Week 1-5 content

Question 1

Empiricism

Answer 1

Knowledge is what can be observed / or ‘you gotta see it to believe it’

Question 2

Operationalise

Answer 2

To formalise and define a measurement

Question 3

Latent construct

Answer 3

Phenomenon that is not directly observed but are rather estimated from an operationalisation

Question 4

Atomisation

Answer 4

Evaluate parts of the whole (in chemistry and physics, literally sub-atomic behaviour)

Question 5

An operationalisation of a phenomenon should be:

Answer 5

Specific
(such as not measuring happiness by measuring how energetic a person feels)

Tied to specific theories/models of behaviour
(such as theoretical distinction between anxiety, fear and stress)

Developed in a replicable and recognisable style
(so that anyone could identify how someone arrived at this measure)

Question 6

Falsifiability

Answer 6

The ability of an idea to be able to be demonstrably wrong

Question 7

Hypothesis

Answer 7

A prediction about the nature of phenomenon

Question 8

Null Hypothesis

Answer 8

There is no notable effect in the investigation.

Any small effects are due to chance alone.

Question 9

Alternative Hypothesis

Answer 9

There is an effect of the investigation.

These differences are more notable than chance alone.

Question 10

Inferential statistics/testing

Answer 10

Statistics used to make assessments of probabilities of ‘true’ effects (rather than just descriptions) of data

Question 11

Research Questions

Answer 11

What is of interest in the current piece of work

Question 12

HARKing

Answer 12

Claiming to have made a prediction after having seen the results of the test data

Question 13

Variables

Answer 13

A variable is anything that varies between people, over time or within context: 
Number of apples eaten per week
Ratings of happiness
Alcohol tolerance
Personality (e.g. extraversion) 
Average running speed 
Beliefs 

Even labels or categories (i.e. nominal data)
Which football team you support or how you vote

Even how much you enjoy research methods and statistics will vary (across time and individuals!)

Question 14

Variable Changes

Answer 14

Variables can change across time

Variables can differ between people

Variables can differ over contexts

Question 15

What are not variables

Answer 15

Things that are NOT variables:
People (only the things that they do/think/feel)

Conditions or groups – but these might represent levels (i.e values) of variables

Question 16

Random Variation

Answer 16

Sometimes called “Noise” or Error

Means that individuals differ naturally

Statistical tests try to account for or explain as much variation in data as it can

But there will always be some random Error that remains unexplained

Question 17

Normal Distribution

Answer 17

X axis represents the scores on the scale
Y axis is the number of people who scored in each 5 point range (e.g. 51 to 55 = 19)

Natural variation

Some low scorers, some high scorers

Most people fall on the middle of the scale

If you were to randomly pick someone from the population what sort of score would they most likely have?

Question 18

Independent Variables

Answer 18

Commonly noted as just “IV”

“Independent” because it’s value is fixed or controlled, and is NOT dependent on something else in our study.

A hypothesised cause – something that we think will have an effect on another variable

In an experiment, we manipulate the IV:
By assigning people to different groups and comparing them
By comparing people across different conditions

Question 19

Levels of the IV

Answer 19

Levels are values taken on by an IV, usually categories of some kind

Usually, levels are simply the conditions or groups used in a study

Question 20

Dependent Variables

Answer 20

Commonly noted as just “DV”

“Dependent” because it’s value depends on something else in our study (hopefully the IV)

A DV is a hypothesised effect – something that we think is affected by another variable

We always measure the DV

Question 21

Nominal Data

Answer 21

Numbers used to represent names or categories or simple identifiers

Numbers used as categories or labels

The numbers have no mathematical value

Most statistical procedures don’t apply to nominal data (mean, standard deviation, etc.)

Some more advanced stats look at category membership

Question 22

Ordinal Data

Answer 22

Order of performance.

The “distance” between each data point is not taken into account.

Ordinal data is used a lot in response scales to psychometric questionnaires (but is debated)

For example, a likert scale question on it’s own is ordinal, but when a questionnaire uses several questions to calculate a total score, this can be thought of as interval data.

Question 23

Interval Data

Answer 23

Measurements with an equal interval between each point on the scale

Numbers on the scale can be positive or negative

Question 24

Ratio Data

Answer 24

Ratio data also has measurements with an equal interval between each point on the scale

Also has a mathematically meaningful absolute zero

The absolute zero allows ratio judgements to be made (multiplication/division - e.g., Susan is twice as tall as her daughter)

Notice the similarity with Interval data

Operationalisation is also key – money in the bank v money in the pocket

Question 25

Continuous and Discrete Variables

Answer 25

Ratio and Interval data are usually continuous
A variable is continuous if it can be subdivided further
10.62 cm makes sense
10.62th place in a marathon does not make sense

The opposite of continuous is discrete (not “discreet”)
Discrete data can be counted in whole numbers
Subdivision of the values doesn’t make sense
Number of siblings you have
Number of students in a class
Number of correct answers on an exam

Question 26

A crisis of confidence

Answer 26

Prestige bias in publication

Prestige bias in resource concentration

‘Publish or Perish’ systems

Questionable Research Practices (QRPs; both systemic and personal)

Lack of transparency / data openness (‘file draw’ problems)

Question 27

Internal Reliability

Answer 27

Within a study, how often do measures agree?

Question 28

External Reliability / Replicability

Answer 28

Across a study, how often do findings agree?

Conceptual Replication – same idea, different method, different sample/data

Direct Replication – same idea, same methods, different sample/data

[Reanalysis of existing data is Reproducibility – same idea, same method, same data

Question 29

Replication

Answer 29

Demonstrating the same findings as another study

Question 30

Meta-Analysis

Answer 30

Statistical analysis of literature consistency

Question 31

Systematic Review

Answer 31

A formal approach to evaluating arguments, practices and findings in the literature

Question 32

Validity

Answer 32

The accuracy or realism of an operationalisation

Question 33

Convergent Validity

Answer 33

Statistical agreement between two similar measures

Question 34

Concurrent Validity

Answer 34

Statistical agreement between two measures of the same latent phenomenon

Question 35

Discriminant Validity

Answer 35

The statistical ability to separate out target categories

Question 36

Face Validity

Answer 36

Does this have the appearance of good measurement?

Question 37

Content Validity

Answer 37

The value of the included/excluded content for representing a construct.

Question 38

Generalisability

Answer 38

How representative a sample size or method is

Question 39

W.E.I.R.D.

Answer 39

Western, Educated, Industrialised, Rich and Democratic Countries

Question 40

Practicality/Utility

Answer 40

How useful a study is for an everyday application