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Penalty Analysis:

Penalty analysis is a method used in sensory data analysis to identify potential directions for the improvement of products, i.e. Overall Liking, on the basis of the other sensory attributes presented to consumers or experts.

Index of this Web Page:
  1. Example SIMS 2000 Excel Export Screen - Total Penalty.
  2. Example SIMS 2000 Excel Export Screen - JAR Distribution.
  3. Print Screen of Main Report Selection Form.
  4. Notes that will be available by Clicking the 'Notes' Button on Main Report Form.

1.   Example SIMS 2000 Excel Export Screen - Total Penalty.



2.   Example SIMS 2000 Excel Export Screen - JAR Distribution.



3.   Print Screen of Main Report Selection Form.


SIMS software express package file: EXAMPLE6.SIMS2000ExpressPackage.sql.ZIP



4.   Notes that will be available by Clicking the 'Notes' Button on Main Report Form.

 *****   SIMS 2000:  Penalty Analysis - Basic Usage Notes   *****

  Purpose:
  --------
    Penalty analysis is a method used in sensory data analysis to identify potential directions for
    the improvement of products, i.e. Overall Liking, on the basis of the other sensory attributes
    presented to consumers or experts.

    Penalty importance is measured by comparing the Overall Liking ratings of those consumers who
    thought the product was Too Much (TM) or Not Enough (NE) on a particular JAR attribute with
    the ratings of those consumers indicating that the JAR attribute was Just About Right (JAR).
  
    The sensory test for Penalty Analysis must include: 
      a) An Overall Liking attribute for the global satisfaction index for a product/sample.
         Typically this Overall Liking attribute would be a standard 9 point scale.
  
      b) One or more JAR attributes for individual characteristics of the product.
         Typically these JAR scales are a standard 5 point scale.
           Example:  Way Too Little ; Too Little ; Just About Right ; Too Much ; Way to Much

  Interpretation:
  ---------------
    Penalty analysis shows the amount that the Overall Liking was penalized by the Not-JAR respondents.
    The Overall Liking score would increase by the Total Penalty.
    The JARs with the larger values for Total Penalty may be of interest.
    The graph values, units of measure, will depend on questionnaire design and panelist's data.
    Generally speaking, with a typical Overall Liking 9pt scale, 1 to 9 return values,
    a JAR with a total penalty > 0.50 is high and > 0.25 is noteworthy.
  
    Interpretation is the responsibility of the Sensory professional taking all aspects into account.


  When to Use Penalty Analysis:
  -----------------------------
    - Studies with Overall Liking and JAR scales.
    - Monadic or multi-sample studies.  One or more samples.
    - More valuable with larger test populations (N).
    - More valuable when parallel description sensory data is available.


  Qualifying Attributes:
  ----------------------
    Overall Liking, SIMS 2000 will *try* to automatically identify your Overall Liking Scale 
    by comparing the attribute's data export label with common ones, such as 'Overall Liking'
    You may need to manually select the proper attribute for your ballots 'Overall Liking', see 2nd Tab.

    JAR scales must have an ODD number categories. The middle category is assumed to be JAR.
    JAR scales actual Not-JAR responses must be >= 20% to be included, in either direction, NE or TM.
    JAR scales Mean-drop calculation must be > 0 to be included.


  Mathematics Information:
  ------------------------
    SIMS 2000 penalty analysis calculations are simple mathematics.  SAS software is not required.
        The mathematic specifications per Tom Carr, 2007.

    The qualified and selected JAR scales are independently analyzed with overall liking.

    Collapse the JAR scale responses by --Panelists-- into three, NE, JAR, and TM.

    Calculate the % of panelists for the JAR scale for the two non-JAR groups, NE and TM.
        JAR(NE%) = N of JAR's(NE) / Total JAR's N
        JAR(TM%) = N of JAR's(TM) / Total JAR's N

    If JAR(NE%) >= 20%       (i.e. If at least 20 percent of panelists answered NE)
        Mean Drop =   Average Overall Liking(only using JAR's JAR panelists.)
                    - Average Overall Liking(only using JAR's NE  panelists.)
        Total Penalty =  Mean Drop  X  JAR(NE%)

    If JAR(TM%) >= 20%       (i.e. If at least 20 percent of panelists answered TM)
        Mean Drop =   Average Overall Liking(only using JAR's JAR panelists.)
                    - Average Overall Liking(only using JAR's TM  panelists.)
        Total Penalty =  Mean Drop  X  JAR(TM%)


  Other related reading materials:
  --------------------------------
    Are available on the Internet.  Try searching for:  Sensory "Penalty Analysis"


  SIMS 2000 Examples and related reading materials:
  -------------------------------------------------
    http://www.SIMS2000.com/ReportsShowPenaltyAnalysis.asp






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