Statistical Software in Research: SPSS
Dr.Dharmesh P. Raykundaliya
Assistant Professor (SS)
Department of Statistics,
Sardar Patel University,
Vallabh Vidangar – 388 120
Anand-Gujarat
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Is Pizza Delivery Efficient?
• Customers want their pizza delivered fast!
• Guarantee = “30 minutes or less”
• What if we measured performance and found an average delivery time of 23.5 minutes?
• On-time performance is great, right?
• Our customers must be happy with us, right?
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Using VOC to Measure Performance
Processes
Measure process performance as compared
to VOC
CTQ
Deliver within 30
Min
Response Time (in Min)
0
5
10
15
20
25
30
35
# o
f R
esp
on
ses
Average = 23.5 Min
Goal = <30 Min
Customers Don’t Experience The Average, Customers Experience The Variation.
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Research and Research Methodology
• Research is a careful investigation or inquiry, specifically search for
new facts in any branch of knowledge. It is an original contribution to
the existing knowledge and making for its advancement.
• Research Methodology is the specific procedures or techniques used
to identify, select, process and analyze information about the specific
research problem. In research paper, the methodology section allows
the readers to critically evaluate study’s overall validity and reliability
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How methodology is differ between Literature and Social or Experimental Science?
• Research Methodologies in literature and Social Science or
Experimental Science have different approach. In literature, research
findings are abstract whenever they are concrete in Social Science or
Experimental Science. In literature, methodology focuses on more
reading and library work or at most on live personality in the form of
personal interview. It is based on its rationale and relevance. However,
in social science it is based on data or evidence and at the end of
study we have concrete results about our hypothesis.
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Types of Research…
• Descriptive Research
• Analytical Research
• Applied Research
• Fundament Research
• Quantitative Research
• Qualitative Research
• Empirical Research
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Steps of Conducting Research
1. Problem Definition
2. Development of an Approach to the Problem
3. Research Design Formulation
4. Fieldwork or Data Collection
5. Data Preparation and Analysis
6. Report Preparation and Presentation
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1. Problem Definition
It is saying good beginning half work done….in a same manner identification
of appropriate research problem will finish your half of research work….so
literature review is highly important….. One has to do extensive review of
literature in area where one is interested to conduct research….list out
logical development of area….with its relevance to specific time and future
relevance….. What we can add in existing……which has application in
present context and validity for future……
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The first step of any marketing research project is to define the problem. In
defining the problem, the researcher should take into account the purpose
of the study, the relevant background information, the information needed,
and how it will be used in decision making. Problem definition involves
discussion with the decision makers, interviews with industry experts,
analysis of secondary data and perhaps some qualitative research, such
as focus group discussion. Once the problem is precisely defined, the
research can be designed and conducted properly.
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2. Development of an Approach to the Problem
Development of an approach to the problem includes formulating
an objective or theoretical framework, analytical models,
research questions, and hypotheses and identifying information
needed. This process is guided by discussions with
management and industry experts, analysis of secondary data,
qualitative research, and pragmatic (realistic) considerations
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3. Research Design Formulation
A research design is a framework or blueprint for conducting the marketing research
project. It details the procedures necessary for obtaining the required information,
and its purpose is to design a study that will test the hypotheses of interest,
determine possible answers to the research questions, and provide the information
needed for decision making. Conducting exploratory research, precisely defining the
variables, and designing the appropriate scales to measure them are also part of
the research design. The issue of how the data should be obtained from the
respondents (for examples by conducting a survey or an experiment) must be
addressed. It is also necessary to design a questionnaire and a sampling plan to
select respondents for the study. More formally, formulating the research design
involves the following steps:
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i. Definition of the information needed
ii. Secondary data analysis
iii. Qualitative Research (Focus Group Discussion)
iv. Methods of collecting quantitative data (survey, observation, and experimentation)
v. Measurement and scaling procedure
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Measurement and Scaling Procedure
One of the important features of conducting any research is collection and organization of data. What is Data? How We collect? And How we organize?
Statistics has its own vocabulary. Many of the terms thatcomprise statistical nomenclature are familiar: some commonlyused in everyday language like sample, proportion, average etc.Statistics is a science of decision making. The decision shouldbe taken based on availability of data. Now the first questionarises to us what is data? “Data is a collection of information”.The information may be quantitative or qualitative. Therefore,we categories data mainly in two parts:
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What is Data
• “Data is a collection of information”. The information may be
quantitative or qualitative. Therefore, we categories data mainly
in two parts:
• Qualitative Data
• Quantitative Data
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Scale of Measurement
Data
Qualitative
Nominal
Ordinal
Quantitative
Interval
Ratio
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Types of Data
Qualitative Data: Data which cannot measure through physical measurement
scale or another word attribute data e.g. eye colour, types of injury, gender many
more.
The Qualitative data also classified in Nominal data as well as Ordinal data.
• Nominal Data: A label which can help to identify or classify the object such data is
said to be nominal data. For example, gender, subject number, unique patient id etc.
• Ordinal Data: A label which can help to identify or classify the object as well as the
magnitude of ordering property associate with categories such data is said to be
ordinal data. For example, types of injury, types of cancer, preference of selection of
doctors
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Types of Data
Quantitative Data: A data which can be quantified or measure by some measurement
scale e.g. height, weight, blood sugar level, cholesterol level.
The quantitative data also classify in two categories namely interval data and Ratio scale.
• Interval Data: In interval data difference between consecutive categories is known and
equal. For example, satisfaction measure in five point scaling. Here is this data zero is
arbitrary it is not fixed. For example, 0 0C temperature, it does not mean there is no
temperature but one can measure it in other measurement like in Kelvin or Fahrenheit.
• Ratio Data: This is data which satisfy the properties of all previous scale as well as here
zero is fixed. For example height, weight.
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Why Scale is Important
• The reason is statistical techniques were developed with
respect to measurement of scale. One cannot use the statistical
techniques like t-test, ANOVA for qualitative response. It is
applicable only if response measured in quantitative scale only.
• It’s important
• To select correct statistical technique
• To select correct graphical tool
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Some More Examples
Measurement
Scale
Common
Example
Descriptive
StatisticsInferential Statistics
NominalGender, Color,
Region
Frequency,
Percentage, Mode
Binomial Test, Chi-Square
Test
Ordinal
Types of Injury,
Socio Economic
Status
Median, Percentile
Spearmen’s Rank
Correlation, Kruskal-Wallis
Test, Friedman Test
Interval
Satisfaction
measured on
five point scale
Mean, Variance,
Standard
Deviation,
Geometric Mean,
Harmonic Mean
t-test, ANOVA, Regression,
Factor Analysis
Coefficient of Variation
Ratio Height, Weight
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4. Fieldwork or Data Collection
Data collection involves a field force or staff that operates either in
the field, in the case of personal interviewing (in-home, mall
intercept, or computer-assisted personal interviewing), from an
office by telephone (or telephone or computer-assisted telephone
interviewing), through mail (traditional mail and mail panel surveys
with prerecruited households), or electronically (e-mail or Internet).
Proper selection, training, supervision and evaluation the field force
help minimize data-collection errors.
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5. Data Preparation and Analysis
Data preparation includes the editing, coding, transcription, and
verification of data. Each questionnaire or observation form is
inspected or edited and, if necessary, corrected. Number or letter
codes are assigned to present each response to each question in
the questionnaire. The data from the questionnaires are transcribed
or keypunched into magnetic tape or disks, or input directly into the
computer. The data are analyzed to derive information related to
the components of the marketing research problem, thus, to
provide input into the management decision problem.
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6. Report Preparation and Presentation
The entire project should be documented in a written report that
addresses the specific research questions identified; describes the
approach, the research design, data collection, and a data analysis
procedure adopted, and presents the results and the major findings.
The findings should be presented in a comprehensible format so that
management can readily used them in the decision making process. In
addition, an oral presentation should be made to management using
tables, figures, and graphs to enhance clarity and impact.
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About SPSS
SPSS is a comprehensive and flexible statistical analysis and
data management solution. SPSS can take data from almost
any type of file and use them to generate tabulated reports,
charts, and plots of distributions and trends, descriptive
statistics, and conducted complex statistical analyses. SPSS is
available from several platforms, Windows, Macintosh, and the
UNIX systems
It is highly used in every industries, including
telecommunications, banking, finance, healthcare,
manufacturing, retail, consumer packaged goods, higher
education, government and market research.
The Most IBM SPSS products include SPSS statistics and SPSS
Modelers.
What is SPSS?
• Originally it is an acronym of Statistical Package for the Social
Science but now it stands for Statistical Product and Service
Solutions
• One of the most popular statistical packages which can perform
highly complex data manipulation and analysis with simple
instructions
Why SPSS?
• Menu driven software.
• Easy to handle
• Connectivity with many Data Base like excel, txt data, SQL, JAVA etc.
• Can also work with Server
• Easy to manipulate the data
• Easy to analyze complex data
• Perform as data mining software
The Four Windows:Data editor
Output viewerSyntax editorScript window
• Data Editor
Spreadsheet-like system for defining, entering, editing, and displaying data. Extension of the saved file will be “sav.”
Output Viewer
Displays output and errors. Extension of the saved file will be “spo.”
• Syntax Editor
Text editor for syntax composition. Extension of the saved file will be “sps.”
Script Window
Provides the opportunity to write full-blown programs, in a BASIC-like language. Text editor for syntax composition. Extension of the saved file will be “sbs.”
The basics of managing data files
Opening SPSS
• Start → All Programs → SPSS Inc→ SPSS 16.0 →
SPSS 16.0
Opening SPSS
•The default window will have the data editor
•There are two sheets in the window:
1. Data view 2. Variable view
Data View
Variable View
Click
Variable View window• This sheet contains information about the data set that is
stored with the dataset
• Name
•The first character of the variable name must be alphabetic
•Variable names must be unique, and have to be less than 64 characters.
•Spaces are NOT allowed.
Variable View window: Type•Type
• Click on the ‘type’ box. The two basic types of variables that you will use are numeric and string. This column enables you to specify the type of variable.
Variable View window: Width
•Width• Width allows you to determine the number of characters SPSS
will allow to be entered for the variable
Variable View window: Decimals
•Decimals• Number of decimals
• It has to be less than or equal to 16
3.14159265
Variable View window: Label
•Label• You can specify the details of the variable
• You can write characters with spaces up to 256 characters
Variable View window: Values
•Values• This is used and to suggest which numbers represent which
categories when the variable represents a category
Data Import from Excel
Data Import from Excel
Exported data in SPSS
Click
Click
Saving the data•To save the data file you created simply click ‘file’ and click ‘save as.’ You can save the file in different forms by clicking “Save as type.”
Click
Sorting the dataClick ‘Data’ and then click Sort Cases
Sorting the data (cont’d)
• Double Click ‘Income.’ Then click ok.
Transforming data
Click ‘Transform’ and then click ‘Compute Variable…’
Transforming data (cont’d)• Example: Adding a new variable named ‘lnheight’
which is the natural log of height • Type in ln_income in the ‘Target Variable’ box. Then type in
‘ln(income)’ in the ‘Numeric Expression’ box. Click OK
Click
Click
Transforming data (Con’d)
Why Visualization?
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What do you understand from this data?
1.60 0.53 0.44 1.22 1.12 2.31 2.90 5.31 4.24 0.280.84 1.01 2.49 6.33 0.78 2.56 2.87 1.92 3.21 0.573.01 1.13 3.91 3.80 4.14 4.72 2.19 1.23 3.48 0.711.30 2.57 2.28 4.23 5.30 1.75 1.05 0.76 6.67 2.852.24 4.23 0.97 4.33 3.80 1.63 1.26 2.40 2.44 6.262.64 1.35 4.99 0.43 3.25 5.64 1.97 4.96 3.51 3.380.34 2.85 5.79 1.20 5.05 4.64 0.84 1.97 8.03 3.246.97 0.76 2.53 3.44 3.07 3.95 3.33 1.35 1.14 6.083.47 2.78 3.08 2.51 2.45 4.39 4.15 4.79 4.28 1.671.41 0.64 3.83 8.09 4.69 3.24 2.58 2.21 2.06 2.12
Data on growth of the plant (in cm)
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Now what do you understand?
What you can make
out?1. Center of the data
2. Variation in the data
3. Shape of the distribution
4. Outliers
5. Count of the data
Histogram of Growth of plant
Growth in cm
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Univariate
• When one wants to find frequency of categories with respect to
only one variable, then it is said to be univariate frequency table.
• E.g. Gender
Gender
Male
Male
Female
Male
Female
Gender Frequency
Female 2
Male 3
Frequency Table
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Bivariate
• When one wants to find frequency of categories with respect to
two variables, then it is said to be univariate frequency table.
• E.g. Gender vs. Smoking habits
Gender Smoking Habit
Male Yes
Male No
Female Yes
Male Yes
Female No
Gender Yes No
Female 1 1
Male 2 1
Frequency Table
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20.0
17.5
15.0
12.5
10.0
7.5
5.0
OT p
er
we
ek
Boxplot of OT per week
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Shapes of Histogram (Types)
Single peak, symmetric, bell
shaped, commonly observed
pattern of a stable process
Single peak, positively
skewed (long tail on
the right)
Many characteristics follow such
patterns.
For example, power generation data
are negatively skewed while
breakdown data are positively
skewed. However such shapes may
also indicate process instability.
LSL USL
Single peak, thick
tail
Two peaks (bi-modal)
Single peak,
negatively skewed
(Long tail on the left)
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Statistical Analysis
• Frequencies AnalysisThis analysis produces frequency tables showing frequency counts and
percentages of the values of individual variables.
• Descriptive Statistics
This analysis shows the maximum, minimum, mean, and standard deviation of the variables
• One Sample and Two Sample t-test
• Analysis of Variance
• Cross Tabulation Analysis
Opening the sample data
• Open ‘Employee data.sav’ from the SPSS• Go to “File,” “Open,” and Click Data
Opening the sample data
•Go to Program Files,” “SPSSInc,” “SPSS16,” and “Samples” folder.
•Open “Employee Data.sav” file
Frequencies•Click ‘Analyze,’ ‘Descriptive statistics,’ then click ‘Frequencies’
Frequencies
• Click gender and put it into the variable box.
• Click ‘Charts.’
• Then click ‘Bar charts’ and click ‘Continue.’
Click Click
Frequencies
•Finally Click OK in the Frequencies box.
Click
Using the Syntax editor• Click ‘Analyze,’ ‘Descriptive statistics,’ then
click ‘Frequencies.’
• Put ‘Gender’ in the Variable(s) box.
• Then click ‘Charts,’ ‘Bar charts,’ and click ‘Continue.’
• Click ‘Paste.’
Click
Using the Syntax editor
• Highlight the commands in the Syntax editor and then click the run icon.
• You can do the same thing by right clicking the highlighted area and then by clicking ‘Run Current’
ClickRight Click!
•Do a frequency analysis on the variable “minority”
•Create pie charts for it
•Do the same analysis using the syntax editor
Answer
Click
Descriptives• Click ‘Analyze,’ ‘Descriptive statistics,’ then
click ‘Descriptives…’
• Click ‘Educational level’ and ‘Beginning Salary,’ and put it into the variable box.
• Click Options
Click
Descriptives• The options allows you to analyze other
descriptive statistics besides the mean and Std.
• Click ‘variance’ and ‘kurtosis’
• Finally click ‘Continue’
Click
Click
Descriptive• Finally Click OK in the Descriptive box. You will
be able to see the result of the analysis.
SPECIAL COMMANDS OF SPSS
• Select Cases
• Split File
• Explore
93
Select Cases (AML Survival.sav)
94
95
Split File: (AML Survival.sav)
96
97
Explore Command: (Serum Cholesterol Changes.sav)
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99
Introduction to Testing of Hypothesis
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Hypothesis Testing
• The Hypothesis Testing is a statistical test used to determine
whether the hypothesis assumed for the sample of data stands
true for the entire population or not. Simply, the hypothesis is
an assumption which is tested to determine the relationship
between two data sets.
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Sample and Population
Claim: Average age of the people in the city is 40 Years with Standard Deviation
of 3 yearsµ = 40
Sample of 50 People are taken and calculated their average
age which is 𝑥 = 37
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Null & Alternate Hypothesis
Hypothesis Statements
• H0 : The average age of the population is 40 years
• H1 : The average age of the population is not 40 years
In other words,
• H0 : µ = 40
• H1 : µ ≠ 40
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Rejection Region and Acceptance Region
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
X
Den
sity
34.12
0.025
45.88
0.025
40
Distribution PlotNormal, Mean=40, StDev=3
Let’s consider alpha level is 5% i.e. 0.05
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Interpretation Hypothesis # Example 1
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
X
Den
sity
34.12
0.025
45.88
0.025
40
Distribution PlotNormal, Mean=40, StDev=3
37
Accept H0 i.e. µ = 40
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Interpretation Hypothesis # Example 2
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
X
Den
sity
34.12
0.025
45.88
0.025
40
Distribution PlotNormal, Mean=40, StDev=3
32
Reject H0 i.e. µ = 40
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Two Types of Test
• Requires an assumption about the population from which the sample is drawn
• Applicable to only Interval/ Ratio data
Parametric Test
• It is a distribution free test
• Applicable to all types of data i.e. Nominal, Ordinal, Interval Ratio
Non-Parametric
Test
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Two Types of Test
Parametric Test
• One Sample t-test
• Independent Sample t-test
• Paired t-test
• One Way ANOVA
• Two Way ANOVA and many more
Non-Parametric Test
• Sign Test
• Wilcoxon Sign Test
• Mann-Whitney U test
• Kruskal Wallis Test and many more
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Testing for Mean(s)
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Testing for Means
• Hypothesis test can be done for different parameters of the
population like, mean, median, variance, standard deviation,
proportion etc.
• Here we are going to see test for means.
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One Sample t-test
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Assumptions for One Sample t-test
1. Data follows normal distribution (using Kolmogorov Smirnov
Test)
2. Sample collected is independent of order (using runs test)
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Case Study
• A manufacturer of high-performance automobiles produces disc brakes that
must measure 322 millimeters in diameter. Quality control randomly draws 16
discs made by each of eight production machines and measures their
diameters.
• This example uses the file brakes.sav. Use One Sample T Test to determine
whether or not the mean diameters of the brakes in each sample significantly
differ from 322 millimeters.
• A nominal variable, Machine Number, identifies the production machine used
to make the disc brake. Here consider production of Machine 1 is to be tested.
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Assumptions
1. Sample collected is independent of order (using runs test)
2. Data follows normal distribution (using Kolmogorov Smirnov
Test)
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Select Cases in SPSS
Step 1 Step 2 Step 3
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Assumptions # 1 Runs TestStep 2
Step 1
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Runs Test
Here our Hypothesis would beH0 : Data collected are independentH1 : Data collected are not independent
Accept H0
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Assumptions # 1 Normality of Data
Step 2Step 1 Step 3
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Test of Normality
Here our Hypothesis would beH0 : Data follows Normal DistributionH1 : Data does not follows Normal Distribution
Accept H0
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Normal QQ Plot
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Let’s run t-test
Step 2Step 1
Here our Hypothesis would beH0 : Average Diameter of Disk is 322 mm (µ = 322 mm)H1 : Average Diameter of Disk is not 322 mm (µ ≠ 322 mm)
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Interpretation of t-test
Accept H0
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Independent Sample t-test
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Independent Sample t-test
• The Independent-Samples t-test procedure tests the
significance of the difference between two sample means.
• Both the samples are independent, hence it is known as
Independent Sample t-test.
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Assumptions for Two sample t-test
1. Sample collected is independent of order (using runs test)
2. Data follows normal distribution (using Kolmogorov Smirnov
Test)
3. Variances are equal in both the samples (Levene’s Test)
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Case Study
• An analyst at a department store wants to evaluate a recent
credit card promotion. To this end, 500 cardholders were
randomly selected. Half received an ad promoting a reduced
interest rate on purchases made over the next three months,
and half received a standard seasonal ad.
• This example uses the file creditpromo.sav. Use Independent-
Samples T Test to compare the spending of the two groups
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Assumptions # 1 Runs Test
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Assumptions # 2 Normality of Data
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Assumptions # 3 Equal Variances
•It will be handled by Independent Sample t-test procedure
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Let’s perform Independent Sample t-test
Step 3
Step 1
Step 2
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Interpretation of Independent Sample t-test
Reject H0
Here our Hypothesis would beH0 : There is no significant difference in two groups (µ1 = µ2)H1 : There is no significant difference in two groups(µ1 ≠ µ2)
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ANOVA (Analysis of Variance)
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ANOVA
• In real life many times happen we want to compare more than two machines,
methods, brands, drugs, fertilizers, income group or value of money with different
quarters when it is given that all groups are independently distributed which each
other.
• Our objective is to compare average response (let us say average waiting time of a
bank) between various groups (more than two branches of same bank) we will use
Analysis of Variance (ANOVA).
• Here it is to note that in ANOVA the response variable is always quantitative and
independent variable is always categorical (what we call it factor)
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Assumptions for Two sample t-test
1. Data follows normal distribution (using Kolmogorov Smirnov
Test)
2. Sample collected is independent of order (using runs test)
3. Variances are equal in both the samples
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Case Study
• A sales manager wishes to determine the optimal number of
product training days needed for new employees. He has
performance scores for three groups: employees with one, two,
or three days of training. The data are in the file
salesperformance.sav.
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Assumptions # 1 Runs Test
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Assumptions # 1 Normality of Data
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Assumptions # 3 Equal Variances
•It will be handled by ONE WAY ANOVA Procedure
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Let’s perform ONE WAY ANOVA
Step 4
Step 1Step 2
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Step 3
Output
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Chi-Square Test(for association)
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Chi-Square Test for Association
• The Chi-Square Test for Association is used to determine if
there is any association between two variables. It is really a
hypothesis test of independence. The null hypothesis is that the
two variables are not associated, i.e., independent. The
alternate hypothesis is that the two variables are associated.
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Case Study
• In order to determine customer satisfaction rates, a retail company
conducted surveys of 582 customers at 4 store locations. From the
survey results, you found that the quality of customer service was the
most important factor to a customer's overall satisfaction.
• Given this information, you want to test whether each of the store
locations provides a similar and adequate level of customer service.
• The results of the survey are collected in satisf.sav. Use Crosstabs to
obtain ordinal measures of the association
between Store and Service Satisfaction.
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Steps in SPSS
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Step 1 Step 2 Step 3
Output
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References
• SPSS Software Version 15/16 & IBM SPSS Version 20
• www.SPSS.com
• Malhotra, N.K. and Dash, S. (2011). Marketing
Research: An Applied Orientation, Sixth Edition,
Pearson Publication
• www.statmodeller.com
Further Questionsdp_raykundaliya@spuvvn.
edu
Thanks To
Department of StatisticsSardar Patel University,
Vallabh Vidyanagar
Department of Higher Education, Gandhinagar
&All Participants