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BNAD 276: Statistical Inference in Management Spring 2016 Welcome Green sheets

By the end of lecture today 1/26/16 Use this as your study guide By the end of lecture today 1/26/16 Independent and dependent variables Experimental versus quasi-experimental methodology Control versus treatment Correlational methodology Random assignment Constructs versus measurement Operational definitions Validity of operational definitions Reliability of measurements Population versus sample Within-participant and between-participant design Single blind (placebo) procedure Double blind procedure

writing assignment forms notebook and clickers to each lecture Remember bring your writing assignment forms notebook and clickers to each lecture A note on doodling Remember to register your clicker soon

Homework Assignment #2 (Has 2 parts) Go to D2L - Click on “Content” Click on “Interactive Online Assignments” Please complete the homework modules on the D2L website under the “Content” tab HW2 – Part 1 - Sampling Techniques HW2 – Part 2 - Integrating Methodologies Due: Thursday, January 28th

Schedule of readings Before next exam: Please read Chapters 1 - 4 in OpenStax Supplemental reading (Appendix D) Supplemental reading (Appendix E) Supplemental reading (Appendix F) Please read Chapters 1, 5, 6 and 13 in Plous Chapter 1: Selective Perception Chapter 5: Plasticity Chapter 6: Effects of Question Wording and Framing Chapter 13: Anchoring and Adjustment

On a the top half of a writing assignment form Categorical data (also called qualitative data) - a set of observations where any single observation is a word or a number that represents a class or category Numerical data (also called quantitative data) - a set of observations where any single observation is a number that represents an amount or count Please note we’ll use the bottom half for something else On a the top half of a writing assignment form please generate two examples of categorical data and two examples of numerical data

What are the four “levels of measurement”? Categorical data Nominal data - classification, differences in kind, names of categories Ordinal data - order, rankings, differences in degree Numerical data Interval data - measurable differences in amount, equal intervals Ratio data - measurable differences in amount with a “true zero”

What are the four “levels of measurement”? Ratio Absolute zero Most numeric Categories Intrinsic ordering Equal sized intervals Units meaningful Interval Names Categories Intrinsic ordering Ordinal Approaching Numeric Categories Least numeric Names Nominal Weakest

What are the four “levels of measurement”? Categorical data Nominal data - classification, differences in kind, names of categories Ordinal data - order, rankings, differences in degree Numerical data Interval data - measurable differences in amount, equal intervals Ratio data - measurable differences in amount with a “true zero” Gender - male or female Family size Jersey number Place in a foot race (1st, 2nd, 3rd, etc) Handedness - right handed or left handed

What are the four “levels of measurement”? Categorical data Nominal data - classification, differences in kind, names of categories Ordinal data - order, rankings, differences in degree Numerical data Interval data - measurable differences in amount, equal intervals Ratio data - measurable differences in amount with a “true zero” Age Hair color Telephone number Ethnic group Breed of dog Temperature Yearly salary

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What are the four “levels of measurement”? Categorical data Nominal data - classification, differences in kind, names of categories Ordinal data - order, rankings, differences in degree Numerical data Interval data - measurable differences in amount, equal intervals Ratio data - measurable differences in amount with a “true zero” Look at your examples of qualitative and quantitative data. Which levels of measurement are they?

Time series versus cross-sectional comparisons: Trends over time versus a snapshot comparison Time series design: Each observation represents a measurement at some point in time. Repeated measurements allow us to see trends. Cross-sectional design: Each observation represents a measurement at some point in time. Comparing across groups allows us to see differences. Traffic accidents Please note: Any one piece of data can often (not always) be used in either a time series comparison or a cross-sectional comparison. It depends how you set up your question. Does Tucson or Albuquerque have more traffic accidents (they have similar population sizes)? Does Tucson have more traffic accidents as the year ends and winter approaches?

Time series versus cross-sectional comparisons: Trends over time versus a snapshot comparison Time series design: Each observation represents a measurement at some point in time. Repeated measurements allow us to see trends. Cross-sectional design: Each observation represents a measurement at some point in time. Comparing across groups allows us to see differences. Unemployment rate Is there an increase in workers calling in sick as the summer months approach? Do more young workers call in sick than older workers? Grade point average (GPA) Does GPA tend to go up or down as students move from freshman to sophomores to juniors to seniors? Does GPA tend to go up or down when you compare Mr. Chen’s class with Mr. Frank’s Freshman English classes?

Random sampling vs Random assignment Random assignment of participants into groups: Any subject had an equal chance of getting assigned to either condition (related to quasi versus true experiment) We know this one Let’s explore this one Random sampling of participants into experiment: Each person in the population has an equal chance of being selected to be in the sample Population: The entire group of people about whom a researcher wants to learn Sample: The subgroup of people who actually participate in a research study

Descriptive or inferential? To determine this we have to consider the methodologies used in collecting the data Descriptive or inferential? Descriptive statistics - organizing and summarizing data Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected What is the average height of the basketball team? Measured all of the players and reported the average height Measured only a sample of the players and reported the average height for team In this class, percentage of students who support the death penalty? Measured all of the students in class and reported percentage who said “yes” Measured only a sample of the students in class and reported percentage who said “yes” Based on the data collected from the students in this class we can conclude that 60% of the students at this university support the death penalty Measured all of the students in class and reported percentage who said “yes”

Descriptive or inferential? Descriptive statistics - organizing and summarizing data Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected Men are in general taller than women Measured all of the citizens of Arizona and reported heights Shoe size is not a good predictor of intelligence Measured all of the shoe sizes and IQ of students of 20 universities Blondes have more fun Asked 500 actresses to complete a happiness survey The average age of students at the U of A is 21 Asked all students in the fraternities and sororities their age

Sample versus population (census) How is a census different from a sample? Census measures each person in the specific population Sample measures a subset of the population and infers about the population – representative sample is good What’s better? Use of existing survey data U.S. Census Family size, fertility, occupation The General Social Survey Surveys sample of US citizens over 1,000 items Same questions asked each year

Population (census) versus sample Parameter versus statistic Parameter – Measurement or characteristic of the population Usually unknown (only estimated) Usually represented by Greek letters (µ) pronounced “mew” pronounced “mu” Statistic – Numerical value calculated from a sample Usually represented by Roman letters (x) pronounced “x bar”

Simple random sampling: each person from the population has an equal probability of being included Sample frame = how you define population Let’s take a sample …a random sample Question: Average weight of U of A football player Sample frame population of the U of A football team Random number table – List of random numbers Pick 24th name on the list Or, you can use excel to provide number for random sample =RANDBETWEEN(1,115) Pick 64th name on the list (64 is just an example here) 64

Systematic random sampling: A probability sampling technique that involves selecting every kth person from a sampling frame You pick the number Other examples of systematic random sampling 1) check every 2000th light bulb 2) survey every 10th voter

Stratified sampling: sampling technique that involves dividing a sample into subgroups (or strata) and then selecting samples from each of these groups - sampling technique can maintain ratios for the different groups Average number of speeding tickets 12% of sample is from California 7% of sample is from Texas 6% of sample is from Florida 6% from New York 4% from Illinois 4% from Ohio 4% from Pennsylvania 3% from Michigan etc Average cost for text books for a semester 17.7% of sample are Pre-business majors 4.6% of sample are Psychology majors 2.8% of sample are Biology majors 2.4% of sample are Architecture majors etc

Cluster sampling: sampling technique divides a population sample into subgroups (or clusters) by region or physical space. Can either measure everyone or select samples for each cluster Textbook prices Southwest schools Midwest schools Northwest schools etc Average student income, survey by Old main area Near McClelland Around Main Gate etc Patient satisfaction for hospital 7th floor (near maternity ward) 5th floor (near physical rehab) 2nd floor (near trauma center) etc

Non-random sampling is vulnerable to bias Convenience sampling: sampling technique that involves sampling people nearby. A non-random sample and vulnerable to bias Snowball sampling: a non-random technique in which one or more members of a population are located and used to lead the researcher to other members of the population Used when we don’t have any other way of finding them - also vulnerable to biases Judgment sampling: sampling technique that involves sampling people who an expert says would be useful. A non-random sample and vulnerable to bias

What is the independent variable? Amount of sleep Does amount of sleep (4 vs 8 hours) affect class attendance? Selected 350 students from 38,000 undergraduates at U of Washington and randomly assigned students into two groups. What is the independent variable? Amount of sleep How many levels are there of the IV? 2 levels (4 hours vs 8 hours) What is the dependent variable? Group 1 gets 4 hours sleep Class attendance What is population and sample? Population: whole school Sample: group of 350 students Note: Parameter would be what we are guessing for the whole school based on these 350 students What is statistic ? Group 2 gets 8 hours sleep Average class attendance for 350 students Quasi versus true experiment (random assignment)? True Random sample? Doesn’t say in the problem, so we have to assume “no”

What is the independent variable? Gender of teacher Does gender of the teacher affect test scores for the students in California? Selected 150 students from Santa Monica and created two groups. What is the independent variable? Gender of teacher How many levels are there of the IV? 2 levels (male vs female teacher) What is the dependent variable? Group 1 gets a female teacher Test Scores What is population and sample? Population: California Sample: group of 150 students from Santa Monica What is statistic ? Group 2 gets a male teacher Average test score for 150 students Quasi versus true experiment (random assignment)? Doesn’t say in the problem, so we have to assume “no” Random sample? No – Random sample would require that everyone in California be equally likely to be chosen.

Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The independent variable in this study was a. the performance of the subjects on the vision exam b. the subjects who ate the carrots c. whether or not the subjects ate the carrots d. whether or not the subjects had their vision tested

Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The dependent variable in this study was a. the performance of the subjects on the vision exam b. the subjects who ate the carrots c. whether or not the subjects ate the carrots d. whether or not the subjects had their vision tested

Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. This experiment was a a. within participant experiment b. between participant experiment c. mixed participant experiment d. non-participant experiment

Let’s try one When Martiza was preparing her experiment, she knew it was important that the participants not know which condition they were in, to avoid bias from the subjects. This is called a _____ study. She also was careful that the experimenters who were interacting with the participants did not know which condition those participants were in. This is called a ____ study. a. between participant; within participant b. within participant; between participant c. double blind design; single blind d. single blind; double blind design

Let’s try one A measurement that has high validity is one that a. measures what it intends to measure b. will give you similar results with each replication c. will compare the performance of the same subjects in each experimental condition d. will compare the performance of different subjects

Let’s try one A study explored whether conservatives or liberals had more bumper stickers on their cars. The researchers ask 100 activists to complete a conservative/liberal values test, then used those results to categorize them as liberal or conservative. Then they identified the 30 most conservative activists and the 30 most liberal activists and measured how many bumper stickers each activist had on their car. The independent variable in this study was a. the performance of the activists b. the number of bumper stickers found on their car c. political status of participant (liberal versus conservative) as determined by their performance on the liberal/conservative test d. whether or not the subjects had bumper stickers on their car

Let’s try one A study explored whether conservatives or liberals had more bumper stickers on their cars. The researchers asked 100 activists to complete a conservative/liberal values test, then used those results to categorize them as liberal or conservative. Then they identified the 30 most conservative activists and the 30 most liberal activists and measured how many bumper stickers each activist had on their car. The dependent variable in this study was a. the performance of the activists b. the number of bumper stickers found on their car c. political status of participant (liberal versus conservative) as determined by their performance on the liberal/conservative test d. whether or not the subjects had bumper stickers on their car

Let’s try one A study explored whether conservatives or liberals had more bumper stickers on their cars. The researchers 100 activists to complete a conservative/liberal values test, then used those results to categorize them as liberal or conservative. Then they identified the 30 most conservative activists and the 30 most liberal activists and measured how many bumper stickers each activist had on their car. This study was a a. within participant experiment b. between participant experiment c. mixed participant experiment d. non-participant experiment

Let’s try one A study explored whether conservatives or liberals had more bumper stickers on their cars. They had 100 activists complete liberal/conservative test. Then, they split the 100 activists into 2 groups (conservatives and liberals). They then measured how many bumper stickers each activist had on their car. This study used a a. true experimental design b. quasi-experiment design c. correlational design d. mixed design

Thank you! See you next time!!