Main difference
between METRIC and CATEGORICAL data
-Metric: data that you can add, subtract, and compute
averages for
-Categorical: can NOT add, subtract, and compute averages
for
Scales
-primary scales:
1.
Categorical
1) Nominal
公稱
: no ranking-ordering between values
2) Ordinal
序數
: ranking ordering between values
2.
Metric
1) Interval
間 隔
: most measures of the strength of feelings,
emotions, attitude and beliefs, or extent of behavior are based
on an interval scale.
2) Ratio
比 例
: use ratio scale to provide exact quantity
measures of tangible things
Wk5
Sampling
-the process of choosing a sample for your research project
-objective: the sample should be representative of the target
population of your study.
Sampling process
for your survey
1.
Define your target population
1.1
Choose a sampling approach
1.2
Specify the sampling frame

1.3
Specify the survey method
1.4
What is the sampling technique?
1.5
Determine the sample size
Sampling Technique
Use probability sampling techniques
-Effective in reducing sampling error
-Less subjective
-Results conductive to statistical analysis
-Findings easily generalizable to the whole population
1) Simple Random Sampling
簡單隨機抽樣
: each element in
the population has a known and equal probability of selection.
2) Systematic Sampling
系統抽樣
: chosen by picking every
i-th element in succession from the sampling frame.
3) Stratified Sampling
分 層 抽 樣
: first divide target
population into groups, picking elements from each
group using
a probability-based procedure.
4) Cluster Sampling
整群抽樣
: divide target population into
groups, each group by itself is representative of the whole
population, randomly pick one cluster, either use the picked
cluster to be your sample or pick a sample from the cluster using
a probability-based procedure.
Coding
-means assigning a code, usually a number, to each possible
response
to each question
-ways to code the response comes down to convenience and
convention
Get to know your data
-target population
-sample size
-information has been collected
-describing individual variables (categorical, metric)
*”average” attitude/behaviour and average effect
Applies to all comparative RQs, and true for many rational
RQs.
Re-coding
-need to make changes to the collected data for your
research
-two types of re-coding that you would encounter in this unit
1) Creating new variables that allow you to classify/group
your responses differently

2) Use a different set of values to code an existing variable
Hypothesis testing
1.
Problem definition
e.g. do male and female students differ on average in
their grade expectation for MKF2121?
2.
Clearly state null and alternative hypothesis
Null: H
0
-No difference/relationship
Alternative: H
1
-difference or association is expected.
3.
Choose the relevant statistical test
Test of association-relational RQ
Test of difference-comparative RQ
4.
Calculate p-value
5.
Is p-value<0.05?
No-do not reject null
Yes-reject null hypothesis
Wk8
Tests of difference
-t-test
1.
one sample: Compare the average
value of one
variable to
a content
2.
paired samples: Compare the average
value of two
variables
3.
two independent samples: H
0
: µ
1
=µ
2
H
1
: µ
1
≠µ
2
-ANOVA
More than two samples: e.g. Do students in different
faculties differ in their average marks?

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