1
Q
What is EMPIRICISM in psychology?
A
- the principle that knowledge about behaviour and mental processes (hypotheses and theories) are tested with regard to OBSERVATIONS rather than solely relying on intuition or revelation.
- fundamental part of scientific method
- based on philosophical method
2
Q
What guides the scientific method in psychology?
A
empirical research
With experiments established and validated measurement tools.
3
Q
What is PRIORI reasoning?
A
From what is before.
Relies on deduction from self evident principles or general knowledge, rather than empirical research
4
Q
What are some ways to ensure that the science of psychology is empirical?
A
- well designed studies (experimental, correlational)
- sound hypothesis or research questions that are clearly testable
- clear definitions of variables (predictors/outcomes)
- use or development of established measures
- appropriate sample sizes
5
Q
Describe the five stages of the research process.
A
- Conceptual framework (models, hypotheses)
- Planning research (designs, sampling)
- Collecting data (instruments, measurements of scale)
- Analysing data (software, transformation)
- Publishing research (critique, collaboration, consumption)
6
Q
Is observation a sufficient tool for research?
A
Observation alone is not sufficient - research must be systematic.
Should be structured so that the results of the observation reveal something about the underlying nature of the world.
7
Q
Is it possible to prove a theory?
A
No.
Just because the data supports a model, doesn’t mean that the model is proven, as it could just be a random finding (ie, it may not REPLICATE).
8
Q
What term should be used instead of ‘proven’?
A
- support
- find evidence for
- positively link
9
Q
When it comes to scientific theory - how do we avoid using random observations?
A
- data collection is theory driven
- research is constructed to explain and predict phenomena
10
Q
What must research and scientific theory say about future observations?
A
it should make definitive predictions about the results of future observations.
11
Q
What must a scientific theory accurately describe?
A
a large class of observants on the basis of a model with contains FEW arbitrary elements
12
Q
What is the role of scientific theory?
A
- provides a conceptual structure that is supported by large and varied set of data.
- hypotheses are specific predictions derived from theories
-hypotheses should be falsifiable: we can reject them with scientific testing
- allows us to test a theory
13
Q
What are the three requirements of scientific theory?
A
- are specific: must state what will happen (and what will not)
- theories are modified and rejected, based on evidence
- no theory is infallible
14
Q
What are testable questions in scientific theory?
A
- does age predict job satisfaction?
NOT - what is the meaning of life?
15
Q
Why do we need testable questions in research?
A
Scientific theories must be stated so that the prediction proposed can be demonstrated to be false.
It cannot be falsifiable if it is not a testable question.
16
Q
What is falsifiability of a theory?
A
the logical possibility that it can be contradicted by an observation or the outcome of an experiment.
17
Q
Give an example of falsifiability in action.
A
Freud’s description of ‘refrigerator mothers’ creating autism in children.
Many of Freuds theories are not falsifiable because they cannot be tested
18
Q
Why do Freuds theories have no predictive ability?
A
- have no explanatory power
- cannot be falsified
- overlooks alternative
19
Q
What is verifiability and what are the requirements?
A
- philosophical doctrine suggests that science must be publicly verifiable to be evaluated.
- requires research to be submitted to the scientific community for criticism and empirical testing
- peer review, scientific publication and replication are minimal requirements
20
Q
What do we need to do to ensure verified and quality research?
A
- TRANSPARENCY in experimental methodology, observation and collection of data.
- PUBLIC ACCESSABILITY and transparency of scientific communication.
- Public availability and REUSABILITY of scientific data.
- Using web-based tools to facilitate scientific collaboration.
21
Q
Why publish research?
A
- communicates knowledge and methods for obtaining knowledge.
- important for furthering science.
- methods are corrected and improved.
22
Q
Why peer review?
A
research is a continual process that is advanced through collaboration and critique.
23
Q
When are journal articles reviewed?
A
Journal articles are critiqued by several scientists PRIOR to publication.
24
Q
What is open access (with reference to publishing research)?
A
the idea that scientific research should be published in such a way that the findings of the study are accessible.
25
Describe (roughly) the sequence of the peer review process for publishing research.
1. author submits article
2. assessed by editor (can be rejected here)
3. sent to reviewers (further reviews needed?)
4. reviews assessed by editor (can be rejected here)
5. accepted
6. production
7. publication
26
What is REPLICATION in research?
- replication: the same experiment for the same results
-replication is viewed as the safeguard for creep in subjectivity.
27
Are replication studies easy to publish?
no
28
What are the three types of data collection in research?
QUANTITATIVE research: conducting statistical analysis using numerical data.
QUANTITATIVE research: rich textual data rather than numerical.
MIXED METHODS: combines the above two - techniques, methods, approaches, concepts or language in a single study.
29
Define DATA
The translation of phenomena in the real world into something that is deliberately recorded and collected.
(translation of psychological data is different).
30
What are some instruments that can be used to collect data for research in psychology?
- questionnaries
- tests (aptitude, intelligence)
- observations
- archival data
- experimental tasks (eg. implicit association tasks)
- physiological measures
- interview and focus groups
31
A phenomenon can take on different values.
What are some constructs that could be variable in psychology?
depression, anxiety, psychopathy
32
Why do variables need to be OPERATIONALISED?
- variables need to be operationalised before we can measure them.
- need to define the concept so it is clearly DISTINGUISHABLE, MEASURABLE and UNDERSTANDABLE in empirical observations.
- definitions can change across contexts over time.
33
How can stress be operationalised?
- difficulty relaxing
- nervous arousal
- impatient
this can be measured in the DASS-21 scale (Depression, Anxiety & Stress Scale)
eg. Q "i find it difficult to unwind" rate on a scale of 1-10 is then 'operationalised'
34
Describe the basics of Quantitative Models
- most have only two variables
- the association between the 2 can be +ve or -ve
+ve: increases in value of the 1st variable is associated with an increase in the 2nd variable.
-ve: increases in the 1st variable is associated with decreases in the values of the second variable.
35
What are the two types of variables in quantitative research?
In experimental research:
INDEPENDENT VARIABLE - changing this, then what happens to the dependant variable.
DEPENDANT VARIABLE
In correlational research, the independent variable is called the PREDICTOR VARIABLE and the dependant variable is called the CRITERION/OUTCOME VARIABLE.
36
Describe independent or predictor variables.
variables which are assumed to cause changes in another variable.
- typically occurs before a particular outcome
- in some designs - the researcher knows the values prior to the start of the study.
In others, it is measured as part of the study.
37
Can independent variable have multiple levels?
yes
38
What are dependent or criterion variables?
thought to change in response to variations in independent variables.
39
What about COVERIATE or control variables?
- may also effect the dependent/criterion/outcome variable.
- typically not of primary interest.
- could be taken into account when designing research.
40
What the heck are CONFOUNDING VARIABLES?
- the covariate should NOT be a better independent variable than the one chosen!
- if the covariate is a more sensible explanation, then it is a confounding variable.
- means that the association that the researcher was originally interested in becomes meaningless. =(
41
Quantitative research can possess different PROPERTIES depending on ....?
WHAT is measured.
HOW it is measured.
The more PROPERTIES, the more flexible our options are.
42
What are scales of measurement (re: measuring the mind)?
- nominal/categorical
- ordinal
- interval
- ratio
43
Describe nominal/categorical as a scale of measurement
- limited
- no magnitude
- cannot perform operations (+ or -)
- data in only 2 categorical options is referred to as DICHOTOMOUS DATA (eg. smoker vs non-smoker)
eg. eye colour, gender at birth, nationality
44
Describe scales of measurement: ORDINAL
- property of order, but values do not represent magnitude
- cannot perform operations (+ or -)
- may still represent categories
eg. placing in competition,
very slow, slow, normal, fast, very fast
45
Describe scales of measurement: INTERVAL
- property of magnitude, but not relative magnitude
- meaningful (equal) differences between intervals
- no true zero point (can have -ve values)
- zero does not represent the absence of the property
- can perform multiple operations
- subtraction and addition (but NOT multiply or divide)
eg. temperature or clock time
46
Describe scales of measurement: RATIO
- best type of data for research
- property of magnitude (including relative magnitude)
- true zero point (cannot have negatives)
- zero represents the absence of the property
- can perform many operations
- add, subtract, divide and multiply
- the most useful scale of measurement for analysis
eg. height, weight, reaction time, exam score
47
Describe a quick way to categorise the four scales of measurement.
(this can go into a chart)
- categories and label variables
- ranks categories in order
- has known equal intervals
- has a true or meaningful zero
48
What is discrete data?
- no intermediate values between data values
- nominal and ordinal values are discrete
- there CANNOT be infinite decimals between points of the scale
49
What is continuous data?
- intermediate values between data values
- common in interval and ratio scales (but not always)
- can be infinite decimals between points on the scale
50
What is reliability in reasearch?
- refers to the consistency of measures
- measures that are not reliable cannot be trusted
- there are three main types of reliability in research
51
What are the three types of reliability in research?
- internal consistency
- test re-test reliability
- inter-rater reliability
52
Define, and give a description of INTERNAL CONSISTENCY
(reliability)
- consistency among items in a measure
- test takers response to all items on a scale should be similar/consistent
53
Define, and give a description of TEST RE-TEST RELIABILITY
- consistency over time
- test takers responses to items at time one should be similar to responses at time two.
54
Define, and give a description of INTER-RATER RELIABILITY
- consistency across observers/raters
- multiple observers/raters should provide similar accounts of the same event/occurrence/behaviour
55
Define VALIDITY
What question do we need to ask (when researching)?
The extent to which a concept or measurement corresponds accurately with the world.
Are we measuring what we think we are measuring?
56
How does evidence for validity build up over time?
with repeated use
57
What are the four main types of validity (in reasearch)
- face validity
- content validity
- criterion validity
- construct validity
58
What is Construct validity?
Example (i.e., extraversion measure)
CONSTUCT VALIDITY: related to other measures of X (convergent) and is not related to the measures of Y and Z (discriminant) Does it behave consistently with theoretical predictions?
People with high scores on this measure also score highly
on the NEO-PI Extraversion subscale; scores on this measure
are not related to scores on the Beck Depression Inventory
or the NEO-PI Openness subscale
59
What is face validity?
Example (i.e., extraversion measure)
FACE VALIDITY: looks like it is measuring X. Items appear to be measuring X.
Items appear (to the naïve observer) to be
measuring extraversion/sociability
60
What is content validity?
Example (i.e., extraversion measure)
CONTENT VALIDITY: samples the full breadth of X.
Items cover all aspects of extraversion (e.g.,
talkative AND adventurous AND active etc.)
61
What is criterion validity?
Example (i.e., extraversion measure)
CRITERION VALIDITY: A test's correspondence with a concrete outcome: measured now (concurrent)and in the future (predictive).
eg. Scores on the measure predict current base rate cortical
arousal levels; as well as future sociable behaviour at
parties, work, etc.
62
Can an unreliable measure be valid?
a measure can be reliable, but not valid,
however, an unreliable measure cannot be valid.
63
What are the four scales of measurement?
Nominal, Ordinal, Interval, and Ratio.
64
What is a nominal scale?
A nominal scale uses numbers as labels to categorize data, but the numbers have no real order or mathematical meaning.
## Footnote
Example: Assigning '1' for male and '2' for female.
65
What is an ordinal scale?
An ordinal scale ranks data in a specific order, but the intervals between the ranks are not necessarily equal.
## Footnote
Example: Ranking race finishers as 1st, 2nd, and 3rd.
66
What is an interval scale?
An interval scale has equal intervals between values, but it lacks a true zero point.
## Footnote
Example: Temperature in Celsius or Fahrenheit.
67
What is a ratio scale?
A ratio scale has equal intervals and a true zero point, which means you can make meaningful ratio comparisons (e.g., 'twice as much').
## Footnote
Example: Height, weight, or age.
68
What are the three measures of central tendency?
Mean, Median, and Mode.
69
What is the mean?
The mean is the arithmetic average of a set of scores, calculated by adding all scores together and dividing by the number of scores. It is best used for symmetrical data distributions.
70
What is the median?
The median is the middle score in a dataset when the scores are arranged in order. It is the preferred measure of central tendency for skewed data.
71
What is the mode?
The mode is the most frequently occurring score in a dataset.
72
What are measures of variability (or dispersion)?
Measures of variability describe the spread or dispersion of scores in a dataset. Common measures include:
* Range
* Interquartile range
* Variance
* Standard deviation
73
What is the range?
The range is the difference between the highest and lowest scores in a dataset.
74
What is the standard deviation?
The standard deviation is a measure of how much the scores in a dataset deviate from the mean. A small standard deviation indicates that the data points are close to the mean, while a large standard deviation indicates they are spread out over a wider range.
PSY1101
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