Psychology Statistics for Dummies

The quick, easy way to master all the statistics you'll ever need The bad news first: if you want a psychology degree you'll need to know statistics. Now for the good news: Psychology Statistics For Dummies. Featuring jargon-free explanations, step-by-step instructions and dozens of real-life examples, Psychology Statistics For Dummies makes the knotty world of statistics a lot less baffling. Rather than padding the text with concepts and procedures irrelevant to the task, the authors focus only on the statistics psychology students need to know. As an alternative to typical, lead-heavy statistics texts or supplements to assigned course reading, this is one book psychology students won't want to be without. Ease into statistics – start out with an introduction to how statistics are used by psychologists, including the types of variables they use and how they measure them Get your feet wet – quickly learn the basics of descriptive statistics, such as central tendency and measures of dispersion, along with common ways of graphically depicting information Meet your new best friend – learn the ins and outs of SPSS, the most popular statistics software package among psychology students, including how to input, manipulate and analyse data Analyse this – get up to speed on statistical analysis core concepts, such as probability and inference, hypothesis testing, distributions, Z-scores and effect sizes Correlate that – get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more Analyse by inference – master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs Open the book and find: Ways to describe statistical data How to use SPSS statistical software Probability theory and statistical inference Descriptive statistics basics How to test hypotheses Correlations and other relationships between variables Core concepts in statistical analysis for psychology Analysing research designs Learn to: Use SPSS to analyse data Master statistical methods and procedures using psychology-based explanations and examples Create better reports Identify key concepts and pass your course

The effects and side-effects of statistics education: Psychology students’ (mis-)conceptions of probability

In three studies we looked at two typical misconceptions of probability: the representativeness heuristic, and the equiprobability bias. The literature on statistics education predicts that some typical errors and biases (e.g., the equiprobability bias) increase with education, whereas others decrease. This is in contrast with reasoning theorists’ prediction who propose that education reduces misconceptions in general. They also predict that students with higher cognitive ability and higher need for cognition are less susceptible to biases. In Experiments 1 and 2 we found that the equiprobability bias increased with statistics education, and it was negatively correlated with students’ cognitive abilities. The representativeness heuristic was mostly unaffected by education, and it was also unrelated to cognitive abilities. In Experiment 3 we demonstrated through an instruction manipulation (by asking participants to think logically vs. rely on their intuitions) that the reason for these differences was that these biases originated in different cognitive processes.

Stress, Positive Psychology and the National Student Survey

The aim was to explore the predictive ability of sources of stress and a range of dispositional and coping behaviours on student satisfaction and motivation. Most research exploring sources of stress and coping in students construes stress as psychological distress, with little attempt to consider positive experiences of stress. A questionnaire was administered to 120 first-year UK psychology students. Questions were asked which measured sources of stress when rated as likely to contribute to distress (a hassle) and likely to help one achieve (an uplift). The sources of stress were amended from the UK National Student Survey (NSS, 2011). Support, control, self-efficacy, personality and coping style were also measured, along with their potential affect on
course satisfaction, motivation and feeling part of a learning community. The sources of stress likely to lead to distress were more often significant than sources of stress likely to lead to positive, eustress states. Ironically,
factors one would consider would help students, such as the university support facilities, only did so when rated as a hassle, not as an uplift. Published university league tables draw heavily on student course satisfaction but this negatively correlated with intellectual motivation and feeling part of a learning
community. This suggests course satisfaction alone reveals an incomplete picture of the student experience. Course educators need to consider how course experiences contribute not just to potential distress but to
potential eustress. Teaching quality, effective support and work-life balance are key to student satisfaction and motivation. How educators interact with their students and the opportunities they create in and outside the class to promote peer support are likely to enhance satisfaction and motivation.