Scientific Method

Question 1

Variable

Answer 1

something that varies (e.g. aggression)
Scientists (that subscribe to the empiricist epistemology) aim to discover these truths by looking at how variables change and what causes them to change

Question 2

Nominal Scale

Answer 2

Categorical

A Nominal Scale is a measurement scale, in which numbers serve as “tags” or “labels” only, to identify or classify an object. This measurement normally deals only with non-numeric (quantitative) variables or where numbers have no value.

Yes/No, Hair color, marital status, gender, origin

Statistics: You can only look at Frequencies

If you only have nominal scaled data, you will be a bit limited in your choice of available statistical methods

Question 3

Ordinal Scale

Answer 3

Categorical

variable measurement scale used to simply depict the order of variables and not the difference between each of the variables. These scales are generally used to depict non-mathematical ideas such as frequency, satisfaction, happiness, a degree of pain, etc. It is quite straightforward to remember the implementation of this scale as ‘Ordinal’ sounds similar to ‘Order’, which is exactly the purpose of this scale.

Property type: Detached > Semi-detached > Terraced > Flat

Transforming data from higher-level scales to ordinal reduces information

Question 4

Interval Scale

Answer 4

Continuous/Scalar

numerical scale where the order of the variables is known as well as the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale. It is easy to remember the primary role of this scale too, ‘Interval’ indicates ‘distance between two entities’, which is what Interval scale helps in achieving.

These scales are effective as they open doors for the statistical analysis of provided data. Mean, median, or mode can be used to calculate the central tendency in this scale. The only drawback of this scale is that there no pre-decided starting point or a true zero value.

80 degrees is always higher than 50 degrees and the difference between these two temperatures is the same as the difference between 70 degrees and 40 degrees.

Many measures in psychology are interval (e.g., IQ)

Question 5

Ratio Scale

Answer 5

Continuous/Scalar

variable measurement scale that not only produces the order of variables but also makes the difference between variables known along with information on the value of true zero. It is calculated by assuming that the variables have an option for zero, the difference between the two variables is the same and there is a specific order between the options.

With the option of true zero, varied inferential statistics, and descriptive analysis techniques can be applied to the variables. In addition to the fact that the ratio scale does everything that a nominal, ordinal, and interval scale can do, it can also establish the value of absolute zero.

The best examples of ratio scales are weight and height. In market research, a ratio scale is used to calculate market share, annual sales, the price of an upcoming product, the number of consumers, etc.

Question 6

Reliability

Answer 6

How accurate measurement of variables is

Thought experiment: Assume you want to estimate the reliability of a depression questionnaire
A group of patients fills out a psychometric questionnaire (PHQ-9)

A week later, the same group fills out the same questionnaire again. If the questionnaire has perfect measurement precision (and invidual levels of depression have remained stable during the week), then the two measurements should correlate perfectly (r = 1.0)

In reality, test-retest reliability will never be perfect, because other factors, unrelated to depression, will also influence the obtained scores
Fatigue
Boredom
Memory effects
Halo effects
Unclear or ambiguous items
Unclear or ambiguous response alternatives

Question 7

Validity

Answer 7

The degree to which a test measures what it aims to measure. In an experimental context, a valid study is one that allows correct inferences about the question it was aimed to answer.

Question 8

Classical test theory

Answer 8

Quantify the reliability of a test

In classical test theory (CTT), on which the definition of reliability is based, each Observed score (X) contains a True component (T) and an Error component (E).
Core assumption of CTT: X=T+E
Which can be rearranged to: E=X-T
Thus, measurement error E is the discrepancy between the numbers used to represent the thing that we are measuring and the actual value of the thing we are measuring (i.e., the value we would get if we could measure it directly)

Within the CTT, reliability is defined as as the ratio of true score variability, relative to the variability of observed scores

Question 9

Estimating reliability with repeated measures

Answer 9

Parallel form method
(by comparing performance
on two tests that are parallel
or equivalent alternative forms)

Test-Retest method
(by comparing several
administrations of
the same test)

Question 10

Estimating reliability with single measure

Answer 10

Split-half method
(by comparing
performance in
two halves of
the same test)

Reliability quantified as
correlation between test-halves
(corrected for test-length
using Spearman-Brown
prediction formula)

Internal consistency
method
(by comparing
performance
item by item
within the same test)

Reliability quantified as
Cronbach‘s α

Question 11

Content Validity

Answer 11

evidence that the content of a test corresponds to the content of the construct it was designed to cover.
For example: According to Baddeley, working memory includes a phonological and a visuo-spatial aspect. A working memory test with content validity should therefore include BOTH of these aspects

Question 12

Criterion Validity

Answer 12

evidence that scores from an instrument correspond with (concurrent validity) or predict (predictive validity) external measures conceptually related to the measured construct
A new intelligence test with high criterion validity should correlate with existing, established intelligence test (concurrent v) and school grades (predictive v)

Question 13

Correlational Method

Answer 13

Observe what naturally happens or take a snapshot of many variables and look for relationships (questionnaire data)

Question 14

Experimental Method

Answer 14

Manipulate some aspect of the environment and observe the effect (Experimental method)

Question 15

3 Conditions to infer Causality

Answer 15

Cause has to precede effect
Cause and effect should correlate
All other explanations of the cause-effect relationship should be ruled out

Question 16

True vs. Quasi-Experiments

Answer 16

A true experiment is where participants are randomly assigned to experimental groups/conditions and
Only true experiments allow causal inferences
In a quasi-experiment, assignment of participants to groups is not random (meaning other explanations unrelated to the independent variables cannot be ruled out!)

Question 17

Inductive reasoning

Answer 17

Or induction, is reasoning from a specific case or cases to a general rule (e.g., observe 100 white swans => conclude that all swans are white).

Question 18

Measurement Error

Answer 18

the discrepancy between the numbers used to represent the thing that we are measuring and the actual value of the thing we are measuring (i.e., the value we would get if we could measure it directly)

Question 19

Levels of measurement

Answer 19

the relationship between what is being measured and the numbers that represent what is being measured