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| Frage | Antworten |
| Concept | A family of related conceptions, may be simple and concrete or complex and abstract |
| Constructs | Are concepts made measurable |
| Nominal (Categorical) Scale | (A≠B≠C) Things from one category are different from things in another category in terms of many aspects that are not usually, or clearly, specified or quantified eg. gender (male, female) |
| Ordinal (Rank) Scale | (A<B<C) Things in one category are more or less than things in another category in terms of units of an attribute that is specified but not quantified eg. ATAR Score, academic grades (A to D) |
| Interval and Ratio Scales | (A-B=B-C) Things in one category are more or less than, and to some comparable extent, things in another category in terms of units of one attribute that is quantified eg. height of a person (cm), income (in $) |
| Hypothesis | An 'educated guess' about what a study might find, should be specific, testable, and directional |
| Operationalisation of a Hypothesis | The process by which we select/create quantifiable representations of concepts, they must be reliable and valid |
| Reliability | It is the extent to which a score is consistent (reproducible) across time and between observers (repeatability, consistency, agreement) |
| Reliability Coefficient | Reliability = (true score)/(true score + Error) |
| Test-retest | Correlation between scores obtained across time (establishing reliability) |
| Parallel-form | Correlation between scores obtained two versions of the test across time (establishing reliability) |
| Inter-rater | Correlation between scores by two observers (establishing reliability) |
| Split-half | Correlation between two halves of the same test (establishing reliability) |
| Internal Consistency (Cronbach Alpha) | Averaged correlation between all possible two halves of the same test (establishing reliability) |
| Validity | The extent to which the score 'behaves' as expected from theory |
| Measurement Validity | How good are the measures being used? |
| Internal Validity | How much we can trust the finding of the experiment |
| External Validity | How well can the results of the study be applied (generalised) to other similar situations with different people at different times? |
| Ecological Validity | We need evidence that use of the test is valid for its context |
| Face Validity | 'Looks' like a measure of self-esteem? |
| Content Validity | Items covers various aspects of self-esteem? |
| Construct Validity | Convergent validity (correlates with other measures of self-esteem), discriminant validity (does not correlate with measures of a different trait, such as IQ) |
| Convergent Validity | Demonstrated by moderate to high correlations between measures of the same trait (eg. two measures of self-esteem, or self-efficacy) |
| Discriminant Validity | Demonstrated by low correlations between measures of different traits (eg. self-esteem and memory) even when the methods employed are the same (both questionnaires) |
| Measurement Error | Arises due to discrepancies between the construct we intend to measure an how well we actually capture it |
| Between-subjects Design | Different participants in each group |
| Within-subjects Design | Same participants in each group tested across occasions |
| Mixed Design | Different participants in each group tested across different occasions |
| Quasi-experimental Design | When it is not possible to randomly assign participants to a control group and an experimental group |
| Correlation Research | Is a description between variables, not a cause and effect relationship, used to provide ideas of cause-and-effect hypotheses that can be tested by experimental research |
| Field Observation | Recording behaviours during clearly specified sampling periods |
| Questionnaire Surveys | Interviews, mail, internet |
| Secondary Data Analysis | Historical records (e.g., medical records, newspapers, archival data) |
| Biased Sample | When the data is not representative of the population |
| The Third Variable Problem | There could be another variable that exists that influences the two variables you are looking at that you didn't measure or take into account |
| Experimental Research | Manipulation of X to study its effect on Y while keeping other factors constant |
| Selection Bias (internal validity) | Non-random factors responsible for participants being in one group or condition and not another |
| Regression to the Mean (internal validity) | Tendency for extreme scores on one occasion to be closer to the mean on another occasion |
| Maturation (internal validity) | Changes within participants over an extended period of time |
| History (internal validity) | Concurrent events happening between pretest and posttest |
| Testing (internal validity) | Aspects of testing (not hypothesised) that affects the participants response |
| Mortality (internal validity) | Loss (attrition) of participants that is related to the treatment |
| Instrument Change (internal validity) | Changes in the instrument (or observer) over repeated testing |
| Experimenter Bias (internal validity) | Contamination of participant's response by the experimenter |
| Representative Sample of Participants (external validity) | Random selection is rare due to ethics and logistics |
| Representative Sample of X's and Y's (external validity) | Accurate and complete operationalisation of a concept (X, Y) is rare due to artificial and multi-factorial nature of X and Y, ethics and logistics |
| Representative Sample of Situations (external validity) | Behaviour is often dependent on context |
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