012 types of data slides

8/6/2019 012 Types of Data Slides

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TYPES OF DATA


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IS STATISTICS 100% CORRECT?


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SecondaryData Compilation

Observation

Experimentation

Print or Electronic

Survey

PrimaryData Collection

DATA SOURSES


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Data

Categorical Numerical

Discrete Continuous

Examples:

Marital Status

Political Party Eye Color

(Defined categories)Examples:

Number of Children

Defects per hour

(Counted items)

Examples:

Weight

Voltage

(Measured characteristics)

TYPES OF DATA


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Quantitative Data (Numerical) consists ofnumbers representing counts ormeasurements.

Qualitative Data (Categorical) can beseparated into different categories that are

distinguished by some nonnumeric

characteristic.

DEFINITIONS


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Discrete Data result when the number ofpossible values is either a finite number ora countable number.

Continuous Data result from infinitelymany possible values that correspond to

some continuous scale that covers a range

of values without gaps.

DEFINITIONS


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A variable - a characteristic of a populationor a sample, e.g. Examination marks Stock price The waiting time for medical services

Data - Observed values of variables

WHAT IS A VARIABLE?


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Data - Observed values of variables46 49 46 48 45 49 46 45 47 43

45 46 44 47 44 45 49 46 42 4746 44 42 45 46 46 42 45 41 47

48 43 43 49 40 44 46 43 45 44

41 47 43 47 48 42 44 48 48 45

Scores on a Test

EXAMPLE


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TYPES OF VARIABLES

A. Qualitative or Attribute variable - thecharacteristic being studied is nonnumeric.

EXAMPLES: Gender, religious affiliation, type of automobile

owned, state of birth, eye color are examples.

B. Quantitative variable - information isreported numerically.

EXAMPLES: balance in your checking account, minutesremaining in class, or number of children in a family.


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QUANTITAIVE VARIABLES Classifications

Quantitative variables can be classified as eitherdiscrete or continuous.

A. Discrete variables: can only assume certain values

and there are usually gaps between values.EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the localHome Depot (1,2,3,,etc).

B. Continuous variable can assume any value within a

specified range.

EXAMPLE: The pressure in a tire, the weight of a pork chop, or the height of students in aclass.


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SUMMARY: TYPES OFVARIABLES


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Scales of Measurement

1. Nominal Scale Categorical/qualitative observations Use number to represent the categories. Example: Single=1, Married=2

2. Ordinal Scale Ordered categorical observations Value are in order Example: Poor-1 Fair-2 Good-3

3. Interval Scale Numerical/quantitative observations Numerical bring the meaning of value.

Example: marks, temperature, IQ

4. Ratio Scale Numerical/quantitative observations Have absolute zero value Example: weight, height, income

SCALES OF MEASUREMENT


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SCALES OF MEASUREMENT

Nominal level

data that isclassified into categories andcannot be arranged in anyparticular order.

EXAMPLES: eye color,gender, religious affiliation.

Ordinal level involves dataarranged in some order, but thedifferences between data valuescannot be determined or aremeaningless.

EXAMPLE: During a taste testof 4 soft drinks, MellowYellow was ranked number1, Sprite number 2, Seven-up number 3, and OrangeCrush number 4.

Interval level

similar to the ordinallevel, with the additional property thatmeaningful amounts of differencesbetween data values can bedetermined. There is no natural zeropoint.

EXAMPLE: Temperature on theFahrenheit scale.

Ratio level the interval level with aninherent zero starting point.Differences and ratios aremeaningful for this level ofmeasurement.

EXAMPLES: Monthly income ofsurgeons, or distance traveledby manufacturersrepresentatives per month.


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Nominal Scale is characterized by datathat consists of names, labels, orcategories only.

Ordinal Scale data can be arranged insome order, but differences between data

values either cannot be determined or are

meaningless.

DEFINITIONS


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Interval Scale is like the ordinal scale, withadditional property that the differencebetween any two data values is

meaningful. However, data at this level donot have a natural zerostarting point.

Ratio Scale is similar to the interval scalewith additional property that there is an

absolute zero(where zero indicates thatnone of the quantity is present). In thisscale ratios are meaningful.

DEFINITIONS


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SUMMARY: SCALES OFMEASUREMENT


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Ratio/Interval dataAge - income55 75000

42 68000. .

. .eightgain+10+5..

NominalPerson Marital status

Ahmad married

Siva singleAh Keong single. .. .Computer Brand

1 IBM

2 Dell3 IBM. .. .

EXAMPLES


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Ratio/Interval dataAge - income55 75000

42 68000

. .

. .eightgain+10+5..

NominalWith nominal data,

all we can do is,

calculate the proportion

of data that falls intoeach category.

IBM Dell Compaq Other Total25 11 8 6 5050% 22% 16% 12%

EXAMPLES


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Knowing the type of data is necessary to properly select thesuitable technique to be used when analyzing data.

Type of analysis allowed for each type of data

Ratio/Interval data arithmetic calculations/Average

67,74,71,83,93,55,48,82,68,62

Average=70.3

Nominal data counting the number of observation/frequency in each category

Single:1 ,Married:2 Divorced:3, Widowed:4 Data record: 1,2,2,2,4,1,2,2,1,3

Average=2.0; Does this mean average person ismarried????

TYPES of DATA TYPES ofANALYSIS


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Solution of Nominal data Category Code Frequency

Single 1 3

Married 2 5 Divorced 3 2

Widowed 4 4

Ordinal data - computations based on anordering process

TYPES of DATA TYPES ofANALYSIS


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Ratio/Interval* Values are real numbers

All calculations are valid

Data may be treated as ordinal or nominal

Example : Examination Marks

Ordinal

Value must represent the ranked order of the data

Calculation based on an ordering process are valid

Data may be treated as nominal but not as interval

Nominal

Value are the arbitrary numbers that represent

categories. Only calculation based on the frequencies of occurrence

are valid.

Data may not be treated as ordinal or interval

*Higher-level data type may be treated as lower-level ones.

HIERARCHY OF DATA


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This is often a preferred source of data due tolow cost and convenience.

Published data is found as printed material,tapes, disks, and on the Internet.

Data published by the organization that hascollected it is called PRIMARY DATA

For example:Data published by the US

Bureau of Census.

Data published by an organization different than the

organization that has collected it is called

SECONDARY DATA.

For example:The Statistical abstracts of the United States,

compiles data from primary sources

Compustat, sells variety of financial data tapes

compiled from primary sources

PUBLISHED DATA

O S O


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Observational study is one in which measurementsrepresenting a variable of interest are observed andrecorded, without controlling any factor that mightinfluence their values.

Experimental study is one in which measurements

representing a variable of interest are observed andrecorded, while controlling factors that might influencetheir values.

When published data is unavailable, oneneeds to conduct a study to generate thedata.

OBSERVATIONAL orEXPERIMENTAL


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StatisticalStudies

Do youmake observations

only, or do you modify thesubjects?

ExperimentObservational

Whenobservationsare made?

Retrospectivestudy

Prospective

study

Cross-sectionalstudy

Past

At

onepoint

Future Design:1. Control effects of variables2. Use replication

3. Use randomization

STATISTICAL STUDIES


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Voluntary Response Sample (or self-selected sample) is one in which therespondents themselves decide whether to

be included in the sample. Voluntary response sample might not be

representative of the intended population.

DEFINITIONS


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A good questionnaire must be well designed:

Keep the questionnaire as short as possible.

Ask short,simple, and clearly worded questions.

Start with demographic questions to helprespondents get started comfortably.

Use dichotomous and multiple choice questions.

Use open-ended questions cautiously.

Avoid using leading-questions.

Pretest a questionnaire on a small number of people. Think about the way you intend to use the

collected data when preparing the questionnaire.

QUESTIONNAIRE


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