Forfatter av avsnitt: Danielle J. Navarro and David R. Foxcroft

Introduksjon til psykologisk måling

The first thing to understand is data collection can be thought of as a kind of measurement. That is, what we are trying to do here is measure something about human behaviour or the human mind. What do I mean by “measurement”?

Måling er i seg selv et subtilt begrep, men i bunn og grunn handler det om å finne en måte å tilordne tall, etiketter eller andre veldefinerte beskrivelser til «ting» på. Alle de følgende tingene kan regnes som psykologiske målinger:

  • Min alder er 33 år.

  • Jeg liker ikke ansjos.

  • Mitt kromosomale kjønn er mann.

  • Mitt selvidentifiserte kjønn er kvinne.

I den korte listen ovenfor er det som er skrevet fet «tingen som skal måles», og det som er skrevet kursivt er «selve målingen». Vi kan faktisk utvide dette litt, ved å tenke på mengden av mulige målinger som kunne ha vært gjennomført i hvert enkelt tilfelle:

  • My age (in years) could have been 0, 1, 2, 3 …, etc. The upper bound on what my age could possibly be is a bit fuzzy, but in practice you would be safe in saying that the largest possible age is 150, since no human has ever lived that long.

  • På spørsmål om jeg liker ansjos, kunne jeg ha sagt at jeg gjør det, eller jeg gjør det ikke, eller jeg har ingen mening, eller jeg gjør det av og til.

  • Mitt kromosomale kjønn kommer nesten helt sikkert til å være mann (XY) eller kvinne (XX), men det finnes noen andre muligheter. Jeg kan også ha Klinfelters syndrom (XXY), som ligner mer på mann enn på kvinne. Og jeg kan tenke meg at det finnes andre muligheter også.

  • My self-identified gender is also very likely to be male or female, but it does not have to agree with my chromosomal gender. I may also choose to identify with neither, or to explicitly call myself transgender.

As you can see, for some things (like age) it seems fairly obvious what the set of possible measurements should be, whereas for other things it gets a bit tricky. But I want to point out that even in the case of someone’s age it is much more subtle than this. For instance, in the example above I assumed that it was okay to measure age in years. But if you are a developmental psychologist, that is way too crude, and so you often measure age in years and months (if a child is 2 years and 11 months this is usually written as “2;11”). If you are interested in newborns you might want to measure age in days since birth, maybe even hours since birth. In other words, the way in which you specify the allowable measurement values is important.

Looking at this a bit more closely, you might also realise that the concept of “age” is not actually all that precise. In general, when we say “age” we implicitly mean “the length of time since birth”. But that is not always the right way to do it. Suppose you are interested in how newborn babies control their eye movements. If you are interested in kids that young, you might also start to worry that “birth” is not the only meaningful point in time to care about. If Baby Alice is born three weeks premature and Baby Bianca is born one week late, would it really make sense to say that they are the “same age” if we encountered them “two hours after birth”? In one sense, yes. By social convention we use birth as our reference point for talking about age in everyday life, since it defines the amount of time the person has been operating as an independent entity in the world. But from a scientific perspective that is not the only thing we care about. When we think about the biology of human beings, it is often useful to think of ourselves as organisms that have been growing and maturing since conception, and from that perspective Alice and Bianca are not the same age at all. So you might want to define the concept of “age” in two different ways: the length of time since conception and the length of time since birth. When dealing with adults it will not make much difference, but when dealing with newborns it might.

Moving beyond these issues, there is the question of methodology. What specific “measurement method” are you going to use to find out someone’s age? As before, there are lots of different possibilities:

  • Du kan bare spørre folk «hvor gammel er du?». Metoden med selvrapportering er rask, billig og enkel. Men den fungerer bare med folk som er gamle nok til å forstå spørsmålet, og noen lyver om alderen sin.

  • You could ask an authority (e.g., a parent) “how old is your child?” This method is fast, and when dealing with kids it is not all that hard since the parent is almost always around. It does not work as well if you want to know “age since conception”, since a lot of parents can not say for sure when conception took place. For that, you might need a different authority (e.g., an obstetrician).

  • Du kan slå opp i offisielle registre, for eksempel fødsels- eller dødsattester. Dette er et tidkrevende og frustrerende arbeid, men det har sine fordeler (f.eks. hvis personen nå er død).

Operasjonalisering: definere målingen din

Alle ideene som er diskutert i forrige avsnitt, er knyttet til begrepet operasjonalisering. For å være litt mer presis: Operasjonalisering er prosessen der vi tar et meningsfylt, men noe vagt begrep og gjør det om til en presis måling. Operasjonaliseringsprosessen kan innebære flere forskjellige ting:

  • Vær presis med hensyn til hva du prøver å måle. Betyr for eksempel «alder» «tid siden fødsel» eller «tid siden unnfangelse» i forbindelse med forskningen din?

  • Determining what method you will use to measure it. Will you use self-report to measure age, ask a parent, or look up an official record? If you are using self-report, how will you phrase the question?

  • Defining the set of allowable values that the measurement can take. Note that these values do not always have to be numerical, though they often are. When measuring age the values are numerical, but we still need to think carefully about what numbers are allowed. Do we want age in years, years and months, days, or hours? For other types of measurements (e.g., gender) the values are not numerical. But, just as before, we need to think about what values are allowed. If we are asking people to self-report their gender, what options to we allow them to choose between? Is it enough to allow only “male” or “female”? Do you need an “other” option? Or should we not give people specific options and instead let them answer in their own words? And if you open up the set of possible values to include all verbal response, how will you interpret their answers?

Operationalisation is a tricky business, and there is no “one, true way” to do it. The way in which you choose to operationalise the informal concept of “age” or “gender” into a formal measurement depends on what you need to use the measurement for. Often you will find that the community of scientists who work in your area have some fairly well-established ideas for how to go about it. In other words, operationalisation needs to be thought through on a case-by-case basis. Nevertheless, while there a lot of issues that are specific to each individual research project, there are some aspects to it that are pretty general.

Før vi går videre, vil jeg bruke et øyeblikk på å rydde opp i terminologien, og i samme slengen introdusere et nytt begrep. Her er fire forskjellige ting som er nært beslektet med hverandre:

  • A theoretical construct. This is the thing that you are trying to take a measurement of, like “age”, “gender” or an “opinion”. A theoretical construct can not be directly observed, and often they are actually a bit vague.

  • Et mål. Tiltaket refererer til metoden eller verktøyet du bruker for å gjøre observasjonene dine. Et spørsmål i en spørreundersøkelse, en atferdsobservasjon eller en hjerneskanning kan alle regnes som et tiltak.

  • En operasjonalisering. Begrepet «operasjonalisering» refererer til den logiske forbindelsen mellom målingen og det teoretiske konstruktet, eller til prosessen der vi forsøker å utlede en måling fra et teoretisk konstrukt.

  • En variabel. Endelig et nytt begrep. En variabel er det vi ender opp med når vi bruker målet vårt på noe i verden. Det vil si at variabler er de faktiske «dataene» som vi ender opp med i datasettene våre.

In practice, even scientists tend to blur the distinction between these things, but it is very helpful to try to understand the differences.