1. AN INTRODUCTION DATA COLLECTION AND TERMS POSTGRADUATE METHODOLOGY COURSE

2. Gathering/Collecting Data n Specify the objective of the study, survey or experiment. n Identify the variables of interest. n Determine method of collecting data. –Sampling Survey –Observational Study –Experimental Study.

3. Sampling Survey n Sampling Techniques. –Simple random sampling –Stratified sampling –Systematic sampling –Cluster sampling

4. n Data collecting Techniques –Personal Interviews –Telephone Interviews –Questionnaire –Direct Observation –Secondary Data ( Data collected by others, eg. Government)

5. Observational Study n The researcher merely observes what is happening or what has happened in the past to draw conclusions based on these observation. n Example, data from the Motorcycle Industry Council stated that “Motorcycle owners are getting older and richer”. n Data were collected on the ages and incomes of motorcycle owners for the years 1980 and 1998 and then compared.

6. Experimental Study n Basically scientific experiments n Factors are controlled by the researcher and data in the form of output of the experiment is collected. n Example –Testing weight lost cream n Group A and Group B is put under the same conditions except Group A is given the weight lost cream. Data is collected after 1 month to see if there is any weight change between the 2 groups.

7. Variable and Data n Variable –Characteristic or attribute that can assume different values. n Data –Values (measurements or observations ) that the variables can assume.

8. n Random Variables –Variables whose values are determined by chance n Data set –A collection of data

9. Variable and types of data n Variables can be classified into 2 –Qualitative Variables –Quantitative Variables n Qualitative Variables –Variables that can be placed into distinct categories, according to some characteristic or attribute –Example gender, religion, colour, …

10. n Quantitative Variables –Numerical in nature and can be ordered or ranked. –Example age, height –Can be classified into 2 groups : Discrete and Continuous n Discrete variables –Assume values that can be counted –Example age

11. n Continuous variables –Assume all value between any two specific values. Obtained by measuring –Example Height

12. Measurement Levels n Variables can also be classified by how they are categorized, counted or measured and this type of classification uses measurement scales. n Measurement scales can yield 4 levels of measurement precision (kejituan) –Nominal –Ordinal –Interval –Ratio

13. n It is best to collect data at the highest level of precision if needed, because we cannot increase precision once it has been collected. n Greater precision yields better results and provides greater flexibility

14. Nominal Measurement n Lowest level of precision n Involves the simple classification of subjects/cases based on some distinguishing (mutually exclusive) characteristics. n No order or ranking can be imposed on the data. n Example: –Race –Gender –Colour

15. Ordinal Measurement n Classifies data into categories that can be ranked; However, precise differences between the ranks does not exist. n Example: Order of finish in a race (1 st, 2 nd,3 rd ), letter grading system (A, B, C ), size ( S, M, L, XL ), satisfactory level (Excellent, Good, Average, Poor, Bad )

16. Interval Measurement n Classifies data into categories that can be ranked and precise differences between units do exist. n However, it does not have true zero. ( Nilai sifar merujuk kepada nilai ukuran ciri itu) n Example: Temperature, 0 degree Celsius does not mean no heat.

17. Ratio Measurement n Classifies data into categories that can be ranked and precise differences between units do exist. n True zero exist and indicates absence of some characteristic. ( Nilai sifar merujuk kepada ketidakwujudan sesuatu ciri ) n True ratio exist when the same variable is measured on two different members of the population. n Example: Counted data such as number of crime, total population. Measured data such as height, weight, time

18. n Level of measurement determines statistical procedures that can be used. n Develop measures that yield greatest precision even if you don’t think you need it. n High level data can be altered into lower levels, but lower level data cannot be converted to higher levels. n However, there is no complete agreement among statisticians about the classification of data into one of the four categories n Example,IQ – Is it Interval or Ratio ?