1. I can analyse quantitative data and represent is graphically

2. Today’s objectives  Distinguish between quantitative and qualitative data;  Understand what is meant by ‘measures of central tendency’ & ‘measures of dispersion’ and how to apply these techniques to data;  Draw and interpret Bar Charts & Histograms.

3. Types of data  There are two types of data that a researcher can collect; qualitative data & quantitative data;  Quantitative data is…  Qualitative data is… Numbers Words What research method mostly produces quantitative data?

4. Quantitative Data  Once we have the quantitative data (numbers) measures of central tendency are used to analyse it…

5. Measures of Central Tendency  Tells the researcher which value is most typical for our set of data;  There are 3 measures of central tendency… Mean Median Most

6. Measures of Central Tendency Definition: How is it calculated? MeanThe most frequently occurring score. Median ‘Average’ – Calculated by adding up all the scores and then dividing by the number of scores. Mode ‘Middle score’- calculated by putting all scores in order then picking the middle score. Task 1: Match the definition above, with the measure of central tendency (mean, median and mode).

7. Measures of Central Tendency Definition: How is it calculated? Mean ‘Average’ – Calculated by adding up all the scores and then dividing by the number of scores. Median ‘Middle score’- calculated by putting all scores in order then picking the middle score. ModeThe most frequently occurring score. Task 1: Match the definition above, with the measure of central tendency (mean, median and mode).

8. Common Exam Question Mean, Median or Mode Which measure of central tendency is best…

9. Common Exam Question Question: If your data set contains outliers (extreme scores) what measure(s) of central tendency should you use? Why?  Median or mode as the outliers will not distort them. Outliers (extreme scores) can distort the mean.

10. Robinson (2014) Because we don’t need Loftus (1979) anymore…

11. Data Collection  Now we will collect some quantitative data which you will analyse using measures of central tendency.

12. Robinson (2014) Experiment  I am carrying out a study where I can’t gain your fully informed consent as I do not want to give away what the experiment is on. What ethical guideline am I breaking?  Do I still have your consent to take part?  You have the right to withdraw at any point.

13. Robinson (2014) – Experimental Design  I am going to use an independent groups design, so what am I going to do with my participants?

14. Group 1  I will show you a photo of a man, you have 10 seconds to look at it.

16. Group 2  I will show you a photo of a man, you have 10 seconds to look at it.

18. Questions… Task: On your own and in silence, complete the following seven questions, in relation to the picture you saw… 1. What colour hair did he have? 2. What was the colour of his shirt? 3. What was the colour of his tie? 4. What was the colour of his gloves? 5. What were the colour of his eyes? 6. Was he smiling? 7. Did he have a side or central hair parting?

19. Answers…

20. Answers Task: On your own and in silence, complete the following seven questions, in relation to the picture you saw… 1. What colour hair did he have? - Blonde 2. What was the colour of his shirt? - White 3. What was the colour of his tie? - Black 4. What was the colour of his gloves? – Black 5. What were the colour of his eyes? - Brown 6. Was he smiling? - No 7. Did he have a side or central hair parting? - Side

21. Data Collection We will now collect in the data from my experiment…  What was my independent variable (IV)?  What was my dependent variable (DV)?

22. Measures of Central Tendency 1. Calculate the Mean, Median and Mode on both sets of data (group 1 and group 2); 2. Identify any outliers in the data; 3. What measure of central tendency would you use to analyse this set of data? Why? 4. What conclusion can you draw from the data? Extension – Can you evaluate my study – were there issues in terms of my:  Methodology  Sampling  Ethical Issues

23. Measures of Central Tendency 1. Calculate the Mean, Median and Mode on both sets of data (group 1 and group 2); 2. Identify any outliers in the data; 3. What measure of central tendency would you use to analyse this set of data? Why? 4. What conclusion can you draw from the data? Extension – Can you evaluate my study – were there issues in terms of my:  Methodology  Sampling  Ethical Issues

24. Measures of Central Tendency Group 1Group 2 WeaponNo Weapon Mean Median Mode

25. Measures of Central Tendency LPSY5Group 1Group 2 WeaponNo Weapon 6051 6061 6161 6161 7162 71 Mean6.33330.66675.81.2 Median6161 Mode6161

26. Measures of Dispersion What does ‘dispersion’ mean?

27. Measures of dispersion  As well as wanting to know where the data typically lies, it is also important to know how much variability there is in the data. 6, 6, 6, 7, 6, 5, 6, 6, 6,6 What is the mean for these two sets of data? 12, 12, 12, 1, 1, 2, 12, 1, 1, 6 Measures of central tendency does not always paint a true picture of the data. 6

28. Measures of Dispersion  Describe the spread of data around a central value (mean);  Tells us how much variability there is in the data; You need to know two measures of dispersion  Range (subtract the lowest score from the highest scores)  Standard Deviation (SD)

29. What does the SD tell us? Large SD Small SD Zero SD There was a lot of variation around the mean All the data was closely clustered around the mean All of the data was the same 6, 6, 6, 7, 6, 5, 6, 6, 6,6 12, 12, 12, 1, 1, 2, 12, 1, 1, 6 6 6 6 6 6

30. What will the SD look like…….  Are the following examples of large, small or zero standard deviation? 26, 100, 12, 1, 8, 2, 2, 1, 1, 66 68, 66, 70, 68, 65, 68, 68, 67, 60 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 Look back at our data set, is our SD – zero, small or large? Zero SDLarge SDSmall SD

31. Graphical Representation of Data  Using Graphs and Tables

32. Presentation of Quantitative Data Question: Why do they present their data in graphs?  Having a visual representation of the data, allows us to easily see patterns in the data;  It also helps us summarise the results

33. Bar Charts  Used when data is in categories or you want to display mean scores from different groups;  Task: Draw a bar graph for the four sets of results found in our class experiment. Always - label the axes and give the graph a title!

35. Presenting our Data  Task: Create a bar chart, presenting the data we collected on the weapon focus effect. Always - label the axes and give the graph a title!

36. Histograms  Consists of vertical bars of equal width but varying height to represent the frequency of each score  Unlike bar charts, its used to present continuous data, like salary's, test scores, time, age… Bars touch each other The continuous scores should increase along the x axes

37. Homework  Complete the homework – constructing graphs handout, ready for next week’s lesson.