1. Design and Data Analysis in Psychology I English group (A) Salvador Chacón Moscoso Susana Sanduvete Chaves Milagrosa Sánchez Martín School of Psychology Dpt. Experimental Psychology

2. Lesson 1 Fundamental concepts Design and Data Analysis in Psychology I

3. Data analysis Univariate Data analysis of only one variable Bivariate Data analysis of two variables Multivariate Data analysis of more than one variable (bivariate analysis is an especial case) 3

4. General concepts 4 1. Population2. Sample 3. Parameter4. Statistics 5. Subjects6. Variables

5. 1. Population (N)  Set of all elements (people, animals, things...) that have one or more common characteristic or property: Students in the University of Seville in the present academic year. Dogs in Seville. Apartments for sale in Seville.  Each element of the population is called individual, subject or case: One of the students in the University of Seville in the present academic year. One of the dogs in Seville. One of the apartments for sale in Seville. 5

6. 2. Sample (n)  Subset of a population.  Requirements of a sample to have the possibility of obtaining conclusions (inferences) from the population: Representative of the population. Appropriate method of elements selection (all the elements of the population could have been chosen as element of the sample). Number of elements big enough. 6

7. 3. Parameter  Values that determine the descriptive properties of a population  They are not usually known: N usually numerous: not available to work with them. They are continuously changing.  We estimate the parameters of the population through the properties of the sample.  Greek letters: e.g., proportion ( ) -pi-, standard deviation ( σ ) - sigma-, mean ( μ ) -mu-, etc. 7   2      

8. 4. Statistics  Descriptive properties of a sample.  Although influenced by errors of different types, they are used to determine the approximated value of the parameters. 8 MEDIAN MODE MEAN STANDARD DEVIATION VARIANCE PROPORTION Mdn Mo S S 2 p

9. 5. Subjects 9 - Individuals - Subjects - Cases - Participants - People - Animals - Things  Each element of the sample.  They don’t have the same features in the same way nor in the same amount; e.g. height: 1.50, 1.85, 1.63, etc.

10. 6. Variables 10  A set of different values.  A constant has only one value. Height, marital status, size, etc.  They can be measured with statistical techniques.  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).

11. Stevens’ classification 11  Nominal scale: nominal variable  Ordinal scale: ordinal variable  Interval scale: quantitative variable  Ratio measurement: quantitative variable

12. Nominal variable  They do not take numerical values, as they describe qualities.  We can assign numbers.  Measurement level allows to identify or distinguish between elements.  Dichotomous nominal variable: Habitat: rural - urban. Answer to an item: True – False.  Polytomous nominal variable : Political group : PA – PP – PSOE – IU – CIU... Marital status: married – divorced – widow. Type of neurosis: hysterically obsessive – phobic - depressive 12

13. Ordinal variable (quasi-quantitative)  One whose elements can be ordered.  Measurement level allows to: Identify or distinguish between values (like nominal scales). order (higher, lower or equal).  Examples: Social Class: Low - Medium - High. Satisfaction: High - Medium - Low. Opinion: Totally disagree (1)... Totally agree (5) 13

14. Quantitative variable  They can be measured by two types of scale : Interval scales: the distance between any two consecutive values is constant respect to a particular property and the zero is relative (arbitrary); e.g., temperature (in Celsius scale).  Measurement level allows to:  differentiate between values (like nominal scales)  order -higher, lower or equal- (like ordinal scales).  Add and subtract: (5-4) = (28-27). Ratio scale: the distance between any two consecutive values is constant and the zero is absolute (absence of the feature that measures the variable); e.g., number of wrong answers.  Measurement level allows to:  differentiate between values (like nominal scales)  order -higher, lower or equal- (like ordinal scales).  Add and subtract (like interval scales).  Ratios; e.g., 15/3=5. 14

15.  Quantitative variables can be: Discrete: values are integers. Continuous: there are infinite number of values between two consecutive values.

16. Variables presentation Identify the types of variables 16 CaseGenderTreatment (Drug) Patient checkNumber of attacks Heart rate 1131172 2212275 3223486 4114592 5123384 6132381 7231274 8124571 9113380 10112277

17. 17 CaseGenderTreatment (Drug) Patient checkNumber of attacks Heart rate 1ManCMuch better172 2WomanABetter275 3WomanBEqual486 4ManAWorse592 5ManBEqual384 6ManCBetter381 7WomanCMuch better274 8ManBWorse571 9ManAEqual380 10ManABetter277