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INTRO TO PL2131Learn how to conduct psychological research |
- Turning a question into research
- Designing an experiment
- Collecting and analysing data
- Presenting findings SCIENTIFIC RESEARCH – the scientific approach1) 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 RESEARCH2) 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 RESEARCHConceptualisation | 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 MODESNominal | 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 RESEARCHQuant. 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 VARIABLESLevels 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 VARIABLESIn experimental designs | They can be continuous or categorical in nature | Number of DVs | There can be alternatives! -> accuracy/response time |
EXTRANEOUS VARIABLES- 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 CVEV | 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 |
DESIGNSBetween (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 CONTROLBetween | 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 RESEARCHExperimental | 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 METHODS1. 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 SCORESMean | 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 STATISTICS1. 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. EFFECTSCeiling 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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