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Dr. Ji Son

Dr. Ji Son

Research Design

Slide Duration:

Table of Contents

I. Introduction
Descriptive Statistics vs. Inferential Statistics

25m 31s

Intro
0:00
Roadmap
0:10
Roadmap
0:11
Statistics
0:35
Statistics
0:36
Let's Think About High School Science
1:12
Measurement and Find Patterns (Mathematical Formula)
1:13
Statistics = Math of Distributions
4:58
Distributions
4:59
Problematic… but also GREAT
5:58
Statistics
7:33
How is It Different from Other Specializations in Mathematics?
7:34
Statistics is Fundamental in Natural and Social Sciences
7:53
Two Skills of Statistics
8:20
Description (Exploration)
8:21
Inference
9:13
Descriptive Statistics vs. Inferential Statistics: Apply to Distributions
9:58
Descriptive Statistics
9:59
Inferential Statistics
11:05
Populations vs. Samples
12:19
Populations vs. Samples: Is it the Truth?
12:20
Populations vs. Samples: Pros & Cons
13:36
Populations vs. Samples: Descriptive Values
16:12
Putting Together Descriptive/Inferential Stats & Populations/Samples
17:10
Putting Together Descriptive/Inferential Stats & Populations/Samples
17:11
Example 1: Descriptive Statistics vs. Inferential Statistics
19:09
Example 2: Descriptive Statistics vs. Inferential Statistics
20:47
Example 3: Sample, Parameter, Population, and Statistic
21:40
Example 4: Sample, Parameter, Population, and Statistic
23:28
II. About Samples: Cases, Variables, Measurements
About Samples: Cases, Variables, Measurements

32m 14s

Intro
0:00
Data
0:09
Data, Cases, Variables, and Values
0:10
Rows, Columns, and Cells
2:03
Example: Aircrafts
3:52
How Do We Get Data?
5:38
Research: Question and Hypothesis
5:39
Research Design
7:11
Measurement
7:29
Research Analysis
8:33
Research Conclusion
9:30
Types of Variables
10:03
Discrete Variables
10:04
Continuous Variables
12:07
Types of Measurements
14:17
Types of Measurements
14:18
Types of Measurements (Scales)
17:22
Nominal
17:23
Ordinal
19:11
Interval
21:33
Ratio
24:24
Example 1: Cases, Variables, Measurements
25:20
Example 2: Which Scale of Measurement is Used?
26:55
Example 3: What Kind of a Scale of Measurement is This?
27:26
Example 4: Discrete vs. Continuous Variables.
30:31
III. Visualizing Distributions
Introduction to Excel

8m 9s

Intro
0:00
Before Visualizing Distribution
0:10
Excel
0:11
Excel: Organization
0:45
Workbook
0:46
Column x Rows
1:50
Tools: Menu Bar, Standard Toolbar, and Formula Bar
3:00
Excel + Data
6:07
Exce and Data
6:08
Frequency Distributions in Excel

39m 10s

Intro
0:00
Roadmap
0:08
Data in Excel and Frequency Distributions
0:09
Raw Data to Frequency Tables
0:42
Raw Data to Frequency Tables
0:43
Frequency Tables: Using Formulas and Pivot Tables
1:28
Example 1: Number of Births
7:17
Example 2: Age Distribution
20:41
Example 3: Height Distribution
27:45
Example 4: Height Distribution of Males
32:19
Frequency Distributions and Features

25m 29s

Intro
0:00
Roadmap
0:10
Data in Excel, Frequency Distributions, and Features of Frequency Distributions
0:11
Example #1
1:35
Uniform
1:36
Example #2
2:58
Unimodal, Skewed Right, and Asymmetric
2:59
Example #3
6:29
Bimodal
6:30
Example #4a
8:29
Symmetric, Unimodal, and Normal
8:30
Point of Inflection and Standard Deviation
11:13
Example #4b
12:43
Normal Distribution
12:44
Summary
13:56
Uniform, Skewed, Bimodal, and Normal
13:57
Sketch Problem 1: Driver's License
17:34
Sketch Problem 2: Life Expectancy
20:01
Sketch Problem 3: Telephone Numbers
22:01
Sketch Problem 4: Length of Time Used to Complete a Final Exam
23:43
Dotplots and Histograms in Excel

42m 42s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Previously
1:02
Data, Frequency Table, and visualization
1:03
Dotplots
1:22
Dotplots Excel Example
1:23
Dotplots: Pros and Cons
7:22
Pros and Cons of Dotplots
7:23
Dotplots Excel Example Cont.
9:07
Histograms
12:47
Histograms Overview
12:48
Example of Histograms
15:29
Histograms: Pros and Cons
31:39
Pros
31:40
Cons
32:31
Frequency vs. Relative Frequency
32:53
Frequency
32:54
Relative Frequency
33:36
Example 1: Dotplots vs. Histograms
34:36
Example 2: Age of Pennies Dotplot
36:21
Example 3: Histogram of Mammal Speeds
38:27
Example 4: Histogram of Life Expectancy
40:30
Stemplots

12m 23s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
What Sets Stemplots Apart?
0:46
Data Sets, Dotplots, Histograms, and Stemplots
0:47
Example 1: What Do Stemplots Look Like?
1:58
Example 2: Back-to-Back Stemplots
5:00
Example 3: Quiz Grade Stemplot
7:46
Example 4: Quiz Grade & Afterschool Tutoring Stemplot
9:56
Bar Graphs

22m 49s

Intro
0:00
Roadmap
0:05
Roadmap
0:08
Review of Frequency Distributions
0:44
Y-axis and X-axis
0:45
Types of Frequency Visualizations Covered so Far
2:16
Introduction to Bar Graphs
4:07
Example 1: Bar Graph
5:32
Example 1: Bar Graph
5:33
Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs?
11:07
Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs?
11:08
Example 2: Create a Frequency Visualization for Gender
14:02
Example 3: Cases, Variables, and Frequency Visualization
16:34
Example 4: What Kind of Graphs are Shown Below?
19:29
IV. Summarizing Distributions
Central Tendency: Mean, Median, Mode

38m 50s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
Central Tendency 1
0:56
Way to Summarize a Distribution of Scores
0:57
Mode
1:32
Median
2:02
Mean
2:36
Central Tendency 2
3:47
Mode
3:48
Median
4:20
Mean
5:25
Summation Symbol
6:11
Summation Symbol
6:12
Population vs. Sample
10:46
Population vs. Sample
10:47
Excel Examples
15:08
Finding Mode, Median, and Mean in Excel
15:09
Median vs. Mean
21:45
Effect of Outliers
21:46
Relationship Between Parameter and Statistic
22:44
Type of Measurements
24:00
Which Distributions to Use With
24:55
Example 1: Mean
25:30
Example 2: Using Summation Symbol
29:50
Example 3: Average Calorie Count
32:50
Example 4: Creating an Example Set
35:46
Variability

42m 40s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Variability (or Spread)
0:45
Variability (or Spread)
0:46
Things to Think About
5:45
Things to Think About
5:46
Range, Quartiles and Interquartile Range
6:37
Range
6:38
Interquartile Range
8:42
Interquartile Range Example
10:58
Interquartile Range Example
10:59
Variance and Standard Deviation
12:27
Deviations
12:28
Sum of Squares
14:35
Variance
16:55
Standard Deviation
17:44
Sum of Squares (SS)
18:34
Sum of Squares (SS)
18:35
Population vs. Sample SD
22:00
Population vs. Sample SD
22:01
Population vs. Sample
23:20
Mean
23:21
SD
23:51
Example 1: Find the Mean and Standard Deviation of the Variable Friends in the Excel File
27:21
Example 2: Find the Mean and Standard Deviation of the Tagged Photos in the Excel File
35:25
Example 3: Sum of Squares
38:58
Example 4: Standard Deviation
41:48
Five Number Summary & Boxplots

57m 15s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Summarizing Distributions
0:37
Shape, Center, and Spread
0:38
5 Number Summary
1:14
Boxplot: Visualizing 5 Number Summary
3:37
Boxplot: Visualizing 5 Number Summary
3:38
Boxplots on Excel
9:01
Using 'Stocks' and Using Stacked Columns
9:02
Boxplots on Excel Example
10:14
When are Boxplots Useful?
32:14
Pros
32:15
Cons
32:59
How to Determine Outlier Status
33:24
Rule of Thumb: Upper Limit
33:25
Rule of Thumb: Lower Limit
34:16
Signal Outliers in an Excel Data File Using Conditional Formatting
34:52
Modified Boxplot
48:38
Modified Boxplot
48:39
Example 1: Percentage Values & Lower and Upper Whisker
49:10
Example 2: Boxplot
50:10
Example 3: Estimating IQR From Boxplot
53:46
Example 4: Boxplot and Missing Whisker
54:35
Shape: Calculating Skewness & Kurtosis

41m 51s

Intro
0:00
Roadmap
0:16
Roadmap
0:17
Skewness Concept
1:09
Skewness Concept
1:10
Calculating Skewness
3:26
Calculating Skewness
3:27
Interpreting Skewness
7:36
Interpreting Skewness
7:37
Excel Example
8:49
Kurtosis Concept
20:29
Kurtosis Concept
20:30
Calculating Kurtosis
24:17
Calculating Kurtosis
24:18
Interpreting Kurtosis
29:01
Leptokurtic
29:35
Mesokurtic
30:10
Platykurtic
31:06
Excel Example
32:04
Example 1: Shape of Distribution
38:28
Example 2: Shape of Distribution
39:29
Example 3: Shape of Distribution
40:14
Example 4: Kurtosis
41:10
Normal Distribution

34m 33s

Intro
0:00
Roadmap
0:13
Roadmap
0:14
What is a Normal Distribution
0:44
The Normal Distribution As a Theoretical Model
0:45
Possible Range of Probabilities
3:05
Possible Range of Probabilities
3:06
What is a Normal Distribution
5:07
Can Be Described By
5:08
Properties
5:49
'Same' Shape: Illusion of Different Shape!
7:35
'Same' Shape: Illusion of Different Shape!
7:36
Types of Problems
13:45
Example: Distribution of SAT Scores
13:46
Shape Analogy
19:48
Shape Analogy
19:49
Example 1: The Standard Normal Distribution and Z-Scores
22:34
Example 2: The Standard Normal Distribution and Z-Scores
25:54
Example 3: Sketching and Normal Distribution
28:55
Example 4: Sketching and Normal Distribution
32:32
Standard Normal Distributions & Z-Scores

41m 44s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
A Family of Distributions
0:28
Infinite Set of Distributions
0:29
Transforming Normal Distributions to 'Standard' Normal Distribution
1:04
Normal Distribution vs. Standard Normal Distribution
2:58
Normal Distribution vs. Standard Normal Distribution
2:59
Z-Score, Raw Score, Mean, & SD
4:08
Z-Score, Raw Score, Mean, & SD
4:09
Weird Z-Scores
9:40
Weird Z-Scores
9:41
Excel
16:45
For Normal Distributions
16:46
For Standard Normal Distributions
19:11
Excel Example
20:24
Types of Problems
25:18
Percentage Problem: P(x)
25:19
Raw Score and Z-Score Problems
26:28
Standard Deviation Problems
27:01
Shape Analogy
27:44
Shape Analogy
27:45
Example 1: Deaths Due to Heart Disease vs. Deaths Due to Cancer
28:24
Example 2: Heights of Male College Students
33:15
Example 3: Mean and Standard Deviation
37:14
Example 4: Finding Percentage of Values in a Standard Normal Distribution
37:49
Normal Distribution: PDF vs. CDF

55m 44s

Intro
0:00
Roadmap
0:15
Roadmap
0:16
Frequency vs. Cumulative Frequency
0:56
Frequency vs. Cumulative Frequency
0:57
Frequency vs. Cumulative Frequency
4:32
Frequency vs. Cumulative Frequency Cont.
4:33
Calculus in Brief
6:21
Derivative-Integral Continuum
6:22
PDF
10:08
PDF for Standard Normal Distribution
10:09
PDF for Normal Distribution
14:32
Integral of PDF = CDF
21:27
Integral of PDF = CDF
21:28
Example 1: Cumulative Frequency Graph
23:31
Example 2: Mean, Standard Deviation, and Probability
24:43
Example 3: Mean and Standard Deviation
35:50
Example 4: Age of Cars
49:32
V. Linear Regression
Scatterplots

47m 19s

Intro
0:00
Roadmap
0:04
Roadmap
0:05
Previous Visualizations
0:30
Frequency Distributions
0:31
Compare & Contrast
2:26
Frequency Distributions Vs. Scatterplots
2:27
Summary Values
4:53
Shape
4:54
Center & Trend
6:41
Spread & Strength
8:22
Univariate & Bivariate
10:25
Example Scatterplot
10:48
Shape, Trend, and Strength
10:49
Positive and Negative Association
14:05
Positive and Negative Association
14:06
Linearity, Strength, and Consistency
18:30
Linearity
18:31
Strength
19:14
Consistency
20:40
Summarizing a Scatterplot
22:58
Summarizing a Scatterplot
22:59
Example 1: Gapminder.org, Income x Life Expectancy
26:32
Example 2: Gapminder.org, Income x Infant Mortality
36:12
Example 3: Trend and Strength of Variables
40:14
Example 4: Trend, Strength and Shape for Scatterplots
43:27
Regression

32m 2s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Linear Equations
0:34
Linear Equations: y = mx + b
0:35
Rough Line
5:16
Rough Line
5:17
Regression - A 'Center' Line
7:41
Reasons for Summarizing with a Regression Line
7:42
Predictor and Response Variable
10:04
Goal of Regression
12:29
Goal of Regression
12:30
Prediction
14:50
Example: Servings of Mile Per Year Shown By Age
14:51
Intrapolation
17:06
Extrapolation
17:58
Error in Prediction
20:34
Prediction Error
20:35
Residual
21:40
Example 1: Residual
23:34
Example 2: Large and Negative Residual
26:30
Example 3: Positive Residual
28:13
Example 4: Interpret Regression Line & Extrapolate
29:40
Least Squares Regression

56m 36s

Intro
0:00
Roadmap
0:13
Roadmap
0:14
Best Fit
0:47
Best Fit
0:48
Sum of Squared Errors (SSE)
1:50
Sum of Squared Errors (SSE)
1:51
Why Squared?
3:38
Why Squared?
3:39
Quantitative Properties of Regression Line
4:51
Quantitative Properties of Regression Line
4:52
So How do we Find Such a Line?
6:49
SSEs of Different Line Equations & Lowest SSE
6:50
Carl Gauss' Method
8:01
How Do We Find Slope (b1)
11:00
How Do We Find Slope (b1)
11:01
Hoe Do We Find Intercept
15:11
Hoe Do We Find Intercept
15:12
Example 1: Which of These Equations Fit the Above Data Best?
17:18
Example 2: Find the Regression Line for These Data Points and Interpret It
26:31
Example 3: Summarize the Scatterplot and Find the Regression Line.
34:31
Example 4: Examine the Mean of Residuals
43:52
Correlation

43m 58s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Summarizing a Scatterplot Quantitatively
0:47
Shape
0:48
Trend
1:11
Strength: Correlation ®
1:45
Correlation Coefficient ( r )
2:30
Correlation Coefficient ( r )
2:31
Trees vs. Forest
11:59
Trees vs. Forest
12:00
Calculating r
15:07
Average Product of z-scores for x and y
15:08
Relationship between Correlation and Slope
21:10
Relationship between Correlation and Slope
21:11
Example 1: Find the Correlation between Grams of Fat and Cost
24:11
Example 2: Relationship between r and b1
30:24
Example 3: Find the Regression Line
33:35
Example 4: Find the Correlation Coefficient for this Set of Data
37:37
Correlation: r vs. r-squared

52m 52s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
R-squared
0:44
What is the Meaning of It? Why Squared?
0:45
Parsing Sum of Squared (Parsing Variability)
2:25
SST = SSR + SSE
2:26
What is SST and SSE?
7:46
What is SST and SSE?
7:47
r-squared
18:33
Coefficient of Determination
18:34
If the Correlation is Strong…
20:25
If the Correlation is Strong…
20:26
If the Correlation is Weak…
22:36
If the Correlation is Weak…
22:37
Example 1: Find r-squared for this Set of Data
23:56
Example 2: What Does it Mean that the Simple Linear Regression is a 'Model' of Variance?
33:54
Example 3: Why Does r-squared Only Range from 0 to 1
37:29
Example 4: Find the r-squared for This Set of Data
39:55
Transformations of Data

27m 8s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Why Transform?
0:26
Why Transform?
0:27
Shape-preserving vs. Shape-changing Transformations
5:14
Shape-preserving = Linear Transformations
5:15
Shape-changing Transformations = Non-linear Transformations
6:20
Common Shape-Preserving Transformations
7:08
Common Shape-Preserving Transformations
7:09
Common Shape-Changing Transformations
8:59
Powers
9:00
Logarithms
9:39
Change Just One Variable? Both?
10:38
Log-log Transformations
10:39
Log Transformations
14:38
Example 1: Create, Graph, and Transform the Data Set
15:19
Example 2: Create, Graph, and Transform the Data Set
20:08
Example 3: What Kind of Model would You Choose for this Data?
22:44
Example 4: Transformation of Data
25:46
VI. Collecting Data in an Experiment
Sampling & Bias

54m 44s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Descriptive vs. Inferential Statistics
1:04
Descriptive Statistics: Data Exploration
1:05
Example
2:03
To tackle Generalization…
4:31
Generalization
4:32
Sampling
6:06
'Good' Sample
6:40
Defining Samples and Populations
8:55
Population
8:56
Sample
11:16
Why Use Sampling?
13:09
Why Use Sampling?
13:10
Goal of Sampling: Avoiding Bias
15:04
What is Bias?
15:05
Where does Bias Come from: Sampling Bias
17:53
Where does Bias Come from: Response Bias
18:27
Sampling Bias: Bias from Bas Sampling Methods
19:34
Size Bias
19:35
Voluntary Response Bias
21:13
Convenience Sample
22:22
Judgment Sample
23:58
Inadequate Sample Frame
25:40
Response Bias: Bias from 'Bad' Data Collection Methods
28:00
Nonresponse Bias
29:31
Questionnaire Bias
31:10
Incorrect Response or Measurement Bias
37:32
Example 1: What Kind of Biases?
40:29
Example 2: What Biases Might Arise?
44:46
Example 3: What Kind of Biases?
48:34
Example 4: What Kind of Biases?
51:43
Sampling Methods

14m 25s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Biased vs. Unbiased Sampling Methods
0:32
Biased Sampling
0:33
Unbiased Sampling
1:13
Probability Sampling Methods
2:31
Simple Random
2:54
Stratified Random Sampling
4:06
Cluster Sampling
5:24
Two-staged Sampling
6:22
Systematic Sampling
7:25
Example 1: Which Type(s) of Sampling was this?
8:33
Example 2: Describe How to Take a Two-Stage Sample from this Book
10:16
Example 3: Sampling Methods
11:58
Example 4: Cluster Sample Plan
12:48
Research Design

53m 54s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Descriptive vs. Inferential Statistics
0:51
Descriptive Statistics: Data Exploration
0:52
Inferential Statistics
1:02
Variables and Relationships
1:44
Variables
1:45
Relationships
2:49
Not Every Type of Study is an Experiment…
4:16
Category I - Descriptive Study
4:54
Category II - Correlational Study
5:50
Category III - Experimental, Quasi-experimental, Non-experimental
6:33
Category III
7:42
Experimental, Quasi-experimental, and Non-experimental
7:43
Why CAN'T the Other Strategies Determine Causation?
10:18
Third-variable Problem
10:19
Directionality Problem
15:49
What Makes Experiments Special?
17:54
Manipulation
17:55
Control (and Comparison)
21:58
Methods of Control
26:38
Holding Constant
26:39
Matching
29:11
Random Assignment
31:48
Experiment Terminology
34:09
'true' Experiment vs. Study
34:10
Independent Variable (IV)
35:16
Dependent Variable (DV)
35:45
Factors
36:07
Treatment Conditions
36:23
Levels
37:43
Confounds or Extraneous Variables
38:04
Blind
38:38
Blind Experiments
38:39
Double-blind Experiments
39:29
How Categories Relate to Statistics
41:35
Category I - Descriptive Study
41:36
Category II - Correlational Study
42:05
Category III - Experimental, Quasi-experimental, Non-experimental
42:43
Example 1: Research Design
43:50
Example 2: Research Design
47:37
Example 3: Research Design
50:12
Example 4: Research Design
52:00
Between and Within Treatment Variability

41m 31s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Experimental Designs
0:51
Experimental Designs: Manipulation & Control
0:52
Two Types of Variability
2:09
Between Treatment Variability
2:10
Within Treatment Variability
3:31
Updated Goal of Experimental Design
5:47
Updated Goal of Experimental Design
5:48
Example: Drugs and Driving
6:56
Example: Drugs and Driving
6:57
Different Types of Random Assignment
11:27
All Experiments
11:28
Completely Random Design
12:02
Randomized Block Design
13:19
Randomized Block Design
15:48
Matched Pairs Design
15:49
Repeated Measures Design
19:47
Between-subject Variable vs. Within-subject Variable
22:43
Completely Randomized Design
22:44
Repeated Measures Design
25:03
Example 1: Design a Completely Random, Matched Pair, and Repeated Measures Experiment
26:16
Example 2: Block Design
31:41
Example 3: Completely Randomized Designs
35:11
Example 4: Completely Random, Matched Pairs, or Repeated Measures Experiments?
39:01
VII. Review of Probability Axioms
Sample Spaces

37m 52s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
Why is Probability Involved in Statistics
0:48
Probability
0:49
Can People Tell the Difference between Cheap and Gourmet Coffee?
2:08
Taste Test with Coffee Drinkers
3:37
If No One can Actually Taste the Difference
3:38
If Everyone can Actually Taste the Difference
5:36
Creating a Probability Model
7:09
Creating a Probability Model
7:10
D'Alembert vs. Necker
9:41
D'Alembert vs. Necker
9:42
Problem with D'Alembert's Model
13:29
Problem with D'Alembert's Model
13:30
Covering Entire Sample Space
15:08
Fundamental Principle of Counting
15:09
Where Do Probabilities Come From?
22:54
Observed Data, Symmetry, and Subjective Estimates
22:55
Checking whether Model Matches Real World
24:27
Law of Large Numbers
24:28
Example 1: Law of Large Numbers
27:46
Example 2: Possible Outcomes
30:43
Example 3: Brands of Coffee and Taste
33:25
Example 4: How Many Different Treatments are there?
35:33
Addition Rule for Disjoint Events

20m 29s

Intro
0:00
Roadmap
0:08
Roadmap
0:09
Disjoint Events
0:41
Disjoint Events
0:42
Meaning of 'or'
2:39
In Regular Life
2:40
In Math/Statistics/Computer Science
3:10
Addition Rule for Disjoin Events
3:55
If A and B are Disjoint: P (A and B)
3:56
If A and B are Disjoint: P (A or B)
5:15
General Addition Rule
5:41
General Addition Rule
5:42
Generalized Addition Rule
8:31
If A and B are not Disjoint: P (A or B)
8:32
Example 1: Which of These are Mutually Exclusive?
10:50
Example 2: What is the Probability that You will Have a Combination of One Heads and Two Tails?
12:57
Example 3: Engagement Party
15:17
Example 4: Home Owner's Insurance
18:30
Conditional Probability

57m 19s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
'or' vs. 'and' vs. Conditional Probability
1:07
'or' vs. 'and' vs. Conditional Probability
1:08
'and' vs. Conditional Probability
5:57
P (M or L)
5:58
P (M and L)
8:41
P (M|L)
11:04
P (L|M)
12:24
Tree Diagram
15:02
Tree Diagram
15:03
Defining Conditional Probability
22:42
Defining Conditional Probability
22:43
Common Contexts for Conditional Probability
30:56
Medical Testing: Positive Predictive Value
30:57
Medical Testing: Sensitivity
33:03
Statistical Tests
34:27
Example 1: Drug and Disease
36:41
Example 2: Marbles and Conditional Probability
40:04
Example 3: Cards and Conditional Probability
45:59
Example 4: Votes and Conditional Probability
50:21
Independent Events

24m 27s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Independent Events & Conditional Probability
0:26
Non-independent Events
0:27
Independent Events
2:00
Non-independent and Independent Events
3:08
Non-independent and Independent Events
3:09
Defining Independent Events
5:52
Defining Independent Events
5:53
Multiplication Rule
7:29
Previously…
7:30
But with Independent Evens
8:53
Example 1: Which of These Pairs of Events are Independent?
11:12
Example 2: Health Insurance and Probability
15:12
Example 3: Independent Events
17:42
Example 4: Independent Events
20:03
VIII. Probability Distributions
Introduction to Probability Distributions

56m 45s

Intro
0:00
Roadmap
0:08
Roadmap
0:09
Sampling vs. Probability
0:57
Sampling
0:58
Missing
1:30
What is Missing?
3:06
Insight: Probability Distributions
5:26
Insight: Probability Distributions
5:27
What is a Probability Distribution?
7:29
From Sample Spaces to Probability Distributions
8:44
Sample Space
8:45
Probability Distribution of the Sum of Two Die
11:16
The Random Variable
17:43
The Random Variable
17:44
Expected Value
21:52
Expected Value
21:53
Example 1: Probability Distributions
28:45
Example 2: Probability Distributions
35:30
Example 3: Probability Distributions
43:37
Example 4: Probability Distributions
47:20
Expected Value & Variance of Probability Distributions

53m 41s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Discrete vs. Continuous Random Variables
1:04
Discrete vs. Continuous Random Variables
1:05
Mean and Variance Review
4:44
Mean: Sample, Population, and Probability Distribution
4:45
Variance: Sample, Population, and Probability Distribution
9:12
Example Situation
14:10
Example Situation
14:11
Some Special Cases…
16:13
Some Special Cases…
16:14
Linear Transformations
19:22
Linear Transformations
19:23
What Happens to Mean and Variance of the Probability Distribution?
20:12
n Independent Values of X
25:38
n Independent Values of X
25:39
Compare These Two Situations
30:56
Compare These Two Situations
30:57
Two Random Variables, X and Y
32:02
Two Random Variables, X and Y
32:03
Example 1: Expected Value & Variance of Probability Distributions
35:35
Example 2: Expected Values & Standard Deviation
44:17
Example 3: Expected Winnings and Standard Deviation
48:18
Binomial Distribution

55m 15s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Discrete Probability Distributions
1:42
Discrete Probability Distributions
1:43
Binomial Distribution
2:36
Binomial Distribution
2:37
Multiplicative Rule Review
6:54
Multiplicative Rule Review
6:55
How Many Outcomes with k 'Successes'
10:23
Adults and Bachelor's Degree: Manual List of Outcomes
10:24
P (X=k)
19:37
Putting Together # of Outcomes with the Multiplicative Rule
19:38
Expected Value and Standard Deviation in a Binomial Distribution
25:22
Expected Value and Standard Deviation in a Binomial Distribution
25:23
Example 1: Coin Toss
33:42
Example 2: College Graduates
38:03
Example 3: Types of Blood and Probability
45:39
Example 4: Expected Number and Standard Deviation
51:11
IX. Sampling Distributions of Statistics
Introduction to Sampling Distributions

48m 17s

Intro
0:00
Roadmap
0:08
Roadmap
0:09
Probability Distributions vs. Sampling Distributions
0:55
Probability Distributions vs. Sampling Distributions
0:56
Same Logic
3:55
Logic of Probability Distribution
3:56
Example: Rolling Two Die
6:56
Simulating Samples
9:53
To Come Up with Probability Distributions
9:54
In Sampling Distributions
11:12
Connecting Sampling and Research Methods with Sampling Distributions
12:11
Connecting Sampling and Research Methods with Sampling Distributions
12:12
Simulating a Sampling Distribution
14:14
Experimental Design: Regular Sleep vs. Less Sleep
14:15
Logic of Sampling Distributions
23:08
Logic of Sampling Distributions
23:09
General Method of Simulating Sampling Distributions
25:38
General Method of Simulating Sampling Distributions
25:39
Questions that Remain
28:45
Questions that Remain
28:46
Example 1: Mean and Standard Error of Sampling Distribution
30:57
Example 2: What is the Best Way to Describe Sampling Distributions?
37:12
Example 3: Matching Sampling Distributions
38:21
Example 4: Mean and Standard Error of Sampling Distribution
41:51
Sampling Distribution of the Mean

1h 8m 48s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Special Case of General Method for Simulating a Sampling Distribution
1:53
Special Case of General Method for Simulating a Sampling Distribution
1:54
Computer Simulation
3:43
Using Simulations to See Principles behind Shape of SDoM
15:50
Using Simulations to See Principles behind Shape of SDoM
15:51
Conditions
17:38
Using Simulations to See Principles behind Center (Mean) of SDoM
20:15
Using Simulations to See Principles behind Center (Mean) of SDoM
20:16
Conditions: Does n Matter?
21:31
Conditions: Does Number of Simulation Matter?
24:37
Using Simulations to See Principles behind Standard Deviation of SDoM
27:13
Using Simulations to See Principles behind Standard Deviation of SDoM
27:14
Conditions: Does n Matter?
34:45
Conditions: Does Number of Simulation Matter?
36:24
Central Limit Theorem
37:13
SHAPE
38:08
CENTER
39:34
SPREAD
39:52
Comparing Population, Sample, and SDoM
43:10
Comparing Population, Sample, and SDoM
43:11
Answering the 'Questions that Remain'
48:24
What Happens When We Don't Know What the Population Looks Like?
48:25
Can We Have Sampling Distributions for Summary Statistics Other than the Mean?
49:42
How Do We Know whether a Sample is Sufficiently Unlikely?
53:36
Do We Always Have to Simulate a Large Number of Samples in Order to get a Sampling Distribution?
54:40
Example 1: Mean Batting Average
55:25
Example 2: Mean Sampling Distribution and Standard Error
59:07
Example 3: Sampling Distribution of the Mean
1:01:04
Sampling Distribution of Sample Proportions

54m 37s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Intro to Sampling Distribution of Sample Proportions (SDoSP)
0:51
Categorical Data (Examples)
0:52
Wish to Estimate Proportion of Population from Sample…
2:00
Notation
3:34
Population Proportion and Sample Proportion Notations
3:35
What's the Difference?
9:19
SDoM vs. SDoSP: Type of Data
9:20
SDoM vs. SDoSP: Shape
11:24
SDoM vs. SDoSP: Center
12:30
SDoM vs. SDoSP: Spread
15:34
Binomial Distribution vs. Sampling Distribution of Sample Proportions
19:14
Binomial Distribution vs. SDoSP: Type of Data
19:17
Binomial Distribution vs. SDoSP: Shape
21:07
Binomial Distribution vs. SDoSP: Center
21:43
Binomial Distribution vs. SDoSP: Spread
24:08
Example 1: Sampling Distribution of Sample Proportions
26:07
Example 2: Sampling Distribution of Sample Proportions
37:58
Example 3: Sampling Distribution of Sample Proportions
44:42
Example 4: Sampling Distribution of Sample Proportions
45:57
X. Inferential Statistics
Introduction to Confidence Intervals

42m 53s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Inferential Statistics
0:50
Inferential Statistics
0:51
Two Problems with This Picture…
3:20
Two Problems with This Picture…
3:21
Solution: Confidence Intervals (CI)
4:59
Solution: Hypotheiss Testing (HT)
5:49
Which Parameters are Known?
6:45
Which Parameters are Known?
6:46
Confidence Interval - Goal
7:56
When We Don't Know m but know s
7:57
When We Don't Know
18:27
When We Don't Know m nor s
18:28
Example 1: Confidence Intervals
26:18
Example 2: Confidence Intervals
29:46
Example 3: Confidence Intervals
32:18
Example 4: Confidence Intervals
38:31
t Distributions

1h 2m 6s

Intro
0:00
Roadmap
0:04
Roadmap
0:05
When to Use z vs. t?
1:07
When to Use z vs. t?
1:08
What is z and t?
3:02
z-score and t-score: Commonality
3:03
z-score and t-score: Formulas
3:34
z-score and t-score: Difference
5:22
Why not z? (Why t?)
7:24
Why not z? (Why t?)
7:25
But Don't Worry!
15:13
Gossett and t-distributions
15:14
Rules of t Distributions
17:05
t-distributions are More Normal as n Gets Bigger
17:06
t-distributions are a Family of Distributions
18:55
Degrees of Freedom (df)
20:02
Degrees of Freedom (df)
20:03
t Family of Distributions
24:07
t Family of Distributions : df = 2 , 4, and 60
24:08
df = 60
29:16
df = 2
29:59
How to Find It?
31:01
'Student's t-distribution' or 't-distribution'
31:02
Excel Example
33:06
Example 1: Which Distribution Do You Use? Z or t?
45:26
Example 2: Friends on Facebook
47:41
Example 3: t Distributions
52:15
Example 4: t Distributions , confidence interval, and mean
55:59
Introduction to Hypothesis Testing

1h 6m 33s

Intro
0:00
Roadmap
0:06
Roadmap
0:07
Issues to Overcome in Inferential Statistics
1:35
Issues to Overcome in Inferential Statistics
1:36
What Happens When We Don't Know What the Population Looks Like?
2:57
How Do We Know whether a sample is Sufficiently Unlikely
3:43
Hypothesizing a Population
6:44
Hypothesizing a Population
6:45
Null Hypothesis
8:07
Alternative Hypothesis
8:56
Hypotheses
11:58
Hypotheses
11:59
Errors in Hypothesis Testing
14:22
Errors in Hypothesis Testing
14:23
Steps of Hypothesis Testing
21:15
Steps of Hypothesis Testing
21:16
Single Sample HT ( When Sigma Available)
26:08
Example: Average Facebook Friends
26:09
Step1
27:08
Step 2
27:58
Step 3
28:17
Step 4
32:18
Single Sample HT (When Sigma Not Available)
36:33
Example: Average Facebook Friends
36:34
Step1: Hypothesis Testing
36:58
Step 2: Significance Level
37:25
Step 3: Decision Stage
37:40
Step 4: Sample
41:36
Sigma and p-value
45:04
Sigma and p-value
45:05
On tailed vs. Two Tailed Hypotheses
45:51
Example 1: Hypothesis Testing
48:37
Example 2: Heights of Women in the US
57:43
Example 3: Select the Best Way to Complete This Sentence
1:03:23
Confidence Intervals for the Difference of Two Independent Means

55m 14s

Intro
0:00
Roadmap
0:14
Roadmap
0:15
One Mean vs. Two Means
1:17
One Mean vs. Two Means
1:18
Notation
2:41
A Sample! A Set!
2:42
Mean of X, Mean of Y, and Difference of Two Means
3:56
SE of X
4:34
SE of Y
6:28
Sampling Distribution of the Difference between Two Means (SDoD)
7:48
Sampling Distribution of the Difference between Two Means (SDoD)
7:49
Rules of the SDoD (similar to CLT!)
15:00
Mean for the SDoD Null Hypothesis
15:01
Standard Error
17:39
When can We Construct a CI for the Difference between Two Means?
21:28
Three Conditions
21:29
Finding CI
23:56
One Mean CI
23:57
Two Means CI
25:45
Finding t
29:16
Finding t
29:17
Interpreting CI
30:25
Interpreting CI
30:26
Better Estimate of s (s pool)
34:15
Better Estimate of s (s pool)
34:16
Example 1: Confidence Intervals
42:32
Example 2: SE of the Difference
52:36
Hypothesis Testing for the Difference of Two Independent Means

50m

Intro
0:00
Roadmap
0:06
Roadmap
0:07
The Goal of Hypothesis Testing
0:56
One Sample and Two Samples
0:57
Sampling Distribution of the Difference between Two Means (SDoD)
3:42
Sampling Distribution of the Difference between Two Means (SDoD)
3:43
Rules of the SDoD (Similar to CLT!)
6:46
Shape
6:47
Mean for the Null Hypothesis
7:26
Standard Error for Independent Samples (When Variance is Homogenous)
8:18
Standard Error for Independent Samples (When Variance is not Homogenous)
9:25
Same Conditions for HT as for CI
10:08
Three Conditions
10:09
Steps of Hypothesis Testing
11:04
Steps of Hypothesis Testing
11:05
Formulas that Go with Steps of Hypothesis Testing
13:21
Step 1
13:25
Step 2
14:18
Step 3
15:00
Step 4
16:57
Example 1: Hypothesis Testing for the Difference of Two Independent Means
18:47
Example 2: Hypothesis Testing for the Difference of Two Independent Means
33:55
Example 3: Hypothesis Testing for the Difference of Two Independent Means
44:22
Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means

1h 14m 11s

Intro
0:00
Roadmap
0:09
Roadmap
0:10
The Goal of Hypothesis Testing
1:27
One Sample and Two Samples
1:28
Independent Samples vs. Paired Samples
3:16
Independent Samples vs. Paired Samples
3:17
Which is Which?
5:20
Independent SAMPLES vs. Independent VARIABLES
7:43
independent SAMPLES vs. Independent VARIABLES
7:44
T-tests Always…
10:48
T-tests Always…
10:49
Notation for Paired Samples
12:59
Notation for Paired Samples
13:00
Steps of Hypothesis Testing for Paired Samples
16:13
Steps of Hypothesis Testing for Paired Samples
16:14
Rules of the SDoD (Adding on Paired Samples)
18:03
Shape
18:04
Mean for the Null Hypothesis
18:31
Standard Error for Independent Samples (When Variance is Homogenous)
19:25
Standard Error for Paired Samples
20:39
Formulas that go with Steps of Hypothesis Testing
22:59
Formulas that go with Steps of Hypothesis Testing
23:00
Confidence Intervals for Paired Samples
30:32
Confidence Intervals for Paired Samples
30:33
Example 1: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means
32:28
Example 2: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means
44:02
Example 3: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means
52:23
Type I and Type II Errors

31m 27s

Intro
0:00
Roadmap
0:18
Roadmap
0:19
Errors and Relationship to HT and the Sample Statistic?
1:11
Errors and Relationship to HT and the Sample Statistic?
1:12
Instead of a Box…Distributions!
7:00
One Sample t-test: Friends on Facebook
7:01
Two Sample t-test: Friends on Facebook
13:46
Usually, Lots of Overlap between Null and Alternative Distributions
16:59
Overlap between Null and Alternative Distributions
17:00
How Distributions and 'Box' Fit Together
22:45
How Distributions and 'Box' Fit Together
22:46
Example 1: Types of Errors
25:54
Example 2: Types of Errors
27:30
Example 3: What is the Danger of the Type I Error?
29:38
Effect Size & Power

44m 41s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Distance between Distributions: Sample t
0:49
Distance between Distributions: Sample t
0:50
Problem with Distance in Terms of Standard Error
2:56
Problem with Distance in Terms of Standard Error
2:57
Test Statistic (t) vs. Effect Size (d or g)
4:38
Test Statistic (t) vs. Effect Size (d or g)
4:39
Rules of Effect Size
6:09
Rules of Effect Size
6:10
Why Do We Need Effect Size?
8:21
Tells You the Practical Significance
8:22
HT can be Deceiving…
10:25
Important Note
10:42
What is Power?
11:20
What is Power?
11:21
Why Do We Need Power?
14:19
Conditional Probability and Power
14:20
Power is:
16:27
Can We Calculate Power?
19:00
Can We Calculate Power?
19:01
How Does Alpha Affect Power?
20:36
How Does Alpha Affect Power?
20:37
How Does Effect Size Affect Power?
25:38
How Does Effect Size Affect Power?
25:39
How Does Variability and Sample Size Affect Power?
27:56
How Does Variability and Sample Size Affect Power?
27:57
How Do We Increase Power?
32:47
Increasing Power
32:48
Example 1: Effect Size & Power
35:40
Example 2: Effect Size & Power
37:38
Example 3: Effect Size & Power
40:55
XI. Analysis of Variance
F-distributions

24m 46s

Intro
0:00
Roadmap
0:04
Roadmap
0:05
Z- & T-statistic and Their Distribution
0:34
Z- & T-statistic and Their Distribution
0:35
F-statistic
4:55
The F Ration ( the Variance Ratio)
4:56
F-distribution
12:29
F-distribution
12:30
s and p-value
15:00
s and p-value
15:01
Example 1: Why Does F-distribution Stop At 0 But Go On Until Infinity?
18:33
Example 2: F-distributions
19:29
Example 3: F-distributions and Heights
21:29
ANOVA with Independent Samples

1h 9m 25s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
The Limitations of t-tests
1:12
The Limitations of t-tests
1:13
Two Major Limitations of Many t-tests
3:26
Two Major Limitations of Many t-tests
3:27
Ronald Fisher's Solution… F-test! New Null Hypothesis
4:43
Ronald Fisher's Solution… F-test! New Null Hypothesis (Omnibus Test - One Test to Rule Them All!)
4:44
Analysis of Variance (ANoVA) Notation
7:47
Analysis of Variance (ANoVA) Notation
7:48
Partitioning (Analyzing) Variance
9:58
Total Variance
9:59
Within-group Variation
14:00
Between-group Variation
16:22
Time out: Review Variance & SS
17:05
Time out: Review Variance & SS
17:06
F-statistic
19:22
The F Ratio (the Variance Ratio)
19:23
S²bet = SSbet / dfbet
22:13
What is This?
22:14
How Many Means?
23:20
So What is the dfbet?
23:38
So What is SSbet?
24:15
S²w = SSw / dfw
26:05
What is This?
26:06
How Many Means?
27:20
So What is the dfw?
27:36
So What is SSw?
28:18
Chart of Independent Samples ANOVA
29:25
Chart of Independent Samples ANOVA
29:26
Example 1: Who Uploads More Photos: Unknown Ethnicity, Latino, Asian, Black, or White Facebook Users?
35:52
Hypotheses
35:53
Significance Level
39:40
Decision Stage
40:05
Calculate Samples' Statistic and p-Value
44:10
Reject or Fail to Reject H0
55:54
Example 2: ANOVA with Independent Samples
58:21
Repeated Measures ANOVA

1h 15m 13s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
The Limitations of t-tests
0:36
Who Uploads more Pictures and Which Photo-Type is Most Frequently Used on Facebook?
0:37
ANOVA (F-test) to the Rescue!
5:49
Omnibus Hypothesis
5:50
Analyze Variance
7:27
Independent Samples vs. Repeated Measures
9:12
Same Start
9:13
Independent Samples ANOVA
10:43
Repeated Measures ANOVA
12:00
Independent Samples ANOVA
16:00
Same Start: All the Variance Around Grand Mean
16:01
Independent Samples
16:23
Repeated Measures ANOVA
18:18
Same Start: All the Variance Around Grand Mean
18:19
Repeated Measures
18:33
Repeated Measures F-statistic
21:22
The F Ratio (The Variance Ratio)
21:23
S²bet = SSbet / dfbet
23:07
What is This?
23:08
How Many Means?
23:39
So What is the dfbet?
23:54
So What is SSbet?
24:32
S² resid = SS resid / df resid
25:46
What is This?
25:47
So What is SS resid?
26:44
So What is the df resid?
27:36
SS subj and df subj
28:11
What is This?
28:12
How Many Subject Means?
29:43
So What is df subj?
30:01
So What is SS subj?
30:09
SS total and df total
31:42
What is This?
31:43
What is the Total Number of Data Points?
32:02
So What is df total?
32:34
so What is SS total?
32:47
Chart of Repeated Measures ANOVA
33:19
Chart of Repeated Measures ANOVA: F and Between-samples Variability
33:20
Chart of Repeated Measures ANOVA: Total Variability, Within-subject (case) Variability, Residual Variability
35:50
Example 1: Which is More Prevalent on Facebook: Tagged, Uploaded, Mobile, or Profile Photos?
40:25
Hypotheses
40:26
Significance Level
41:46
Decision Stage
42:09
Calculate Samples' Statistic and p-Value
46:18
Reject or Fail to Reject H0
57:55
Example 2: Repeated Measures ANOVA
58:57
Example 3: What's the Problem with a Bunch of Tiny t-tests?
1:13:59
XII. Chi-square Test
Chi-Square Goodness-of-Fit Test

58m 23s

Intro
0:00
Roadmap
0:05
Roadmap
0:06
Where Does the Chi-Square Test Belong?
0:50
Where Does the Chi-Square Test Belong?
0:51
A New Twist on HT: Goodness-of-Fit
7:23
HT in General
7:24
Goodness-of-Fit HT
8:26
Hypotheses about Proportions
12:17
Null Hypothesis
12:18
Alternative Hypothesis
13:23
Example
14:38
Chi-Square Statistic
17:52
Chi-Square Statistic
17:53
Chi-Square Distributions
24:31
Chi-Square Distributions
24:32
Conditions for Chi-Square
28:58
Condition 1
28:59
Condition 2
30:20
Condition 3
30:32
Condition 4
31:47
Example 1: Chi-Square Goodness-of-Fit Test
32:23
Example 2: Chi-Square Goodness-of-Fit Test
44:34
Example 3: Which of These Statements Describe Properties of the Chi-Square Goodness-of-Fit Test?
56:06
Chi-Square Test of Homogeneity

51m 36s

Intro
0:00
Roadmap
0:09
Roadmap
0:10
Goodness-of-Fit vs. Homogeneity
1:13
Goodness-of-Fit HT
1:14
Homogeneity
2:00
Analogy
2:38
Hypotheses About Proportions
5:00
Null Hypothesis
5:01
Alternative Hypothesis
6:11
Example
6:33
Chi-Square Statistic
10:12
Same as Goodness-of-Fit Test
10:13
Set Up Data
12:28
Setting Up Data Example
12:29
Expected Frequency
16:53
Expected Frequency
16:54
Chi-Square Distributions & df
19:26
Chi-Square Distributions & df
19:27
Conditions for Test of Homogeneity
20:54
Condition 1
20:55
Condition 2
21:39
Condition 3
22:05
Condition 4
22:23
Example 1: Chi-Square Test of Homogeneity
22:52
Example 2: Chi-Square Test of Homogeneity
32:10
XIII. Overview of Statistics
Overview of Statistics

18m 11s

Intro
0:00
Roadmap
0:07
Roadmap
0:08
The Statistical Tests (HT) We've Covered
0:28
The Statistical Tests (HT) We've Covered
0:29
Organizing the Tests We've Covered…
1:08
One Sample: Continuous DV and Categorical DV
1:09
Two Samples: Continuous DV and Categorical DV
5:41
More Than Two Samples: Continuous DV and Categorical DV
8:21
The Following Data: OK Cupid
10:10
The Following Data: OK Cupid
10:11
Example 1: Weird-MySpace-Angle Profile Photo
10:38
Example 2: Geniuses
12:30
Example 3: Promiscuous iPhone Users
13:37
Example 4: Women, Aging, and Messaging
16:07
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Research Design

Lecture Slides are screen-captured images of important points in the lecture. Students can download and print out these lecture slide images to do practice problems as well as take notes while watching the lecture.

  • Intro 0:00
  • Roadmap 0:06
    • Roadmap
  • Descriptive vs. Inferential Statistics 0:51
    • Descriptive Statistics: Data Exploration
    • Inferential Statistics
  • Variables and Relationships 1:44
    • Variables
    • Relationships
  • Not Every Type of Study is an Experiment… 4:16
    • Category I - Descriptive Study
    • Category II - Correlational Study
    • Category III - Experimental, Quasi-experimental, Non-experimental
  • Category III 7:42
    • Experimental, Quasi-experimental, and Non-experimental
  • Why CAN'T the Other Strategies Determine Causation? 10:18
    • Third-variable Problem
    • Directionality Problem
  • What Makes Experiments Special? 17:54
    • Manipulation
    • Control (and Comparison)
  • Methods of Control 26:38
    • Holding Constant
    • Matching
    • Random Assignment
  • Experiment Terminology 34:09
    • 'true' Experiment vs. Study
    • Independent Variable (IV)
    • Dependent Variable (DV)
    • Factors
    • Treatment Conditions
    • Levels
    • Confounds or Extraneous Variables
  • Blind 38:38
    • Blind Experiments
    • Double-blind Experiments
  • How Categories Relate to Statistics 41:35
    • Category I - Descriptive Study
    • Category II - Correlational Study
    • Category III - Experimental, Quasi-experimental, Non-experimental
  • Example 1: Research Design 43:50
  • Example 2: Research Design 47:37
  • Example 3: Research Design 50:12
  • Example 4: Research Design 52:00

Transcription: Research Design

Welcome to www.educator.com.0000

Today we are going to be talking about research designs, also known as research strategies0002

Here is the roadmap for today.0007

First we have been talking about descriptive versus inferential statistics.0010

You now that now were talking about different ways of doing inferential statistics so we are just going to review this again.0015

Then we are going to talk about variables in relationships.0021

There are different kinds of variables and different kinds of relationships you need to know about.0025

We are going to introduce three different categories of research strategies or designs0029

and then we are talking about why cannot other strategies besides the experimental strategy determine causation.0035

We are going to talk about what is so special about experiments.0042

Finally, we are going to end with some experimental terminology.0046

Now that we have been doing a lot of descriptive statistics so far looking at visualizing data, summarizing it,0050

and we could do that for both one and two variables.0059

Then we talked about why inferential statistics is different.0062

Number 1, we are interested in generalization and because of that we covered some sampling methods that allow our samples to generalize to the population.0066

We also introduced the idea that we would like to have comparison in order to examine causation.0077

We would like to know whether one variable causes another not just the relationship between them in terms of a regression line0085

but we like to know does one cause the other one.0093

In order to do that, we are going to have to learn about experiments.0098

But before we learn about experiments, it is going to be helpful to learn about other kinds of research strategies in addition to experiments,0103

just so we could see why experiments are so special and different.0112

but in order to understand those different kinds of research strategies we are going to need to review variables and the relationship that they can have.0117

One thing you need to know about variables is that we have covered 2 broad kinds of variables so far.0125

Categorical, categories as the variable levels instead of actual numerical values or continuous0132

where the values of the variables are numbers that are meaningful in some way.0144

An example of categorical variables are whether kids have pets or not, versus continuous which might be something like a score on a biology test.0149

Then you can have maybe 1 – 100 and each of those numbers are meaningful.0163

In terms of relationships, there is a bunch of different relationships we actually covered.0169

All of those relationships so far have been descriptive relationships.0175

We have covered relationship such as linear relationship, curvilinear, we have also looked0180

at relationships such as negative correlation and positive correlation.0188

But all of those relationships are descriptive.0199

They are just describing what the relationship is like.0204

They are not actually telling you whether one variable causes the other variable.0208

Those are causal relationships.0213

They are not necessarily telling you that one variable does not cause the other one either.0215

This is a new kind of relationship that we are going to be looking at.0219

Only one kind of study, what kind of research design can actually look at causal relationships.0224

And that is going to be through the experimental research strategy or design.0234

This is the only one.0245

That one is pretty special.0248

So far we have only covered these other kinds of relationships.0251

Before we go on to the nitty-gritty of an experiment, it helps to know what are the non-experiment.0255

Not every type of research study is an experiment just because you collect data and some variables does not mean you have an experiment.0266

That is a very meaning to start using a mere context.0276

A lot of people especially in the media or popular press they only use it in a more loose way before they call it an experiment.0280

Usually we use that for a very specific kind of research strategy.0288

First, category 1 are the kind of studies that are only descriptive.0293

These are descriptive studies.0302

A variable can either be categorical or continuous but they do not look at any relationships.0304

No relationships.0314

They do not even describe relationship between variables.0316

They only look at variables in isolation.0321

They are really only interested in the distribution of peoples heights, I.Q, or the distribution of a bacteria in the stomach.0323

They are only interested in describing one variable or a couple of different variables but not looking at the relationships between them.0337

That is very important.0345

That is the descriptive study.0347

That is category one. Category two moves on a bit.0349

Here we are interested only in continuous variable.0350

Only continuous variables can be used in category 2, no categorical ones.0357

Here the only relationship we can look at is the descriptive.0362

Here because we have multiple continuous variables multiple, we often look at these on a scatter plot.0367

Those are called correlational studies and that is category two.0381

Category 3 has three different kinds of research strategies in their.0393

One is experimental, the other is quarter experimental, and the third kind is non experimental.0399

In these, you need to have at least one categorical variable.0406

You can have also continuous ones and other categorical variables but you need to have at least one categorical variable.0416

Here you can look at both descriptive relationships, both for one of these the experimental one, you could also look at causality.0426

You cannot look at causality with cause experimental, not quite, not all the way, nor non experimental.0438

Even though you can look at causality within a category 3 research strategies it is really only experimental research strategy that can look at causality.0449

In category 3, let us break down these three different types.0465

Experimental research strategy you will know that you need at least one categorical and that is actually the case for all of these at least one categorical.0469

That is what these three have in common but for experimental, you can look at both the descriptive relationship and causal relationships.0493

You can look at both of those kinds of things.0512

For quasi experimental, we could look at descriptive and we can come up with causal explanation, but not quite.0515

And that is what called quasi experimental.0521

It gets close to get pretty close as closer you can be without actually being experimental and for non experimental you can only look at descriptive.0523

These are the three different kinds and quasi experimental ones are also often called accidental experiments0549

or one very common example is using pretest and post tests where you are looking at how something in the middle is causing you,0562

but you also have compound where because your testing people two times or your measuring something twice.0577

It might just be an artifact of measuring something twice.0586

For example, if you wanted to know how good a math software is, you might have a pretest0590

and post test but it might just get better just because they did the same test twice.0596

That is why quasi experimental sometimes has issues.0601

Non experimental variables are going to be quite different from experimental, categorical variable.0606

We are going to talk about next what the difference is.0614

Okay, actually before we do that we are going to talk about why cannot the other strategies determine causation.0617

It is because it seems exclusive for experimental research strategy.0626

What is wrong with the other one?0631

All of the other strategies have 2 fundamental problems that they cannot escape.0633

Here is the big issue.0641

We have some variable one and we want to know whether they will want causes changes in variable 2.0643

For instance, let us say we want to know if having a radio in developing countries will lead to having women having fewer children.0656

If your household has a radio does it mean you have fewer children in your house or greater use of birth control?0671

That might be variable 1 radio and available 2 might be something like use of birth control.0679

We want to know whether one variable causes the other variable.0691

Because if we knew about causation and we want to reduce or increase the use of birth control somewhere then we could adjust radios in households.0699

We could give people free radios or something, and then by doing that change the use of birth control.0721

It would be nice to know whether this causal relationship actually exists or not.0727

Now, one thing you can do is to ask people whether they have radios and then ask people how much they use birth control.0730

Whenever you do studies like that and that would largely be a non experimental design because this is a categorical variable radio, no radio.0737

Maybe use of birth control might be how many times do you use birth control in a month and maybe that is continuous.0747

Here one issue is you do not know whether one causes the other because we have what is called a third variable problem.0755

A third variable problem can be described like this.0766

There might be another variable that actually impacts both your variable 1 and 2.0770

This is what we call a extraneous variable.0776

One that is sort of outside your scope of study.0779

You are not looking at it in your study.0783

This extraneous variable might actually impact both variable 1 and variable 2.0787

You might see variable 1 and 2 having a relationship together like when one goes up, the other goes up0794

or the other one goes down and the other one goes down.0802

You might see that relationship.0804

That relationship might actually be caused by a third variable that is affecting both of those.0806

For instance, perhaps how much money your household earns affects whether you have a radio but affects the use of birth control.0814

Or the level of education that a woman has might affect whether they own a radio and whether she uses a birth control.0823

Those things might be caused by something else.0831

They look like they are moving together but they are actually not causing each other.0834

One sort of picture that I like to draw from my students, sort of a mental picture is this idea of let us say there are 2 boats.0839

There is a little blue boat and there is little red boat.0851

And you whenever you see the blue boat sort of rise up in sea level, the red boat rises up in sea level.0866

When the blue boat goes down in sea level, the red boat goes down in sea level but actually both of those are caused by the water.0875

It is not that the blue boat is actually causing the red boat to go up or down but it is really the water0888

that is causing both the blue boat and red boat to go up and down.0893

In this case, this might be variable 1, variable 2, and the water is the extraneous variable.0897

It might really be this extraneous variable that you are not studying for some reason that is really causing the both of these things.0906

That is always the problem in a non experimental correlational designs.0915

They always have this problem of the third variable that might be lurking back there.0925

The third variable is often also called a confound.0931

Something that is moving along with variable 2, the variable of interest, but it is sneaking in there.0935

We are not measuring it, so we do not know about it.0946

Even when you solve this third variable problem, sometimes you still have another problem that remains and this is called the directionality problem.0951

We do not actually know whether variable 1 impacts variable 2 or is it that variable 2 impacts variable 1.0961

We do not know how to distinguish between those and that is called the problem of directionality.0974

Even if two variables have a relationship to each other either a positive correlation or negative correlation, we do not know whether X causes Y or Y causes X.0979

That is hard to distinguish in a correlational design or non experimental design.0991

Let us say we take a correlational picture.0999

For instance, here, let us say that variable 1 is a woman's educational attainment and variable 2 let us say is use of birth control.1003

Well, we do not know whether a woman's education actually impacts the use of birth control1028

or perhaps it is that using birth control allows women to stay in school longer.1034

It could be either one of those directions that we do not know from doing a correlational study.1041

Let us say we ask women how many years of schooling do you have and we ask women how often in a month do you use birth control?1046

That would be a correlational design, but one issue that we will have is we do not know whether X causes Y or Y causes x.1055

On top of that right now we also do not know whether there is a third variable lurking that might be more explanatory.1064

Experiments have found a way to solve both of these problems based on the third variable problem and the directionality problem.1076

That is why experiments are special.1085

Here is how experiments have solve the problem.1087

They are special because they use manipulation and what we call control.1090

Manipulation and control these two solve the third variable and directionality problem.1097

Here is what we mean by manipulation.1105

Manipulation means that in an experiment a categorical variable, the variation is created by experimenter or researcher team.1107

It is not that we just ask women if they own a radio, it is that the experiments has actually go in there and create that variation.1133

They might take a whole bunch of people, none of whom have radios, they are all the same at first1141

but then the experimenters create variation by randomly assigning half of them to get a free radio.1146

In that way, the experimenters are the one who is creating.1155

They are the ones walking around in there and that is manipulation.1160

To the categorical variable gets manipulated.1165

Whenever you think manipulation I want you to think create it, like rolling up your sleeves and get in there making them a certain way.1169

That is one thing that experiments do differently.1178

How does these address the third variable problem?1181

Well, it addresses the third variable problem because the experimenters created some condition right.1188

They created the radio condition.1196

Then once the experimenters introduced this then you can look for changes in the second variable.1198

Now the experimenters did not do anything to that second variable.1213

If that second variable changes after the man made change, then you know that it is caused by that man-made change because all of the things stay the same.1216

Their level of education and their socioeconomic status.1227

Everything else remain the same.1231

The only thing that changed was the introduction of that radio.1233

They addressed the third variable problem.1238

What about directionality?1243

The way that they solve directionality is like this.1247

They gave them the radio.1253

The experimenters gave the participants the radio.1255

Is the radio caused by birth control?1258

No, because the experimenters caused it.1263

We already know how variable 1 was caused.1266

It was caused artificially by the experimenters.1270

Because we have ruled out that Y causes X or variable 2 causes variable 1.1275

We know there is only one direction that could possibly be the case because we ruled out the other one.1282

This one, we rule out variable 1 causes variable 2.1290

We basically ruled this arrow out because we know what caused variable 1, the experimenters.1297

That is one of the important components of an experiment that they had manipulation of your categorical variable.1308

The second component is what we call control.1320

Now often people mistake the word control to mean manipulation, but I need you to separate that in your mind.1323

When you think of the word control I want you to think isolation.1331

What you need to do in order to have control is isolate the variable of interest.1340

Everything else has to be the same, except for this one thing that is different for instance, having a radio.1347

The problem with other studies is that they do not have control.1356

The women who have a lot of education, they might also be wealthy or live in a different neighborhood or already been using birth control.1361

Those women are different in many ways and variables.1372

What we want to do in control is isolate the variable of interest and how do we do that?1377

We make sure we have 2 equivalent groups first.1390

Only difference is manipulated variable.1404

What we want to do is start off with two groups of people that look exactly the same for all intensive purposes.1416

We have two groups.1421

They are all the same.1424

They are women, there are some rich women here and some poor women here.1425

There are some educated women here.1431

There are some uneducated women there.1433

Also this other group has the same mix of women.1436

Once you have two equivalent groups then one group get something different.1443

And that is the manipulated condition or the treatment condition.1452

So then, one gets the manipulated condition.1458

The other that does not get anything special is called the control group.1464

It is because we want to compare these two groups because they only have one thing that is different.1469

There is only one variable that is different then we know that any other changes that come from that point on is caused by that one difference.1476

Because for everything else they are the same.1486

That is what we call control.1490

Remember you have to stop reading your mind.1492

Remember control, we do not mean less when we mean control.1495

That is manipulation.1499

When we mean control think of this picture, separating out, isolating the variable of interest,1501

making two equivalent groups, having only one thing the different.1507

When you have two equivalent groups and only one thing is different, that one stands out.1514

That is how we isolate the variable.1517

How is this solved the third variable problem?1521

While here is the thing, I had 2 equivalent groups and you have ruled out the other variables.1527

Having the two groups, 2 equivalent groups that rules out the third variable because on all these other variables like how rich or poor, ethnicity, whatever.1534

On all these other things, these women are roughly equivalent.1551

How do we solve directionality?1556

Actually control by itself does not actually address directionality.1562

It has to be control plus manipulation in order to get directionality.1567

Directionality is taken care of by manipulation.1572

These 2 things are what make experiment special.1580

The manipulation of a categorical variable and the control or what we call isolation of that variable.1584

Having a control group that is identical in every other way except for that one manipulated difference.1591

There are a couple of different methods of control that one could use.1602

The question is how do we create these equivalent groups but obviously a lot of times you never have exactly the same people.1606

You might have an Asian women and in one condition an Asian women in another condition.1617

They are not exactly the same.1623

How do we create 2 roughly equivalent groups or one thing we can do is hold the other variables constant.1627

Let us say where interested in two groups of women and we are worried about education.1637

Maybe we will have all college educated women.1644

They all have a university degree.1649

Everybody has a university degree and one group gets radios.1659

That might be one way we do.1663

We just have these two groups be the same on some extraneous variable or perhaps you are really interested in whether they are married or not.1665

We might only have a single woman in our sample.1676

Both groups are made up of single women, or perhaps we are interested in age.1680

Maybe we want them all to be a similar age.1687

Holding constants means we are holding some suspected third variable constant.1691

The problem with holding constant, although it is a really good method of control is that it is just really hard to do for every single variable1707

and there are tons of variables.1718

It is really hard to hold more than one or two variables constant.1719

Or else you might have had to find a group of women or educated the same class, the same gender, age, same locale, race.1724

After a while like it gets smaller and smaller and smaller and then you will only have 3 people that fit that description.1738

Often you can only do that for one or two extraneous variables.1747

Another method of matching is that what you have is for every person you have here, you have sort of equivalent person on in the other group.1752

Sometimes we might have age matching.1764

For every 23-year-old, you have a 23-year-old in the other group.1766

For every 30-year-old, you have a 30-year-old in this group.1771

For every 28-year-old, you have a 28 year old in this group.1774

For every 41-year-old, you have a 41 year old in this group.1778

And then one group gets radios.1781

In this case, we are not holding some variable constant but basically roughly on average these participants1785

are roughly equal to these participants on some third variable.1798

You can do point by point matching or you could do by just having roughly even matching.1806

For here we might have a 38-year-old, but we might have a 36-year-old.1815

If we take the means of these two groups we will be roughly equivalent on this variable of age.1820

Making sure on average, participants in both groups, equivalent on some third variable.1830

It might be how much many people make in a year.1861

It might be whether they are married or single.1865

You might have a group of women who are half married, half single but then here also you have a group that half married, half single.1868

Matching is a little bit more loose than holding constant but matching still has the same problems.1876

You can only really match on a couple of extraneous variables that you have to plan ahead of time.1885

There might be other variables that are important, but you just did not know how important they are.1891

Maybe whether they live in an apartment or house or have roommates or not.1896

Maybe those are really important but perhaps you did not match for those variables.1900

But if you think about it, there is a big problem.1908

There are so many variables that might be important like something we do not even know like1911

having some fold in the powerless region of your brain might be really important to birth control.1921

Or maybe you know some prior experience with your first boyfriend is really important or maybe how hungry you tend to be is really important.1929

Who knows?1939

They are billion extraneous variables.1940

They are infinite number of them.1943

How could we possibly match for all of them?1945

It is impossible.1948

We cannot hold those things all constant.1949

We cannot match for all of those things.1951

What are we going to do?1953

One thing is to trust again in randomness.1955

One way of doing it is create two groups and women are randomly assigned to these two groups.1959

It is like flipping a coin for each person like heads, in the other group tails.1968

By doing it randomly, hopefully you will have an even mix of women who are hungry and not hungry here and hungry and not hungry and here.1973

Or people who had bad experiences with their first boyfriend in here but they are randomly put into groups1983

so that the two groups are roughly equivalent on all kinds of extraneous variables that you did not even know existed.1993

With random assignment, the issue is you are putting your trust in randomness.2004

Randomness does not always mean that you will get equivalent groups.2009

In both of these methods of control you definitely end up with the equivalent groups for those variables that you control.2014

But for random assignment, you could roughly have 2 equivalent groups for lots of variables2023

that you do not even know about but in exchange, you do not have a guarantee that these two groups are equivalent.2030

There is no guarantee but often random assignment is used just because there are so many variables that are just really hard to control.2039

Let us talk about experiment terminology.2052

When we say it is a true experiment, what I really mean is that we really did use the experimental research strategy.2055

That means manipulation and control.2068

Sometimes people will just say that this test was on the lab, it was an experiment.2073

But that does not necessarily mean that the use of experimental research strategy so you have to check for that.2078

What we call all other studies that seem like you know like they are using some research2085

and collecting data with variables and all of that stuff, we call all of those a study.2090

If it is a correlational design can it be an experiment?2097

No, it can only be a study.2103

Whereas an experiment can actually also be called a study.2107

You just have to be careful about how you use those words from now on.2112

One important new piece of terminology is the independent variable.2118

This is being manipulated variable, categorical variable in an experiment.2124

That variable we are going to give it a special name because we talked about it frequently.2138

We are going to call that the ID or independent variable.2142

The variable that you are interested in measuring the outcome of, that is called the dependent variable.2146

This is how we measure the outcome of an experiment or a study and that is called the dependent variable.2161

Factors that is just a different name for independent variables.2169

You might have 2 factors that means you have 2 IV.2174

You might have 3 factors which means you have 3 IV.2177

Factor is just another name for IV.2180

Treatment conditions are the different situations caused by manipulating that IV.2185

Once you have an IV, you will end up with multiple treatment conditions.2190

Often at least one treatment, one group that get something special, for instance getting a radio versus the control condition where they do not get anything.2195

Nothing is done to them.2208

If the control condition, sometimes they just do not have anything else.2212

Often in medical studies, you might see randomized trials where that means they did random assignment for patients2217

to get into the treatment condition where they get some sort of special drug.2225

But the other group they do not go into the control condition, they go into what we call the placebo condition.2229

One group will get a pill that works and have effect.2235

The other group gets sugar pills.2243

Just so that it is not just the fact of believing that it will help in order to rule that out.2246

Placebo conditions are very similar to controls except that they do get something.2253

It is just that the thing is chemically inert like sugar.2258

Levels of an IV is the same thing as treatment conditions.2264

In this IV we have 2 levels.2271

We have this level and that level.2273

It is another way of saying different conditions of IV and confounds or extraneous variables,2277

these are those variables that you do not measure necessarily but affects your DV.2289

These affect DV but they are not part of the your study design.2297

Those are what we call confounds or extraneous variables.2314

Sometimes you might hear the terminology that this is a blind experiment.2321

This means that participants in the study do not know what the conditions are and they do not know what the condition they are in.2326

There participants do not know what condition they are in.2335

This is often a piece terminology that you hear in medical studies where patients are blind.2348

It just means that they do not know which condition there in.2362

They do not know whether they are in the treatment condition or the placebo condition2365

Blind experiments take it one step further.2370

Not only do the participants not know, but also the research team that is interacting with the participant those people do not know either.2373

Also, research team interacts with participant do not know what condition participant is in.2391

For instance, let us say I am like the pharmacist who gives out the medication so the medication might already be labeled with the person's name.2414

I do not know what is inside of it.2423

I do not know if the placebo is inside of that or the drug is actually inside of it.2425

When I interact with the participants and give them instructions and I say if you want to take this two times a day.2431

You definitely do not want to drink milk if you are you taking this medication.2439

Then my interaction with the participant is the same regardless of whether they are in the placebo condition or the drug condition.2446

Those are called double-blind experiments.2455

Double blind experiments are also important in psychology, where off and research assistants administer the study2458

do not know what condition the participant is that so the computer or some other recording system will record which condition that the purchased an event.2465

The experimenter does not know they usually the person who is in charge of the entire thing they know what condition everybody2478

but the people interacting with the participants need to be blind in order to be a double-blind experiment.2487

Finally, let us summarize how categories are related to statistics.2499

In category 1, in the script that studies usually all you can do is summarize data which we know how to do or visualize using things like histogram.2504

You know box plots and all-kinds of things by in correlational studies what you can do is also summarize the visualize data.2523

It could still do not summarize visualize, but now you could also analyze your data with regression because this is part of summary and find correlation.2535

Now you can apply those as well to correlational study now with experimental quasi experimental and non experimental designs of category three.2559

Obviously, you could summarize and visualize but we cannot necessarily use regression lines2570

because regression and correlation are saved for when you are interested in two continuous variables.2578

But one of them is categorical.2587

We have learn to deal of those yet we are going to learning them to the next eventually on the next lesson.2592

We got to set up probability ideas and advance but we are going to be learning about t-test and f-test and clusters.2601

These are going to depend on probability some probability principles so we are going to go cover those first.2617

but then we are going to go on and talk about this analysis later on.2625

Let us move on to some examples.2631

Example 1, an educational psychologist has found a significant relationship between college students grade point average and their parents annual income.2634

Students with affluent parents have a higher grade point averages and students with poor parents.2645

She concluded that a student level of academic success depends on how much many the student’s parents earn.2651

What research strategy was used?2657

While we know that grade point average is continuous and annual income is continuous, so this must be category two correlational.2660

What statistical analysis were probably conducted?2673

This sounds like a positive relationship and that seems like it is correlation.2677

Here it says the academic success depends on the money from their parents.2699

That is a causal word.2707

You have to be careful because a lot of times, people would not just come out and say straight that the cause is this.2710

There are a lot of different ways of saying causes impacts depends on these code words for causality.2715

Is it true that money causes academic success?2725

Well, in a correlational design can you look at causality?2732

No why?2737

Because there is the third variable problem.2741

It might be other things.2750

For instance, their parents might have really good work ethic and because of that they have higher incomes.2752

Maybe they insist that work ethic in their children so that they have higher grade point averages.2764

That might be one explanation of a third variable work ethic that really explains it.2769

Another thing might be that the parents income gives these kids access to extra tutoring.2775

It is really the tutoring that helps their grade point averages.2783

Let us say they have free tutoring available then maybe this relationship would just go away.2791

Another thing might be that maybe parents income is affected by you know by their ability to delay gratification.2797

They have these values about delaying gratification and how education delays gratification.2809

Maybe because that these college students have been raised in a household where delay gratification has been highly valued2816

and because of that, there are able to say, although I'm not going to drink because I'm going to take my final tomorrow.2825

They can delay gratification.2832

It might be that these third variable are at play.2834

Probably directionality is not as big of an issue because probably in common is not quite affected by their student’s scores.2837

GPA affecting current income that the harder link to sort of imagine.2850

That is probably not likely to be the case.2854

Example 2, a psychologist wanted to compare children in the first, third and fifth grade on their persistence on a difficult task.2859

What kind of research design is this?2867

They want to know about two variables.2870

Variable one is age or grade level right and variable 2 is persistence.2873

This is a categorical variable and maybe persistence since they might measure it by saying times spent on a difficult task.2886

It is like a really hard puzzle but they look at how long kids spent on it.2898

Maybe times spent and if that is the case, that is a continuous variable.2903

We know that we are in category three.2911

Is this an experiment?2919

Okay is this categorical variable manipulated experimenters or control it.2922

And if everything except for age controlled for.2929

No, that is not the case.2934

They did not randomly assigned these children to be these ages.2936

The children were not made to be instantaneously older.2941

They do not start off as a major just magically made older.2944

It is not experimental.2948

It is not quasi experimental.2951

It is not really pre-post.2953

It is not really cluster experiment.2954

This is non experimental.2955

Can we conclude that age, or rather like experience causes persistence?2966

No, not necessarily.2972

It might be that somehow the third grade curriculum causes persistent or maybe it is time spent in school causes persistence.2974

There might be all these other variables involved, and so we do not necessarily know that age causes persistence.2984

Is it that age does not cause persistence?3004

That is not what I’m saying.3006

It is just that we do now know.3008

We cannot say that it does or does not.3009

Example 3, a biologist wanted to know whether complex sugars can sustain life longer than simple sugars.3013

She prepares six petri dishes, each containing 10 bee insects.3020

2 dishes are assigned to the control group and 2 are assigned to simple sugars and 2 our assigned to complex sugars.3026

She took 2 dishes and put nothing in one.3034

Simple sugars in 2 of them and complex sugars on the other 2.3036

The DV is the time it takes for half of the leaf hopper insects to die.3041

What are the cases?3047

Are the cases that the leaf hoppers?3052

No because what we are looking for is how fast half of the leaf hoppers dies?3053

It is not about the leaf hoppers itself.3063

It is actually about the dishes. The petri dishes.3068

Each of those is the case, and for each of those petri dishes they are going to have a dependent variable.3072

How long it took for half of the week the leaf hoppers to die?3080

What is the sample size?3089

The sample size is 6 dishes and is this an experiment.3091

Yes, it is presumably these dishes all started off the same and then 2 we are randomly like this that the two were special,3097

but 2 are randomly assigned to have nothing.3105

2 randomly assigned to artificially by the experiment with extra to put simple sugars in it.3108

The experimenter put complex sugars and the other one.3114

This is an experiment.3117

Example 4, if you wanted to test the hypothesis that hamster were raised in less daylight3119

have higher hormone concentrations than hamsters raised in more daylight.3129

What would you do to show that daylight exposure causes hormone concentrations to increase?3136

What you would want to do is first start off with hamsters that were always sort of similar?3144

Start with similar hamsters.3153

Maybe I will get hundred hamsters that were raised similarly.3157

They are the same amount of day light.3161

I would randomly assigned these hamsters to two groups.3164

In one of the groups I would raise in less daylight and the other group would get more daylight.3171

The IV is daylight exposure.3189

How much daylight they got?3193

The DV that I'm interested in is their hormone concentrations.3195

Once they are in these two groups and now I change the way that their raised.3204

One gets more daylight.3209

One gets less daylight.3210

Than I would measure their hormone concentrations and see if there are many changes.3213

That is our research strategies.3219

Join us next time on www.educator.com3223

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