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Executive Summary Samples

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									 SPSS Tutorial (2)

Research Methods & Data
        Analysis
     Assignment example
 Methodology (about 200 words)
 Results (about 400 words)
 Discussion (about 400 words)
 Executive summary (max 200 words –
  structured into bullet points)
 APPENDIX – including a small selection
  of tables, graphs and statistical output
NOTE: the following examples of the
  final report are purely imaginary!!!
  They are not based on actual results!!!
     Methodology section
• State the objective
• Briefly describe the methodology
  you chose:
  – What does the methodology do?
  – Why is it useful for your objective
     Methodology (example)
The objective of this study was to test whether being
  vegetarian influences the amount spent in GCS
  stores.
An independent sample t-test was chosen as the
  methodology to investigate such hypothesis.
The t-test allows to compare two means from
  different consumer groups and test the null
  hypothesis that the two means are equal. If the
  probability of the t-statistics falls below a
  threshold level (set at 0.05, i.e. 5%) then the null
  hypothesis is rejected in favour of the alternative.
The t-test is based upon the normal distribution of
  the target variable (I.e. the amount spent) within
  each of the groups. If the sample size is reasonably
  large (>40-50 units) it is possible to exploit the
  normal approximation. […]              118 words
             Results
• Show/summarise the most relevant
  output of your SPSS analysis
• Describe and comment statistically
  (i.e. not in marketing terms) such
  output
• Illustrate any limit/problem that
  might have emerged from your
  application
               Results (example)
The t-test was carried out to check whether the following customer
  characteristics led to statistically significant differences in the
  group means:
   – Vegetarian
   – Use coupon
   – Gender
Table 1 summarises the output of the Independent Samples T-test
   for the 3 above customer characteristics
[Table 1 here]
Table 1 shows that the t-statistic for the vegetarian characteristic
   has a p-value of 0.77. As this p-value is above 0.05, the null
   hypothesis of equal means can not be rejected. This means the
   vegetarian factor is not influencing the amount spent. However,
   the mean comparison hypothesis test does not take explicitly
   into account the potential influence of other disturbing factors
   (e.g. store size). Partial correlation and regression analysis could
   give further information in that direction, but for the objective of
   this study and given the very high p-value we can confidently
   assume that the t-test result are reliable. […]
            Discussion
• Now interpret the result you have
  presented in the previous section
  under an operational (marketing)
  perspective, leave out technicalities
  and focus on the main findings.
       Discussion (example)
This study showed that being vegetarian is not an influential
  factor in determining the amount spent, while there are
  significant differences in terms of gender and the use of
  coupon. More specifically, table 2 gives the average
  amount spent for male/female and user/non users of
  coupon. It looks that the amount spent by men is
  significantly higher, and also the use of coupon lead to a
  higher expenditure.
This could lead to a strategy for increasing the amount spent
  as follows:
• Create an advertising campaign to attract males into the
  GCS chain
• Improve the distribution of coupon (after an accurate
  cost/benefit analysis)
      Executive summary
• Now just extract a bullet point list
  summarising the key passages of
  your study.
        Executive summary
            (example)
• The objective of the study is to investigate what
  factors influence the amount spent
• We used hypothesis testing (independent
  samples t-test) as a methodology
• Other methodologies (ANOVA, partial
  correlations) could give further indication
• Being vegetarian is not a relevant factor
• Gender and use of coupon are relevant factor
• An male-targeted advertising strategies and the
  calibration of the distribution of coupon could
  increase the profits for GCS
                           Task A
 • Examine the relationship between the
   amount spent and the following
   customer characteristics:
            –   Being male/female
            –   Being vegetarian
            –   Shopping for himself / for himself and others
            –   Shopping style (weekly, bi-weekly, etc.)


Potential methods:
• Battery of hypothesis testing & Analysis of variance
• Correlation / Regression Analysis
 •
                              B
                     Task between the amount
     Examine the relationship
     spent and the following customer
     characteristics:
     – Hypothesis: the average amount spent in health-
       oriented shop is higher than those of other shops.
       True or false?
     – Test the same hypothesis accounting for different shop
       sizes

Potential methods:
• Battery of hypothesis testing & Analysis of variance
• Partial correlation (accounting for size)
• Regression Analysis
•
                     Task C average amount
    Find a relationship between the
    spent per store and the following store
    characteristics:
    – Size of store
    – Health-oriented store
    – Store organisation

Potential methods:
• Transform the customer data set into a store data set
• Battery of ANOVA
• Correlation / Regression Analysis
 •
                         Task D those that use
     Hypothesis: is the amount spent by
   coupon significantly higher?
 • What is the most effective way of distributing coupons:
    – By mail
    – On newspapers
    – Both
Potential methods:
• Recode the variable into 1=not using coupon and 2=using
coupon
• Hypothesis testing
• Analysis of variance
            SPSS basics
•   Opening SPSS files
•   Defining variables
•   Restructuring data
•   Saving data
•   The output window
•   Cross-tabulation
•   Graphs
Variable view
Data view
        Case summaries
• Analyze / Report / Case
  summaries
  – Select target variable(s)
  – Select grouping variable(s)
  – Include additional statistics
                                       Variable(s) you are
                                          interested in



                                                 Grouping
                                                 variables




Do not limit/display
       cases
                Click here to choose
                 the statistics you
                        need
Output window
   Categorising variables
• Transform/categorize variables
  – Select variable
  – Choose number of categories
 Computing new variables
• Transform/Compute
 – Choose expression
 – Define “if” category
                 Define a
Name the new     numeric
  variable       expression to
                 compute it




               Define a condition
  If you want to work with
       ‘stores’ as rows
• Data / Aggregate   Aggregating variable




                              Name the
                              new file
   Descriptive statistics in
            SPSS
• Click on Analyze / Descriptive
  Statistics / Frequencies
• Select the variable you are
  interested in
• Select the STATISTICS you are
  interested in
                             First select the variable




Then choose the statistics
      SPSS output
                   Statistics

Amount spent
N                  Valid                   779
                   Missing                   0
Mean                                  404.4871
Std. Error of Mean                     4.13528
Median                                394.0800 a
Mode                                    274.70b
Std. Deviation                       115.41804
Percentiles        10                 262.4020 c
                   20                 310.5520
                   30                 335.6920
                   40                 366.2010
                   50                 394.0800
                   60                 427.1540
                   70                 455.4820
                   80                 497.3320
                   90                 556.5080
  a. Calculated from grouped data.
  b. Multiple modes exist. The smallest value is shown
  c. Percentiles are calculated from grouped data.
Charts & descriptive stats




         Charts
                 Amount spent
            80




            60




            40




            20
Frequency




                                Std. Dev = 115.42
                                Mean = 404.5
            0                   N = 779.00
                   10
                   15
                   20
                   25
                   30
                   35
                   40
                   45
                   50
                   55
                   60
                   65
                   70
                   75
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                     0.
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                       0
                 Amount spent
 Grouped statistics in SPSS
• Click on Analyze / Custom Tables /
  General tables
• Select the target variable(s) as rows
• Select the grouping variable(s) as
  column
• Choose the statistics you want to be
  computed
                             Tick here to have
                             the statistics in
                             the table



Grouping
 variable




     Select the statistics
          you want
                                         Output
                                                          Amount spent
                                                                                 Standard
                   Mean     Median    Mode     Percentile 10   Percentile 95   Error of Mean    Std Deviation    Valid N
Size of   Small    391.55    380.75   274.70        241.48          635.48              10.36         121.21    N=137
store     Medium   399.53    388.12   106.03        259.92          625.23               5.99         117.15    N=383
          Large    418.67    416.59   170.03        288.17          626.61               6.74         108.53    N=259
 Hypothesis testing in SPSS
• One-sample test (value of the mean in the population)
• Analyze / Compare Means / One sample test


                                                Click on
                                                OPTIONS to
                                                choose the
                                                confidence level
                              Output
     T-Test                               One-Sample Statistics

                                                                                  Std. Error
                                         N           Mean        Std. Deviation     Mean
                   Amount spent              779   404.4871         115.41804      4.13528


                                   One-Sample Test

                                             Test Value = 400
                                                                       95% Confidence
                                        p value                          Interval of the
                                                           Mean            Difference
                    t       df         Sig. (2-tailed)   Difference   Lower         Upper
    Amount spent   1.085         778             .278        4.4871   -3.6305      12.6047


The null hypothesis is not rejected (as the p-value is larger than 0.05)
      Test on two means
   (independent samples)
• Analyze / Compare means /
  Independent samples t-test



                           Specify which
                           groups you
                           are comparing
                                                       Output
                                                       Independent Samples Test

                                 Levene's Test for
                               Equality of Variances                                  t-test for Equality of Means
                                                                                                                            95% Confidence
                                                                                                                             Interval of the
                                                                                                   Mean       Std. Error       Difference
                                  F          Sig.        t          df         Sig. (2-tailed)   Difference   Difference   Lower        Upper
Amount spent Equal variances
                                  1.244        .265     -2.270           394            .024      -27.1146    11.94454 -50.59764     -3.63162
             assumed
             Equal variances
                                                        -2.194   251.912                .029      -27.1146    12.35795 -51.45270     -2.77657
             not assumed




   The null hypothesis is rejected
   (as the p-value is smaller than 0.05)
• Analyze / ANOVA in SPSS
            Compare means / One-way
  ANOVA
ANOVA dialog box
                  ANOVA output

                            ANOVA

Total output
                 Sum of
                 Squares   df        Mean Square    F       Sig.
Between Groups   309.600         2      154.800    14.221     .000
Within Groups    293.900        27        10.885
Total            603.500        29
Correlation and covariance
          in SPSS
                      Choose
                      between
                      bivariate &
                      partial
              Bivariate correlation
                              Select the variables
                              you want to analyse




   Require the
significance level
   (two tailed)
                                 Ask for additional
                                   statistics (if
                                    necessary)
 Bivariate correlation output
                                     Correlations

                                           Shopping
                                             style      Use coupons Amount spent
Shopping style    Pearson Correlation               1          .157**       .159**
                  Sig. (2-tailed)                    .         .000         .000
                  N                               779           779          779
Use coupons       Pearson Correlation            .157**           1         .291**
                  Sig. (2-tailed)                .000              .        .000
                  N                               779           779          779
Amount spent      Pearson Correlation            .159**        .291**          1
                  Sig. (2-tailed)                .000          .000             .
                  N                               779           779          779
  **. Correlation is significant at the 0.01 level (2-tailed).
Partial correlationsList of
                       variables to be
                       analysed




                        Control
                        variables
          Partial correlation output
- - -   P A R T I A L      C O R R E L A T I O N      C O E F F I C I E N T S     - - -


Controlling for..         SIZE       STYLE            Partial correlations still
            AMTSPENT         USECOUP            ORG   measure the correlations
AMTSPENT       1.0000            .2677       -.0116   between two variables, but
              (      0)      (   775)    (    775)    eliminate the effect of other
              P= .           P= .000     P= .746      variables, i.e. the correlations
                                                      are computed on consumers
USECOUP           .2677      1.0000          .0500    shopping in stores of identical
              (   775)       (      0)   (    775)    size and with the same
              P= .000        P= .        P= .164      shopping style

ORG            -.0116            .0500       1.0000
              (   775)       (   775)    (       0)
              P= .746        P= .164     P= .


(Coefficient / (D.F.) / 2-tailed Significance)
" . " is printed if a coefficient cannot be computed
Bivariate regression in
         SPSS
       Regression dialog box
                          Dependent
                              variable




                           Explanatory
Leave this                 variable
unchanged!
                Regression output
                                  Coefficientsa

                       Unstandardized        Standardized
                        Coefficients          Coefficients
Model                  B        Std. Error       Beta            t       Sig.
1       (Constant)   140.359      34.715                        4.043      .001
        Age            4.577         .838            .807       5.464      .000
  a. Dependent Variable: Cholesterol (mg/100 ml)




           Value of the                                         Statistical
           coefficients                                        significance
                                                             Is the coefficient
                                                             different from 0?
  Multivariate regression in
            SPSS
• Analyze / Regression / Linear


                            Simply select
                            more than one
                            explanatory
                            variable
                                    Output
                                       Coefficientsa

                               Unstandardized          Standardized
                                 Coefficients           Coefficients
Model                           B        Std. Error        Beta         t        Sig.
1       (Constant)          296.482        19.792                      14.980      .000
        Health food store       9.721      15.012               .024      .648     .517
        Size of store           9.753        6.070              .059    1.607      .109
        Gender               -69.598         7.483             -.302   -9.301      .000
        Vegetarian             -1.910      12.570              -.005    -.152      .879
        Shopping style        22.760         6.069              .123    3.750      .000
        Use coupons           30.417         3.512              .285    8.662      .000
  a. Dependent Variable: Amount spent
     How good is the model?
                      Model Summary

                                    Adjusted     Std. Error of
  Model       R       R Square      R Square     the Estimate
  1            .439 a     .193          .187       104.08167
    a. Predictors: (Constant), Use coupons, Vegetarian,
       Gender, Health food store, Shopping style, Size of store



• The regression model explain less than 19% of the
total variation in the amount spent

								
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