measurements and variables Flashcards

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1
Q

the “how” of quantitative research

A
  • the conclusions that we can draw from research depends on how the knowledge was generated
  • for any piece of research we plan, we must be able to answer:
    1) how do we actually test hypotheses appropriately?
    2) how do we generalise our findings?
    3) how do we quantify seemingly unquantifiable things?
  • the answer to these questions lies in research design
  • research designs can vary on lots of different dimensions:
    1) some designs involve multiple measurements from the same people and some design compare groups.
    2) Some designs take all their measurement at one point and others follow participants across time.
  • the design we choose depends on:
    1) our hypothesis
    2) the resources we have (time, money, facilities)
    3) logistical considerations
    4) ethical considerations
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2
Q

structure

A
  • start with a research question - hope it will answer our research
  • come up with a hypothesis - we specify the outcome we expect
  • to test the hypothesis we’ll design an experiment
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3
Q

testing our hypothesis

A

experiment might be like:
1) Invite a group into the lab.
2) Give half the people some ice cream to eat, and don’t give any ice cream to the other half (our manipulation).
3) We then get all participants to look at pictures of people (the stimuli) and rate how much they want to eliminate them on a scale from 0 (no desire) to 9 (all the desire possible).
- after the experiment we might thank the pps and debrief them by describing the aims of the study
- in our study we’re manipulating one thing and measuring on thing
- our study has an independent and dependent variable

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4
Q

dependent variable

A
  • you analyse it
  • its value depends on the value of other variables
  • its the things we measure and is sometimes called the outcome
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5
Q

independent variable

A
  • influences the values of your dependent variable
  • is the thing we’re manipulating and is sometimes called the predictor
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6
Q

features of a good study

A
  • in a well-designed experiment, we can be confident in saying our manipulation caused a change in our outcome
  • but this isn’t the case with our study, because we’re missing a lot of things.
  • including:
    1) controls
    2) randomisation
    3) blinding
    4) a theoretical framework
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7
Q

controls

A
  • Our imaginary study didn’t use any controls.
  • We recruited all kinds of people without giving consideration to how different characteristics might affect our results.
    1) Children and adults in our sample
    2) People with lactose intolerance in our sample who would’ve experienced discomfort eating ice-cream.
  • We didn’t have standardised instructions for participants who enrolled in the study.
    3) Maybe some participants arrived very hungry, and others arrived very full, and the hungry participants were just hangry.
  • We didn’t control our IV appropriately - we might have often changed the brand, the flavour, or the amount of ice cream, maybe one day we gave frozen yoghurt instead of ice cream.
    4) Now we don’t know exactly what caused any changes in the outcome.
    5) It could be that inky strawberry mini milks cause murderous tendencies.
  • We didn’t control the lab environment it was conducted in, on some days the heating was up super high and on others we had the windows wide open.
  • Maybe people only felt murderous when they were made to eat ice cream in the cold.
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8
Q

randomisation

A
  • Another feature that might have been missing from our study is randomisation.
  • We didn’t randomly assign people to the groups.
    1) Maybe we recruited all our participants for the ice cream conditions first, and we did this outside of a dentists office.
    2) It might be that most of these participants had sensitive teeth and so eating cold food made them angry.
  • A well-designed experiment should randomise both participant allocation and stimulus presentation order.
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9
Q

blinding

A
  • Another feature that might have been missing from our study is blinding.
  • Maybe we told participants that we were interested in the effects of ice cream on murderous tendencies
    1) Participants may have modified their behaviour to fit or contradict our hypothesis.
  • Maybe we also gave all the participants the ice cream ourselves.
    2) If participant are naive to group allocation then the study is said to be single-blind.
  • If neither the participants nor the researcher know which condition the participants are put in, the study design is known as double-blind.
    Allocation is recorded but only revealed once the study is over and the data are being analysed.
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10
Q

theoretical framework

A
  • the choice of predictor (IV) and outcome (DV) variables does not happen in a theoretical vacuum
  • these choices should be base on theory, but in our experiment these choices weren’t based on theory
  • it could be that murder causes people to eat ice cream, in which case we should probably swap the IV and DV
  • or it might be that they’re completely unrelated and any effect is just a coincidence
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11
Q

types of experimental studies

A
  • true experiments
  • quasi-experiments
  • natural experiments
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12
Q

true experiments

A
  • usually have tight controls
  • can be somewhat artificial, because they abstract away from the real world
  • lack ecological validity - ability to generalise the results from an experiment to the real world.
  • provide the most rigorous methodology for investigating casual relationships
  • experiments can be difficult to perform from a logistical point of view, because randomisation can be difficult, and sometimes manipulating IVs directly can be difficult or impossible
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13
Q

quasi-experiments

A
  • similar to true experiments except for participant randomisation
  • this makes them useful in situations were randomisation isnt possible
  • in situation like this, we should still try to match the pps so that the groups dont differ on any relevant characteristics, except for the ones we’re investigating
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14
Q

natural experiments

A
  • studies where randomisation and manipulation occur through natural or socio-political processes
  • one example might be twin studies:
    1) Identical twins share essentially 100% of their genes.
    2) Fraternal twins share on average 50% of their genes.
    3) Both kinds of twins tend to share the same home environment (raised together).
    4) Comparing similarities between identical twins and similarities between fraternal twins, we can estimate the role of genes and environment in all sorts of things (physical, mental health, personality, cognitive ability, etc).
  • Other kinds of natural experiments might be a result of policy changes (like smoking bans, or changes in the length of compulsory education) or natural events.
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15
Q

within-subject and between-subjects design

A
  • in between-subjects or independent designs we compare different groups of pps - different pps are assigned to different conditions
  • in within-subjects or repeated measure we take repeated measurements from pps - where each pps gets assigned to all the conditions and we compare
  • mixed designs have both within-subject and between-subject manipulations - we split people into 2 groups but still measure each person under multiple conditions
  • within-subject have some disadvantages like order effects, but with within-subject it can sometimes be easier to detect differences between conditions
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16
Q

time frame

A
  • studies can also vary in terms of whether pps are measured at one points in time or whether they’re followed over time
  • cross-sectional designs:
    1) take a cross-section of the sample at a single point in time
    2) logistically easier than other types of studies
    3) not very useful for telling us how things change over time
17
Q

longitudinal designs

A
  • involves repeated measurements of the same characteristics from the same pps at multiple different points in time.
  • logistically very difficult to do and can be expensive, some can run for years or even decades.
  • very useful for seeing how things change over time, particularly useful for studying
  • becasue they can run for so long there can be issues with missing data
    1) Missing data can be complex to deal with because sometimes data is missing at random, but other times it can be tricking something you’re interested in.
    2) E.g., a study on whether dating apps help you find love might show that no people find love on the apps, but that might just be because those that do find love drop out of the study.
18
Q

construct validity

A
  • in psych we measure lots of things that are difficult to observe directly - this includes things like happiness, cognitive ability, and aspects of personality
  • we try to measure these things using a range of tools including questionnaires, and experimental tasks
  • we design these tools using the theoretical underpinnings behind the constructs we’re trying to measure
  • construct validity is the extent to which a tool can be justifiably trusted to actually measure the construct it is supposed to measure
19
Q

external validity

A
  • want to be able to generalise the finidngs from our studies beyond the particular people that took part in our study
  • we want to be able to generalise the findings from our studies beyond the exact experimental tasks and setup used in our study.
  • a study has external validity if it can be generalised to the population of people with relevant characteristics - it might be the case that if our study only used white men in western cultures that the findings might only generalise to white men in western cultures.
  • ecological validity is a type of external validity that is particularly relevant to experimental designs - refers to whether the findings of a study apply to the “real world”
20
Q

WEIRD samples

A
  • Researchers have questioned whether the results from typical psych studies are generalisable.
  • Most psychology studies are conducted in a small handful of countries in the Global North, e.g., America, Europe, Australia.
  • Many of these studies also make use of undergraduate psychology students for their participants.
  • More generally, typical psychology studies are conducted in societies that are WEIRD:
    1. Western
    2. Educated
    3. Industrialised
    4. Rich
    5. Democratic
  • Understanding exactly whether and how these impact the generalisability of psychology findings means running more studies with samples that aren’t WEIRD.
21
Q

reliability

A
  • about the consistency of a measure
  • a measure is reliable if it produces the same results each time its used on the same participant
  • e.g., if we’re measuring maths anxiety with a questionnaire then our questionnaire is reliable if we get similar scores each time we test a particular participant.
  • this kind of stability over time is known as test re-test reliability.
22
Q

levels of measurement

A

1) nominal
2) ordinal
3) interval
4) ratio
- sometimes a construct can fall into many of these levels, and it’s on the researcher to decide what measurement level is the most appropriate to use

23
Q

nominal data

A
  • refers to names, categories, labels or group membership
  • e.g., eye colour, occupation, study condition, age
  • cant compare the different groups in any quantifiable way
24
Q

ordinal level

A
  • individual observations can be ordered in a meaningful way
  • for example:
    1) We could order marathon runners ranked in order of who came 1st, 2nd, or 3rd
    2) However, doesn’t give information about the differences between individual points, e.g., we don’t know how much faster the winner is compared to the runner-up.
    3) The distance between the 1st and 2nd doesn’t have to be the same as the distance between 2nd and 3rd.
  • Common in psychology because of Likert scale.
25
Q

interval level

A
  • at the internal level, the differences (intervals) between pairs of adjacent values are the same
  • but there is no absolute zero point, e.g., IQ is measured at the interval level.
26
Q

ratio level

A
  • similar to interval, but there is a meaningful 0 point
  • e.g., reaction time, number of correct responses, score on an exam
27
Q

variable/data types

A
  • when we represent variables with numbers we can have different types depending on the types of data
  • continuous variable - can contain any numerical value within a certain range - e.g., time, height and weight
  • discrete variables - only contain some values - e.g., the number of children
  • binary variables - only take one of 2 possible values - e.g., heads/tails, pass/fail.
  • our IVs and DVs can be any tupe of any level of measurement, it all depends on the study