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.