Data Analys

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ISDATA ANALYSIS USING
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.

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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





Main menus
Data Editor
SPSS viewer
Importing and Exporting data

Data Analysis Using SPSS
Dr. Nelson Michael J.

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Data Analysis





Descriptive Statistics
Frequency distribution
Cross - tabulation
Comparison of means

Data Analysis Using SPSS
Dr. Nelson Michael J.

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