### 15.18 RELIABILITY ¶

    RELIABILITY
            /VARIABLES=var_list
            /SCALE (name) = {var_list, ALL}
            /MODEL={ALPHA, SPLIT[(n)]}
            /SUMMARY={TOTAL,ALL}
            /MISSING={EXCLUDE,INCLUDE}

The `RELIABILITY` command performs reliability analysis on the data.

The `VARIABLES` subcommand is required. It determines the set of variables upon which analysis is to be performed.

The `SCALE` subcommand determines the variables for which reliability is to be calculated. If `SCALE` is omitted, then analysis for all variables named in the `VARIABLES` subcommand are used. Optionally, the name parameter may be specified to set a string name for the scale.

The `MODEL` subcommand determines the type of analysis. If `ALPHA` is specified, then Cronbach’s Alpha is calculated for the scale. If the model is `SPLIT`, then the variables are divided into 2 subsets. An optional parameter n may be given, to specify how many variables to be in the first subset. If n is omitted, then it defaults to one half of the variables in the scale, or one half minus one if there are an odd number of variables. The default model is `ALPHA`.

By default, any cases with user missing, or system missing values for any variables given in the `VARIABLES` subcommand are omitted from the analysis. The `MISSING` subcommand determines whether user missing values are included or excluded in the analysis.

The `SUMMARY` subcommand determines the type of summary analysis to be performed. Currently there is only one type: `SUMMARY=TOTAL`, which displays per-item analysis tested against the totals.

#### 15.18.1 Example - Reliability ¶

Before analysing the results of a survey – particularly for a multiple choice survey – it is desireable to know whether the respondents have considered their answers or simply provided random answers.

In the following example the survey results from the file `hotel.sav` are used. All five survey questions are included in the reliability analysis. However, before running the analysis, the data must be preprocessed. An examination of the survey questions reveals that two questions, viz: v3 and v5 are negatively worded, whereas the others are positively worded. All questions must be based upon the same scale for the analysis to be meaningful. One could use the `RECODE` command (see RECODE), however a simpler way is to use `COMPUTE` (see COMPUTE) and this is what is done in Example 15.8.

    get file="hotel.sav".

    * Recode V3 and V5 inverting the sense of the values.
    compute v3 = 6 - v3.
    compute v5 = 6 - v5.

    reliability
    	/variables= all
    	/model=alpha.

Example 15.8: Investigating the reliability of survey responses

In this case, all variables in the data set are used. So we can use the special keyword ‘`ALL`’ (see Backus-Naur Form).

Screenshot 15.8: Reliability dialog box with all variables selected

Result 15.6 shows that Cronbach’s Alpha is 0.11 which is a value normally considered too low to indicate consistency within the data. This is possibly due to the small number of survey questions. The survey should be redesigned before serious use of the results are applied.

Scale: ANY

Case Processing Summary
Cases | N | Percent
--- | --- | ---
Valid | 17 | 100.0%
Excluded | 0 | .0%
Total | 17 | 100.0%

Reliability Statistics
Cronbach's Alpha | N of Items
--- | ---
.11 | 5

Result 15.6: The results of the reliability command on `hotel.sav`