hypothesis testing & spss

8/3/2019 Hypothesis Testing & SPSS

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Research Methodology

for your project

Hypothesis and analysis of data


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Research Process

1. Research Problem

2. Review of Literature

3. Hypothesis development

4. Sampling design

5. Data Collection

i. Method

ii. Measurement6. Analysis and interpretation

7. Research Report


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3

Scales of Measurement


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4

Variable

A variable can be defined as anything that can ordoes change.

Change Within, between

Types

Nominal

Ordinal Interval and

ratio variables


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5

Scales of Measurement

Various scales are used in measurement

4 Types1. Nominal Scale or categorical variable

2. Ordinal Scale or Rank variable

3. Interval Scale or Scaled variable

4. Ratio Scale


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Why scaling important

The kinds ofdescriptive statistics and

significance tests that are appropriate

depend on the level of measurement of the

variables concerned ie., nominal, ordinal,

interval, or ratio.


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Nominal Scale

Numbers used to categorise objects or events.

Numbers are interchangeable (1) Eg : Gender : 1 for male & 2 for female

(2) Eg. : Numbering Athletes Jerseys

The players on a Football team have numbers on their jerseys: e.g. 2,6,10, 12, etc.

E.g. religion, area of residence, occupation, store types, dept, etc

The rule is: Do not assign the same numeral to differentclasses or different numerals to the same class. Beyond that,anything goes with the nominal scale.

Statistical Tools Descriptive Statistical techniques :

Mode, Frequency tables & percentage analysis

Inferential : Cross tabulation and chi square


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Ordinal Scale

Numbers used to rank items.

Determination of greater or less.Hierarchical order

Ranks are not interchangeable, unequal intervals

Egs: Ranking of brands, Other instances are found among scales ofintelligence, measuring education by degrees attained, i.e. bachelors,masters, or doctorate.

Statistics : - Median, Mode, Percentages, frequency tablesRank correlation, Cross tabulation

NB : Arithmetic mean (or average) should not be used on theordinal scale.


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Nominal and Ordinal Scales

Nominal and ordinal scales are considered tobe lower level of scales.

(higher levels of scales interval and ratio)

The branch of statistics that deals with

nominal and ordinal measurement is callednon parametric statistics.


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Interval Scale

Quantitative scale classification, order, & equal intervals Most psychological tests, measures of attitude, personality, and the like,

are interval measures.

Business Research : Satisfaction level, preference rating, Attitudes

Analysis : To analyse response in Likert scale, each response isassigned a numerical value. Item by item basis

Single score for each respondent

NB : High (Low) score must consistently reflect a favourableresponse.

Statistical Technique : All advanced tests t test, ANOVA, regression, Factor analysis,.....


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Ratio Scale

Scales on which the value of zero means

none, or the total absence of the variable,

are ratio scale of measurement.

Eg:- Measures of physical dimensions weight, height,distance

Business research :- Sales, profit, ROTA,

income.


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ScaleClassific

ationOrder Distance Origin Examples

Nominal YES NO NO NO Gender, Store type

Ordinal YES YES NO NO Ranking for brands, Marketposition

Interval YES YES YES NOAttitudes, opinions, Many

psychological measures

Ratio YES YES YES YESAge in years, income in Rs,Sales, Costs , No of

consumers, Market share

Source: Adapted from U. Sekaran.(2006). Research Methods for Business. (4th ed.). New Delhi : Wiley India (P) Ltd, p.189.

Properties of Four scales


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Scales and Statistical Techniques

All statistical techniques applicable to a given

scale are also applicable to any higher scale.

For eg. All statistics applicable to an ordinal scale are

also applicable to interval and ratio scales.


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Reducing scales of Measurement

If a variable can be measured on an interval or

ratio scale, it can also be reduced and measured

on an ordinal scale.

To simplify things by reducing interval or ratio

data to ordinal categories.

Egs


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Scales and Descriptive Statistics

-

ScaleMeasure of central

TendencyMeasure of Dispersion

Nominal Mode, percentages

Ordinal Median Percentiles

Interval Mean, Range Standard Deviation

Ratio

Arithmetic mean,

Geo metric Mean,

Harmonic Mean

Standard Deviation or

Variance or

coefficient of variation


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Scales and statistical test

Level of

Data

No of

Samples

Independent

Samples?

Sample

Size

Appropriate Statistical

Technique

Interval 2 Yes 30 Z test

Interval 2 Yes


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Small sample and large sample

If sample size is


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Independent samples &

Related samples

Indept samples (Equal or unequal sample size)

Eg. Male and female samples

Related samples (Equal sample size)

Eg. (i) Husband and wife

(ii) Father, mother and child(iii) Before and after data same sample


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Data Analysis

Tabulation of data (Excel)

Reliability of the instrument (if interval scaled)

Crobanchs alpha - SPSS

Descriptive statistics and charts (Excel)

Hypothesis Testing (SPSS)


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Crobanchs alpha

Used to describe the reliability

Alpha coefficient ranges in value from 0 to 1

The higher the score, the more reliable thegenerated scale is.

0.7 is considered to be an acceptablereliability coefficient but lower thresholds aresometimes used in the literature.


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Hypothesis

A hypothesis is a precise testable statementA prediction of what the researcher expects to find

or prove

Types

Alternate hypothesis

is the expected conclusion

proposition that is accepted if the null hypothesis is rejected.

Null hypothesis is the hypothesis being tested.

No difference/no relationship


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Hypothesis Development in your

research

Two variables

One demography and one from your research

question

Two research questions

Relation or Difference between two variables


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Hypothesis e.g???

Difference hypotheses

Relation hypotheses


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Hypothesis testing

Develop alternate and null hypothesis

Hypothesis tests


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Hypothesis Tests

DifferenceOne nominal variable and an interval scalet test (if there are only 2 groups)

1. Independent Samples t test

2. Paired Sample t test

Anova (if there are more than two groups)

Relationship/ Association

Between two interval scaled variables Regression analysis

Between two nominal/ordinal variables

Chi square


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Hypothesis Tests in SPSS

1. Import data from EXCEL to SPSS

2. Give values for nominal variables

3. Hypothesis testing


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About SPSS

SPSS is a computer program used for statisticalanalysis. It is used by market researchers, health researchers,

survey companies, government, education researchers,

marketing organizations and others.

SPSS (originally, Statistical Package for the SocialSciences) was released in its first version in 1968.

The company SPSS Inc. announced on July 28, 2009,that it was being acquired by IBM for US$1.2 billion. Asof January 2010, it became "SPSS: An IBM Company". No abbreviation now


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SPSS Versions

Version 10 (SPSS-X) in 1983

SPSS 15.0.1 - November 2006

SPSS 16.0.2 - April 2008

SPSS Statistics 17.0.1 - December 2008

PASW Statistics 17.0.3 - September 2009

PASW Statistics 18.0 - August 2009

PASW Statistics 18.0.1 - December 2009

PASW Statistics 18.0.2 - April 2010PASW Statistics 18.0.3 - September 2010

IBM SPSS Statistics 19.0 - August 2010


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Statistics and SPSS

Statistics included in the base software:

Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive

Ratio Statistics

Bivariate statistics: Means, t-test, ANOVA, Correlation, Nonparametric tests

Prediction for numerical outcomes: Linear regression

Prediction for identifying groups: Factor analysis, cluster analysis (two-step, K-means, hierarchical),

Discriminant analysis


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Statistical Terms

Degrees of freedom, level of

significance, reliability and validity


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Degrees of freedom (df)

In statistics, the number ofdegrees of freedom is thenumber of values in the final calculation of a statistic thatare free to vary.

The number of independent pieces of information that gointo the estimate of a parameter is called the degrees offreedom (df).

In general, the degrees of freedom of an estimate is equalto the number of independent scores that go into theestimate minus the number of parameters estimated asintermediate steps in the estimation of the parameter itself(which, in sample variance, is one, since the sample meanis the only intermediate step).


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Level of Significance

The probability that observed or greater differences occurred

by chance.

In statistics, a result is called statistically significant if it is

unlikely to have occurred by chance - test of significance

The amount of evidence required to accept that an event is

unlikely to have arisen by chance is known as the significance

level or critical p-value

If the obtained p-value is small, then it can be said either the

null hypothesis is false or an unusual event has occurred.


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Reliability and Validity

How precise are your measurements?

The two most important aspects of precision are reliabilityand validity.

Reliability refers to the reproducibility of a measurement.

Validity of a measurement tool (i.e. test in education) isconsidered to be the degree to which the tool measureswhat it claims to measure.

Validity is often assessed along with reliability


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t test and Levenes test

Levene's test is often used before a comparison of means. Levene's test is an inferential statistic used to assess the

equality of variances in different samples.

Some common statistical procedures assume that variances of the

populations from which different samples are drawn are equal. Levene's

test assesses this assumption.

It tests the null hypothesis that the population variances are

equal (called homogeneity of variance).

If the resulting p-value of Levene's test is less than some critical value(typically 0.05), the obtained differences in sample variances are

unlikely to have occurred based on random sampling. Thus, the null

hypothesis of equal variances is rejected and it is concluded that there

is a difference between the variances in the population.

hypothesis testing & spss

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