SPSS

Overview

• Variable

• Types of variables

Qualitative

Quantitative

• Reliability and Validity

• Hypothesis Testing

• Type I and Type II Errors

• Significance Level

• SPSS

• Data Analysis

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Variable

• A characteristic of an individual or

object that can be measured

• Types:

Qualitative and Quantitative

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Types of Variables

• Qualitative variables: Variables which

differ in kind rather than degree

• Measured on:

1) Nominal scale indicates categorizing into

groups

groups or classes.

Eg. Gender, religion, race, colour,

occupation, etc

2) Ordinal scale indicates ordering of items.

Eg. Agreement – disagreement scale,

customer satisfaction ratings, etc

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Quantitative variables: Variables which

differ in degree rather than kind.

• Measured on:

1) Interval scale indicates rank and

distance from an arbitrary zero

measured in unit intervals.

Eg. Temperature, examination scores,

etc.

2) Ratio scale indicates rank and distance

from a natural zero.

Eg. Height, monthly consumption,

annual budget, etc

Data Analysis Using SPSS

Dr. Nelson Michael J.

5

Reliability

• The confidence we can place on the

measuring instrument to give us the same

numerical value when measurement is

repeated on the same object.

Eg. Instrument to measure the number of

things a child can recall

Assessing reliability:

Cohen’s kappa coefficient for categorical

data

Cronbach’s alpha for internal reliability for

a set of questions

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Validity

• To see how accurate is the relationship

between the measure and the underlying

trait it is trying to measure

• Eg. An instrument claimed to measure IQ

may just be testing memory

• Assessing validity:

Face validity

Predictive validity

Content validity

Construct validity

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Hypothesis Testing

• A Hypothesis is an assumption or claim

about some characteristic of a population,

which we should be able to support or

reject on the basis of empirical evidence.

• Null Hypothesis (H0) – It is the presumption

that is accepted as correct unless there is

strong evidence against it.

• Alternative Hypothesis (H1) – it is

accepted if H0 is rejected

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Marketing: Did the advertising strategy have

any impact in increasing the level of

product awareness?

Production: Is the average output of two

factories the same?

Finance: Is the average stock price of the

company’s stocks less than that of the

competitor’s stocks?

Human Resource: Has there been any

significant impact of 360 degree feedback

system on employee’s performance?

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Type I and Type II Errors

• While testing a hypothesis, if we

reject a hypothesis when it should be

accepted,

accepted, it amounts to TYPE I error

• Accepting a hypothesis when it

should be rejected amounts to TYPE

II error

• Both types of errors can be reduced

if we increase the sample size

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Significance level (p – value)

• The criterion that is used for accepting

or rejecting a null hypothesis is called pvalue

• A p-value of 0.05 means that there is

95% confidence of making the right

decision.

Data Analysis Using SPSS

Dr. Nelson Michael J.

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SPSS

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Main menus

Data Editor

SPSS viewer

Importing and Exporting data

Data Analysis Using SPSS

Dr. Nelson Michael J.

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Data Analysis

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Descriptive Statistics

Frequency distribution

Cross - tabulation

Comparison of means

Data Analysis Using SPSS

Dr. Nelson Michael J.

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