DATA PREPARATION AND COLLECTION Prepared by; Miss Syazwani Mahmad Puzi School of Bioprocess...
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Transcript of DATA PREPARATION AND COLLECTION Prepared by; Miss Syazwani Mahmad Puzi School of Bioprocess...
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