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) CategoriesFrequencyNumber of employees 15 (25%) (50%) (25%)>501

L. Liu PM Outreach, USyd.5 Means Average of a variable Meaningful for ratio or ordinal variables but nominal variables

L. Liu PM Outreach, USyd.6 Crosstab analysis Tabular presentation of the co-variation between two (nominal or ordinal)variables Useful for initial data analysis Computer training Training course attended C1C2C3C4C5 Yes12525 No13121

L. Liu PM Outreach, USyd.7 Graphs Bar chart- nominal, ordinal, ratio (grouped) Pie chart – nominal, ordinal and ratio (grouped) Line graph - ratio

L. Liu PM Outreach, USyd.8 Statistical analysis

L. Liu PM Outreach, USyd.9 Measures of central tendency Mean: average of scores Mode: most frequent score Median: mid point or mid score

L. Liu PM Outreach, USyd.10 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

L. Liu PM Outreach, USyd.11 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

L. Liu PM Outreach, USyd.12 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

L. Liu PM Outreach, USyd.13 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

L. Liu PM Outreach, USyd.14 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

L. Liu PM Outreach, USyd.15 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.

L. Liu PM Outreach, USyd.16 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

L. Liu PM Outreach, USyd.17 Example Cont’ Gender Training course attended C1C2C3Total Actual (Male)99725 Expected (Male) Actual (Female) Expected (Female)

L. Liu PM Outreach, USyd.18 Example Cont’ Chi-square test Value df Asymp. Sig (2-sided) Person Chi-square Likelihood ratio Linear-by-linear assoc No. of cases 50

L. Liu PM Outreach, USyd.19 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

L. Liu PM Outreach, USyd.20 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

L. Liu PM Outreach, USyd.21 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