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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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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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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.