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Design and Data Analysis in Psychology I English group (A)

Fundamental concepts of data analysis in psychology including population, sample, parameter, statistics, subjects, and variables.

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Design and Data Analysis in Psychology I English group (A)

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

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

  3. Data analysis

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

  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.

  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.  •  • 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. MEDIAN MODE MEAN STANDARD DEVIATION VARIANCE PROPORTION Mdn Mo S S2 p

  9. 5. Subjects • 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. - Individuals - Subjects - Cases - Participants - People - Animals - Things

  10. 6. Variables • 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 • 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

  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)

  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.

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

  16. Variables presentationIdentify the types of variables

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