Main difference between METRIC and CATEGORICAL data Metric data that you can

Main difference between metric and categorical data

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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
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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
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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 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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