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Lecture 2: Basic steps in SPSS and some tests of statistical inference

Lecture 2: Basic steps in SPSS and some tests of statistical inference. Basic steps in SPSS Error checking Missing values analysis Grouping variables Graphical representation Break Hypothesis testing, inferential statistics and parametric testing in SPSS. Exercises.

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Lecture 2: Basic steps in SPSS and some tests of statistical inference

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  1. Lecture 2: Basic steps in SPSS and some tests of statistical inference • Basic steps in SPSS • Error checking • Missing values analysis • Grouping variables • Graphical representation • Break • Hypothesis testing, inferential statistics and parametric testing in SPSS

  2. Exercises • Exercise data can be found on the website: www.ex.ac.uk/ebrg

  3. Statistical inference: the null and alternative hypotheses (H0,H1) • Statistical inference is concerned with differences between samples / populations • Null hypothesis: “There is no significant difference between A and B” • Alternative hypothesis: “There is a significant difference between A and B”

  4. One and Two tailed tests • Very often, we will want to examine the ‘direction’ of a difference • One-tailed test: specifying the direction • Two-tailed test: no direction specified • Alternative hypothesis changes for one-tailed test: • “A is significantly greater or less than B”

  5. Determining your data types • Population: all possible cases • Sample: your selection of the population • Tied or ‘paired’ samples: Samples that are linked, perhaps in time (e.g. before/after samples)

  6. What type of test is appropriate? • Parametric: Classical tests • Non-parametric: Less powerful tests • Knowing your data

  7. Test selection • The choice of statistical test is crucial • Various tests depending on test requirements: Parametric Non-parametric • Test calculation can be undertaken in SPSS

  8. Significance testing • How significant is the test result? Does it show a ‘real’ difference, or could it have occurred by chance? • With every test, there will be a probability distribution, which looks like the normal distribution • This distribution specifies the probability of the result (test statistic) occurring by chance or random variation

  9. Significance - continued • Probability is measured as 100% = 1.00 • Each test has a critical ‘rejection’ area on its distribution where test statistics must be rejected, as they are too large than to have occurred by chance, according to the number in the sample • Accordingly, all tests have critical values for different levels of significance and differently tailed tests

  10. Interpreting significance • To simplify matters, we use significance ‘levels’ to determine if a test is significant • We usually use 0.05 (5%) or you could use 0.01 (1%) • This means that there is a 5% probability that the result occurred by chance, i.e. we can be 95% confident in its importance

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