DATA PREPARATION AND DATA PREPARATION AND COLLECTIONCOLLECTION

Prepared by;Prepared by;

Miss Syazwani Mahmad PuziMiss Syazwani Mahmad Puzi

School of Bioprocess EngineeringSchool of Bioprocess Engineering

Types of dataAccording to research type

QualitativeNon-numerical Non-numerical measurementsmeasurements

e.g. thick, thin, slow, fast.e.g. thick, thin, slow, fast.

Quantitative Numerical measurementsNumerical measurements

e.g. Weight, length, e.g. Weight, length, Temperature, etc.Temperature, etc.

Scales of Data

Ordinal NominalIntervalRatio

Nominal data scaleNominal data scaleThe nominal data scale is the lowest level of data.Nominal scales are therefore qualitative rather than quantitative. Quantitative information can only obtain by doing counts of the number of occurrences with a particular property.Have no order. It is only for identity.Nominal scale has no zero.Numbers themselves are not the nominal scale; they are just values.

Hair color

Number

Black 47

Brown 16

Gray 7

Also called categorical

data

Ordinal data scaleOrdinal data scale

Grade Number

Excellent 47

Very good

26

Good 21

Pass 15

Fail 7

Have an order (unlike nominal data)

The intervals between the numbers are not necessarily equal

There is no "true" zero point

Have the properties of the nominal data

In the example it is reasonable to say that grade is an ordinal scale because fail/pass/good/very good/excellent form a sequence that would not make sense in any other form.

Also called ordered data

Interval data scale

Have an equal sequenceHave the properties of nominal and ordinalHave not true zero point Most sophisticated data scale

Day Temperature(oC)

Monday 29

Tuesday 28

Wednesday

30

Thursday 31

Friday 32

Also called score data

Ratio data scaleRatio data scaleThe ratio between any two pairs of values that are the same 'distance apart' is the same anywhere on the scale .The data has true zero point. The closest to real number system

For example: Kelvin scale of temperature. This scale has an absolute zero. Thus, a temperature of 300 Kelvin is twice as high as a temperature of 150 Kelvin.

Also called score data

OrderOrder IntervalInterval OriginOriginNominalNominal nonenone nonenone

nonenone

OrdinalOrdinal yesyes unequal unequal none none

IntervalInterval yesyes equal orequal or none none

unequal?unequal?

RatioRatio yesyes equalequal zero zero

As you go from nominal to interval scales, you get more information about thing being measured.Example:

Nominal Scales:DO you use CNN for online news?

Yes/NoOrdinal Scales:How many times do you use CNN in a day?

(a) 0 times a day(b) 1-5 times a day(c) more than 5 times a day Yes/No

Interval/Ratio Scales:How many times do you use CNN in a day?

_____ times a day

Types of dataAccording to source

Primaryoriginal data collected for original data collected for

a specific purpose.a specific purpose.

Secondarycollected by someone else collected by someone else

for another purposefor another purpose

Direct observation Direct observation ExperimentationExperimentation Survey Survey InterviewsInterviews

Trade journals Newspapers Press releases Demographic data Industry analysts' reports Marketing research reports Public opinion polls

Key Data Collection Techniques

Observations

Surveys Interviews Experimentation

ObservationssObservation means that the situation

of interest is checked. Observation does not tell why it happened. Used for quantitative researchIt can be conducted by ways: Mechanically Personally

Surveys Surveys or questioning involve using a

questionnaire (data collection instrument) to ask respondents questions to secure the desired information. 

Used for quantitative research Questionnaires may be administered by:

Mail (slow; low respond) Telephone (easy to administer; allow data to

be collected quickly at a relatively low cost ) Computer/internet (rapid; low cost) In-person

Dr. Mohamed Mahmoud Nasef

Criteria in selection of survey type

VersatilityQuantity of the dataSample controlQuality of the dataResponse rateSpeed Cost

Dr. Mohamed Mahmoud Nasef

InterviewsA focus group is a small group (6-8) of people (respondent) headed by a moderator, carefully selected, deliberate certain topic.  They are used to generate concepts and hypotheses.In-depth interview:An in-depth interview is an unstructured, direct, personal interview in which a single respondent is probed by a highly skilled interviewer to uncover underlying motivations, beliefs, attitudes and feelings on a topic. Used in qualitative research.

Dr. Mohamed Mahmoud Nasef

ExperimentationExperimentation Selection of matched groups, giving them different experimental treatments controlling for other related factors, and checks for differences in the responses of the experimental group and the control group.  Data in an experiment may be collected through:ObservationSurveys.

Experimentation can be in a form of:  Laboratory experiments.  Field experiments Clinical experiments

Dr. Mohamed Mahmoud Nasef

Consideration for Data Selection Technique

Technical adequacy: reliability, validity, freedom from bias, etc. Practicality: cost, political consequences, duration, personnel needs, etc. Ethics: protection of human rights, privacy, legality, environment, etc.

Dr. Mohamed Mahmoud Nasef

Data PreparationData Preparation involves:Data Preparation involves:

Checking or logging the data inChecking or logging the data in

Checking the data for accuracy Checking the data for accuracy

Entering the data into the computerEntering the data into the computer

Transforming the data;Transforming the data;

Developing and documenting a Developing and documenting a database structure that integrates database structure that integrates the various measures.the various measures.

Dr. Mohamed Mahmoud Nasef

Logging the DataLogging the Data

Set up a procedure for logging the data and keeping track of it until you are ready to do a comprehensive data analysis. Database that enables you to assess at any time is recommended.Retain data records for at least 5-7 years.

Checking the Data For Checking the Data For AccuracyAccuracy

As soon as data is received you should screen it for accuracy. In some circumstances doing this right away will allow you to go back to the sample to clarify any problems or errors. There are several questions you should ask as part of this initial data screening:

Are the responses legible/readable? Are all important questions answered? Are the responses complete? Is all relevant contextual information included (e.g., data, time, place, researcher)?

Dr. Mohamed Mahmoud Nasef

Developing a Database Structure

Two options available for developing a database:Database programs (Microsoft access,

Claris Filemaker)Statistical programs (e.g., SPSS, SAS,

Minitab, Datadesk)

Dr. Mohamed Mahmoud Nasef

Entering the Data into the Entering the Data into the ComputerComputer

Type the data directly.Type the data directly.

Check it for errors.Check it for errors.

Alternative you can use double Alternative you can use double entry programs to check data.entry programs to check data.

Summarize the data.Summarize the data.

Data TransformationsData Transformations

Transform the raw data into variables Transform the raw data into variables that are usable in the analyses.that are usable in the analyses.

End of SessionEnd of Session