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20 Cards in this Set
- Front
- Back
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Causality
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The cause-and-effect relationship between variables.
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Demand characteristics
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The case of factors affecting the motivational state of participants during studies which may make the observed behaviour unnatural.
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Evaluation
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Research involving systematic appraisal of organisations, processes or programs leading to feedback on improvement or performance.
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Experimentation
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A research approach in which attempts are made to
identify causal relationships among variables under controlled conditions. |
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Falsifiability
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Popper's (1959) assertion that the scientific method is ultimately based on our ability to prove an assertion is false (by finding a counterexample to it), but not to prove one is true.
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Introspection
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The consideration of the contents of one's mind, and the reports of that consideration. Example of an introspective technique - a verbal protocol.
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Qualitative research
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A body of research techniques which seeks insights through loosely structured, mainly verbal data rather than measurements. Analysis is interpretative, subjective, impressionistic and diagnostic.
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Quantitative research
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Research which seeks to make measurements as distinct from qualitative research.
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Self-report
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The gathering of data by having the participants report on their own behaviour or thought; a research technique used to study problem solving and knowledge representation, including methods such as verbal protocols, self-ratings and focused diary keeping.
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Surveys
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A research method that collects information from people about opinions or facts. Participants are asked to reconstruct experience, rather than behaviour being manipulated, in order to establish a causal relationship.
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α-level
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the probability of making a Type I error (usually this value is .05).
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β-level
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the probability of making a Type II error (Cohen, 1992, suggests a maximum value of 0.2).
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Central limit theorem
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this theorem states that when samples are large (above about 30) the sampling distribution will take the shape of a normal distribution regardless of the shape of the population from which the sample was drawn. For small samples the t-distribution better approximates the shape of the sampling distribution. We also know from this theorem that the standard deviation of the sampling distribution (i.e. the standard error of the sample mean) will be equal to the standard deviation of the sample (s) divided by the square root of the sample size (N)
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Confidence interval
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for a given statistic calculated for a sample of observations (e.g. the mean), the confidence interval is a range of values around that statistic that are believed to contain, with a certain probability (e.g. 95%), the true value of that statistic (i.e. the population value)
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Degrees of freedom
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an impossible thing to define in a few pages let alone a few lines. Essentially it is the number of 'entities' that are free to vary when estimating some kind of statistical parameter. In a more practical sense, it has a bearing on significance tests for many commonly used test statistics (such as the F-ratio, t-test, chi-square statistic) and determines the exact form of the probability distribution for these test statistics. The explanation involving rugby players in Chapter 8 is far more interesting...
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Deviance
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the difference between the observed value of a variable and the value of that variable predicted by a statistical model.
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Effect size
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an objective and (usually) standardized measure of the magnitude of an observed effect. Measures include Cohen's d, Glass' g and Pearson's correlations coefficient, r.
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Fit
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how sexually attractive you find a statistical test. Alternatively, it's the degree to which a statistical model is an accurate representation of some observed data. (Incidentally, it's just plain wrong to find statistical tests sexually attractive.)
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Linear model
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a model that is based upon a straight line.
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Meta-analysis
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this is a statistical procedure for assimilating research findings. It is based on the simple idea that we can take effect sizes from individual studies that research the same question, quantify the observed effect in a standard way (using effect sizes) and then combine these effects to get a more accurate idea of the true effect in the population.
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