StatPsy - Prelims Part 1.pptx
Page 1: Introduction
Psychological Statistics
Instructor: Jade G. Villanueva, CSPE, RPm
Page 2: Definition
Psychological Statistics: The art and science of collecting, presenting, analyzing, and interpreting data for psychological reports and materials.
Page 3: Types of Statistics
Descriptive Statistics: Collection and presentation of data.
Inferential Statistics: Interpretation and use of data obtained from descriptive statistics.
Page 4: Types of Measurement
Continuous Data: Can be measured with varying degrees of precision.
Non-Continuous Data: Expressed in whole units.
Page 5: Measurement Scales
Four Types of Scales:
Nominal: Measures identity (e.g., gender, religion).
Ordinal: Ranks individuals or objects.
Interval: Reflects differences (e.g., test scores).
Ratio: Measures of length, weight, etc. (highest type).
Page 6: Statistical Symbols
Σ: Sum of
f: Frequencies
F: Cumulative frequencies
n: Sample size
N: Population size
i: Interval
X: Independent variable
Y: Dependent variable
μ: Population mean
Page 7: Sample and Population
Population: Total number of objects under investigation, can be the whole for small, manageable groups.
Sample: Representative subset of the population, determined through sampling methods.
Page 8: Sampling Types
Probability Sampling: Random selection for strong statistical inference.
Non-Probability Sampling: Non-random selection based on convenience.
Page 9: Probability Sampling Techniques
Simple Random Sampling: Every member has an equal chance of selection; example using a random number generator.
Page 10: Probability Sampling Techniques
Systematic Sampling: Selection at regular intervals from a numbered list; example using alphabetical listing of employees.
Page 11: Probability Sampling Techniques
Stratified Sampling: Dividing the population into subpopulations to ensure representation of important subgroups; example with gender balance.
Page 12: Probability Sampling Techniques
Cluster Sampling: Randomly selecting entire subgroups. Example using offices from a company.
Page 13: Probability Sampling Techniques
Area Sampling: Sampling areas when complete reference is unavailable; example using mapped blocks.
Page 14: Probability Sampling Techniques
Multi-Stage Sampling: Further filtering clusters when they are still too large using additional sampling techniques.
Page 15: Non-Probability Sampling Techniques
Judgment Sampling: Researcher selects samples based on expertise; example with disabled students.
Page 16: Non-Probability Sampling Techniques
Convenience Sampling: Selecting easily accessible individuals; example from classroom settings where representativeness is an issue.
Page 17: Non-Probability Sampling Techniques
Quota Sampling: Selecting a specific proportion of predetermined categories; example focusing on dietary preferences.
Page 18: Non-Probability Sampling Techniques
Panel Sampling: Randomly choosing a group to participate multiple times; example with vaccine trials.
Page 19: Non-Probability Sampling Techniques
Snowball Sampling: Recruiting participants through referrals; example with researching homelessness.
Page 20: Sample Size Calculation
Slovin's Formula: Used for determining sample size; standard margins set at 1%-10%.
Page 21: Example 1 Sample Size
Calculate Sample Size: For a population of 2500 at 95% accuracy; found using Slovin's formula.
Page 22: Example 1 Result
Sample size needed: 345 out of total population 2500.
Page 23: Example 2 Sample Size
Calculate Sample Size: For a population of 200 at 97% accuracy; using Slovin's formula.
Page 24: Example 2 Result
Sample size needed: 169 out of total population 200.
Page 25: Exercise 1
Find Sample Size: For a population of 9550 at 96% accuracy.
Page 26: Exercise 2
Find Sample Size: For a population of 11550 with a margin of error of 0.07.