1. YAAAAAAY!

2. What is it? In psychology we have an interest in studying human behaviour... which requires research! Research methods (or scientific methods) involves the testing of the truth of a proposition (or idea) by using careful measurement and controlled observation One of the most popular and powerful research tools is an experiment which is a formal trial to confirm or disconfirm a hypothesis

3. Statistics Descriptive Statistics: Summarise, organise and describe important features of the data so it can be more easily interpreted eg) graps, tables, calculating the mean (average) etc. Inferential Statistics: Allow the researcher to draw conclusions, based on evidence, about the results in the study and whether they can be generalised to a wider population We will look more at this later on

5. Selection and Allocation A participant is any person or group of people used in any kind of research study, and how they are selected and allocated to groups is very important to the study The process of selecting participants for research is called sampling. The participants actually selected for the research form the sample, which is the portion or subset of the larger population of interest

6. Sample Is a group of participants selected from, and representative of the population of interest EG: - 20 students from year 12 - 10 17 year old boys There are 2 different ways you can select a sample: - Random - Stratified

7. Population Is an entire group of people or animals that belong to a particular category EG: - All Sale College students - All year 12 students - All year 12 psychology students - 15 year old boys - Under 5 feet tall girls

8. Random Sampling Means that every member of the population of interest has an equal chance of being selected for the sample to be used in the study Some ways this can be done include: - Putting names in a hat and drawing them out - Giving each member of the population a number and then choosing every 3 rd or 5 th number for the sample

9. Stratified Sampling Is where the sample contains exactly the same proportions of participants as in the population. They are divided into distinct groups (strata) and then a sample from each stratum (each group) is chosen for the sample EG: - If your sample is 100 people and you are looking at the relationship between age and intelligence in Australia; the number of people in each age category in the sample should be exactly the same of the proportion of ages in the population A Stratified Random Sample is where each member of a stratum has equal chance of being selected for the sample

10. Allocation to Groups Experimental group: the group that is exposed to the treatment (the independent variable) Control group: the group that is exposed to the controlling condition. It provides a standard against which to compare the performance of the experimental group to EG: - Chocolate before class affects performance - Experimental group: Eats a block of chocolate before class - Control Group: Doesn’t eat the chocolate

11. Random Allocation (to groups) Is where the participants basically have a 50/50 chance of being in the control group or experimental group. So they have equal chance if being in either This means that any change in behaviour most likely has something to do with the independent variable

12. Placebo and Experimenter Effects Placebo effect Occurs when a participants response is influenced by their expectations rather than by the treatment Experimenter effect Occurs when there is a chance that the participant’s response is due to the actions of the experimenter rather than the independent variable. It is possible for the experimenter to unintentionally sway the participant if they want to see a particular result Experimenter bias: happens when the person measuring the dependent variable is aware of the purpose or hypothesis and may misread the data

13. Eliminating the Placebo Effect Single-blind procedure Is where the participants do not know whether they are in the control group or the experimental group

14. Eliminating the Experimenter Effect Double-blind procedure Is when neither the participant or experimenter knows which group the participant has been allocated to. In this case the person collecting the data is usually not the experimenter

16. Experimental Design Is one of the most rigorous and controlled methods used in psychology Is used to test the cause-effect relationship between variables

17. Variables Any event, state or condition that can be varied to see what the outcome is Independent variable (IV): the condition that is manipulated to see what the effect on the dependent variable will be Dependent variable (DV): is affected by the IV EG: - IV: Whether chocolate is eaten before class - DV: Behaviour/ability to work by the students

18. Extraneous Variables Is an variable other than the IV that might affect the DV EG: - Some of the children in the class had no sleep last night because of weather disturbances - Other examples include temperament, attitudes, mood, motivation The experimenter should ensure that extraneous variables are eliminated from the experiment by pre empting them

19. Confounding Variables When an experimenter cannot be sure whether changes in the dependent variable were caused by the independent variable or an uncontrolled variable, and the effects of the uncontrolled variable are confused with the effects of the independent variable, it is known as a confounding variable It is basically a second, unintended independent variable - Extraneous variables unaccounted for lead to confounding variables

20. Minimising effects of extraneous variables There are different experimental designs that minimise the effects of extraneous variables, these are: Repeated measures design Matched participants design Independent groups design - Describe the differences between these 3 experimental designs, what are the pros and cons of each (table)?

21. Hypothesis Is a testable prediction of the relationship between two or more characteristics of events – its basically an educated guess about what the outcome of an experiment will be EG: - Eating chocolate before class will have a negative impact on a child’s ability to concentrate during class

22. Operational Hypothesis Is a defined and precise prediction oh how each variable is measured and the effect it is expected to have on behaviour. It states how the variables will be manipulated and measured, as well as the population from which the sample has been drawn Operational definitions: Is where an experimenter defines all the terms and subject matter being measured by describing precisely how they are going to measure it - See page 334 of text for examples

23. Null Hypothesis States that there is NO relationship between the variables In this case the experimenter is usually expecting the hypothesis to be disproved EG: - It is predicted that there will be no relationship between the amount of sugar consumes before class and ability to concentrate on a task

24. Non-directional hypothesis If an researcher is unsure of the direction of the relationship between variables they will propose a non-directional hypothesis (two-tailed) EG: - It is predicted that there is a relationship between the amount of chocolate consumed before class and ability to concentrate on a task

25. Directional Hypothesis Is used when researcher is confident of the direction of the relationship (one-tailed) they will propose a directional hypothesis EG: - It is predicted that the consumption of chocolate (1 100 bar or more) of chocolate will decrease the students ability to concentrate on a task

26. Descriptive Statistics Includes graphs, tables, tables and calculations of mean median, mode and correlation Task: Read pages 335-343 - What are correlations and how is the strength of a correlation measured? - What are distributions and what do they tell us about the data? - Define central tendency and its measures: mean, median, mode, standard deviation

27. Central Tendency A measure of central tendency is a number that describes a typical score around which other scores fall It is a measure of the middle, or average of a data set

28. Mean Is the average, the sum of all the number divided by the total number of numbers EG: - 4, 5, 6, 7, 2, 1, 10 - The sum of all these is 35 - Divided by 7 = 5 - Mean = 5

29. Median Is the number that falls in the middle of the data set To get the median you arrange the data from lowest to highest value, the value in the middle is the median EG: - 78, 95, 97, 101, 110, 127, 129 - 101 is the median as there are 3 values above and 3 below it - If there are even numbers, eg: - 78, 95, 97, 101, 110, 127, 129, 131 - Then you would add 101 and 110 and divide by 2 to get the average

30. Mode The mode is the most frequently occurring value EG: - 1, 3, 4, 4, 6, 4, 7, 8, 7, 1, 4 - The mode is 4

31. Variability Refers to how spread out the scores (data) are Range: is the difference between the highest and lowest score – calculated by subtracting the lowest value from the highest value Standard deviation: describes how much a score differs from the mean

32. Correlation Correlation allows us to identify the relationship between two variables – it indicates the extent to which two variables are related, but NOT that one causes the other The strength of a correlation is measured by a correlation coefficient which is a score between -1 and +1 The closer to +1 or -1 the score is the greater the strength of the correlation (relationship)

33. No Correlation There is no relationship between the x and y axis

34. Positive Correlation As one variable increases the other variable increases

35. Negative Correlation When one variable increases the other variable decreases

36. Distribution When graphed, the distribution of data often shows a pattern: Normal distribution: most scores fall in the middle Positively skewed distribution: there are lots of low scores and few high scores Negatively skewed distribution: there are lots of high scores and few low scores

37. Inferential Statistics Allow us to draw conclusions, and to generalise findings about samples to the broader research population Task: Read pages 344-345 - What is a conclusion? - What are generalisations? - What does statistical significance refer to and how is it measured?

38. P- value P = probability, which refers to the likelihood of an event occurring Statistical significance (which is measured using the p-value), gives an estimate of how often results might have occurred through chance alone The results of this significance test are stated as a probability, known as a p-value The scale of the p-value ranges from 0-1

39. P-value An result that could have occurred through chance 5 times or less out of 100 (5%) is considered significant - P < 0.05 In some trials (mainly involving drugs) the p-value needs to be much lower eg) p < 0.01 or less - Look at summary of p-values on page 345 of text

40. Conclusions and Generalisations A conclusion is a decision or judgement about what the results from the research mean eg) hypothesis supported or not, extraneous or confounding variables A generalisation is a decision or judgement about how widely the findings of the study can be applied – especially to other members of the population – sampling techniques must be considered, as well as extraneous and confounding variables

41. Ethics in psychological research Define the following: - The role of the researcher - Participant’s rights - Confidentiality - Voluntary participation - Informed consent - Withdrawal rights - Deception - Debriefing - Professional conduct