Statistics and research

Save this PDF as:

Size: px
Start display at page:

Transcription

1 Statistics and research Usaneya Perngparn Chitlada Areesantichai Drug Dependence Research Center (WHOCC for Research and Training in Drug Dependence) College of Public Health Sciences Chulolongkorn University, Bangkok, THAILAND 1

2 Statistics used in research 1. Sampling technique 2. Sample size 3. Reliability check 4. Data analysis 2

3 Data analysis 1. Building file from questionnaire 2. Reliability check 3. Summarisation of major characteristics of population 4. Summarisation of major characteristics of sample 5. Causal finding 6. Classify groups/cross sectional 7. Prediction 3

4 Data analysis Reliability check 1. Editing - possible check - cross check 2. Cronbach Alpha 4

5 Data analysis Sumarised data Nominal and Ordinal Scale - number - percentage Interval and Ratio Scale Mean, Median, Standard diviation Graph Boxplot etc 5

6 Nominal and Ordinal Scale 1. Univariate Variable -frequency table and percentage Contraceptive use N % Yes No Total Incident Rate: IR IR = Number of new case during the period of study Total population 6

7 2. Bivariate Variable Ratio : R R = Male Female (Sex ratio) Risk Ratio : RR RR is the risk of an event (or of developing a disease) relative to exposure. Relative risk is a ratio of the probability of the event occurring in the exposed group versus a non-exposed group. 7

8 Descriptive Statistics Measures of central tendency Mean, median, mode, geometric mean, midrange Measure of variation Range, Interquartile range, variance and standard deviation, coefficient of variation Shape Symmetric, skewed, using box-and-whisker plots 8

9 Summary Measures Summary Measures Central Tendency Mean Median Mode Quartile Range Variance Variation Coefficient of Variation Geometric Mean Standard Deviation 9

10 Measures of Variation Variation Range Variance Standard Deviation Coefficient of Variation Population Variance Sample Variance Interquartile Range Population Standard Deviation Sample Standard Deviation 10

11 Coefficient of Variation Measures relative variation Always in percentage (%) Shows variation relative to mean Is used to compare two or more sets of data measured in different units CV S = 100% X 11

12 Shape of a Distribution Describes how data is distributed Measures of shape Symmetric or skewed Left-Skewed Mean < Median < Mode Symmetric Mean = Median =Mode Right-Skewed Mode < Median < Mean 12

13 Exploratory Data Analysis Box-and-whisker plot Graphical display of data using 5-number summary X smallest 1 Q 2 Median( ) Q Q 3 X largest

14 Distribution Shape and Box-and-Whisker Plot Left-Skewed Symmetric Right-Skewed Q1 Q2 Q 3 Q1Q Q 2 3 Q1 Q2 Q3 14

15 Data analysis Testing Hypothesis: All hypothesis tests are conducted the same way. The researcher states a hypothesis to be tested, formulates an analysis plan, analyzes sample data according to the plan, and accepts or rejects the null hypothesis, based on results of the analysis. 15

16 Statistical Testing Hypothesis Every hypothesis test requires the analyst to state -a null hypothesis (H o ) and an alternative hypothesis (H 1 ) -Significance level = 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. -Test method: The test method involves a test statistic and a sampling distribution. Computed from sample data, the test statistic might be a mean score, proportion, difference between means, difference between proportions, z-score, t-score, chi-square, etc. 16

17 Statistical Testing Hypothesis Quantitative data Qualitative data: p, p 1 -p 2 Correlation test 17

18 Statistical Testing Hypothesis 1. Null Hypothesis: H o 2. Alternative Hypothesis: H 1 or H a H 1 or H a must be in the difference direction H o : average income =10,000 US\$/month H 1 : average income 10,000 US\$/month ( µ =10,000) ( µ 10,000) ( µ <10,000) ( µ 10,000) 18

19 ERROR 1. Type I error is the error of rejecting the null hypothesis when it is true -- of saying an effect or event is statistically significant when it is not. The projected probability of committing type I error is called the level of significance. For example, for a test comparing two samples, a 5% level of significance (a =.05) means that when the null hypothesis is true (i.e. the two samples are part of the same population), you believe that your test will conclude "there's a significant difference between the samples" 5% of the time. α = P ( reject H 0 / H 0 true ) α = Level of Significance 19

20 ERROR 2. Type II error is the error of accepting the null hypothesis (H 0 ) when alternative hypothesis (H 1 ) is true H 0 not true β = P ( accept H 0 / H 0 not true) Reducing α made β increased Reducing β made α increased Adjusting the sample size can reduce both α and β 20

21 Proportional test 1. Binomial Test : small n 2. Z test : large n 3. Chi-Square Test for 1-way classification 3.1 Pearson Chi-Square test 3.2 Likelihood ratio test 3.3 Fisher s Exact test 21

22 Correlation analysis >2 Variables Multiple Regression Discriminant Analysis Logistic Regression ANOVA (Analysis of Variance) ANOVA (Analysis of Covariance) MANOVA (Multivariate Analysis of Variance) Log-Linear Model 22

23 H o : Var(gr 1.) = Var(gr. 2)=.... = var(gr.k) H 1 : At least 1 pairs of var(gr.i) # Var(gr. J) Reject H o (Accept H 1 ) if Sig. < alpha 23

24 Multiple Comparison 1. Variance of each group is equal: LSD, Bonferronni, Tukey, Duncan 2. Variance of each group is unequal: Dunnett, Tamhane etc. 24

25 Two-Way table or Contingency Table Drinking coffee Smoking Drink coffee (# per day) Smoking (# per day) Total Total Pearson Chi-Square test Likelihood ratio Chi-Square Fisher Exact test Etc. 25

26 Analysis of 2-group correlation for 2X2 table ĉ control other factors Exposure variable Disease Variable Cochran s Mantel-Haenszel Confound Variable 26

27 Multivariate Analysis Discriminant Analysis, Regression and Correlation Analysis Cluster Analysis Factor Analysis Principal Analysis Multidimensional Scaling Path Analysis etc. 27

28 Statistics for Research By using SPSS for Windows Chitlada Areesantichai Usaneya Perngparn WHO CC for Research and Training in Drug Dependence College of Public Health Sciences Chulalongkorn University Bangkok THAILAND

29 Mean is the average and is computed as the sum of all the observed outcomes from the sample divided by the total number of events. Median is the 'middle value' in your list. Mode: the mode of a set of data is the number with the highest frequency. Range of a set of data is the difference between the highest and lowest values in the set.

30 Level of Data Interval / Ratio Stat used for Central Tendency measurement Mode, Median, Mean Ordinal Mode, Median Nominal Mode

31 Variance: In statistics, a variance is also called the mean squared error. The variance is one of several measures that statisticians use to characterize the dispersion among the measures in a given population. To calculate the variance, it is necessary to first calculate the mean or average of the scores. The next step is to measure the amount that each individual score deviates or is different from the mean. Finally, you square that deviation by multiplying the number by itself. Numerically the variance equals the average of the squared deviations from the mean. ( Small variance Large variance

32 Standard Deviation or Std dev or SD or S: A measure of the dispersion of a set of data from its mean. The more spread apart the data is, the higher the deviation. Standard deviation can also be calculated as the square root of the variance. Standard Error or Std err or SE of a statistic is the standard deviation of the sampling distribution of that statistic. Sample random sampling Small sample size causes higher error Large sample size causes low error

33 Skewness is a measure of the degree of asymmetry of a distribution. If the left tail (tail at small end of the distribution) is more pronounced than the right tail (tail at the large end of the distribution), the function is said to have negative skewness. If the reverse is true, it has positive skewness. If the two are equal, it has zero skewness.

34 Normal distribution Symmetric Mean = Median = Mode

35 Skewed to the left or negative skew Mean < Median < Mode

36 Skewed to the right or positive skew Mode < Median < Mean Note: Skewness = - Skewed to the left Skewness = 0 No skew Skewness = + Skewed to the right

37 Normal distribution Reliability test Relation test Comparison test t-test 1.Independent t-test 2. Paired t-test

38 Normal distribution test SPSS: Analyze Descriptive Statistics Descriptive Analyze Descriptive Statistics Explore

39 Reliability(Alpha Coefficient or Cronbach) Use for Rating Scale questions. Cronbach's alpha can be written as a function of the number of test items and the average inter-correlation among the items

40 1. SPSS: Analyze Scale Reliability Analysis 2. Choose item in Data, Put into item 3. Choose Alpha Model 4. If the statistic detail is needed Click Statistic button 5. If the individual case is needed Choose List item labels

41 Output Alpha Coefficient or Cronbach ****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) Reliability Coefficients N of Cases = 50.0 N of Items = 10 Alpha =.7358

42 Pearson Correlation 1. SPSS: Analyze Correlate Bivariate Correlation 2. 2 Variables or more, put variable math, stat 3. At Correlation Coefficients, Choose Pearson 4. Test of significance, Choose Two-tailed 5. At Flag significant correlation, Set a symbol to identify the significant correlation of those two variables. 6. Click option to identify other values 7. Click ok

43 Chi-Square Test the distribution ratio H 0 : Population ratio is equal H 1 : Population ratio is not equal 1. SPSS: Analyze Nonparametric test 2. Choose variable in Test variable list, identify value 3. Click Option to identify other related values, Click OK

44 Chi-Square test 1. Observed number 2. Expected number 3. Residual is the different between observation and expectation number 4. Chi-square test (χ 2 test) is any hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true, or any in which this is asymptotically true, meaning that the sampling distribution can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough.

45 Chi-Square Ratio 1. SPSS: Analyze Nonparametric test 2. Move variables in Test variable list, specify value 3. Define Expected Values, click value 4. Set expected ratio 5. Click option, press OK

46 Mann-Whitney U-test For 2 independent groups 1. SPSS: Analyze Nonparametric test 2. Move variables in Test variable list, put variable score_m 3. Move target variables into Grouping variable 4. Click Define Group to verify variable value, e.g. sex : 1=male 2=female 5. In Test Type, choose Mann-Whitney Utest 6. Define others 7. Click OK

47 Independent t-test Independent t-test is used to test for a difference between two independent groups (like males and females) on the means of a continuous variable. SPSS: Analyze Compare Means Independent Sample t-test

48 Paired t-test Paired t test provides an hypothesis test of the difference between population means for a pair of random samples whose differences are approximately normally distributed. The most common design is that one nominal variable represents different individuals, while the other is "before and "after" some treatment. SPSS: Analyze Compare Means Paired Sample T Test

49 ANOVA (Analysis of Variance) At least 2 independent variables and one dependent variable SPSS: Analyze Compare Means One-Way ANOVA 1. Move to variable in Dependent list and move another variable to factor 2. Click Post Hoc to set sig level. If it is significant, then analyze Post Hoc Comparison to find out which couple are significant. If the samples are not equal, choose Scheffe 3. Click continue 4. Click contrast or option 5. Click ok

Descriptive Statistics

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

CHAPTER 3 COMMONLY USED STATISTICAL TERMS

CHAPTER 3 COMMONLY USED STATISTICAL TERMS There are many statistics used in social science research and evaluation. The two main areas of statistics are descriptive and inferential. The third class of

SPSS Tests for Versions 9 to 13

SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list

Variables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test.

The Analysis of Research Data The design of any project will determine what sort of statistical tests you should perform on your data and how successful the data analysis will be. For example if you decide

Module 9: Nonparametric Tests. The Applied Research Center

Module 9: Nonparametric Tests The Applied Research Center Module 9 Overview } Nonparametric Tests } Parametric vs. Nonparametric Tests } Restrictions of Nonparametric Tests } One-Sample Chi-Square Test

Chapter 5 Analysis of variance SPSS Analysis of variance

Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,

How to choose a statistical test. Francisco J. Candido dos Reis DGO-FMRP University of São Paulo

How to choose a statistical test Francisco J. Candido dos Reis DGO-FMRP University of São Paulo Choosing the right test One of the most common queries in stats support is Which analysis should I use There

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

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

Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

Outline of Topics. Statistical Methods I. Types of Data. Descriptive Statistics

Statistical Methods I Tamekia L. Jones, Ph.D. (tjones@cog.ufl.edu) Research Assistant Professor Children s Oncology Group Statistics & Data Center Department of Biostatistics Colleges of Medicine and Public

ANSWERS TO EXERCISES AND REVIEW QUESTIONS

ANSWERS TO EXERCISES AND REVIEW QUESTIONS PART FIVE: STATISTICAL TECHNIQUES TO COMPARE GROUPS Before attempting these questions read through the introduction to Part Five and Chapters 16-21 of the SPSS

Data Analysis. Lecture Empirical Model Building and Methods (Empirische Modellbildung und Methoden) SS Analysis of Experiments - Introduction

Data Analysis Lecture Empirical Model Building and Methods (Empirische Modellbildung und Methoden) Prof. Dr. Dr. h.c. Dieter Rombach Dr. Andreas Jedlitschka SS 2014 Analysis of Experiments - Introduction

A Guide for a Selection of SPSS Functions

A Guide for a Selection of SPSS Functions IBM SPSS Statistics 19 Compiled by Beth Gaedy, Math Specialist, Viterbo University - 2012 Using documents prepared by Drs. Sheldon Lee, Marcus Saegrove, Jennifer

II. DISTRIBUTIONS distribution normal distribution. standard scores

Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

We know from STAT.1030 that the relevant test statistic for equality of proportions is:

2. Chi 2 -tests for equality of proportions Introduction: Two Samples Consider comparing the sample proportions p 1 and p 2 in independent random samples of size n 1 and n 2 out of two populations which

Inferential Statistics. Probability. From Samples to Populations. Katie Rommel-Esham Education 504

Inferential Statistics Katie Rommel-Esham Education 504 Probability Probability is the scientific way of stating the degree of confidence we have in predicting something Tossing coins and rolling dice

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

F. Farrokhyar, MPhil, PhD, PDoc

Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

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

Introduction to Statistics and Quantitative Research Methods

Introduction to Statistics and Quantitative Research Methods Purpose of Presentation To aid in the understanding of basic statistics, including terminology, common terms, and common statistical methods.

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,

Once saved, if the file was zipped you will need to unzip it.

1 Commands in SPSS 1.1 Dowloading data from the web The data I post on my webpage will be either in a zipped directory containing a few files or just in one file containing data. Please learn how to unzip

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,

We will use the following data sets to illustrate measures of center. DATA SET 1 The following are test scores from a class of 20 students:

MODE The mode of the sample is the value of the variable having the greatest frequency. Example: Obtain the mode for Data Set 1 77 For a grouped frequency distribution, the modal class is the class having

2. Describing Data. We consider 1. Graphical methods 2. Numerical methods 1 / 56

2. Describing Data We consider 1. Graphical methods 2. Numerical methods 1 / 56 General Use of Graphical and Numerical Methods Graphical methods can be used to visually and qualitatively present data and

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

Lecture - 32 Regression Modelling Using SPSS

Applied Multivariate Statistical Modelling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 32 Regression Modelling Using SPSS (Refer

Objective of the course The main objective is to teach students how to conduct quantitative data analysis in SPSS for research purposes.

COURSE DESCRIPTION The course Data Analysis with SPSS was especially designed for students of Master s Programme System and Software Engineering. The content and teaching methods of the course correspond

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

INTERPRETING THE REPEATED-MEASURES ANOVA

INTERPRETING THE REPEATED-MEASURES ANOVA USING THE SPSS GENERAL LINEAR MODEL PROGRAM RM ANOVA In this scenario (based on a RM ANOVA example from Leech, Barrett, and Morgan, 2005) each of 12 participants

SPSS for Exploratory Data Analysis Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav)

Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav) Organize and Display One Quantitative Variable (Descriptive Statistics, Boxplot & Histogram) 1. Move the mouse pointer

An introduction to IBM SPSS Statistics

An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive

Homework 3. Part 1. Name: Score: / null

Name: Score: / Homework 3 Part 1 null 1 For the following sample of scores, the standard deviation is. Scores: 7, 2, 4, 6, 4, 7, 3, 7 Answer Key: 2 2 For any set of data, the sum of the deviation scores

EBM Cheat Sheet- Measurements Card

EBM Cheat Sheet- Measurements Card Basic terms: Prevalence = Number of existing cases of disease at a point in time / Total population. Notes: Numerator includes old and new cases Prevalence is cross-sectional

Some Critical Information about SOME Statistical Tests and Measures of Correlation/Association

Some Critical Information about SOME Statistical Tests and Measures of Correlation/Association This information is adapted from and draws heavily on: Sheskin, David J. 2000. Handbook of Parametric and

UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates

UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates 1. (a) (i) µ µ (ii) σ σ n is exactly Normally distributed. (c) (i) is approximately Normally

AP Statistics: Syllabus 3

AP Statistics: Syllabus 3 Scoring Components SC1 The course provides instruction in exploring data. 4 SC2 The course provides instruction in sampling. 5 SC3 The course provides instruction in experimentation.

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...

Biostatistics Lab Notes

Biostatistics Lab Notes Page 1 Lab 1: Measurement and Sampling Biostatistics Lab Notes Because we used a chance mechanism to select our sample, each sample will differ. My data set (GerstmanB.sav), looks

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.

Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing

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,

Data Mining Part 2. Data Understanding and Preparation 2.1 Data Understanding Spring 2010

Data Mining Part 2. and Preparation 2.1 Spring 2010 Instructor: Dr. Masoud Yaghini Introduction Outline Introduction Measuring the Central Tendency Measuring the Dispersion of Data Graphic Displays References

STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance

Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis

Analysis of numerical data S4

Basic medical statistics for clinical and experimental research Analysis of numerical data S4 Katarzyna Jóźwiak k.jozwiak@nki.nl 3rd November 2015 1/42 Hypothesis tests: numerical and ordinal data 1 group:

Research Methods & Experimental Design

Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) 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,

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

The Statistics Tutor s

statstutor community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence Stcp-marshallowen-7 The Statistics Tutor s www.statstutor.ac.uk

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

Research Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement

Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.

Stats Review Chapters 3-4

Stats Review Chapters 3-4 Created by Teri Johnson Math Coordinator, Mary Stangler Center for Academic Success Examples are taken from Statistics 4 E by Michael Sullivan, III And the corresponding Test

STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members

SPSS Guide How-to, Tips, Tricks & Statistical Techniques

SPSS Guide How-to, Tips, Tricks & Statistical Techniques Support for the course Research Methodology for IB Also useful for your BSc or MSc thesis March 2014 Dr. Marijke Leliveld Jacob Wiebenga, MSc CONTENT

WHAT IS A JOURNAL CLUB?

WHAT IS A JOURNAL CLUB? With its September 2002 issue, the American Journal of Critical Care debuts a new feature, the AJCC Journal Club. Each issue of the journal will now feature an AJCC Journal Club

Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS

Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction

Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality

Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality 1 To help choose which type of quantitative data analysis to use either before

Introduction to Statistics with SPSS for Social Science

New Introduction to Statistics with SPSS for Social Science Gareth Norris Faiza Qureshi Dennis Howitt Duncan Cramer Aberystwyth University City University London University of Loughborough University of

Previous lecture. Lecture 6. Learning outcomes of this lecture. Today. Entering data into SPSS. Data coding. Data preparation methods

Lecture 6 Empirical Research Methods IN4304 Data preparation methods Previous lecture participant-observation and non-participant observation Does the observer act as a member of the group? Sampling strategies

7. Tests of association and Linear Regression

7. Tests of association and Linear Regression In this chapter we consider 1. Tests of Association for 2 qualitative variables. 2. Measures of the strength of linear association between 2 quantitative variables.

By Hui Bian Office for Faculty Excellence

By Hui Bian Office for Faculty Excellence 1 K-group between-subjects MANOVA with SPSS Factorial between-subjects MANOVA with SPSS How to interpret SPSS outputs How to report results 2 We use 2009 Youth

Chapter 21 Section D

Chapter 21 Section D Statistical Tests for Ordinal Data The rank-sum test. You can perform the rank-sum test in SPSS by selecting 2 Independent Samples from the Analyze/ Nonparametric Tests menu. The first

Statistics Chapter 3 Averages and Variations

Statistics Chapter 3 Averages and Variations Measures of Central Tendency Average a measure of the center value or central tendency of a distribution of values. Three types of average: Mode Median Mean

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

dbstat 2.0

dbstat Statistical Package for Biostatistics Software Development 1990 Survival Analysis Program 1992 dbstat for DOS 1.0 1993 Database Statistics Made Easy (Book) 1996 dbstat for DOS 2.0 2000 dbstat for

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics

Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

Assumptions. Assumptions of linear models. Boxplot. Data exploration. Apply to response variable. Apply to error terms from linear model

Assumptions Assumptions of linear models Apply to response variable within each group if predictor categorical Apply to error terms from linear model check by analysing residuals Normality Homogeneity

A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes

A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes together with the number of data values from the set that

Bivariate Analysis. Correlation. Correlation. Pearson's Correlation Coefficient. Variable 1. Variable 2

Bivariate Analysis Variable 2 LEVELS >2 LEVELS COTIUOUS Correlation Used when you measure two continuous variables. Variable 2 2 LEVELS X 2 >2 LEVELS X 2 COTIUOUS t-test X 2 X 2 AOVA (F-test) t-test AOVA

Chapter 16 Multiple Choice Questions (The answers are provided after the last question.)

Chapter 16 Multiple Choice Questions (The answers are provided after the last question.) 1. Which of the following symbols represents a population parameter? a. SD b. σ c. r d. 0 2. If you drew all possible

Data Analysis Tools. Tools for Summarizing Data

Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool

Hypothesis Testing & Data Analysis. Statistics. Descriptive Statistics. What is the difference between descriptive and inferential statistics?

2 Hypothesis Testing & Data Analysis 5 What is the difference between descriptive and inferential statistics? Statistics 8 Tools to help us understand our data. Makes a complicated mess simple to understand.

Chapter 3: Data Description Numerical Methods

Chapter 3: Data Description Numerical Methods Learning Objectives Upon successful completion of Chapter 3, you will be able to: Summarize data using measures of central tendency, such as the mean, median,

Statistics for Management II-STAT 362-Final Review

Statistics for Management II-STAT 362-Final Review Multiple Choice Identify the letter of the choice that best completes the statement or answers the question. 1. The ability of an interval estimate to

ACTM State Exam-Statistics

ACTM State Exam-Statistics For the 25 multiple-choice questions, make your answer choice and record it on the answer sheet provided. Once you have completed that section of the test, proceed to the tie-breaker

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

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:

T-test & factor analysis

Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue

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

AP Statistics 2001 Solutions and Scoring Guidelines

AP Statistics 2001 Solutions and Scoring Guidelines The materials included in these files are intended for non-commercial use by AP teachers for course and exam preparation; permission for any other use

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

DATA INTERPRETATION AND STATISTICS

PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

Simple Linear Regression Chapter 11

Simple Linear Regression Chapter 11 Rationale Frequently decision-making situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related

The Logic of Statistical Inference-- Testing Hypotheses

The Logic of Statistical Inference-- Testing Hypotheses Confirming your research hypothesis (relationship between 2 variables) is dependent on ruling out Rival hypotheses Research design problems (e.g.

1. Why the hell do we need statistics?

1. Why the hell do we need statistics? There are three kind of lies: lies, damned lies, and statistics, British Prime Minister Benjamin Disraeli (as credited by Mark Twain): It is easy to lie with statistics,

Seminar paper Statistics

Seminar paper Statistics The seminar paper must contain: - the title page - the characterization of the data (origin, reason why you have chosen this analysis,...) - the list of the data (in the table)

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,

1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96

1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years

Simple linear regression

Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between

Guide for SPSS for Windows

Guide for SPSS for Windows Index Table Open an existing data file Open a new data sheet Enter or change data value Name a variable Label variables and data values Enter a categorical data Delete a record

SPSS TUTORIAL & EXERCISE BOOK

UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS

The general form of the PROC MEANS statement is

Describing Your Data Using PROC MEANS PROC MEANS can be used to compute various univariate descriptive statistics for specified variables including the number of observations, mean, standard deviation,

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,