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Week 14

Week 14. EXPLORING DATA. Today’s items. Check the items on the last exam Review some chapters of the book Learn basic functions of SPSS Using flash video Chapter on “Exploring data”. Terms must be remembered.

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Week 14

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  1. Week 14 EXPLORING DATA Discovering Statistics Using SPSS

  2. Today’s items • Check the items on the last exam • Review some chapters of the book • Learn basic functions of SPSS • Using flash video • Chapter on “Exploring data” Discovering Statistics Using SPSS

  3. Terms must be remembered • Independent variable: A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated. • Dependent variable: A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome. • Predictor variable: A variable thought to predict an outcome variable. This is basically another term for independent variable (although some people won’t like me saying that; I think life would be easier if we talked only about predictors and outcomes). • Outcome variable: A variable thought to change as a function of changes in a predictor variable. This term could be synonymous with ‘dependent variable’ for the sake of an easy life. Discovering Statistics Using SPSS

  4. Level of Measurement • Categorical (entities are divided into distinct categories): • Binary variable: There are only two categories (e.g. dead or alive). • Nominal variable: There are more than two categories (e.g. whether someone is an omnivore, vegetarian, vegan, or fruitarian). • Ordinal variable: The same as a nominal variable but the categories have a logical order (e.g. whether people got a fail, a • pass, a merit or a distinction in their exam). • Continuous (entities get a distinct score): • Interval variable: Equal intervals on the variable represent equal differences in the property being measured (e.g. the difference between 6 and 8 is equivalent to the difference between 13 and 15). • Ratio variable: The same as an interval variable, but the ratios of scores on the scale must also make sense (e.g. a score of 16 on an anxiety scale means that the person is, in reality, twice as anxious as someone scoring 8). Discovering Statistics Using SPSS

  5. When we collect data in an experiment, we can choose between two methods of data collection. • The first is to manipulate the independent variable using different participants. This method is the one described above, in which different groups of people take part in each experimental condition (a between-groups, between-subjects or independent design). • The second method is to manipulate the independent variable using the same participants. Simplistically, this method means that we give a group of students positive reinforcement for a few weeks and test their statistical abilities and then begin to give this same group negative reinforcement for a few weeks before testing them again, and then finally giving them no reinforcement and testing them for a third time (a within-subject or repeated measures design). As you will discover, the way in which the data are collected determines the type of test that is used to analyse the data. Discovering Statistics Using SPSS

  6. Z score • http://www.regentsprep.org/Regents/math/algtrig/ATS7/ZChart.htm Discovering Statistics Using SPSS

  7. Last week • We talked briefly about: • A number of scores to describe a “variable”: • Measure of central tendency (mean, median, mode) • Measure of variability (range, standard deviation, variance, quartile splits) • Measure of shapes (kurtosis and skewness) Discovering Statistics Using SPSS

  8. Skewness and kurtosis • ## the values of skewness and kurtosis should be 0 in a normal distribution • Positive values of skewness indicates a pile-up of scores on the left (positively skewed) • Negative values of skewness indicates a pile-up of scores on the left (negatively skewed) • Positive values of kurtosis indicate a pointy distribution whereas negative values indicate a flat distribution. Discovering Statistics Using SPSS

  9. Assumption of Parametric date • Normally distributed data • Homogeneity of variance – each of the group you tested should have the same variance • Interval data – the distance between points of your scale should be equal at all parts along the scale • Independency – data from different subjects are independent, which means that the behavior of one participant does not influence the behavior of another. Discovering Statistics Using SPSS

  10. Let’s use SPSS to see if some data match with the assumptions • Use example/data set provided by the book – SPSSexam.sav • Non-parametric test • Chi-square Discovering Statistics Using SPSS

  11. Figure 3.10 Discovering Statistics Using SPSS

  12. Figure 3.12 Discovering Statistics Using SPSS

  13. Figure 3.15 Discovering Statistics Using SPSS

  14. Fig 3.16 Discovering Statistics Using SPSS

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