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. 

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.