Measuring Variable

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MEASURING VARIABLES

Muhammad Arsyad, Ph.D.

GRADUATE SCHOOL OF AGRICULTUREHASANUDDIN UNIVERSITY

2011


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FOUNDATIONS OF RESEARCH

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SCIENTIFICMETHOD

1.

Substantial

2. Sistematika

3.Konsistensi


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Introduction

Concept (s) will not have meaningwithout variable (s)Concept into Variable: calledOperationalization Process (moredifficult process in the research)Variable: measured conceptHow the measurement is constructedstrongly depends on the type of variable that we have.

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Data Into Numbers

The Process of assigning numbers toobservations according to a set of rules is

Measurement ( Knoke)

Missing Data Issues and their treatmentsHow important the Data ConstructionCoding Process produces; the codebook (a

complete record of all coding decisions); adata file containing the entire set of numerical values for each variable forevery case (Knoke)

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Dimension Clarity

Conceptually/Dimensionally: has tobe clear What we are going tomeasure Variable (s) (can be measured) -- Conceptualizing -- title (optional) How are you going to measure yourvariable (s)?

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Time as a Variable

The influence of time on dependentvariable (Koutsoyiannis) in variousways;

(1) by introducing explicitly avariable t in the function, measuredin time periods from the first givenyear onwards(2) by a dummy variable

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(3) by removing the trend from thevariables before performing theregression(4) by working with the firstdifferences of the variables(5) by introducing lagged variables inthe function(6) by derivatives of the functionwith respect time

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Lagged Variables

Called Distributed Lag ModelsYt = c + b1 Xt + b2 Xt-1 + b3 Xt-2+ .+ bz Xt- z + + t

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Validity-Reliability in Gettting aGood Quality of MeasurementValidity: the degree to which avariables operationalizationaccurately reflects the concept it isintended to measure (Knoke)Reliability: the extent to whichdifferent operationalizations of thesame concept produce consistentresult (Knoke)

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Different kinds of validity(Rybarova)

face validitythe simplest and least scientific definition of validityit is demonstrated when a measure superficiallyappears to measure what it claims to measure

Based on subjective judgment and difficult to quantifye.g. intelligence and reasoning questions on the IQtestProblem - participants can use the face validity tochange their answers

concurrent validity (criterion validity)is demonstrated when scores obtained from a newmeasure are directly related to scores obtained from amore established measure of the same variable

e.g. new IQ test correlates with an older IQ test


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Different kinds of validity (cont.)(Rybarova)

Different kinds of validity predictive validity

when scores obtained from a measure accurately

predict behavior according to a theorye.g. high scores on need for achievement test predictcompetitive behavior in children (ring toss game)

construct validityis demonstrated when scores obtained from a measureare directly related to the variable itself Reflects how close the measure relates to theconstruct (height and weight example)in one sense, construct validity is achieved byrepeatedly demonstrating every other type of validity


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Different kinds of validity (cont.)(Rybarova)

Different kinds of validity convergent validityis demonstrated by a strong relationship between thescores obtained from two different methods of measuring the same construct

e.g. an experimenter observing aggressive behavior inchildren correlated with teachers ratings of theirbehavior

divergent validityis demonstrated by using two different methods to

measure two different constructsconvergent validity must be shown for each of the twoconstructs and little or no relationship exists betweenthe scores obtained from the two different constructswhen they are measured by the same methode.g. aggressive behavior and general activity level inchildren


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Scales of measurement (Rybarova)Scales define the type categories we use in

measurement and the selection of a scale hasdirect impact on our ability to describerelationships between variablesthe nominal scale simply represents qualitative difference in the variable

measured can only tell us that a difference exists without the

possibility telling the direction or magnitude of thedifference

e.g. majors in college, race, gender, occupation

the ordinal scale the categories that make up an ordinal scale form an

ordered sequence can tell us the direction of the difference but not the

magnitude

e.g. coffee cup sizes, socioeconomic class, T-shirt sizes,food preferences


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Scales of measurement (cont.)(Rybarova)

the interval scale categories on an interval scale are organizedsequentially, and all categories are the samesize

we can determine the direction and the

magnitude of a difference May have an arbitrary zero (convenient pointof reference)

e.g. temperature in Farenheit, time in secondsthe ratio scale consists of equal, ordered categories anchored

by a zero point that is not arbitrary butmeaningful (representing absence of a variable

allows us to determine the direction, themagnitude, and the ratio of the difference

e.g. reaction time, number of errors on a test


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SummaryScale of Measure

-ment

Properties StatisticalAnalysis

Differenti-ating

Ordered Interval Zero

Nominal XNon-ParametricOrdinal X X

Interval X X XParametric

Rasio X X X X

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The relationship between reliabilityand validity (Rybarova)

they are partially related and partiallyindependent reliability is a prerequisite for validity

(measurement procedure can not be

valid unless it is reliable e.g. IQ, hugevariance of repeated measurements isimpossible if we are truly measuringintelligence)

it is not necessary for a measurement tobe valid for it to be reliable (e.g. heightas a measure of intelligence)

A measure may be very reliable, but not

valid(Knoke)


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Muhammad ARSYAD, Ph.D.

Affiliation: Department of Socio-economic of AgricultureFaculty of Agriculture, Hasanuddin UniversityMakassar, South Sulawesi 90245T/F. +62-411-580486; E. [email protected]://www.unhas.ac.idSkype: arsyadryukoku

Measuring Variable

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