# Chapter 6: t test for dependent samples

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Chapter 6: t test for dependent samples ****This chapter corresponds to chapter 11 of your book ( t(ea) for Two (Again) ). What it is: The t test for dependent samples is used to determine whether the means of two related groups are significantly different. It is called a t test for dependent samples because you use it when you are comparing the same group of people that was measured twice (i.e., groups that are related to or dependent on each other). When to use it: Per the flow chart on page 190 of Salkind (2008), you would use the t test for dependent samples when: (1) you are examining differences between groups (as opposed to examining the relationship between two variables) and (2) the participants in the study were tested more than one once (as opposed to once) and (3) You are comparing two groups/time points (as opposed to three or more groups/ time points) Questions asked by the t test for dependent samples: Do the means of two groups of related scores differ from each other? Examples of research questions that would use a dependent t test: o o o Do the students in this class know more about SPSS at the beginning or end of the semester? Does people s meaning in life increase after they have a child? Do people lose weight after attending a weight loss boot camp for 2 months? Using SPSS to Calculate a dependent t test (Data set: Chapter6_Example1.sav) Imagine you ve designed a method that you believe can teach algebra to people in a very short amount of time (15 minutes). To test this method, you recruit 10 random individuals and give them an algebra test of 10 problems. You then have them work through your method for 15 minutes and then they complete another algebra test of 10 problems. Now you want to compare the scores on the pre and post-test to determine if scores improved after participants went through your training. To determine whether your method worked or not, follow the famous 8 steps. 1. Statement of the null and research hypotheses Null H 0 : µ pretest = µ posttest The average score on the pretest and posttest are equal. Research H 1 : X pretest X posttest The average score on the pretest and posttest are not equal.

2 2. Setting the level of risk to p < Selection of the appropriate test statistic Again, using the flowchart on page 190 of Salkind (2008) we see that the t test for dependent samples is the appropriate test statistic because we are comparing the means (of test scores) between two dependent groups (the same people tested twice, once before learning the method and once after. Open the dataset Chapter6_example1.sav. Take a moment to familiarize yourself with the data. Note how data for this type of analysis should be entered. 1) Each participant has one row in the data 2) One column is used to indicate that participant s score on the pretest (i.e. the number of problems out of 10 they answered correctly) 3) Another column indicates each participant s score on the posttest. The data should look something like this in SPSS: If you were to switch to variable view, you would see that more descriptive labels have been added to the variables. These labels will be what shows up in the output and are helpful to remind you (the researcher) what the variables represent. In your own data set, you could use whatever labels you find most helpful.

3 4. Computation of the test statistic. We will use SPSS to compute the test statistic for us. To do so, click on the Analyze dropdown menu, highlight Compare Means, and then click on Paired Samples t Test, as pictured below. The following pop-up window will appear:

4 Highlight the name of the variable that represents the pre-test score and click the arrow to place it in the paired variables: box. Next, highlight the name of the variable that designates the posttest score and click the arrow again to also place it in the paired variables: box. Your screen should look like the picture below: Now, Click OK and navigate to the output window to find your results. The output will look like this (continued on the next 2 pages):

6 Interpreting the Output The third piece of the output (the table labeled paired samples t-test ) includes all of the information you ll need to complete the 8 steps. In particular, you want to locate the obtained value, degrees of freedom, and p-value associated with the analysis you just ran. There is also information that helps you interpret the t-statistic and some information that you can simply ignore. 5. Determination of the value needed for rejection of the null hypothesis If we were doing this example by hand, then this is the point when we would look at the table of critical values for t (Salkind, 2008, pgs ). Recall that we look up the critical value to tell us the smallest value of t needed to reject the null hypothesis. The critical value is the t that corresponds to a p of.05 for a specific number of degrees of freedom. Because p gets smaller as t gets bigger, we know that if our obtained value is bigger than that critical value, then the p is less than.05. However, SPSS gives us an exact p-value! This means we don t have to find the critical value on our own when we use SPSS. 6. Comparison of the obtained value and the critical value is made Because SPSS gives us a p-value, all we have to do now is see whether that p-value given to us (in the output) is greater or less than.05 (the level of risk we are willing to take). In this example, the output tells us that the p-value for our test is.107 (which is greater than our cutoff value of.05). This means that we fail to reject the null hypothesis. Put another way, this means there is a 10.7% chance that we would obtain this pattern of results if the null hypothesis was true and there was no effect of the method on algebra ability. While 10.7% might not seem very likely, recall that we are only willing to take a 5% chance. This means that we cannot reject that null hypothesis in our example. 7/8. Making a Decision Because our p-value (.107) is greater than.05, this means we fail to reject the null hypothesis. In other words, we conclude that there is no significant difference between the pretest and posttest scores. Interpretation of the Findings Now we report our results. Here s an example of how these results would be reported in a journal article: A t test for dependent samples revealed that there was not a significant effect of the learning method on test scores (t (9) = -1.79, p >.05). Although participants performed somewhat better after (M = 5.70, SD = 1.63) compared to before the tutorial (M = 4.7, SD = 1.77), this difference was not statistically significant.

7 Effect Sizes For someone unfamiliar with stats, you might say: The algebra tutorial does not appear to influence algebra ability. Although not covered in Salkind (2008), it can be helpful to compute an effect size of the difference between the scores in your data set. For a refresher, see Salkind (2008) pages for a discussion of why effect sizes are important. To compute an effect size for dependent t, we can easily plug in some of the information from the output we already generated into the following formula: Mean Difference Standard Deviation of the Difference Scores If you refer to your output, you ll see both of these pieces of information in the first part of the table labeled paired samples t-test. For our example then: **Notice that we ve used a 1 rather than the -1 in the output. That s because we are interested in the SIZE of the effect, not the direction. Cohen s guidelines for interpreting an effect size (See page 180 of Salkind) tell us that this is actually a large effect size. This is somewhat contradictory with our non-significant results reported above. While the results of this particular study suggest that the pattern of results likely occurred by chance, this may also mean that all hope might not be lost for your proposed method! At this point, you might try running another study with either more participants or a different methodology. This helps illustrate why it can be important to pay attention to both statistical significance AND effect sizes. This doesn t guarantee you ll find a significant result the next time, but does suggest it might be worth trying. On the other hand, if the effect size had also been very small (i.e., close to zero) then you might want to think about developing a new method

8 Practice Problem #1 (for SPSS) A researcher is interested in changes over time in happiness after a break up. He asks 6 people to complete a Happiness scale the first day after a break up and then complete the same scale again 20 days later. Higher numbers represent more happiness. Use SPSS to enter the data below and answer the questions that follow. Day 1 Day A. What is the null and research hypothesis (in both words and statistical format)? B. What is the level of risk associated with the null hypothesis? C. What is the appropriate test statistic and WHY? D. What is the obtained value for the t-t test and what is its associated p-value? Is the difference between the two groups statistically significant? E. What do you conclude about the effect of video game type on aggression? Write up your results as you would for a journal article. F. Write up your results as you would for an intelligent person who doesn t know stats.

9 Practice Problem #2 (Hand Calculation) The data below are from participants ratings of how jittery they feel before and after drinking a Red Bull (higher scores equal more jittery). Use a dependent samples t-test to assess whether or not the Red Bull increased feelings of jitteriness. Pretest Posttest A. What is the null and research hypothesis (in both words and statistical format)? B. What is the level of risk associated with the null hypothesis? C. What is the appropriate t test statistic and WHY? D. What is the value needed for rejection of the null hypothesis (i.e. the critical value)? E. What is the obtained value? Is it greater than the critical value? F. Can you reject the null hypothesis? What do you conclude about the effectiveness of the pain relief drug? Write up the results as you would for a journal article. You should calculate the means of the two groups for this problem, but you do not have to calculate the standard deviation. G. Write up your results as you would for an intelligent person who doesn t know stats.

10 Practice Problem 3 (SPSS and Hand Calculation) Below are the number of free throws made (out of 5 attempts) by a group of players. During the first set of attempts, the crowd was trying to distract the players. During the second set of attempts, the crowd was quiet. Hand calculate the analysis using the data below and then use SPSS to check your answer. Also calculate an effect size for the data and include this information when you write up your results as you would for a journal article. Time1 Time

### Chapter 9: Correlation Coefficients

Chapter 9: Correlation Coefficients **This chapter corresponds to chapters 5 ( Ice Cream and Crime ) and 14 ( Cousins or Just Good Friends? of your book. What it is: A correlation coefficient (also called

### Chapter 11: Chi-square (χ 2 )

Chapter 11: Chi-square (χ 2 ) *This chapter corresponds with Chapter 16 in your text ( What to do when you re not normal ). What it is: Chi-square is a nonparametric statistic. This means that it can be

### Two Related Samples t Test

Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person

### T-tests. Daniel Boduszek

T-tests Daniel Boduszek d.boduszek@interia.eu danielboduszek.com Presentation Outline Introduction to T-tests Types of t-tests Assumptions Independent samples t-test SPSS procedure Interpretation of SPSS

### ED632G: Research/Applied Educational Psychology

1 ED632G: Research/Applied Educational Psychology This tutorial is designed to help ED632G students have a better understanding on how to run a general pre-test vs. posttest or improvement over semesters

### Chapter 2 Probability Topics SPSS T tests

Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform

### DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS

DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS Using SPSS Session 2 Topics addressed today: 1. Recoding data missing values, collapsing categories 2. Making a simple scale 3. Standardisation

### 13 Two-Sample T Tests

www.ck12.org CHAPTER 13 Two-Sample T Tests Chapter Outline 13.1 TESTING A HYPOTHESIS FOR DEPENDENT AND INDEPENDENT SAMPLES 270 www.ck12.org Chapter 13. Two-Sample T Tests 13.1 Testing a Hypothesis for

### Psyc 250 Statistics & Experimental Design. Single & Paired Samples t-tests

Psyc 250 Statistics & Experimental Design Single & Paired Samples t-tests Part 1 Data Entry For any statistical analysis with any computer program, it is always important that data are entered correctly

### UNDERSTANDING THE DEPENDENT-SAMPLES t TEST

UNDERSTANDING THE DEPENDENT-SAMPLES t TEST A dependent-samples t test (a.k.a. matched or paired-samples, matched-pairs, samples, or subjects, simple repeated-measures or within-groups, or correlated groups)

### Hypothesis Tests: Two Related Samples

Hypothesis Tests: Two Related Samples AKA Dependent Samples Tests AKA -Pairs Tests Cal State Northridge Ψ320 Andrew Ainsworth PhD Major Points Related samples? Samples? Difference scores? An example t

### EXCEL Analysis TookPak [Statistical Analysis] 1. First of all, check to make sure that the Analysis ToolPak is installed. Here is how you do it:

EXCEL Analysis TookPak [Statistical Analysis] 1 First of all, check to make sure that the Analysis ToolPak is installed. Here is how you do it: a. From the Tools menu, choose Add-Ins b. Make sure Analysis

### Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics **This chapter corresponds to chapters 2 ( Means to an End ) and 3 ( Vive la Difference ) of your book. What it is: Descriptive statistics are values that describe the

### Chapter 7. Comparing Means in SPSS (t-tests) Compare Means analyses. Specifically, we demonstrate procedures for running Dependent-Sample (or

1 Chapter 7 Comparing Means in SPSS (t-tests) This section covers procedures for testing the differences between two means using the SPSS Compare Means analyses. Specifically, we demonstrate procedures

### Simple Linear Regression One Binary Categorical Independent Variable

Simple Linear Regression Does sex influence mean GCSE score? In order to answer the question posed above, we want to run a linear regression of sgcseptsnew against sgender, which is a binary categorical

### SPSS on two independent samples. Two sample test with proportions. Paired t-test (with more SPSS)

SPSS on two independent samples. Two sample test with proportions. Paired t-test (with more SPSS) State of the course address: The Final exam is Aug 9, 3:30pm 6:30pm in B9201 in the Burnaby Campus. (One

### Statistics 104: Section 7

Statistics 104: Section 7 Section Overview Reminders Comments on Midterm Common Mistakes on Problem Set 6 Statistical Week in Review Comments on Midterm Overall, the midterms were good with one notable

### SPSS Guide: Tests of Differences

SPSS Guide: Tests of Differences I put this together to give you a step-by-step guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar

### c. The factor is the type of TV program that was watched. The treatment is the embedded commercials in the TV programs.

STAT E-150 - Statistical Methods Assignment 9 Solutions Exercises 12.8, 12.13, 12.75 For each test: Include appropriate graphs to see that the conditions are met. Use Tukey's Honestly Significant Difference

### Hypothesis Testing. Male Female

Hypothesis Testing Below is a sample data set that we will be using for today s exercise. It lists the heights for 10 men and 1 women collected at Truman State University. The data will be entered in the

### T-test in SPSS Hypothesis tests of proportions Confidence Intervals (End of chapter 6 material)

T-test in SPSS Hypothesis tests of proportions Confidence Intervals (End of chapter 6 material) Definition of p-value: The probability of getting evidence as strong as you did assuming that the null hypothesis

### Chapter 9. Two-Sample Tests. Effect Sizes and Power Paired t Test Calculation

Chapter 9 Two-Sample Tests Paired t Test (Correlated Groups t Test) Effect Sizes and Power Paired t Test Calculation Summary Independent t Test Chapter 9 Homework Power and Two-Sample Tests: Paired Versus

### Lesson 1: Comparison of Population Means Part c: Comparison of Two- Means

Lesson : Comparison of Population Means Part c: Comparison of Two- Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis

### SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES

SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR

### Hypothesis Testing hypothesis testing approach formulation of the test statistic

Hypothesis Testing For the next few lectures, we re going to look at various test statistics that are formulated to allow us to test hypotheses in a variety of contexts: In all cases, the hypothesis testing

### Chapter 9: Introduction to the t Statistic

Chapter 9: Introduction to the t Statistic First of all, you need to know who developed the t statistic. His name was William S. Gossett, but he published under the pseudonym Student. (His employer wouldn

### Simple Linear Regression in SPSS STAT 314

Simple Linear Regression in SPSS STAT 314 1. Ten Corvettes between 1 and 6 years old were randomly selected from last year s sales records in Virginia Beach, Virginia. The following data were obtained,

### Multiple Regression Analysis in Minitab 1

Multiple Regression Analysis in Minitab 1 Suppose we are interested in how the exercise and body mass index affect the blood pressure. A random sample of 10 males 50 years of age is selected and their

### Independent t- Test (Comparing Two Means)

Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent

### LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING

LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.

### Measuring Evaluation Results with Microsoft Excel

LAURA COLOSI Measuring Evaluation Results with Microsoft Excel The purpose of this tutorial is to provide instruction on performing basic functions using Microsoft Excel. Although Excel has the ability

### Example for testing one population mean:

Today: Sections 13.1 to 13.3 ANNOUNCEMENTS: We will finish hypothesis testing for the 5 situations today. See pages 586-587 (end of Chapter 13) for a summary table. Quiz for week 8 starts Wed, ends Monday

### SPSS Guide: Regression Analysis

SPSS Guide: Regression Analysis I put this together to give you a step-by-step guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar

### Point-Biserial and Biserial Correlations

Chapter 302 Point-Biserial and Biserial Correlations Introduction This procedure calculates estimates, confidence intervals, and hypothesis tests for both the point-biserial and the biserial correlations.

### Module 5 Hypotheses Tests: Comparing Two Groups

Module 5 Hypotheses Tests: Comparing Two Groups Objective: In medical research, we often compare the outcomes between two groups of patients, namely exposed and unexposed groups. At the completion of this

### The Dummy s Guide to Data Analysis Using SPSS

The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests

### HOW TO USE MINITAB: INTRODUCTION AND BASICS. Noelle M. Richard 08/27/14

HOW TO USE MINITAB: INTRODUCTION AND BASICS 1 Noelle M. Richard 08/27/14 CONTENTS * Click on the links to jump to that page in the presentation. * 1. Minitab Environment 2. Uploading Data to Minitab/Saving

### 6 Comparison of differences between 2 groups: Student s T-test, Mann-Whitney U-Test, Paired Samples T-test and Wilcoxon Test

6 Comparison of differences between 2 groups: Student s T-test, Mann-Whitney U-Test, Paired Samples T-test and Wilcoxon Test Having finally arrived at the bottom of our decision tree, we are now going

### Contrasts ask specific questions as opposed to the general ANOVA null vs. alternative

Chapter 13 Contrasts and Custom Hypotheses Contrasts ask specific questions as opposed to the general ANOVA null vs. alternative hypotheses. In a one-way ANOVA with a k level factor, the null hypothesis

### CHAPTER 15: Tests of Significance: The Basics

CHAPTER 15: Tests of Significance: The Basics The Basic Practice of Statistics 6 th Edition Moore / Notz / Fligner Lecture PowerPoint Slides Chapter 15 Concepts 2 The Reasoning of Tests of Significance

### KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management

KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To

### Statistical Significance and Bivariate Tests

Statistical Significance and Bivariate Tests BUS 735: Business Decision Making and Research 1 1.1 Goals Goals Specific goals: Re-familiarize ourselves with basic statistics ideas: sampling distributions,

### JMP for Basic Univariate and Multivariate Statistics

JMP for Basic Univariate and Multivariate Statistics Methods for Researchers and Social Scientists Second Edition Ann Lehman, Norm O Rourke, Larry Hatcher and Edward J. Stepanski Lehman, Ann, Norm O Rourke,

### SPSS: Expected frequencies, chi-squared test. In-depth example: Age groups and radio choices. Dealing with small frequencies.

SPSS: Expected frequencies, chi-squared test. In-depth example: Age groups and radio choices. Dealing with small frequencies. Quick Example: Handedness and Careers Last time we tested whether one nominal

### SPSS Explore procedure

SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stem-and-leaf plots and extensive descriptive statistics. To run the Explore procedure,

### The Philosophy of Hypothesis Testing, Questions and Answers 2006 Samuel L. Baker

HYPOTHESIS TESTING PHILOSOPHY 1 The Philosophy of Hypothesis Testing, Questions and Answers 2006 Samuel L. Baker Question: So I'm hypothesis testing. What's the hypothesis I'm testing? Answer: When you're

### Recall this chart that showed how most of our course would be organized:

Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical

### Box plots & t-tests. Example

Box plots & t-tests Box Plots Box plots are a graphical representation of your sample (easy to visualize descriptive statistics); they are also known as box-and-whisker diagrams. Any data that you can

### The scatterplot indicates a positive linear relationship between waist size and body fat percentage:

STAT E-150 Statistical Methods Multiple Regression Three percent of a man's body is essential fat, which is necessary for a healthy body. However, too much body fat can be dangerous. For men between the

### Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures

Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone:

### ABSORBENCY OF PAPER TOWELS

ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?

### Introduction to Stata

Introduction to Stata September 23, 2014 Stata is one of a few statistical analysis programs that social scientists use. Stata is in the mid-range of how easy it is to use. Other options include SPSS,

### One-Way Analysis of Variance

One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We

### Hypothesis Testing. April 21, 2009

Hypothesis Testing April 21, 2009 Your Claim is Just a Hypothesis I ve never made a mistake. Once I thought I did, but I was wrong. Your Claim is Just a Hypothesis Confidence intervals quantify how sure

### Example: Multivariate Analysis of Variance

1 of 36 Example: Multivariate Analysis of Variance Multivariate analyses of variance (MANOVA) differs from univariate analyses of variance (ANOVA) in the number of dependent variables utilized. The major

### Getting Started With SPSS

Getting Started With SPSS To investigate the research questions posed in each section of this site, we ll be using SPSS, an IBM computer software package specifically designed for use in the social sciences.

### Chapter 7 Appendix. Inference for Distributions with Excel, JMP, Minitab, SPSS, CrunchIt!, R, and TI-83/-84 Calculators

Chapter 7 Appendix Inference for Distributions with Excel, JMP, Minitab, SPSS, CrunchIt!, R, and TI-83/-84 Calculators Inference for the Mean of a Population Excel t Confidence Interval for Mean Confidence

### Allelopathic Effects on Root and Shoot Growth: One-Way Analysis of Variance (ANOVA) in SPSS. Dan Flynn

Allelopathic Effects on Root and Shoot Growth: One-Way Analysis of Variance (ANOVA) in SPSS Dan Flynn Just as t-tests are useful for asking whether the means of two groups are different, analysis of variance

### Minitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab Step-By-Step

Minitab Guide This packet contains: A Friendly Guide to Minitab An introduction to Minitab; including basic Minitab functions, how to create sets of data, and how to create and edit graphs of different

### THE FIRST SET OF EXAMPLES USE SUMMARY DATA... EXAMPLE 7.2, PAGE 227 DESCRIBES A PROBLEM AND A HYPOTHESIS TEST IS PERFORMED IN EXAMPLE 7.

THERE ARE TWO WAYS TO DO HYPOTHESIS TESTING WITH STATCRUNCH: WITH SUMMARY DATA (AS IN EXAMPLE 7.17, PAGE 236, IN ROSNER); WITH THE ORIGINAL DATA (AS IN EXAMPLE 8.5, PAGE 301 IN ROSNER THAT USES DATA FROM

### Chapter 7 Part 2. Hypothesis testing Power

Chapter 7 Part 2 Hypothesis testing Power November 6, 2008 All of the normal curves in this handout are sampling distributions Goal: To understand the process of hypothesis testing and the relationship

### Single sample hypothesis testing, II 9.07 3/02/2004

Single sample hypothesis testing, II 9.07 3/02/2004 Outline Very brief review One-tailed vs. two-tailed tests Small sample testing Significance & multiple tests II: Data snooping What do our results mean?

### UNDERSTANDING THE INDEPENDENT-SAMPLES t TEST

UNDERSTANDING The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly

### Discriminant Function Analysis in SPSS To do DFA in SPSS, start from Classify in the Analyze menu (because we re trying to classify participants into

Discriminant Function Analysis in SPSS To do DFA in SPSS, start from Classify in the Analyze menu (because we re trying to classify participants into different groups). In this case we re looking at a

### Chapter 7 Section 7.1: Inference for the Mean of a Population

Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used

### Comparing Two or more than Two Groups

CRJ 716 Using Computers in Social Research Comparing Two or more than Two Groups Comparing Means, Conducting T-Tests and ANOVA Agron Kaci John Jay College Chapter 9/1: Comparing Two or more than Two Groups

### For example, enter the following data in three COLUMNS in a new View window.

Statistics with Statview - 18 Paired t-test A paired t-test compares two groups of measurements when the data in the two groups are in some way paired between the groups (e.g., before and after on the

### Projects Involving Statistics (& SPSS)

Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,

### Solutions 7. Review, one sample t-test, independent two-sample t-test, binomial distribution, standard errors and one-sample proportions.

Solutions 7 Review, one sample t-test, independent two-sample t-test, binomial distribution, standard errors and one-sample proportions. (1) Here we debunk a popular misconception about confidence intervals

### SPSS: Descriptive and Inferential Statistics. For Windows

For Windows August 2012 Table of Contents Section 1: Summarizing Data...3 1.1 Descriptive Statistics...3 Section 2: Inferential Statistics... 10 2.1 Chi-Square Test... 10 2.2 T tests... 11 2.3 Correlation...

### INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)

INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of

### EPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST

EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions

### Two-Sample T-Test from Means and SD s

Chapter 07 Two-Sample T-Test from Means and SD s Introduction This procedure computes the two-sample t-test and several other two-sample tests directly from the mean, standard deviation, and sample size.

### TRANSCRIPT: In this lecture, we will talk about both theoretical and applied concepts related to hypothesis testing.

This is Dr. Chumney. The focus of this lecture is hypothesis testing both what it is, how hypothesis tests are used, and how to conduct hypothesis tests. 1 In this lecture, we will talk about both theoretical

### Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish

Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)

### Contrasts and Post Hoc Tests for One-Way Independent ANOVA Using SPSS

Contrasts and Post Hoc Tests for One-Way Independent ANOVA Using SPSS Running the Analysis In last week s lecture we came across an example, from Field (2013), about the drug Viagra, which is a sexual

### Working with SPSS. A Step-by-Step Guide For Prof PJ s ComS 171 students

Working with SPSS A Step-by-Step Guide For Prof PJ s ComS 171 students Contents Prep the Excel file for SPSS... 2 Prep the Excel file for the online survey:... 2 Make a master file... 2 Clean the data

### Testing Hypotheses using SPSS

Is the mean hourly rate of male workers \$2.00? T-Test One-Sample Statistics Std. Error N Mean Std. Deviation Mean 2997 2.0522 6.6282.2 One-Sample Test Test Value = 2 95% Confidence Interval Mean of the

### MATH Chapter 23 April 15 and 17, 2013 page 1 of 8 CHAPTER 23: COMPARING TWO CATEGORICAL VARIABLES THE CHI-SQUARE TEST

MATH 1342. Chapter 23 April 15 and 17, 2013 page 1 of 8 CHAPTER 23: COMPARING TWO CATEGORICAL VARIABLES THE CHI-SQUARE TEST Relationships: Categorical Variables Chapter 21: compare proportions of successes

### Odds ratio, Odds ratio test for independence, chi-squared statistic.

Odds ratio, Odds ratio test for independence, chi-squared statistic. Announcements: Assignment 5 is live on webpage. Due Wed Aug 1 at 4:30pm. (9 days, 1 hour, 58.5 minutes ) Final exam is Aug 9. Review

### t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon

t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com

### One-Sample t-test. Example 1: Mortgage Process Time. Problem. Data set. Data collection. Tools

One-Sample t-test Example 1: Mortgage Process Time Problem A faster loan processing time produces higher productivity and greater customer satisfaction. A financial services institution wants to establish

### Practice 3 SPSS. Partially based on Notes from the University of Reading:

Practice 3 SPSS Partially based on Notes from the University of Reading: http://www.reading.ac.uk Simple Linear Regression A simple linear regression model is fitted when you want to investigate whether

### 7. Comparing Means Using t-tests.

7. Comparing Means Using t-tests. Objectives Calculate one sample t-tests Calculate paired samples t-tests Calculate independent samples t-tests Graphically represent mean differences In this chapter,

### Chapter 23. Inferences for Regression

Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily

### This chapter discusses some of the basic concepts in inferential statistics.

Research Skills for Psychology Majors: Everything You Need to Know to Get Started Inferential Statistics: Basic Concepts This chapter discusses some of the basic concepts in inferential statistics. Details

### A Basic Guide to Analyzing Individual Scores Data with SPSS

A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete

### Hypothesis testing - Steps

Hypothesis testing - Steps Steps to do a two-tailed test of the hypothesis that β 1 0: 1. Set up the hypotheses: H 0 : β 1 = 0 H a : β 1 0. 2. Compute the test statistic: t = b 1 0 Std. error of b 1 =

### Using Excel for inferential statistics

FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied

### Doing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices:

Doing Multiple Regression with SPSS Multiple Regression for Data Already in Data Editor Next we want to specify a multiple regression analysis for these data. The menu bar for SPSS offers several options:

### DDBA 8438: The t Test for Independent Samples Video Podcast Transcript

DDBA 8438: The t Test for Independent Samples Video Podcast Transcript JENNIFER ANN MORROW: Welcome to The t Test for Independent Samples. My name is Dr. Jennifer Ann Morrow. In today's demonstration,

### 1 Hypotheses test about µ if σ is not known

1 Hypotheses test about µ if σ is not known In this section we will introduce how to make decisions about a population mean, µ, when the standard deviation is not known. In order to develop a confidence

### Section 12.2, Lesson 3. What Can Go Wrong in Hypothesis Testing: The Two Types of Errors and Their Probabilities

Today: Section 2.2, Lesson 3: What can go wrong with hypothesis testing Section 2.4: Hypothesis tests for difference in two proportions ANNOUNCEMENTS: No discussion today. Check your grades on eee and

### One-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate

1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,

### Descriptive Statistics

Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

### " Y. Notation and Equations for Regression Lecture 11/4. Notation:

Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through

### Hypothesis testing. c 2014, Jeffrey S. Simonoff 1

Hypothesis testing So far, we ve talked about inference from the point of estimation. We ve tried to answer questions like What is a good estimate for a typical value? or How much variability is there