLSTAT-Pro is a core statistical module of XLSTAT which includes all the mainstream functionalities in statistics and multivariate analysis. More advanced features contained in add-on modules can be added for specific applications. This way you can adapt the software to your needs making the software more cost-efficient.
-
Didactic
Didactic The results of XLSTAT are organized by analysis and are easy to navigate. Moreover useful information is provided along with the results to assist you in your interpretation.
-
Affordable
Affordable XLSTAT is a complete and modular analytical solution that can suit any analytical business needs. It is very reasonably priced so that the return of your investment is almost immediate. Any XLSTAT license comes with top level support and assistance.
-
Accessible - Available in many languages
Accessible - Available in many languages We have ensured XLSTAT is accessible to everyone by making the program available in many languages, including Chinese, English, French, German, Italian, Japanese, Polish, Portuguese and Spanish.
-
Automatable and customizable
Automatable and customizable Most of the statistical functions available in XLSTAT can be called directly from the Visual Basic window of Microsoft Excel. They can be modified and integrated to more code to fit to the specificity of your domain. Adding tables and plots as well as modifying existing outputs becomes easy. Furthermore, XLSTAT includes some special tools on the dialog boxes to generate automatically the VBA code in order to reproduce your analysis using the VBA editor or to simply load pre-set settings. This effortless automation of routine analysis will be a huge time saver on your part.
The conditional logit model
The conditional logit model is a statistical method similar to logistic regression.
The conditional logit model is a method mostly used in its evolved form as part of the conjoint analysis. It is nevertheless useful when to analyze a certain type of data. McFadden introduced this model in 1973. Instead of having one line per individual, there are as many lines as alternatives. Thus, it is no longer the characteristics of the individuals that are modeled but the alternatives.
If one seeks to study travel modes, we will have four travel modes (car / train / air / bus), each travel mode having its own characteristics (price, speed), but an individual can only choose one of the four modes.
As part of a conditional logit model, we have for N individuals, N*4 rows with 4 rows associated with the four choices. The binary response variable will indicate the choice of the individual with the value 1, and 0 will correspond to the options that the individual did not choose.
A column associated with the name of the individuals (with 4 lines per individual for our example) has to be selected in XLSTAT. The explanatory variables will also have N * 4 rows.
Dataset for the conditional logit model
The example discussed below is a classic case in which one seeks to compare the travel modes proposed to go on vacation. It comes from Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.
An Excel file containing both the data and the results can be downloaded by clicking here.
The data correspond to a sample of 210 individuals, each one having 4 possibilities (air, car, bus and train). We asked each of them the travel mode they would choose to go on vacation.
The data set has 840 rows. The first column identifies the individual, the second is the binary variable modeling the travel mode. Then there are two quantitative variables, respectively, the overall cost and the waiting time during the trip associated with each travel mode for each individual. Finally, the categorical variable associated with the transportation is in the last column (air, train, bus or car).
Set a conditional logit model
To activate the dialog box, start XLSTAT, then select XLSTAT / XLSTAT-Conjoint / Conditional Logit , or click the corresponding button on the XLSTAT-Conjoint toolbar (see below).
Once you have clicked the button, the dialog box appears.
Select the data on the Excel sheet.
The response variable corresponds to the binary variable. The subject labels correspond to the numbers associated with the individuals (you can also have names of individuals instead). In our case there are three predictors, one qualitative - the travel mode - and two quantitative - global cost and waiting time. As we selected the labels of the variables, we must select the variable labels option.
Once you click the OK button, the calculations are performed and the results displayed.
Interpret the results of a conditional logit model
The following table gives several indicators of the quality of the model (or goodness of fit). These results are similar to R² and to the analysis of variance table of linear regression and ANOVA. The most important value is the Chi-square associated with the log ratio (LR). This is equivalent to Fisher's F test in the linear model: an attempt to assess whether the variables provide a significant amount of information to explain the variability of the binary variable. In our case, as the probability is less than 0.0001, we can conclude that the variables provide a significant amount of information.
These goodness of fit statistics show that our model is significantly better than the model without any predictor. The following table confirms these initial impressions:
The p-values are all very small and the impact of the three variables is significant in the type III analysis table.
Finally, the coefficients of the model show that the air is preferred and that the waiting time has a significant negative effect on the choice of travel mode.
The analysis of residuals may also be useful and provide other information about individuals' choices.