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Survey Analysis

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Types of research

• Descriptive

• Exploratory

• Evaluative

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Types of data

• Nominal: no numerical difference between categories.

• Ordinal: order of importance but distance between ranks has no numerical meaning

• Ratio: fully numerical.

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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