Chapter 1 Understanding Statistics in the Behavioral Sciences by Robert Pagano Chapter 1: Statistics and Scientific Method Presented by David R. Dunaetz Azusa Pacific University

Why Statistics is Important 1. It helps us understand reality. Statistics is thinking clearly with the information that we have. Humans suffer from bounded rationality. There’s lots of stuff we don’t know. Statistics provides tools to examine the evidence. Example: Should a woman accept to go on a Saturday date with a desirable man if he asks after Wednesday? Example: Does the way someone dresses at a pre-employment interview predict performance?

Why Statistics is Important 2. It helps us love and understand people. Love is based on knowledge. Philippians 1:9 And this is my prayer: that your love may abound more and more in knowledge and depth of insight. Example: Understanding differences between you and your colleagues. -Extraversion and Introversion -Orderliness

8 Key Definitions 1. Data Information that is collected about the people, items, or events in question. Usually numeric (age) but can be categorical (sex). Can often be converted into numbers by assigning numbers to the categories. Data Collection Exercise. Collection Codebook Composite Variables Data entry Excel Tables

8 Key Definitions 2. Population The complete set of individuals, objects, or scores under investigation. Example: If I’m studying APU students, the population is every APU student. Other important populations Employees Potential employees Customers Potential customers

8 Key Definitions 3. Sample A subset of the population that is examined in order to make conclusions about the population. If I’m studying APU students, this class could be a sample.

8 Key Definitions 4. A Statistic A number. Calculated from data from a sample. It gives us information about the sample, and perhaps the population. The most common statistic is the mean (the average). Others include median, mode, and standard deviation. Others are z, r, t, F, and χ2.

8 Key Definitions 5. A Parameter Like a statistic, but calculated from data from the whole population. It gives us information about the whole population. Usually, we don’t have enough information to know a parameter, but we can guess it from a statistic.

8 Key Definitions 6. A Variable Example of a study with 2 variables: Is it more likely that a girl will say yes when a guy asks her out with a text, in person, or by FaceBook? Variable 1: Means of asking her out (phone, in person, or Facebook). Variable 2: Her response (yes or no). A variable is any property or characteristic of some event, object, or person that can have different values depending on the conditions or the time.

8 Key Definitions 7. Dependent variable The result that is measured in an experiment; it depends on the conditions that are chosen at the beginning. Example: Whether a girl says yes or not.

8 Key Definitions 8. An independent variable A condition in the experiment chosen independently of other variables; it’s what the experimenter “manipulates.” Example: How a guy asks a girl out: phone, in person, or FaceBook.

Rule for Remembering IV and DV IV => DV

Practice Problem Chpt 1 Ex. 6a Identify the IV, DV, sample, population, data, and statistic. An I/O psychologist believes that a different arrangement of the keyboard will promote faster typing. Twenty trainees, selected from a large trade school, participate in an experiment designed to test this belief. Ten of the trainees learn to type on the conventional keyboard. The other ten are trained using the new keyboard. At the end of the training period, the typing speed in words per minute of each trainee is measured. The mean typing speeds are calculated for both groups and compared to determine whether the new keyboard has had an effect.

Descriptive and Inferential Statistics Descriptive Statistics First part of the course. Describes how the data looks. Relatively easy. Used most often in business and churches. Example: Shoe size Frequency of each size Average (Mean) Most Popular (Mode) Variability (Standard Deviation)

Descriptive and Inferential Statistics Second part of the course. Analysis is done on data from a sample to describe the whole population. Example: Conclusions about all job candidates. Much more difficult than descriptive statistics. Generally you will want to work with a stats consultant. This course is to help you be good consumer of stats, and perhaps of producer as well.

Descriptive and Inferential Statistics We use special tests to draw conclusions about differences in populations or relationships between variables t- test Correlations Some conclusions that are stated in terms of confidence intervals: “We are 95% sure that between 57% and 62% of APU girls prefer to be asked out in person rather than by FaceBook”

Chapter 2 Understanding Statistics in the Behavioral Sciences by Robert Pagano Chapter 2: Basic Mathematical and Measurement Concepts Presented by David R. Dunaetz Azusa Pacific University

Review Thirty APU students are randomly chosen in a study of shoe size and gender of APU students. The average size for women is 8 and the average size for men is 10. What is the sample? What is the population? What are the variables? What are the statistics?

Chapter 2: Basic Mathematical and Measurement Concepts Why use math? It’s the best way we have of creating and communicating information. Example: How many students are in the class? We can often reduce many numbers to just one or two Measuring stuff (“metrics”) has allowed business and psychology to advance enormously in the last few decades. Metrics in Bible The Book of Numbers (Good use) Acts of the Apostles (Good use) I Chronicles 21 (Bad use)

Learn the Shorthand. . . Think of mathematical symbols like the abbreviations used in messaging. The goal of the mathematical symbols is to make life easier. If you don’t learn what they mean, you’ll quickly get lost.

Common Symbols X bar X1 X sub 1 or X 1 α alpha β beta χ chi φ phi μ mu ρ rho σ, Σ sigma ω omega

Basic Notation: subscripts X stands for the variable measured For example, X = age, N stands for the total number of subjects or scores Xi stands for the i th score where i can vary from 1 to N.

Summation* Summation notation Excel function (formula): =sum(C8:C12) On board On Excel Excel function (formula): =sum(C8:C12) =sum(C8..C12) +sum(C8..C12) =sum(C8,C9,C10,C11,C12) =C8+C9+C10+C11+C12

Rounding Numbers (Reducing the number of numbers after the decimal point) Simple rules: Use Excel to give you two decimal places: Home => Number => Number Example: Stats on basketball players

Measurement scales Nominal Data Can be put in categories. No numbers are necessarily associated with the categories; one’s not more than the other. Any numbers are arbitrary. Ordinal Data Represents an order. Intervals between points are not defined and perhaps not equal. Interval Data The interval between each number is equal, but 0 doesn’t especially mean there is none of what’s being measured Ratio Data Same as interval, but 0 means there is none of what’s measured.

Measurement Scales (8e: p. 30, 9e: p. 34, 10e: p. 34) Beanie Baby Exercise Type of animal Number of arms and legs Color of eyes Name Copyright date Weight Cuteness (0-10) Nominal, Ordinal, Interval, or Ratio?

Continuous and Discrete Variables Continuous variables can take on any value. 180.14 lbs $55,413.44 Ratio and sometimes interval scales Note: Real limits of 180 lbs is 179.5 to 180.5 lbs if measured to the nearest pound Discrete variables can only take on certain values Likert scale scores: Bieber = 1 or 2 or 3 or 4 or 5 Gender = 1 or 2 Year of birth: 1990 Nominal, interval, and sometimes interval scales.