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Analysis of internal customer response

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  • #50392

    Gopal
    Participant

    We periodically carry out internal customer satisfaction survey.There are total 11 departments and the 150 respondents.The questionnaire having 10 questions each on prformance & behavioural aspects.The 5 point rating scale is used ranging form not at all to optimum level of meeting expectations.Till date we heve been carrying out simple analysis using average.Now I want to analyse the data using right statistical tool.I am confused whether I should use ANOVA or Correlation or both to give detailed feed back for actions.  

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    #173181

    Swaggerty
    Participant

    Well,
        if it is subjective data, like strongly agree,agree, disagree, etc, you could use a likert scale
    George

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    #173230

    Michael Mead
    Participant

    What you are talking about is a Likert scale.
    Now, about your analysis. You do not have a big smaple, so a point estimate of your averages is not apporpriate. You need to put a confidence interval around your estimates to identify any real changes.
    I have often seen statements like: “Last month the average was 3.09, and this month is 3.02. This can be attributed to a change in management.”
    What a crock. This is hogwash. There is no reason to believe that this is natural variation. Of course the score will change from time to time, but is it significant? Even if it is not significant at the .05 level, you might be prompted to make some claim about the change. This can be reasonable if there is a trend in the statistics over time.

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    #173232

    dk
    Participant

    Break the responces into “Top 2 Box” (4 and 5 ratings), “Bottom 2 box” (1 and 2 ratings) and “neutral” (3 rating). Pie chart shall tell you the percentage contribution of each of the boxes. Obviously bottom 2 box needs to be enhanced.
    DK

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    #173236

    Gopal
    Participant

    Dear Michael,
    Your response is quite convencing to me.Can you help me to solve last part of my querry that should I use ANOVA or correlation or both.
    Regards
    MADAN   

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    #173238

    Michael Mead
    Participant

    Hello Madan,
    Sorry, I don’t know how to answer your question. I would do a t-test for the difference in means. Set a p-value of .10.
    Another way would be to simply set a 90% confidence interval around your current estimate of the mean. If the new estimate falls within that range, conclude that there is not enough evidence to claim a change has taken place.
    These tests can be done in Excel and many other statistics packages. I hope this helps. Maybe someone else can give you more information. Good luck.

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    #173358

    Fontanilla
    Participant

    It’s a mistake to try to apply correlation in this case.  ANOVA might be somewhat applicable but just barely.  You have ordinal data you’re trying to analyze with “normal” techniques.  The two shouldn’t be mixed in such a way.  If you feel you must put numbers to these, you would be better off using non-parametric analysis.  But the best way to analyze would be to simply chart the responses in a pie chart or bar chart.  You still need to do the “heavy lifting” when it comes to understanding why you receive scores in the lower categories to understand what is going wrong (as well as in the higher categories to understand what you are doing right).  Playing with ANOVA or correlation won’t help you in that respect.

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    #173360

    DR J
    Participant

    Use ordinal or logistic regression so that you will know the causation between the customer overall score vs each behavior. This is critical in the analysis.

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