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Quantitative research – variables, measurement
levels, samples, populations
HEM 4112 – Research methods I
Martina Vukasovic
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Variables (1)• Independent and dependent
– Independent – suspect for cause– Dependent – outcome of interest
• Types (measurement levels):– Nominal/categorical
• Dichotomies as special type
– Ordinal– Interval – Ratio
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Variables (2)
• Why is the choice important?– If not in line with the concept – then
jeopardizing construct validity– Use of statistical tools depends on types of
variables
• How to choose?– What are the attributes of the particular
concept?
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Variables (3)1. Are there more then two categories?
a) NO variable is dichotomous
b) YES go the next question
2. Can the categories be rank ordered?a) NO variable is nominal/categorical
b) YES go to next question
3. Are the distances between categories equal?a) NO Variable is ordinal
b) YES go to next question
4. Does a zero value of the variable make sense?a) NO Variable is interval
b) YES Variable is on the ratio measurement level
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Variables (4) - exercise• Determine measurement level of the following
variables:– Age– Gender– Education attainment– Occupation– Duration of studies– Research productivity– Approach to teaching
• Discuss your choices
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Sources of data (1)• Sources:
– statistical data bases already collected data– questionnaires/surveys you are collecting the data
• Often from a trustworthy source– e.g. Ministry, national statistical bureau, UIS– But are they always trustworthy?
• Sometimes you have to collect data by yourself
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Sources of data (2) - tips
• Make sure you understand the definitions of indicators• Make sure the indicators are comparable (if doing a
comparative study)• Check who is the actual source, esp. for international
data bases• If you are collecting the data on your own, reliability of
data depends on – how good the questionnaire/survey is– how representative is the sample
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Operationalisation (1)From concepts to indicators
• Example 1:– Concept – intelligence
– Indicator – IQ
– Tool for measuring intelligence: tests that yield IQ
• Example 2:– Concept – social intelligence
– Indicator – social IQ
– Tool for measuring social intelligence: tests that yield SIQ (?)
• Example 3:– Concept – intelligence
– Indicator – several different IQs
– Tool for measuring intelligence: tests that yield IQ
• Sometimes you can use several indicators for one concept
– BUT you need to have a reason to do so
– AND you need to be clear how you combine these indicators
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Operationalisation (2)
• Can be presented as a 2-step proces:– Development of an indicator– Development of a tool to measure the indicator
• Sometimes already defined (if using data bases) – make sure you understand the definition and the tool– be critical about them
• Sometimes you define it – be careful about construct validity!
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Operationalisation (3)
• IQ – good indicator of intelligence?
• IQ test – good measuring tool?
• How would you operationalise quality in HE?– Why?
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Sampling (1)
• Sampling – A sample is a representative part of the
population you are interested in• Important for generalization!• An assumption for using statistical tools in the first
place
• Different techniques for sampling– Depends on your research topic
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Sampling (2)
• Random sampling– In some computer programmes (e.g.
SPSS/PASW) this can be done automatically • Generators of random numbers
– Can be done in various ways, although some techniques may introduce bias if not careful
– Selection from the population is done entirely at random no bias (?)
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Sampling (3) - exercise
• Imagine you need a random sample for a study. Discuss what bias, if any, can be introduced by using the following methods:– Stopping people in public and asking questions:
• On the street• In the theatre
– Distributing a questionnaire inside the classroom– Calling people on the phone– Asking people to complete an online survey
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Sampling (4)
• But completely random sampling can in some ways introduce bias even if done correctly
• For some topics and populations, stratified sampling is more appropriate– E.g. when you know in advance that the
distribution is not normal
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Sampling (5)
• Stratified (random) sampling– Divide the population in several groups, or strata
– Identify how many respondents or “cases” you need in each group on the basis of their proportion in the entire population
– Do random sampling within this strata– Check after data collection if your stratification
worked
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Sampling (6) - exercise
• What kind of sampling procedure you need, if interested in the following issues (if you think you need to stratify the sample, also discuss what strata you need):– Female students are more successful than male
students;– Students who pay for their education are more
concerned with quality of education;– Mobile students have difficulties in obtaining jobs after
graduation?
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Constructing a questionnaire (1)• Which data do you need?
– back to conceptual framework (+ research questions)
• How do you operationalise the concepts?– Use the literature, see what others did before you – Several questions can serve as indicators of one
concept– Sometimes “control” questions are used to check the
internal consistency of answers
• Be clear what is the purpose of each question– Useful also to see them terms of how they relate to
independent or dependent variables
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Constructing a questionnaire (2)• Questions MUST NOT be ambiguous
– Piloting is necessary– Be aware of language issues
• Options for answers need to be clear
• Layout needs to be user friendly– The respondent needs to be able to complete
the questionnaire easily– Otherwise, you risk if incomplete
questionnaire incomplete data bases
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Practicalities (1)
• Make sure you do not jeopardize your sample
• Make sure you allow enough time for responses
• The collecting procedures needs to be as simple as possible
• Expect low response rates, not everyone will answer
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Practicalities (2)
• online or paper?
• Think if this affects your sample
• If paper questionnaire – putting data into the data base requires time,
discipline and concentration– useful to label each completed questionnaire
with a unique number and introduce that into the data base as well (for later checks)
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Practicalities (3)
• Analysis– Make a plan beforehand! Statistical packages can be
seductive– Be systematic in building the data base, especially in
terms of variable labels, types etc.
• Always make notes of all the manipulations to the data base that you make
• Keep your data base as well as files with results of analysis safe and make regular backups
SPSS workshop as part of HEM 4113