Design and Data Analysis in Psychology I 2010-11

1. Design and Data Analysis in Psychology I2010-11 School of PsychologyDpt. Experimental Psychology Mila Sánchez Martín

2. Course presentation • Course teachers: • Vicente Manzano Arrondo • Hassan Fazeli Khalili • Mª Angeles Arias Velarde • Milagrosa Sánchez Martín The idea isthatwe can understandus!!

3. Books

4. Diseño y Análisis de Datos en Psicología I DOCUMENTO MONOGRAFICO I SUMATORIOS

5. Diseño y Análisis de Datos en Psicología I DOCUMENTO MONOGRAFICO II CÓMO DESPEJAR UNA VARIABLE

6. Diseño y Análisis de Datos en Psicología I DOCUMENTO MONOGRAFICO III FORMULARIO

7. Class schedule

8. Course objectives • Establish the essential foundations to enable the student of Psychology to conduct the initial data analysis coming from psychological studies. • In the early chapters we develop the concepts of data description, initial steps of any statistical study: the collection, tabulations and graphical presentations of data, and the achievement of its fundamental characteristics. It's known as descriptive analysis. • In the second set of chapters we propose a group of concepts and procedures with the objective that the student understands the logical development of what is known as inferential statistics (estimation theory and statistical decision). These contents are basic for the resolution of any research problem by sampling.

9. Chapter 1: fundamental concepts Role of data analysis in Psychology as a science and professional practice. • General concepts: - Statistical population. - Sample. - Statistical. - Parameter. • Variables and their clasification: - Nominal or qualitative variable. - Ordinal variable. - Discrete and continuous quantitative variable.

10. Chapter 2: Frequency distribution and graphic representation • Introduction. • Frequency distributions. • Graphic representations: • Qualitative variables • Quantitative variables. • Properties of frequency distributions.

11. Chapter 3: Basic statistical characteristics I • Central tendency measures: - Arithmetic mean. - Median. - Mode. • Comparison between central tendency measures. • Position measures based on quantiles.

12. Chapter 4: Basic statistical characteristics II • Measures of variation or dispersion (justification and adequacy): - [total] range. - Semi-interquartile range. - Variance and standard deviation. • Other measures of variation : - Pearson’s coefficient. - Quasi-variance. • Measures of Skewness and Kurtosis (justification): - Skewness. - Kurtosis or "peakedness" .

13. Chapter 5: Zscores and the normal curve • Concept, implications and justification. • Raw scores, differentialsand standard scores (or z scores). • Properties of these scores. • Normal curve. • Standard normal curve. • Some applications.

14. Chapter 6: Inferential statistics I • Basic diagram of the dynamic relationship between sample and population. • Sampling error and statistical reliability. • Sampling distribution: concept and empirical construction. • Concept, uses and implications of the mathematical expectation, bias and standard error. • Some sampling distributions: - Sampling distribution of the mean. - Sampling distribution of the proportions.

15. Chapter 7: Inferential statistics II • Introduction. • Punctual estimation: concept and consequences. • Interval estimation: accuracy, accuracy error and risk. • Probability interval. • Confidence interval. • Estimation of means and proportions. • Calculate the sample size for estimating means and proportions.

16. Chapter 8: Inferential statistics III • Introduction. • State and significance of the null hypothesis. • Alpha risk: concept, associated concepts, decision about their amount and timing for the decision of alpha. • Statistical decision based on standardized distances. • Some cases based on normality assumption: comparison of means and proportions.

17. Chapter 9: Covariation for nominal variables • Comparison of two observed proportions in independent and dependent groups. • Contingency table as a bivariable frequency distribution: expected frequencies, residuals and standardized residuals. • Chi-square as a measure of independence: Pearson test and distribution of associated probability. • Calculation, interpretation and limitations. 17

18. 3 blocks of content: • Block 1: capters 2, 3 and 4 • Block 2: chapters 5 and 6 • Block 3: chapters 7, 8 and 9 (if it’s possible) 18

19. Evaluation system • Written final exam : 0-8 pts. • Paper in small group (optional): 0-2 pts. I will commet the details in practice hour • Voluntary testing (mid semester): 1 pt. –is added to the final mark once required tests are passed-. • Only includes Block 1 (chapters 2, 3 and 4) • To pass the course: minimum of 5 pts. in the final mark (sum of final exam and paper in small group); it’s essential to obtain at least 4 pts. in the written final exam. • On the day of the exam the student will have a standard form, which will include the necessary statistical tables, and a calculator.

20. Classroom methodology (I) • Big group (BG): • Master classes • Approach problems and issues • It includes all registered • Medium group (MG): • Solve case studies • Practice with computer • It includes half of the class • A1: since Armesto Luque to Lopez Cabrera (inc) • A2: since Lopez Navarro to Vazquez Naharro (inc) • Small group (SG): Details in practice hour • Paper in small group (10 persons aprox.) • Presentation of papers for each chapter in writing and in person (every 2 weeks aprox.) • Presentation of assignments in class for each block (3 sessions) • In theory it includes quarter of the class every two weeks • Proposition: half of the class (A1 and A2 complete) every weeks. Last hour reserved for exposure hours and doubts.

21. Classroom methodology (II) • Theory and practice at the same time (more or less) • When we finish chapter’s practice: next week, exercise delivery (SG) • When we finish one block of contents: exposure

22. Schedule (approx.) • Presentation of Block I SG (2h): 1st week April • Presentation of Block II SG (2h): 3rd week May • Presentation of Block III SG (2h): last week • Voluntary testing: last week April

23. Guidelines to study the subject • Attending class • Active approach: study from the first class • To do the exercises by comprehensive learning

24. Chapter 1 General concepts

25. Introduction • What does a subject course like this represent in a degree course like this? • I'll be a psychologist : • Generator of knowledge (researcher) • Consumer of knowledge generated by others (clinical psychologist, human resources, school, ...) • A good psychologist can’t perform their profession properly without the necessary knowledge to make investigations or to consume comprehensively those made by other researchers. • No matter what we do as a psychologist in the future.If we take our work with professionalism, we will be almost continuously collecting data, analyzing and drawing conclusions.

26. Data analysis

27. General concepts 1. Population 2. Sample 3. Parameter 4. Statistics 5. Subjects 6. Variables

28. 1. Statistical population (N) • Set of all elements (people, animals, things ...) that have one or more common characteristic or property: • Freshman Psychology degree during 2010/11 • Each student would be an element of the population • You could consider: size, age, IQ, etc. • Dogs in foster in Seville • Apartments for sale in Murcia • Each element of the population: individual, subject or case • Finite or infinite population • The usual practice is to work with finite populations, but if the number of elements is large it is considered as infinite.

29. 2. Statistical sample (n) • Subset of a population • Requirements of a sample to draw conclusions (inferences) in the population: • Representative of the population (true reflection) • Appropriate method of elements selection (that all elements could have been chosen as sample items) • Number of elements big enough • Big sample (n≥30) and small (n<30) –only with educational purposes-

30. 3. Statistical population • Values that determine the descriptive properties of a population • Not usually known: • N usually numerous: not profitable to work with them (excepc. CI) • Are changing (average weight of Spanish) • Using samples, through their properties, estimate the population • Proportion ( ) -pi-, standard deviation ( σ ) -sigma-, mean ( μ ) -mu-, etc.  •  • 2   •   • 

31. 4. Statistics • Descriptive properties of a sample • Although influenced by errors of different types, are used to determine the approximate value of the parameters Mdn Mo S S2 p MEDIAN MODE MEAN STANDARD DEVIATION VARIANCE PROPORTION

32. 5. Subjects • Don’t have the same features in the same way nor in the same amount • Height: 1.50, 1.85, 1.63, etc. - People - Animals - Things - Numbers - Individuals - Subjects - Cases - Participants

33. 6. Variables • A set of differentvalues • A constant has only one value • Height, marital status, size, etc. • Can bemeasuredwithstatisticaltechniques • There is a classification of variables according to the type of mathematical operations that we are allowed to do with the assigned numbers (Stevens’ classification)

34. Stevens’ classification • Nominal scale: nominal variable • Ordinal scale: ordinal variable • Interval scale: quantitative variable • Ratio measurement: quantitative variable

35. Nominal variable • Do not take numerical values, as they describe qualities • We can assign numbers • Measurement level allows us to identify or distinguish between elements • Dichotomous nominal variable : • Habitat: rural - urban. • Answer to an item: True – False. • Contraceptives: Yes – No. • Sex: Man - Woman. • Polytomous nominal variable : • Political group : PA – PP – PSOE – IU – CIU ... • Marital status: M – D – W. • Type of neurosis: hysterically Obsessive-phobic-Depressive • Smoking: Smoking - No Smoking – former smokers

36. Ordinal variable (quasi-quantitative) • One that can sort its elements, not only to distinguish them. • Used as an ordinal measurement scale. • When in an ordinal variable the differences between immediate values in order remain fairly constant, these can be treated as an interval variable. • Measurement level allows us to: differentiate between values –nominal scale- and order (higher, lower or equal) • Examples: • Social Class: Low - Medium - High. • Satisfaction: High - Medium - Low. • Level of agreement: Do not agree ... Agree • Opinion: Disagree Total (1) ... Total agreement (5)

37. Quantitative variable • Can be measured by two types of scale: • Interval scales: for quantitative variables in that the distance between any two consecutive values is constant respect to a particular property. (5-4)=(28-27) Ratio scale: for quantitative variables in that, in addition to the above, admit the existence of absolute 0, thus establishing ratios between different values. 15/3= 5 • Distinction between scales: absolute zero (absence of the feature that measures the variable) or relative. • Discrete quantitative variable (you can only take integer values) or continuous (may be infinite number of values between two consecutive numbers). • Examples: • Income (in thousands of euros), Temperature (in degrees), Weight (in Kilos), length (in meters, cnt., mm.) Height (in meters, cnt., mm), No. of correct answers Reaction times (in milliseconds ...), Age (years, months, days), work experience (years, months ...), CI.

38. Data analysis Univariate Bivariate Multivariate

39. Variables presentationIdentify the types of variables

41. Issues

42. 1st issue • Usually in a graphical representation of bar chart for a frequency distribution of a variable shows the following disposition: • 1) Values on the vertical axis and frequency on the horizontal axis.2) Values on the vertical axis and cumulative frequency on the horizontal axis.3) Values on the horizontal axis and cumulative frequency on the vertical axis.4) Values on the horizontal axis and absolute frequency on the vertical axis.

43. 2nd issue • The mean for these data "2, 4, 6 and 8" is: • 1) 3. • 2) 4. • 3) 5. • 4) 8. • 5) Depends on the value of the variance (not given).

44. 3rd issue • What is the mode in the next set of data: 2, 4, 4, 4, 6, 8?1) 2, because it is the lowest value.2) 8, because it is the highest value.3) 4, because it is the most frequent value.4) 5, because the value is more focused.5) This data set is not a mode.

45. 4rd issue • What rates you can consider like a dispersion measure1) The median.2) The variance.3) The mean.4) Any of the above.

46. 5thd issue • If a distribution is very homogeneous, what effect can be expected in their index?1) Large standard deviation.2) Small standard deviation.3) Large median.4) Small median.