statistical software in research: spss · spss is a comprehensive and flexible statistical analysis...

Statistical Software in Research: SPSS

Dr.Dharmesh P. Raykundaliya

Assistant Professor (SS)

Department of Statistics,

Sardar Patel University,

Vallabh Vidangar – 388 120

Anand-Gujarat

dp_raykundaliya@spuvvn.edu

www.statmodeller.com 1

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?

Using VOC to Measure Performance

Processes

Measure process performance as compared

to VOC

Deliver within 30

Response Time (in Min)

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

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.

Types of Research…

• Descriptive Research

• Analytical Research

• Applied Research

• Fundament Research

• Quantitative Research

• Qualitative Research

• Empirical Research

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

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……

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.

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

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:

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

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:

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

Scale of Measurement

Qualitative

Nominal

Ordinal

Quantitative

Interval

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

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

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.

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

Some More Examples

Measurement

Common

Example

Descriptive

StatisticsInferential Statistics

NominalGender, Color,

Region

Frequency,

Percentage, Mode

Binomial Test, Chi-Square

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

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.

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.

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.

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

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

Exported data in SPSS

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.”

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

Transforming data (Con’d)

Why Visualization?

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

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

Female

Gender Frequency

Female 2

Male 3

Frequency Table

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

Boxplot of OT per week

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

Two peaks (bi-modal)

Single peak,

negatively skewed

(Long tail on the left)

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.

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.’

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

Descriptives• Click ‘Analyze,’ ‘Descriptive statistics,’ then

click ‘Descriptives…’

• Click ‘Educational level’ and ‘Beginning Salary,’ and put it into the variable box.

• Click Options

Descriptives• The options allows you to analyze other

descriptive statistics besides the mean and Std.

• Click ‘variance’ and ‘kurtosis’

• Finally click ‘Continue’

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

Select Cases (AML Survival.sav)

Split File: (AML Survival.sav)

Explore Command: (Serum Cholesterol Changes.sav)

Introduction to Testing of Hypothesis

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.

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

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

Rejection Region and Acceptance Region

Distribution PlotNormal, Mean=40, StDev=3

Let’s consider alpha level is 5% i.e. 0.05

Interpretation Hypothesis # Example 1

Accept H0 i.e. µ = 40

Interpretation Hypothesis # Example 2

Reject H0 i.e. µ = 40

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

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

Testing for Mean(s)

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.

One Sample t-test

Assumptions for One Sample t-test

1. Data follows normal distribution (using Kolmogorov Smirnov

2. Sample collected is independent of order (using runs test)

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.

Assumptions

Select Cases in SPSS

Step 1 Step 2 Step 3

Assumptions # 1 Runs TestStep 2

Step 1

Runs Test

Here our Hypothesis would beH0 : Data collected are independentH1 : Data collected are not independent

Accept H0

Assumptions # 1 Normality of Data

Step 2Step 1 Step 3

Test of Normality

Here our Hypothesis would beH0 : Data follows Normal DistributionH1 : Data does not follows Normal Distribution

Accept H0

Normal QQ Plot

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)

Interpretation of t-test

Accept H0

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.

Assumptions for Two sample t-test

3. Variances are equal in both the samples (Levene’s Test)

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

Assumptions # 1 Runs Test

Assumptions # 3 Equal Variances

•It will be handled by Independent Sample t-test procedure

Let’s perform Independent Sample t-test

Step 3

Step 1

Step 2

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)

ANOVA (Analysis of Variance)

• 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)

Assumptions for Two sample t-test

3. Variances are equal in both the samples

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.

Assumptions # 1 Runs Test

Assumptions # 3 Equal Variances

•It will be handled by ONE WAY ANOVA Procedure

Let’s perform ONE WAY ANOVA

Step 4

Step 1Step 2

Step 3

Output

Chi-Square Test(for association)

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.

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.

Steps in SPSS

Step 1 Step 2 Step 3

Output

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.

Thanks To

Department of StatisticsSardar Patel University,

Vallabh Vidyanagar

Department of Higher Education, Gandhinagar

&All Participants

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