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PL2131 Cheat Sheet by

psychology

INTRO TO PL2131

Learn how to conduct psycho­logical research
- Turning a question into research
- Designing an experiment
- Collecting and analysing data
- Presenting findings

SCIENTIFIC RESEARCH – the scientific approach

1) Intuition: process of coming to direct knowledge or certainty without reasoning or inferring; forming hypotheses
2) Authority: acceptance of facts stated by author­ities; used in designing stage; expert whose facts are subject to testing using the scientific process
3) Ration­alism: uses reasoning to arrive at knowledge, assumes that valid knowledge is acquired if correct reasoning process is used; identify the outcomes that indicate the truth/­falsity of the hypotheses
4) Empiri­cism: acquire knowledge through experi­ences; cognition and percep­tion; empirical observ­ations to be conducted under controlled conditions
- The goal of science: to understand the world we live in
- To acquire knowledge

ASSUMP­TIONS UNDERLYING SCIENTIFIC RESEARCH

2) Reality in nature: our experi­ences are real; forms basis for further research; scientists assume that there is an underlying reality that they are trying to uncover
3) Discov­era­bility: it is possible to discover the regula­rities and reality; must assume that we can discover laws that make experi­ences real
1) Unifor­mit­y/r­egu­larity in nature
a. Determ­inism: the belief that there are causes or determ­inants of mental processes and behaviour (making sense of the world)
b. Probab­ilistic cause: causes that usually produce outcomes, the interim and what we get instead when we are seeking to attain the end goal that is determ­inism

PSYCHO­LOGICAL RESEARCH

Concep­tua­lis­ation
adopting a scientific approach; definition of terms
Operat­ion­ali­sation
construct vs measure; working definition of the construct - specif­ication
Hypothesis
forming a testable hypoth­esis; science is falsif­iable; embracing the null; can never be proven to be correct
Research study
experi­mental vs non-ex­per­imental
Data collection
how do we treat subjects? measur­ement modes used
Data analysis
samples and sample sizes; comparing group scores
Presen­tation
presenting research findings

MEASUR­EMENT MODES

Nominal
catego­ries, non-qu­ant­ita­tive, uses symbols to classify variable values
Ordinal
rank-order scale of measur­ement; cannot assume equidi­stance
Interval
equal intervals, no absolute zero point (arbit­rary)
Ratio
absolute zero point, rank-o­rde­ring, equal intervals
GOOD MEASUR­EMENTS
- Reliab­ility: consis­tency of scores of your measur­ement instrument
- Validity: extent to which your measur­ement procedure is measuring what you think it is measuring; whether you have used and interp­reted the scores correctly

EXPERI­MENTAL RESEARCH

Quant. exp. research designs
Conducting experi­ments to establish causations by manipu­lating IVs and observing changes on DVs
Required conditions for claiming causation:
- Associ­ation: 2 variables are empiri­cally correlated
- Tempor­ality: cause comes before effect
- Elimin­ation of plausible altern­ative explan­ations: effect cannot be explained by a 3rd variable

INDEPE­NDENT VARIABLES

Levels of the IV and manipu­lation strength
>2 levels of the IV to conclude causality
Strength: levels of the IV must be distinct and different from each other
# of IVs?
>1 IV!!
Having only one -> misleading
In experi­mental designs:
- Event manipu­lation: random assign. into condit­ions, roughly equal profiles
- Instru­ctional manipu­lation
- Individual difference manipu­lation: varying IV by selecting partic­ipants that differ in the amt or type of a measured internal state (cannot conclude causality; inherent charac­ter­istics)

DEPENDENT VARIABLES

In experi­mental designs
They can be continuous or catego­rical in nature
Number of DVs
There can be altern­atives! -> accura­cy/­res­ponse time

EXTRANEOUS VARIABLES

In experi­mental designs
- Third variables besides the IV and DV
- Cloud interp­ret­ations of the IV-DV rship if uncont­rolled
- Blinding to remove bias (syste­matic ways to account for them)

EV vs CV

EV
CV
- Might compete with the IV in explaining the outcome
- An EV that may eliminate the ability to claim that the IV causes changes in the DV
- Affects absolute outcome but not experi­mental outcome
- Creeps in system­ati­cally and affects one level of the IV but not the other

DESIGNS

Between (goes through 1 level of the IV)
Within (goes through all levels of the IV)
Shorter time to obtain results
Elimin­ation of CVs
Random assign. could cause unequal groups of unequal abilities (confo­unding)
Mental fatigue, floor effects
 

EXPERI­MENTAL CONTROL

Between
Within: counte­r-b­ala­ncing to counter sequencing effects (order effects and carryover effects)
- matching: alt. method to/can be combined with random­isation
- random­ised: possib­ility that there is a sequence that has a higher frequency of a certain variable
- random­isation
- intras­ubject: does not solve order effects
 
- complete: N!, N = # of levels of IV; may not have enough partic­ipants
 
- incomp­lete: multiple sequences, control order effects, N sequences, only works for even #; odd # – create a mirror!
Matching:
o Equating partic­ipants
 Precis­ion­-co­ntrol: each partic­ipant matched with another on selected variables (equal identical attrib­utes);
 Freq. distri­bution: match groups by equating overall distri­bution of selected variable – random assign til 2 groups comparable
o Hold variables constant: slicing
o Build the EV into research design

Incomp­lete:
 Each TC appear equal no. of times in each position
 Each TC precede and follow every other TC equal no. of times

NON-EX­PER­IMENTAL RESEARCH

Experi­mental
Non-ex­per­imental
manipu­lated the IV (varia­bility)
did not manipulate the IV (varia­bility due to individual differ­ences
can infer causality
can only infer correl­ation
control over EVs
construct and use good test items

SURVEY RESEARCH METHODS

1. Match the research object­ives.
2. Approp­riate for the respon­dents to be surveyed.
3. Short, simple questions.
4. Avoid loaded or leading questions
5. Avoid double­-ba­rrelled questions
6. Avoid double negatives
7. Determine whether closed­-ended, or open-e­nded, or mixed format questions are needed
8. Construct mutually exclusive and exhaustive response categories for closed­-ended questions
9. Consider the different types of closed­-ended response categories (measu­rement modes) – would an interval scale or ordinal scale be more useful?
10. Use multiple items to measure complex or abstract constructs
11. Make sure questi­onnaire is easy to use; - Limit contin­gency questions (redir­ection) - Control response bias (social desira­bility) - Control response bias (response set) – insert contra­sting items
12. Pilot-test – think-­aloud technique
Need to ensure the validity of questi­onnaire (i.e., the test items measure what we had initially set out to measure)
Construct is too broad for comfort: need to operat­ion­alize
Specific operat­ion­ali­zation of the idea that we want to pursue and not something else

DESCRIBING SCORES

Mean
Variab­ility
 
- Wanting to know how the scores spread around the mean
- Presence of outliers can be misleading
Standard deviation: describing the spread of a group of scores; average amount that scores differ from the mean
 
Variance
Central tendency:
- Make sense of a group of scores
- Know how our data look like centrally

INFERE­NTIAL STATISTICS

1. Converting raw scores to Z-scores
2. Converting Z-scores to raw scores
- Number of SDs a score is above or below the mean
X=(Z)(­SD)+M
Z=(X-M)/SD
Distri­bution of Z-scores: M=1,SD=1
Z-scores
- To describe a score in terms of where it fits into the overall group of scores, create a Z-score
- Number of SDs a score is above or below the mean
- Analogous to a transl­ation; standa­rdi­sation

!! We describe a group of data scores using a repres­ent­ative value (mean + SD)
Obtain a Z-score to infer how a score is ‘perfo­rming’ in comparison to others.

NORMAL CURVE

NORMAL CURVE

EFFECTS

Ceiling effect
when an IV no longer has an effect on the DV
Floor effect
when a data-g­ath­ering instrument has a lower limit to the data values it can reliably specify

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