Filters allow you to perform analysis on a subset of the data. Categories of the sys_RespStatus (Incomplete, Disqualified, or Qualified/Complete) variable may be selected for inclusion in the analysis. Using the Manage Filters... additional filters may be selected from variables in your study, or new variables that you build based on existing variables or combinations of existing variables.
Counts, Logit, and Latent Class can use weights to give some respondents more importance in the calculations than others. You may select an existing variable with numeric responses to use as a weight (such as the numeric variable FamilySize). In that case, values associated with each case are applied as weights. Or, you may specify weights to apply to categories within an existing select-type variable (for example, the select-type variable Gender, where Male is assigned a weight of 1.25 and Female 0.75). Weights may not be applied during HB estimation.
You may also assign weights to categories of a new segmentation variable that you create (under Analysis | Segments and Weights...). New segmentation variables are created by specifying logic relating to one or multiple variables. For example, from the Segments and Weights dialog, you could create a new segmentation variable that categorized people into four buckets depending on their gender and the size of their family: a) Male_Small_Family, b) Male_Large_Family, c) Female_Small_Samily, d) Female_Large_Family. Then, you could assign discrete weights to categories a-d for use in analysis.
This area allows you to select which tasks to include in analysis. Fixed (holdout tasks) by default are omitted from analysis. You may wish to omit other tasks as well, such as the first task as a warm-up.
This dialog lets you select which attributes to include in your analysis, and how to code them (part-worth or linear coding). If you select linear coding, a single utility coefficient is fit to the attributes, such as a slope for price, speed, or weight.
Linear Coding of Quantitative Attributes:
When you specify that an attribute should be coded as a linear term, a single column of values is used for this attribute in the independent variable matrix during utility estimation. A weight (slope) is fit for that independent variable that provides the best fit. A column opens up for you to specify the Value used in the estimation matrix for each level of the quantitative attribute. By default, these are 1, 2, 3, etc. However, you should specify values that metrically correspond to the quantities shown to respondents. For example, if respondents saw levels of "1 lb., 2 lb, and 6 lb." then the values associated with those levels should be 1, 2, and 6. Please note that CBC will automatically zero-center any values you specify when creating the independent variable matrix. So, values of 1, 2, 6 will be converted to values of -2, -1, and +3 in the independent variable matrix prior to utility estimation.
If using linear coding and HB, please note that level values should be specified in the magnitude of about single digits to lead to quick and proper convergence. In other words, rather than specifying 10000, 40000, 70000 one should specify 1, 4, 7. And, rather than specify 0.01, 0.04, 0.07, one should specify 1, 4, 7.
Sometimes estimating a separate set of utility values for each attribute (main effects) does not fit the data as well as when also fitting selected interaction effects. This occurs when the utilities for attributes are not truly independent. We encourage you to consider interaction terms that can significantly improve fit. But, we caution against adding too many interaction terms, as this can lead to overfitting and slow estimation times.
The interaction between two attributes with j and k levels leads to (j-1)(k-1) interaction terms to be estimated. But, when the utilities are "expanded" to include the reference levels in the reports and utility files, a total of jk interaction terms are reported.
If certain attributes have levels with known utility order (best to worst, or worst to best) that you expect all respondents would agree with, you may decide to constrain these attributes so that all respondents (or groups, in the case of logit or latent class) adhere to those utility constraints. For more information, please see the Latent Class or CBC/HB manuals.
Step size (default = 1.0)
This number governs the sizes of changes made in the iterative computation. If set to a smaller number, such as .5 or .1, the computation will be slower but may be a tiny bit more precise.
Maximum number of iterations (default 100)
Controls the maximum number of iterations that will be undertaken in the analysis. In most cases, fewer than 12 iterations are all that is needed for logit estimation to converge with a great deal of precision. Only in rare cases will more iterations be necessary.
Log-likelihood Convergence Limit (default is 1 in the fifth decimal place)
Once the log-likelihood fails to produce a very large gain in the last iteration, the logit routine breaks out of the iteration loops and reports the results. One can change the convergence limit (amount of gain for the most recent iteration) to set a new criterion for breaking out.
Tasks to include for best/worst data
If you have used the best/worst response type, then respondents have provided two answers (best and worst choices) for each choice task. You can estimate utilities using best and worst tasks (default) or using best tasks only or worst tasks only.
If set to Full, more information about variances and covariances of estimates is provided in the output. The variance/covariance matrix may be used for subsequent D-efficiency computations (not a feature of the logit routine).