Research methods Recap last session 1 Outline the

Research methods

Recap: last session 1. Outline the difference between descriptive statistics and inferential statistics? 2. The null hypothesis predicts that there will be a significant difference? True/false. 3. Shorthand for the null hypothesis is Ho? True/false 4. What are Inferential statistics? 5. Why are Levels of measurement important? 6. Ordinal data is data that is measured on a scale? True/false 7. Why is it necessary to have a Null hypothesis?

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 1. The null hypothesis predicts that there will be a significant difference? True/false. False 1. Shorthand for the null hypothesis is Ho? True/false True 1. What are Inferential statistics? Tests designed to assess whether we reject or retain the null hypothesis. 1. Why are Levels of measurement important? To know which is the most appropriate descriptive statistic to calculate, which graph to use and which inferential test to use we need to establish what the level of measurement is. 1. Ordinal data is data that is measured on a scale? True/false False 1. 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

The Null hypothesis is usually stated as : There will be no difference between X and Y or any difference will be due to chance effects.

A team of psychologists was interested in studying the effects of alcohol on peoples' reaction times. Earlier research suggested that an increase in reaction time was due to the alcohol rather than peoples' expectations of alcohol. The psychologists recruited two groups of volunteers (an independent groups design) from a local university. Each participant's reaction time was measured by using a computer game. The participants were then given a drink. The first group received a drink containing a large measure of strong alcohol; the second group received an identical drink without alcohol, but with a strong alcoholic smell. Finally, all participants were required to play the computer game again to assess their reaction time. Once they had completed the task, they were then thanked for their time and allowed to leave. What is the IV? whether the participants have had an alcoholic drink or one that is not alcoholic but smells as if it is What is the DV? reaction times on a computer game Null hypothesis: There will be no difference between the university students‘ reaction times on a computer game between those who have had an alcoholic drink or one that is not alcoholic but smells as if it contains alcohol; any differences are due to chance factors.

A teacher in a small secondary school wanted to find out whethere was any truth in her idea that students who used a computer regularly for their homework achieved higher exam grades than those who did not. She decided to interview a sample of 30 students taken from across the school. She taperecorded all the interviews. She later obtained their end of year exam grades from their reports. What is the IV? whether the participants used a computer regularly for their homework or didn’t use a computer regularly for their homework. What is the DV? Exam grade achieved Null hypothesis: There will be no difference between the exam grades achieved at the end of year between those who regularly used a computer to complete homework and those who did not regularly use a computer to complete homework; any differences are due to chance factors. Page 8 -9 complete assessment 8 a and 8 b

Have you got a coin?

Probability and chance • Read page 9 • Answer the following 1. When analysing data what can we never be certain of? 2. What do statistical tests do? 3. If results are judged to be caused by a genuine effect what are they called? 4. Inferential tests therefore allow us to…. . 5. In many experiments we are looking at a significant difference within our results. How is a correlation different?

Probability and chance • • 1. 2. 3. 4. 5. Read page 9 Answer the following When analysing data what can we never be certain of? That the conclusion in true What do statistical tests do? Calculate the statistical probability of our results occurring through chance alone so that we can decide whether to accept/reject our null hypothesis. If results are judged to be caused by a genuine effect what are they called? significant Inferential tests therefore allow us to…. . Reject or retain the null hypothesis In many experiments we are looking at a significant difference within our results. How is a correlation different? The null hypothesis would state that there is no correlation and that any relationship is due to chance factors

Inferential statistics • Inferential statistics are used to test hypothesis. – Do groups differ on some outcome variable? – Is the difference more than expected by chance • Used to make generalisations from a sample to a population. • Inferential statistics take into account sampling error (chance, random error)

P value The reason for calculating an inferential statistic is to get a p value (p = probability) Inferential statistical tests work by assessing the probability of our results occurring due to chance alone (rather than the IV) The p value determines whether or not we reject the null hypothesis. We use it to estimate whether or not we think the null hypothesis is true. The p value provides an estimate of how often we would get the obtained result by chance, if in fact the null hypothesis were true. If the p value is small, reject the null hypothesis and accept that the samples are truly different with regard to the outcome. If the p value is large, accept the null hypothesis and conclude that the treatment or the predictor variable had no effect on the outcome.

Decision rules – Levels of significance How small is "small? “ Once we get the p value (probability) for an inferential statistic, we need to make a decision. Do we accept or reject the null hypothesis? What p value should we use as a cutoff? The one chosen is called the level of significance.

Levels of significance Researchers can use significance levels of 10%, 5%, 1% (or 0. 1% in very stringent conditions) - expressed as: 10%, 0. 10, 1 in 10, p≤ 0. 10. 5%, 0. 05, 1 in 20, p≤ 0. 05 1%, 0. 01, 1 in 100, p≤ 0. 01 If you use a 5% statistical significance level and this is achieved you are saying that the probability of your results being a fluke and nothing to do with your IV is less than 5%. or you are 95% sure that your change in DV is because of your IV

Using the 0. 05 level of significance means if the null hypothesis is true, we would get our result 5 times out of 100 (or 1 out of 20). We take the risk that our study is not one of those 5 out of 100. When you use a computer program to calculate an inferential statistic (such as a t-test, Chi-square, correlation), the results will show an exact p value (e. g. , p =. 013).

Task for homework Read page 9 -10 of booklet on significance levels. and complete Task 8 on pages 10 -11

Which type of inferential test should be used?

• Depends on: 1) whether the researcher is testing for differences between groups (experiment) or a correlation between two co-variables. 1) Level of measurement (nominal, ordinal interval) 1) Experimental design (independent groups, matched pairs, repeated measures)

Test of difference or correlation Nominal or at least ordinal level data? At ordinal level Independent groups design Spearman’s Rho Chi square Repeated measures/matched pairs or independent groups design? Wilcoxon T Mann-Whitney U Test

Chi-Squared = nominal data = independent groups design Mann-Whitney U Test. Independent groups Design

Wilcoxon T Test Repeated measures. At least ordinal (1 st in the race)

Activity • Have a go at trying to draw the diagram yourself. • Complete pages 13 and 14 of your booklet

And Finally… • Answers Name of test Difference or correlation Level of measurement Experimental design Chi-Square Difference (or association) Nominal (presented in a 2 x 2 contingency table) Independent groups Wilcoxon Difference At least Ordinal Repeated Measures (or matched Pairs) Mann-Whitney Difference At least Ordinal Independent groups Spearman’s Correlation At least Ordinal N/A

Now you need to justify each test Fill in the gaps • The Spearman’s Rho was used because the data can be treated as at least 1)________ and the researchers were studying a possible 2)_________ between two co-variables 1 = Ordinal 2 = Correlation (or relationship)

Now you need to justify each test Fill in the gaps • The Chi-Square test was used because the data can be treated as 1)________ and the researches had hypothesised that there will be 2)__________ between conditions when using the 3) _____________ design. 1 = Nominal 2 = a difference 3 = Independent groups (please note that the Chi-square is also used as a test of association)

Now you need to justify each test Fill in the gaps • The Wilcoxon T test was used because the data can be treated as 1)________ and the researches had hypothesised that there will be 2)__________ between conditions when using the 3) _____________ design. 1 = ordinal 2 = a difference 3 = Repeated Measures (please note that the Wilcoxon T is also used for a matchedpairs design)

Now you need to justify each test Fill in the gaps • The Mann-Whitney U test was used because the data can be treated as 1)________ and the researches had hypothesised that there will be 2)__________ between conditions when using the 3) _____________ design. 1 = ordinal 2 = a difference 3 = independent groups