Discovering Statistics Using SPSS (Introducing Statistical Methods S.) (2nd Edition)

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Argyrous, G. London: Sage. This page links to a number of very useful PDF files which explain a number of procedures. A comprehensive if slightly dated guide to the programme that is written in plain English rather than that weird technical language that IT specialists delight in baffling us mere mortals with! Online Statistics: An interactive multimedia course of study. Another excellent free site. Very comprehensive and uses multimedia and online demonstrations and simulations to clearly illustrate difficult concepts.

Includes small self test exercises which are valuable. Fox, J. Applied regression analysis and generalised linear models 2nd ed. Those of you who are more comfortable with formulae will find this book to be a comprehensive introduction to regression. Pampel, F. Logistic regression: A primer.

Discovering Statistics Using R

Thousand Oaks: Sage publications. An excellent introduction giving detailed explanation of the concepts involved in logistic regression can be found in Pampel This small book is the best resource if you really want to understand all the fundamentals of logistic regression. It has an excellent Appendix on logarithms which is the best explanation I have seen. Applying regression and correlation. Logistic Regression Module Master of applied Statistics.


If you select the tick-box labelled Means and standard deviations then SPSS will produce the mean and standard deviation of all of the variables selected for analysis. The dispersion in a distribution 1. First, we can exclude cases listwise, which means that if a case has a missing value for any variable, then they are excluded from the whole analysis. Some important information about straight lines eD 7. To prove that this is the case, the data file pbcorr. Online Available online.

Point and interval estimation of treatment effect. Correlation Introduction to correlation. The concept of cause and effect. Partial correlation. Linear Regression Model building. Model concepts and least squares estimation.

Discovering Statistics Using SPSS (Introducing Statistical Methods S.) (2nd Edition)

Statistical validity checks. Practical validity of model fit. Diagnostic checking. Beyond linear modelling. The module will be presented using a range of teaching and learning strategies. The learning strategies will build upon the students' maturity and their ability to reflect on their own learning experiences. Students will also be advised to consolidate their knowledge of the content via directed texts and articles, both paper based and electronic.

Appropriate formative exercises will be used to consolidate the students' skills in terms of both presentation and analysis of results. This approach will provide the student with the opportunity to experience how a software package aids quantitative data presentation and analysis within research and to have feedback on their development of quantitative data analysis skills.

Bryman, A. Byrne, D. Fielding, J. Understanding Social Statistics. Our researcher predicted that 1 as anxiety increases, exam performance will decrease, and 2 as the time spent revising increases, exam performance will increase. Both of these are directional hypotheses, so both tests are one-tailed. To ensure that the output displays the one-tailed significance values select and then click on to run the analysis.

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Buy Discovering Statistics Using SPSS (Introducing Statistical Methods S.) (2nd Edition) on ✓ FREE SHIPPING on qualified orders. Buy Discovering Statistics Using SPSS (Introducing Statistical Methods series) on The text is fully compliant with the latest release of SPSS (version 13). . Hardcover: pages; Publisher: Sage Publications Ltd; 2nd edition (April 30, ).

Correlation is significant at the 0. SPSS Output 6.

Using Statistical Regression Methods in Education Research

Underneath each correlation coefficient both the significance value of the correlation and the sample size N on which it is based are displayed. This significance value tells us that the probability of getting a correlation coef- ficient this big in a sample of people if the null hypothesis were true there was no relationship between these variables is very low close to zero in fact. Hence, we can gain confidence that there is a genuine relationship between exam performance and anxiety. Our criterion for significance is usually. In psychological terms, this all means that as anxiety about an exam increases, the per- centage mark obtained in that exam decreases.

Conversely, as the amount of time revising increases, the percentage obtained in the exam increases. So there is a complex interrelationship between the three variables.

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Using R2 for interpretation 1 Although we cannot make direct conclusions about causality from a correlation, we can take the correlation coefficient a step further by squaring it. The correlation coefficient squared known as the coefficient of determination, R2 is a measure of the amount of vari- ability in one variable that is shared by the other. For example, we may look at the relation- ship between exam anxiety and exam performance.

Exam performances vary from person to person because of any number of factors different ability, different levels of preparation and so on. If we add up all of this variability rather like when we calculated the sum of squares in section 2.

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We can then use R2 to tell us how much of this variability is shared by exam anxiety. These two variables had a correlation of 0. This value tells us how much of the variability in exam perform- ance is shared by exam anxiety. If we convert this value into a percentage multiply by we can say that exam anxi- ety shares So, although exam anxiety was highly correlated with exam performance, it can account for only To put this value into perspective, this leaves I should note at this point that although R2 is an extremely useful measure of the substantive importance of an effect, it cannot be used to infer causal relationships.

So, although exam anxiety can account for I was born in England, which has some bizarre traditions. Each year locals are encouraged to attempt to tell the biggest not parametric? Over the years there have been tales of mermaid farms, giant moles, and farting sheep blowing holes in the ozone layer.

I am thinking of entering next year and reading out some sections of this book. Imagine I wanted to test a theory that more creative people will be able to create taller tales. I gathered together 68 past contestants from this com- petition and asked them where they were placed in the competition first, second, third, etc. The position in the competition is an ordinal variable see section 1. The data for this study are in the file The Biggest Liar.

For the Position variable, each of the categories described above has been coded with a numerical value. First place has been coded with the value 1, with positions being labelled 2, 3 and so on. Note that for each numeric code I have provided a value label just like we did for coding variables. I have also set the Measure property of this variable to.

The procedure for doing a Spearman correlation is the same as for a Pearson correlation except that in the Bivariate Correlations dialog box Figure 6.


At this stage, you should also specify whether you require a one- or two-tailed test. I predicted that more creative people would tell better lies. This hypothesis is directional and so a one-tailed test should be selected. The output is very similar to that of the Pearson correlation: a matrix is displayed giving the correlation coefficient between the two variables.

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Note that the relation- ship is negative: as creativity increased, position decreased. This might seem contrary to what we predicted until you remember that a low number means that you did well in the competition a low number such as 1 means you came first, and a high number like 4 means you came fourth.