Inferential Statistics. What are they? When would you use them?


 Bridget Lane
 1 years ago
 Views:
Transcription
1 Inferential Statistics What are they? When would you use them?
2 What are inferential statistics? Why learn about inferential statistics? Why use inferential statistics? When are inferential statistics utilized? Which types of inferential statistics are most commonly used and when? What is important for you to know about inferential statistics?
3 What are inferential statistics? Inferential statistics infer from the sample to the population They determine probability of characteristics of population based on the characteristics of your sample They help assess strength of the relationship between your independent (causal) variables, and you dependent (effect) variables.
4 Why learn about inferential statistics? BEFORE you use any intervention, you should do some research and determine if there is evidence that it works. (i.e., Does the head start program increase educational performance for low income children) BEFORE you work with any group, you want to base your judgments on research, not on stereotypes (i.e., You may want to know what proportion of Latino boys join gangs?) BEFORE you make recommendations, you want to understand the probabilities of success (i.e., What is the probability that a child will have success in school if they participate in your tutorial program?) Before you continue on with a program/intervention, you want to reassure yourself that this program is worth your time and effort. As you apply for grants, you want to ensure the grantees that you can implement a evidence based program. When making policy recommendations or participating in political advocacy, you want to provide empirical support that your intervention actually works.
5 Why use inferential statistics? Many toptiered journals will not publish articles that do NOT use inferential statistics. Allows you to generalize your findings to the larger population. Can determine not just what CAN happen, but what tends to happen in programs like yours. Helps assess strength of the relationship between your independent (causal) variables, and you dependent (effect) variables. Can assess the relative impact of various program inputs on your program outcomes/objectives.
6 When are inferential statistics utilized? Inferential statistics can only be used under the following conditions. You have a complete list of the members of the population. You draw a random sample from this population Using a preestablished formula, you determine that your sample size is large enough. Can you use inferential statistics even if you data do not meet these criteria? Inferential statistics can help determine strength of relationship within your sample. In other words, you can assess the strength of the impact of your independent variables (program inputs) on your outcomes (program outputs) IF it is very difficult to obtain a population list and/or draw a random sample, then you do the best you can with what you have. In this case, you can use inferential statistics and journals may publish it.
7 Which types of inferential statistics are most commonly used and when? The following types of inferential statistics are relatively common and relatively easy to interpret. One sample test of difference/one sample hypothesis test Confidence Interval Contingency Tables and Chi Square Statistic Ttest or Anova Pearson Correlation Bivariate Regression Multivariate Regression
8 First consider univariate statistics. One sample test of difference OR one sample hypothesis test When is it used? To compare responses of program participants on a pre and post test. To determine if implemented program had an impact on one particular outcome. How do you interpret it? If the probability is.05 or less that you will make a mistake in asserting there is a difference between the pre and posttest scores in the population, then you can assert that the program did make a difference on this outcome. In other words, your program is working.
9 First consider univariate statistics. Confidence Interval When is it used? To estimate a value/score in a population based on the score of the participants in your sample. How do you interpret it? A 95% confidence interval indicates you are 95% confident that you can predict/infer the value/score of a population within a specified range based on the value/score of your sample.
10 Next consider bivariate statistics. Contingency tables and ChiSquare statistic When are they used? When you have two categorical variables,. AND you want to know if they are related. (i.e., gender and score on outcome measurement). How do you interpret them? The chisquare statistics can be used to determine the strength of the relationship (i.e., Does knowing someone s gender help you predict their outcome score/value). If the probability associated with the chisquare statistics is.05 of less, then you can assert that the independent variable can be used to predict scores on the dependent or outcome variable. You can also use the contingency table to compare the actual scores across the independent variable on the dependent variable or outcome measurement (i.e., compare the number/percent of males who agreed that the program had a positive impact on their lives to the percent of females who agreed.)
11 Next consider bivariate statistics. Ttest or Anova When is it used? When you have a categorical and continuous variable. And you want to compare mean scores of two or more groups (i.e., you want to compare mean GRP of students you have tutored across race). How do you interpret it? The Ttest or F statistic can be used to determine if the groups have significantly different means. If the probability associated with the F statistics is.05 or less then we can assert that there is a difference in the means.
12 Next consider bivariate statistics. Pearson Correlation When is it used? When you have a continuous independent variable and a continuous dependent variable. How do you interpret it? When the probability associated with the T statistics is.05 of less then you can assume there is a relationship between the dependent and independent variable. For instance you may want to know if the number of hours participants spend in your program is positively related to their scores on school exams.
13 Next consider bivariate statistics. Bivariate Regression When is it used? When you have a continuous independent variable and a continuous dependent (outcome) variable. For instance, you may want to know if the number of hours participants spend in your program is positively related to their scores on school exams. How do you interpret it? When the probability associated with the F statistic is.05 or less then you can assume there is a relationship between the dependent and the independent variable. * NOTE The Pearson Correlation and Bivariate regression are very similar.
14 Finally let us consider multivariate statistics Elaborated ChiSquare statistic When is it used? When you have more than one independent categorical variable, and one dependent categorical variable. How is it interpreted? You divide one of the independent variables into two groups and then do a chi square for each group (i.e., divide gender into males and females, then do a chisquare of males and one for females. So for females you can do a chisquare of outcome measurement by race, and then do the same for males.)
15 Multivariate Regression When is it used? Multivariate regression is used you have more than one independent (causal) variable and one dependent (effect or outcome) variable. You not only want to know if you intervention has an impact on the outcome, but you want to know WHICH aspects of your intervention has an impact and/or the relative impact of different aspects of your intervention. How do you interpret it? If the probability associated with the F statistic is.05 of less, then you can If the probability associated with the T statistic for each of the independent variables is.05 or less, then you can assert that independent variable has an impact on the outcome, independent of the other variables. The value of the T statistics can be compared across the independent variables to determine the relative value of each.
16 What is important for you to know about inferential statistics? You should be able to 1. Read and understand computer printouts 2. Construct tables and graphs from the computer printouts. 3. Interpret and explain these tables and graphs to an audience. 4. Make wise decisions based on valid and accurate data.
17 What if you NEVER intend to use Inferential Statistics? All of us are consumers of information We can learn about inferential statistics and be wiser consumers of information. We are empowered, and have the tools to determine if the information we are reading is accurate/valid. If you implement programs, you are ethically bound to your participants to be able to accurately measure the outcomes of your intervention. If you use government/foundation funding to implement your programs, then you are responsible for using their monies wisely and efficiently.
18 Dr. Carol Albrecht USU Extension Specialist
Results from the 2014 AP Statistics Exam. Jessica Utts, University of California, Irvine Chief Reader, AP Statistics jutts@uci.edu
Results from the 2014 AP Statistics Exam Jessica Utts, University of California, Irvine Chief Reader, AP Statistics jutts@uci.edu The six freeresponse questions Question #1: Extracurricular activities
More informationIntroduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.
Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative
More informationWISE Power Tutorial All Exercises
ame Date Class WISE Power Tutorial All Exercises Power: The B.E.A.. Mnemonic Four interrelated features of power can be summarized using BEA B Beta Error (Power = 1 Beta Error): Beta error (or Type II
More informationBecoming an Educated Consumer of Research: A Quick Look at the Basics of Research Methodologies and Design. Taylor Dimsdale Mark Kutner
Becoming an Educated Consumer of Research: A Quick Look at the Basics of Research Methodologies and Design Taylor Dimsdale Mark Kutner Meeting of the Minds PractitionerResearcher Symposium December 2004
More information8. Comparing Means Using One Way ANOVA
8. Comparing Means Using One Way ANOVA Objectives Calculate a oneway analysis of variance Run various multiple comparisons Calculate measures of effect size A One Way ANOVA is an analysis of variance
More informationNSSE MultiYear Data Analysis Guide
NSSE MultiYear Data Analysis Guide About This Guide Questions from NSSE users about the best approach to using results from multiple administrations are increasingly common. More than three quarters of
More informationWISE Sampling Distribution of the Mean Tutorial
Name Date Class WISE Sampling Distribution of the Mean Tutorial Exercise 1: How accurate is a sample mean? Overview A friend of yours developed a scale to measure Life Satisfaction. For the population
More informationIBM SPSS Direct Marketing 22
IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release
More informationFixedEffect Versus RandomEffects Models
CHAPTER 13 FixedEffect Versus RandomEffects Models Introduction Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small study Confidence interval
More informationASSESSING STUDENTS CONCEPTUAL UNDERSTANDING AFTER A FIRST COURSE IN STATISTICS 3
28 ASSESSING STUDENTS CONCEPTUAL UNDERSTANDING AFTER A FIRST COURSE IN STATISTICS 3 ROBERT DELMAS University of Minnesota delma001@umn.edu JOAN GARFIELD University of Minnesota jbg@umn.edu ANN OOMS Kingston
More informationwww.rmsolutions.net R&M Solutons
Ahmed Hassouna, MD Professor of cardiovascular surgery, AinShams University, EGYPT. Diploma of medical statistics and clinical trial, Paris 6 university, Paris. 1A Choose the best answer The duration
More informationInteraction effects and group comparisons Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised February 20, 2015
Interaction effects and group comparisons Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised February 20, 2015 Note: This handout assumes you understand factor variables,
More informationElements of Scientific Theories: Relationships
23 Part 1 / Philosophy of Science, Empiricism, and the Scientific Method Chapter 3 Elements of Scientific Theories: Relationships In the previous chapter, we covered the process of selecting and defining
More informationEffect Sizes. Null Hypothesis Significance Testing (NHST) C8057 (Research Methods 2): Effect Sizes
Effect Sizes Null Hypothesis Significance Testing (NHST) When you read an empirical paper, the first question you should ask is how important is the effect obtained. When carrying out research we collect
More informationIn 1994, the U.S. Congress passed the SchooltoWork
SchooltoWork Programs Schooltowork programs: information from two surveys Data from the 996 School Administrator's Survey show that threefifths of U.S. high schools offer schooltowork programs,
More informationNational Evaluation of Student Support Services: Examination of Student Outcomes After Six Years
U.S. DEPARTMENT OF EDUCATION National Evaluation of Student Support Services: Examination of Student Outcomes After Six Years Final Report National Evaluation of Student Support Services: Examination of
More informationDraft 1, Attempted 2014 FR Solutions, AP Statistics Exam
Free response questions, 2014, first draft! Note: Some notes: Please make critiques, suggest improvements, and ask questions. This is just one AP stats teacher s initial attempts at solving these. I, as
More informationNew Evidence that Tutoring with Community Volunteers Can Help Middle School Students Improve their Academic Achievement
New Evidence that Tutoring with Community Volunteers Can Help Middle School Students Improve their Academic Achievement Anna Allen and Nancy Feyl Chavkin Abstract is study evaluates the impact of minimally
More informationShiken: JLT Testing & Evlution SIG Newsletter. 5 (3) October 2001 (pp. 1317)
Statistics Corner: Questions and answers about language testing statistics: Point biserial correlation coefficients James Dean Brown (University of Hawai'i at Manoa) QUESTION: Recently on the email forum
More informationRecall this chart that showed how most of our course would be organized:
Chapter 4 OneWay 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
More informationWhich Design Is Best?
Which Design Is Best? Which Design Is Best? In Investigation 28: Which Design Is Best? students will become more familiar with the four basic epidemiologic study designs, learn to identify several strengths
More informationExercise 1: How to Record and Present Your Data Graphically Using Excel Dr. Chris Paradise, edited by Steven J. Price
Biology 1 Exercise 1: How to Record and Present Your Data Graphically Using Excel Dr. Chris Paradise, edited by Steven J. Price Introduction In this world of high technology and information overload scientists
More informationSelecting a Subset of Cases in SPSS: The Select Cases Command
Selecting a Subset of Cases in SPSS: The Select Cases Command When analyzing a data file in SPSS, all cases with valid values for the relevant variable(s) are used. If I opened the 1991 U.S. General Social
More informationKey Measurement Issues in Screening, Referral, and FollowUp Care for Young Children s Social and Emotional Development
Key Measurement Issues in Screening, Referral, and FollowUp Care for Young Children s Social and Emotional Development April 2005 Prepared by Colleen Peck Reuland and Christina Bethell of the Child and
More informationThe InStat guide to choosing and interpreting statistical tests
Version 3.0 The InStat guide to choosing and interpreting statistical tests Harvey Motulsky 19902003, GraphPad Software, Inc. All rights reserved. Program design, manual and help screens: Programming:
More informationPRINCIPAL COMPONENT ANALYSIS
1 Chapter 1 PRINCIPAL COMPONENT ANALYSIS Introduction: The Basics of Principal Component Analysis........................... 2 A Variable Reduction Procedure.......................................... 2
More informationA Guide to Sample Size Calculations for Random Effect Models via Simulation and the MLPowSim Software Package
A Guide to Sample Size Calculations for Random Effect Models via Simulation and the MLPowSim Software Package William J Browne, Mousa Golalizadeh Lahi* & Richard MA Parker School of Clinical Veterinary
More informationFindings from the Michigan School Readiness Program 6 to 8 Follow Up Study
Findings from the Michigan School Readiness Program 6 to 8 Follow Up Study October, 2007 Elena Malofeeva, Marijata DanielEchols, and Zongping Xiang High/Scope Educational Research Foundation 600 North
More informationIBM SPSS Missing Values 22
IBM SPSS Missing Values 22 Note Before using this information and the product it supports, read the information in Notices on page 23. Product Information This edition applies to version 22, release 0,
More informationREAD MORE READ BETTER? A METAANALYSIS OF THE LITERATURE ON THE RELATIONSHIP BETWEEN EXPOSURE TO READING AND READING ACHIEVEMENT M. Lewis S. J.
READ MORE READ BETTER? A METAANALYSIS OF THE LITERATURE ON THE RELATIONSHIP BETWEEN EXPOSURE TO READING AND READING ACHIEVEMENT M. Lewis S. J. Samuels University of Minnesota 1 Abstract While most educators
More information