CHAPTER 2 - NORM, CORRELATION AND REGRESSION.ppt
- 2. By the end of this chapter, students should be able to: • Define norms. • Discuss the types of norms. • Differentiate between correlation and regression. • Explain inference the measurement. Learning Objectives
- 3. • Norms are the accepted standards on particular test. • Norms consist of data that make it possible to determine the relative standing of an individual who has taken a test. • Norms provide a basis for comparing the individual with a group.
- 4. NORMS • to understand psychological test and proper interpretation of scores. • Raw Scores – different units – kg, hour , no. of correct/incorrect responses • interpreted accurately. • Norms are obtained by administering the test to a sample of people and obtaining the distribution of scores for that group.
- 5. Norms • Steps in developing Norms: 1. Defining the target population – Normative group - based on intention of test . 2. Selecting the sample from the target population – True representative sample. – Large sample – Random sampling 3. Standardizing the conditions – Test administration must be standard, valid
- 6. • association / relationship between two variables • Correlational coefficient is a mathematical index that describes the direction and magnitude of a relationship. • r • Both variables are considered to be independent
- 7. POSITIVE CORRELATION High scores on Y correspond to high scores on X. Low scores on Y correspond to low scores on X
- 8. NEGATIVE CORRELATION Higher scores on Y are associated with lower scores on X. Lower scores on Y are associated with higher scores on X
- 9. ZERO CORRELATION The correlation statistic did not indicate a relationship between the two variables
- 10. Regression analysis is a statistical process for estimating the relationships among variables. • In linear regression analysis one variable independent and other independent. • Predict cause and effect relationship between x and y
- 11. COMPARISON CORRELATION REGRESSION Measuring the strength of the relationship between two variables. Develop equation between two variables from sample data and practicing the value of one variable, given the value of other variables. Determines association between two variables x and y. Determines cause and effect relationship between x and y Both variables are considered to be independent In linear regression analysis one variable independent and other independent.
- 12. Branches of statistics • aka types of statistics • Two types 1. Descriptive statistics 2. Inferential statistics
- 13. Descriptive statistics Definition of descriptive statistics 1. mathematical summaries of results. 2. describe the basic features of the data in a study.
- 14. Descriptive statistics Types of descriptive statistics 1. Measures of frequency 2. Measures of central tendency 3. Measures of variation
- 15. Measures of frequency • Aka frequency of distributions • shows how often something occurs • Example: frequency, percentage
- 16. Measures of central tendency • locates the distribution by various points in a data • use this when you want to show how an average or most indicated response.
- 17. Types of Measures of Central Tendency Mode: The most frequently occurring score in a distribution. Mean: The arithmetic average of scores in a distribution (obtained by adding the scores and then dividing by the number of scores that were added together). Median: The middle score in a rank-ordered distribution.
- 18. Measures of variation • to describe the distribution of the data. • Types: 1. graphs like pie charts and bar charts that describe the data. 2. Measurement of variation – The range is a statement of the highest and lowest scores – If our distribution has the following scores: 1, 2, 3, 5, 7, 9, 9, 10, the range is from 1 to 10.
- 19. Inferential Statistics - Inferential statistics makes inferences or predictions about a populations from observations and analyses of a sample.
- 20. Inferential Statistics - Types of inferential statistics a. One Way Anova (F) – to determine difference among variables (3 and above categories) (age) b. t-test (t) – to determine difference among variables (only 2 categories)(gender)
- 21. Inferential Statistics - Types of inferential statistics c. correlation pearson (r) – to determine relationship between 2 variables d. regression (r) – to determine influence among variables.