HAPPY NEW YEAR Welcome back To let you

HAPPY NEW YEAR Welcome back

To let you know… • Every 2 weeks: mini mock exam (Every other Monday) • Revision session held Tuesday/Wednesday timetable will be given to you in the coming weeks. • Update on the website: • exam practice section. (target: to complete exam questions) • Research methods mini handbook and workbook

Plan for the coming weeks • 3 rd January: Levels of measurement • Thursday 5 th: Mock feedback • Monday 9 th: RM - picking the right test • Tuesday 10 th: RM – practice with questions. • Friday 12 th: RM • 3 rd week: Schizophrenia

Today • Hand out homework (5 th due in 16 th) Still need to complete independent study. • New homework cards • What we are going on to • Check homework (features of science)

A 2: what you have covered so far • Reliability across all methods of investigation. Ways of assessing reliability: test-retest and interobserver; improving reliability. • Types of validity across all methods of investigation: face validity, concurrent validity, ecological validity and temporal validity. Assessment of validity. Improving validity. • Content analysis • Features of science: objectivity and the empirical method; replicability and falsifiability; theory construction and hypothesis testing; paradigms and paradigm shifts. • Reporting psychological investigations. Sections of a scientific report: abstract, introduction, method, results, discussion and referencing. • Probability and significance: use of statistical tables and critical values in interpretation of significance; Type I and Type II errors.

A 2: what you still have to cover Inferential testing • Introduction to statistical testing; the sign test. • Factors affecting the choice of statistical test, including level of measurement and experimental design. When to use the following tests: Spearman’s rho, Pearson’s r, Wilcoxon, Mann-Whitney, related t-test, unrelated t-test and Chi-Squared test.

Today Introduction to inferential statistics Null hypothesis Level of significance Probability Type 1 and type 2 errors Carrying out statistical tests Picking the right test Writing up the report

RECAP: Quiz 1. Outline the difference between descriptive statistics and inferential statistics? 1. The null hypothesis predicts that there will be a significant difference? True/false. 1. Shorthand for the null hypothesis is Ho? True/false 1. What are Inferential statistics? 1. Why is it necessary to have a Null hypothesis?

6. If the null hypothesis is retained, this means that the result is…. . 7. What is the chosen p value also known as? 8. When does a type one error occur? 9. When does a type 2 error occur? 10. Which p value would you use if you were conducting a piece of research that is socially sensitive?

1. Outline the difference between descriptive statistics and inferential statistics? Summarising data vs. allowing you to see whether the research hypothesis or null hypothesis is retained 2. The null hypothesis predicts that there will be a significant difference? True/false. False 3. Shorthand for the null hypothesis is Ho? True/false True 4. What are Inferential statistics? Tests designed to assess whether we reject or retain the null hypothesis. 5. Why is it necessary to have a Null? Eliminates bias. Forces researcher to accept the view that the two sets of data has occurred through chance. Means there is no other conclusions that can be made

6. If the null hypothesis is retained, this means that the result is not significant. 7. What is the chosen p value also known as? Level of significance 8. When does a type one error occur? reject the null hypothesis and accept the hypothesis 9. When does a type 2 error occur? retain the null hypothesis even thought the hypothesis is correct 10. Which p value would you use if you were conducting a piece of research that is socially sensitive? 1% (P=0. 01) – there is a 1% chance that we wrongly reject the null hypothesis.

LEVELS OF MEASUREMENT Type of data

Why do I need to know about the levels of measurement? • most appropriate descriptive statistic to calculate • which graph to use • which inferential test to use Levels of measurement relate to quantitative data.

Measuring levels of measurement Activity

Levels of measurement Nominal (category) data This is the most simplest method of classifying information. Involves counting frequency data We must only be able to place each item/person into one category Only classifies each person as ‘tall’ or ‘short’; no distinction at all between ‘tall’ people.

Levels of measurement Ordinal data: This level of measurement involves ranking data into place order, with rating scales often being used to achieve this. These intervals cannot be considered equal They do not tell us about distances between positions We convert raw scores to ranks for statistical testing (1 st , 2 nd, 3 rd) and it is the ranks and not the scores that are used in the calculations.

Levels of measurement Interval data: Standardised measurements units like time, weight and temperature are interval data Most informative and accurate form of measurement Increments on the scale can be measured, and they are equidistant. Tell us how many intervals on the scale each person is from anyone else.

Activity A. Colour code, which statements below refer to each level of measurement B. Which level of measurement: Identify whether the data in each statement is nominal, ordinal or interval. C. Complete activity C and D. Levels of measurement in dogs

Activity How confident do you feel about the levels of measurement? Put your hand in the air showing… 5 fingers if you feel really confident 3 fingers if you feel okay about the topic 0 fingers if you do not feel confident.

CHOOSING THE RIGHT TEST

Statistical tests The seven tests that you can be asked about (but won’t have to calculate) in the exam: Chi-squared (χ2) Wilcoxon T Mann-Whitney U Spearman’s Rho Unrelated t-test Related t-test Pearson’s r How do you know which test to use? You need to learn the following criteria, but there is no need for you to understand why this is the case

Criteria a. Difference or correlation? -Whether the researcher is testing for differences between groups (i. e. an experiment) or a correlation between two co-variables b. Level of measurement (nominal, ordinal, interval and ratio) c. In a test of difference, whether the experimental design is an independent groups, repeated measures or matched pairs

A A test of difference or a correlation? Laboratory experiments, field experiments, natural and quasi experiments are all testing for differences between groups. The researcher is trying to establish the probability that changes in the DV are caused by the experimental manipulation or naturally occurring IV. Correlational research is attempting to show two co-variables are linked. In this context correlation can include correlational analysis as well as investigations that are looking for an association. So look for the word correlation or association in the hypothesis for a correlation.

B Level of Measurement (type of data)- is the data nominal, ordinal or interval/ratio?

C The Experimental Design An independent groups design uses different participants in each group, whereas the repeated measures design uses the same participants in each group. For mathematical reasons, these tests treat the repeated measures and match-pairs design as the same category.