Tables Program
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Overview

The functionality and output of the Tables program resembles the cross-tab capabilities found in many statistical and cross-tab packages today. However, the SMRT Market Simulator is primarily concerned with conjoint analysis; the Tables program is basic and relatively inflexible. If you need to analyze the results with a more sophisticated package, SMRT or SSI Web can export data for use in other software systems.


Analyzing Nominal/Ordinal Data

Nominal data are numeric values, where the numbers refer to categories or classifications. For example, one might code gender as:

Male = 1  
Female = 2  

An example of a type of survey question that might be analyzed is shown below:

Which of the following best describes the  
highest level of education you achieved?  
o Some high school  
o Graduated high school  
o Some college  
o Graduated college  
o Some post-graduate studies  
o Post-graduate degree  
o Doctoral degree  

If we assign the following codes:

Some high school = 1  
Graduated high school = 2  
Some college = 3  
Graduated college = 4  
Some post-graduate studies = 5  
Post-graduate degree = 6  
Doctoral degree = 7  

it is true that larger numbers reflect greater education (most would agree on this point). In contrast to the previous example where the coded value really had no quantitative meaning other than nominal classification, these data reflect ordinal scaling. However, as with nominal data, it still doesn't make sense to apply mathematical operations such as addition, subtraction, multiplication or division to ordinal data. For example, a person with a "post-graduate degree" (code 6) doesn't necessarily have twice as much education as a person who completed "some college" (code 3).

The Table Display

When you analyze survey responses using Tables, a table of results (in this case, frequencies) is generated and displayed in the report window:   
                  
                               Total  
Education  
   Some high school             3  
   Graduated high school       35  
   Some college                68  
   Graduated college           73  
   Some post-graduate studies  18  
   Post-graduate degree        11  
   Doctoral degree              4  
 
Total                         212  
Missing                         -  
 
This simple table displays summary frequencies for the entire sample. Frequencies represent the number of times respondents answered (or were classified) in a particular way. Every time you click Compute!, a new table (or set of tables if you have selected multiple variables) is appended to the report window. You clear the window by clicking Clear. In addition to frequencies, there are other statistics you can include (by clicking Statistics) in the table appropriate for nominal or ordinal variables:

      Column percent  
      Row percent  
      Table percent  
      Chi-Square  

These statistics are described in any good statistics textbook.

You can also analyze the results by another variable, such as Gender. In the example below, Gender is the banner (column) variable, and Education the stub (row) variable. This display is often referred to as a cross-tabulation.

Education by Gender  
                                       Gender  
                                   Male  Female   Total  
Education  
       Some high school               3       -       3  
       Graduated high school         18      17      35  
       Some college                  38      30      68  
       Graduated college             33      40      73  
       Some post-graduate studies     8      10      18  
       Post-graduate degree           6       5      11  
       Doctoral degree                3       1       4  
 
Total                               109     103     212  
Missing                              -       -       -  
 
This display lets us compare educational achievement by gender. For example, 33 males went as far as graduating college (but no farther) vs. 40 females. If we include column percentages, we make better sense of the results, since there are an unequal number of males and females in the sample.
 
Education by Gender  
                                   Gender  
                                   Male  Female   Total  
Education  
       Some high school               3       -       3  
                                      3%      -       1%  
 
       Graduated high school         18      17      35  
                                     17%     17%     17%  
 
       Some college                  38      30      68  
                                     35%     29%     32%  
 
       Graduated college             33      40      73  
                                     30%     39%     34%  
 
       Some post-graduate studies     8      10      18  
                                      7%     10%      8%  
 
       Post-graduate degree           6       5      11  
                                      6%      5%      5%  
 
       Doctoral degree                3       1       4  
                                      3%      1%      2%  
 
       Total                        109     103     212  
                                    100%    100%    100%  
 
Missing                               -       -       -  
Analyzing Continuous Variables

You can also analyze continuous variables with the Tables program.

The following statistics are available in Tables and appropriate for analyzing continuous data:

Mean  
Standard deviation  
Variance  
Minimum  
Maximum  
Standard Error of Mean  
 
These statistics are described in any good statistics textbook.

Continuous variables can only be specified as stub variables; they cannot be used as a banner variable unless you recode them to discrete categories by creating a custom segment.

If a continuous variable you are analyzing has many unique values, the table can become extremely large. A simple remedy is to turn off frequencies and only request summary statistics, such as the mean, minimum, maximum, standard deviation and standard error.


Some Notes on Respondent Weighting

The Tables program lets you weight respondents differentially. Weighting lets some respondents have more impact on the statistical summaries than others. For example, a respondent with a weight of 2.0 is counted twice as much as another respondent with a weight of 1.0. Weighting is useful for adjusting a sample to reflect known population characteristics.

Weights affect every statistic mentioned earlier in this section, except for Minimum and Maximum. When you request weighted tables, both weighted and unweighted totals are reported. We strongly suggest you assign weights so that the average weight is equal to 1.0. If the weights do not average 1.0, the total unweighted number of respondents will not equal the total weighted respondents. More critically, many statistics are incorrect if the average weight is not equal to 1.0, including:

Standard error  
Chi Square  
 
You will also see some inaccuracy in the following statistics because of the division by (n-1), especially if you have small sample sizes:
 
Standard deviation  
Variance