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L. Liu PM Outreach, USyd. 1
Survey Analysis
L. Liu PM Outreach, USyd. 2
Types of research
• Descriptive
• Exploratory
• Evaluative
L. Liu PM Outreach, USyd. 3
Types of data
• Nominal: no numerical difference between categories.
• Ordinal: order of importance but distance between ranks has no numerical meaning
• Ratio: fully numerical.
L. Liu PM Outreach, USyd. 4
Frequencies• The number or percentage of data points in
a specific category of a variable (Nominal or ordinal)
Categories Frequency Number of employees
1 5 (25%) 1-100
2 10 (50%) 101-500
3 5 (25%) >501
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Means
• Average of a variable
• Meaningful for ratio or ordinal variables but nominal variables
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Crosstab analysis
• Tabular presentation of the co-variation between two (nominal or ordinal)variables
• Useful for initial data analysis
Computer training
Training course attended
C1 C2 C3 C4 C5
Yes 1 2 5 2 5
No 1 3 1 2 1
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Graphs• Bar chart- nominal, ordinal, ratio (grouped)
• Pie chart – nominal, ordinal and ratio (grouped)
• Line graph - ratio
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Statistical analysis
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Measures of central tendency
• Mean: average of scores
• Mode: most frequent score
• Median: mid point or mid score
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Measures of dispersion
• Range: the difference between highest and lowest scores
• Variance: average of squared deviations score from the mean
• Standard deviation: square root of the variance
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Normal distribution
• Bell-shaped
• Distribution of sample statistics in population (if repeated samples are drawn)
• E.g. the values found in a sample can be used to estimate population values assuming normal distribution
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Significance
• Used to indicate the “degree” of differences between two values
• Influenced by sample size, data quality and test procedures.
• Typically use 0.05 (significant) and 0.01(highly significant) cutoff points
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Null hypothesis (H0)
• Usually propose no difference/relationship between two values/variables
• Typically, the researcher is interested in alternative (H1) and rejecting the Null
• Eg: • H0: Excel and lotus usage levels are the same• H1: excel and lotus usage levels are different
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Null hypothesis (Cont’)
• Examples• H0: student examination result is influenced by
the student’s intelligence• H1: student examination result is influenced by
the student’s intelligence• Student examination result– dependent variable• Student’s intelligence – independent variable
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Chi-square test
• Chi-square is a statistic based on the sum of the squared differences btw observed and expected values
• Asymp.sig. indicate the level of significance
• <5% of cells with expected frequencies <5
• 0 cells with expected frequencies <1.
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Chi-square Example
• H0: there is no relationship btw course enrolment pattern and gender in the population
• H1: there is a relationship btw course enrolment pattern and gender in the population
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Example Cont’
GenderTraining course attended
C1 C2 C3 Total
Actual (Male) 9 9 7 25
Expected (Male) 6.5 7.0 11.5 25
Actual (Female) 4 5 16 25
Expected (Female) 6.5 7.0 11.5 25
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Example Cont’Chi-square test
Value df Asymp. Sig
(2-sided)
Person Chi-square 6.588 2 0.037
Likelihood ratio 6.750 2 0.034
Linear-by-linear assoc. 5.649 1 0.017
No. of cases 50
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t-test
• t is calculated based on the sample size and comparison btw the two means
• When there is the two means are the same, t follows a known distribution
• Paried sample test
• Group or independent samples test
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Correlation
• A measure of the relationships btw ordinal or ration variables
• Range –1 to 1
• 0 denotes no relationship
• <0 negative relationship
• > 1 positive relationship
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Significance of correlation
• Indicate if the correlation is significantly different from 0
• H0: the correlation btw the variables is zero
• H1: the correlation btw the variables is zero