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# Executive Summary Samples

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Executive Summary Samples document sample

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```									 SPSS Tutorial (2)

Research Methods & Data
Analysis
Assignment example
 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
• 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
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
• 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
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
•
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
•
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)
Variable(s) you are
interested in

Grouping
variables

Do not limit/display
cases
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
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30
35
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70
75
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0
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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)
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