COMM 250 Agenda - Week 12

Housekeeping

• RP2 Due Wed.

• RAT 5 – Wed. (FBK 12, 13)

Lecture

• Experiments

• Descriptive and Inferential Statistics

In-Class Team Exercise # 8 – (Review)

First Do as Individuals, then produce a Team Version:

1) Design a Factorial Experiment to answer these questions:Which can be read faster on a web site - plain text (plain black letters on a white

background, no links) or text supplemented in some way?

What other variables might affect a user’s ability to read text? (Name 3 and then Choose one for Step 2)

2) Draw a table of the design - at least 3 levels of one variable, 2 of another (you choose the second IV)

• Label the 2 IVs and Label Their Levels

3) Write out 2 Hypotheses (H1, H2):• One Predicting Effects of IV 1, the other the Effects of IV 2

4) Declare the DV (It is in your H1, H2)

5) List Two (“People”) Variable you Should “Control for”

Statistics

Why Study Statistics ? Integral to the “Scientific Method” Seeing the Forest Amid the Trees

Describing (Sports Statistics) Inferring (Correlates of Cancer) Predicting (Science, Business)

To Pursue an ‘Objective’ View

Statistics

Descriptive Statistics: a way to summarize data

Inferential Statistics: strategies for estimating population

characteristics from data gathered on a sample

Descriptive Statistics

Measures of Central Tendency Used to describe similarities among scores What number best describes the entire

distribution?

Measures of Dispersion Used to describe differences among scores How much do scores vary?

Descriptive Statistics

Measures of Central Tendency

Mean The Average Medium The Middle Score Mode The Most Common Score

Measures of Dispersion

• Range The Highest & Lowest Scores

Variance A Measure Of Dispersion Equal To The

Average Distance Of The Scores, Squared, From The Mean Of All Scores, Divided By N

Standard Deviation The Square Root Of The Variance

(Dispersion About The Mean, Based In The Original Units)

Inferential Statistics

Used For -

Estimation To Extrapolate from a Sample to a

Population

Significance Testing• To Determine the Importance of

Observed Differences; e.g., Between Groups or Variables

Using Inferential Statistics for EstimationPurpose To Estimate (Extrapolate) from a Sample (Statistic)

to a Population (Parameter)

Reason Why It Saves Time and Money (Don’t Have to Survey/Measure the

Entire Population)

A Statistic Measure of a Sample (E.g., Comm250) on Some Variable

E.g., Average (Mean) Height of Comm250 Students

A Parameter A Characteristic of a Population (E.g., Virginia)

E.g., Average (Mean) Height of Residents of VA

Inferential Statistics

Note: When Statistics are Used for Estimation, this is the “Heart” of Using Statistics to Infer -

Statistic Measure of a Sample (E.g., HSers taking SAT in ‘02) on

Some VariableE.g., Average (Mean) SAT score in 2002E.g., Average (Mean) SAT score Among GMU Freshmen

Parameter A Characteristic of a Population (E.g., All HSers Who Have

Taken the SAT)

E.g., Average (Mean) SAT Across ALL HSers (50 years?)

Parametric & Non-Parametric

Parametric Statistics • Stats Used to Establish Attributes of a

Population Based on Attributes of a Sample

Non-Parametric Statistics Stats Used ONLY to Describe The Attributes

of a Sample No Generalization Back to Its Population is

Attempted

Inferential Statistics

Estimation of Population Parameters Assumes: A Normal Distribution

“Bell-Shaped Curve” Most Variables ARE Normally Distributed

A Random Sample Unless Some Form of Random Sampling is

Used, the Sample Will NOT be “Representative” of the Population (to Which One Wishes to Generalize)

Significance Testing

Analyzing Sample Data To Test Hypotheses About Populations

• By Convention, There are Always Two H:• The Null (H0: No Differences)

• The Experimental (H1: Differences Exist)

Hypotheses One-Tailed = Directional Two-Tailed = 2-Directional (Non-Directional)

Testing The Null Hypothesis

Set The Significance Level

Compute The Calculated Value

Compare This To The Critical Value Needed To Reject The Null

Testing Differences

Nominal Data: Chi Square Test

Ordinal Data: Median Test

Interval Data T-Test, ANOVA, & Ratio Data: Regression

Types of Variables

Definition: “Concepts that take on 2 or more values”

• Nominal = Equal Groupings• (Gender, Race, Political Party)

• Ordered = Some Priority or Rank• Ordinal

• Rank Order: (Hottest Days, Top Ten)

• Interval• Equal Intervals: (Temp., IQ, Scale from 1-7)

• Ratio• Interval, with a “True” Zero (Weight; Height)

t-Test and ANOVA

The t-test 2 independent samples (groups) Are the samples different ? A Different Online Example to Try

Analysis of Variance (ANOVA) 2 or more levels of a (Discrete) IV Often Multiple IVs; Single DV Factorial Designs Main Effects; Interactions

In-Class Team Exercise # 9

First Do as Individuals, then produce a Team Version:

A professor believes that posting “practice quizzes” increases student test scores. He provides online quizzes to class A but not to class B.

1) What is the Research Hypothesis? The Null?2) Conduct a t-test on the Following Quiz 1

Scores:• Scores from Class A: 12, 12, 12, 11, 11, 11, 10, 9• Scores from Class B: 9, 8, 8, 7, 7, 7, 6, 4

3) What Conclusion Can Be Drawn?----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Deliverable: a written version – showing all work

Review of: In-Class Team Exercise # 9

The answers are on the link on the previous page, or click on this link for answers.--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

You will be required to do a similar problem

(a t-test and the accompanying questions)

on P-RAT 6 and in-class RAT 5.----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

In-Class Team Exercise # 10Produce a Team Version only:How does talking on a cell phone affect driving?

Design a 3 x 2 Factorial Experiment (draw a Table)You Must Use These IVs:• Level of Driving Experience (Pick 3 Levels)

• Type of Distraction (Pick 3: Cell Phone, Changing CDs, You choose #3)

Write out 2 Hypotheses (H1, H2):• Your DV should be: MPH deviation from the average speed

on the road

• One Predicting the Effects of Driving Experience• One Predicting Differences Due to Type of Distraction

Label the 2 IVs and Label Their LevelsList Two Other Variables you Should “Control for”