AN AN INTRODUCTIONINTRODUCTION

DATA COLLECTION DATA COLLECTION AND TERMSAND TERMS

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Gathering/Collecting DataGathering/Collecting Data

Specify the objective of the study, Specify the objective of the study, survey or experiment.survey or experiment.

Identify the variables of interest.Identify the variables of interest. Determine method of collecting Determine method of collecting

data.data.– Sampling SurveySampling Survey– Observational StudyObservational Study– Experimental Study.Experimental Study.

Sampling SurveySampling Survey

Sampling Techniques.Sampling Techniques.– Simple random samplingSimple random sampling– Stratified samplingStratified sampling– Systematic samplingSystematic sampling– Cluster samplingCluster sampling

Data collecting TechniquesData collecting Techniques– Personal InterviewsPersonal Interviews– Telephone InterviewsTelephone Interviews– QuestionnaireQuestionnaire– Direct ObservationDirect Observation– Secondary Data ( Data collected by Secondary Data ( Data collected by

others, eg. Government)others, eg. Government)

Observational StudyObservational Study The researcher merely observes what is The researcher merely observes what is

happening or what has happened in the happening or what has happened in the past to draw conclusions based on these past to draw conclusions based on these observation.observation.

Example, data from the Motorcycle Example, data from the Motorcycle Industry Council stated that “Motorcycle Industry Council stated that “Motorcycle owners are getting older and richer”. owners are getting older and richer”.

Data were collected on the ages and Data were collected on the ages and incomes of motorcycle owners for the incomes of motorcycle owners for the years 1980 and 1998 and then compared.years 1980 and 1998 and then compared.

Experimental StudyExperimental Study

Basically scientific experimentsBasically scientific experiments Factors are controlled by the Factors are controlled by the

researcher and data in the form of researcher and data in the form of output of the experiment is output of the experiment is collected.collected.

ExampleExample– Testing weight lost creamTesting weight lost cream

Group A and Group B is put under the Group A and Group B is put under the same conditions except Group A is given same conditions except Group A is given the weight lost cream. Data is collected the weight lost cream. Data is collected after 1 month to see if there is any weight after 1 month to see if there is any weight change between the 2 groups.change between the 2 groups.

Variable and DataVariable and Data Variable Variable

– Characteristic or attribute that can Characteristic or attribute that can assume different values.assume different values.

Data Data – Values (measurements or observations ) Values (measurements or observations )

that the variables can assume.that the variables can assume.

Random VariablesRandom Variables– Variables whose values are Variables whose values are

determined by chancedetermined by chance Data setData set

– A collection of dataA collection of data

Variable and types of dataVariable and types of data

Variables can be classified into 2Variables can be classified into 2– Qualitative VariablesQualitative Variables– Quantitative VariablesQuantitative Variables

Qualitative Variables Qualitative Variables – Variables that can be placed into distinct Variables that can be placed into distinct

categories, according to some categories, according to some characteristic or attributecharacteristic or attribute

– Example gender, religion, colour, …Example gender, religion, colour, …

Quantitative VariablesQuantitative Variables– Numerical in nature and can be ordered Numerical in nature and can be ordered

or ranked.or ranked.– Example age, heightExample age, height– Can be classified into 2 groups : Can be classified into 2 groups :

Discrete and ContinuousDiscrete and Continuous

Discrete variablesDiscrete variables– Assume values that can be countedAssume values that can be counted– Example ageExample age

Continuous variablesContinuous variables– Assume all value between any two Assume all value between any two

specific values. Obtained by specific values. Obtained by measuringmeasuring

– Example Height Example Height

Measurement LevelsMeasurement Levels

Variables can also be classified by Variables can also be classified by how they are categorized, counted or how they are categorized, counted or measured and this type of measured and this type of classification uses measurement classification uses measurement scales.scales.

Measurement scales can yield 4 levels Measurement scales can yield 4 levels of measurement precision (kejituan)of measurement precision (kejituan)– NominalNominal– OrdinalOrdinal– IntervalInterval– RatioRatio

It is best to collect data at the It is best to collect data at the highest level of precision if highest level of precision if needed, because we cannot needed, because we cannot increase precision once it has been increase precision once it has been collected.collected.

Greater precision yields better Greater precision yields better results and provides greater results and provides greater flexibilityflexibility

Nominal MeasurementNominal Measurement Lowest level of precisionLowest level of precision Involves the simple classification of Involves the simple classification of

subjects/cases based on some subjects/cases based on some distinguishing (mutually exclusive) distinguishing (mutually exclusive) characteristics. characteristics.

No order or ranking can be imposed on No order or ranking can be imposed on the data.the data.

Example:Example:– RaceRace– GenderGender– ColourColour

Ordinal MeasurementOrdinal Measurement

Classifies data into categories that Classifies data into categories that can be ranked; However, precise can be ranked; However, precise differences between the ranks does differences between the ranks does not exist.not exist.

Example: Order of finish in a race Example: Order of finish in a race (1(1stst, 2, 2ndnd ,3 ,3rdrd ), letter grading system (A, ), letter grading system (A, B, C ), size ( S, M , L, XL ), satisfactory B, C ), size ( S, M , L, XL ), satisfactory level (Excellent, Good, Average, Poor, level (Excellent, Good, Average, Poor, Bad )Bad )

Interval MeasurementInterval Measurement

Classifies data into categories that Classifies data into categories that can be ranked and precise can be ranked and precise differences between units do exist.differences between units do exist.

However, it does not have true However, it does not have true zero. ( Nilai sifar merujuk kepada zero. ( Nilai sifar merujuk kepada nilai ukuran ciri itu)nilai ukuran ciri itu)

Example: Temperature, 0 degree Example: Temperature, 0 degree Celsius does not mean no heat.Celsius does not mean no heat.

Ratio MeasurementRatio Measurement Classifies data into categories that can be Classifies data into categories that can be

ranked and precise differences between ranked and precise differences between units do exist.units do exist.

True zero exist and indicates absence of True zero exist and indicates absence of some characteristic. ( Nilai sifar merujuk some characteristic. ( Nilai sifar merujuk kepada ketidakwujudan sesuatu ciri )kepada ketidakwujudan sesuatu ciri )

True ratio exist when the same variable is True ratio exist when the same variable is measured on two different members of the measured on two different members of the population.population.

Example: Counted data such as number of Example: Counted data such as number of crime, total population. Measured data such crime, total population. Measured data such as height, weight, timeas height, weight, time

Level of measurement determines statistical Level of measurement determines statistical procedures that can be used.procedures that can be used.

Develop measures that yield greatest Develop measures that yield greatest precision even if you don’t think you need it.precision even if you don’t think you need it.

High level data can be altered into lower High level data can be altered into lower levels, but lower level data cannot be levels, but lower level data cannot be converted to higher levels.converted to higher levels.

However, there is no complete agreement However, there is no complete agreement among statisticians about the classification among statisticians about the classification of data into one of the four categoriesof data into one of the four categories

Example ,IQ – Is it Interval or Ratio ?Example ,IQ – Is it Interval or Ratio ?