1 EAA492/6: FINAL YEAR PROJECT DEVELOPING QUESTIONNAIRES PART 2 1 A H M A D S H U K R I Y A H A Y A E N G I N E E R I N G C A M P U S U S M
2 CONTENTS Reliability And Validity Sample Size Determination Sampling Designs Descriptive And Inferential Statistics Pilot Study 2
3 CHOICE OF QUESTIONNAIRES Adopt the questionnaire based on previous studies 3 Adapt the questionnaire based on previous studies Create a NEW questionnaire
4 RELIABILITY AND VALIDITY 4 Validity can be defined as the extent to which any measuring instrument measures what it is intended to measure. Reliability concerns the extent to which an experiment, test or any measuring procedure yields the same results on repeated trials.
5 VALIDITY 5 Validity is the extent to which any measuring instrument measures what it is intended to measure. Validity is about interpretation of data arising from a specified procedure. It is not a test! Thus, validity is not about the measuring instrument itself but the measuring instrument in relation to the purpose for which it is being used.
6 VALIDITY Three types of validity: (1) Content Validity (2) Criterion-Related Validity (3) Construct Validity 6
7 Content Validity 7 Content validity depends on the extent to which an empirical measurement reflects a specific domain of content. Example: A test in arithmetical operations would not be valid if the test problems focused only on addition and neglecting subtraction, multiplication and division. Thus a researcher must be able to specify the full domain of content that is relevant to the particular measurement situation. Example: We must specify all the words that a standard four student should know how to spell. Choose at random the number of words that should be sampled. Then, they must be put in a form that is testable.
8 Content Validity Some limitations 8 The process of determining the domain of the content is more difficult and complex when dealing with the abstract concepts typically found in the social sciences. There is no agreed upon criterion for determining the extent to which a measure has attained content validity. Thus, a measure can only be considered as strongly or weakly valid (i.e., the alternative is not between fully valid or fully invalid measures).
9 Criterion-Related Validity Also known as Predictive Validity 9 Has the closest relationship to what is meant by the term validity Definition: Is an issue when the purpose is to use an instrument to estimate some important form of behaviour that is external to the measuring instrument itself, the latter being referred to as the criterion Example: We assess the validity of college board examination by showing they accurately predict how well high school college seniors will do in college instruction
10 Criterion-Related Validity 10 Example: We validate a written driver s test by showing that it accurately predicts how well some group of persons can operate an automobile. The indicator between the test and the criterion is usually estimated by the size of the correlation.
11 Criterion-Related Validity 11 Have been used mainly in psychology and education. Should be used in any situation or area of scientific enquiry in which it makes sense to correlate scores obtained on a given test with performance on a particular criterion or set of relevant criteria. For some cases, criterion-related validity cannot be used because we cannot determine the relevant criterion variables.
12 Construct validity 12 It is concerned with the extent to which a particular measure relates to other measures consistent with theoretically derived hypotheses concerning the concepts (or constructs) that are being measured. Example: Suppose a researcher wanted to evaluate the construct validity of a particular measure of self-esteem say Rosenberg s self-esteem scale. Theoretically, Rosenberg has argued that a student s level of self-esteem is positively related to participation in school activities. Determine correlation between Rosenberg s self-esteem scale to a group of students and their extent of involvement in school activities. If correlation is positive and substantial, then it supports one piece of evidence on the validity of Rosenberg s self-esteem scale.
13 Involves three steps: Construct validity 13 (1) Theoretical relationships between the concepts must be specified. (2) Empirical relationships between the measures of the concepts must be examined. (3) Empirical evidence must be interpreted in terms of how it clarifies the construct validity of a particular measure.
14 RELIABILITY 14 Reliability concerns the degree to which results are consistent across repeated measures Basic formulation of measurements where X is the observed score, t is the true score and e is the random error
15 RELIABILITY Assumptions: Note : For (3), it is assumed that two sets of measurements are observed for a single person for a single variable.
16 RELIABILITY Therefore, 16 From Assumption (1), This result is true for repeated measurements of a single variable for a single person.
17 RELIABILITY 17 Reliability refers to the consistency of repeated measurements across persons rather than within a single person. Thus, look at the variance of the measurement. Hence
18 RELIABILITY Thus the ratio of true to observed variance is called the reliability of X as a measure of T. 18 Reliability can also be expressed as
19 RELIABILITY 19 The estimate of a measure s reliability can be obtained by correlating parallel measurements. Two measurements (X and X ) are defined as parallel if they have identical true scores and equal variances as shown below:
20 RELIABILITY 20 Thus, Thus it follows that the estimate of reliability is simply the correlation between parallel measures.
21 RELIABILITY 21 There are four basic methods to estimate the reliability of empirical measurements namely the retest method, the alternative-form method, the split-halves method and the internal consistency method The range of values for the reliability method is from 0 to 1. Values near 1 show good reliability. Usually, if the value is more than 0.7 than the method is reliable.
22 RELIABILITY In SPSS, the reliability analysis is obtained from the following commands: ANALYZE SCALE RELIABILITY ANALYSIS Under reliability, there are five different types of methods namely (1) The alpha Cronbach s method 22 (2) The split-half method (3) The Guttman method (4) The parallel method (5) The strict parallel method
23 The easiest method. The Retest Method 23 Suppose that a set of questionnaires or tests are given to some respondents. Then if the same set of questionnaires or tests are given to the same respondents after some specified time period, then this is known as the retest method. The interval between the two tests are usually taken to be from two to four weeks.
24 The Retest Method The equations for the two tests are as follows: Assumptions: (i) (ii) Thus and 24 X 1 X t t X X t V V 1 2, x X X
25 The Retest Method 25 Weaknesses of this method 1. Researches usually cannot do more than one tests 2. The reaction of the respondents about certain surveys. Example: If a respondent is being interviewed about whether he/she will vote in a coming election at time 1, the respondent might make a decision at time 2 and will actually vote at time 3 due to the fact that he/she was sensitized to the election through the interview.
26 The alternative form method 26 The most frequently used method in the field of education Similar to the retest method as it requires two sets of tests which is given to the same respondents Different from the retest method in that an alternative form of the questions are given The two sets of questions must measure the same thing. Example: If two tests are designed to measure the understanding of mathematical operators using 20 questions for each test then the sets of questions must be of equal difficulty
27 The alternative form method Superior than the retest method 27 Weakness of this method is to design questionnaires or tests which are of equal level.
28 The split-half method Suppose there are N questions in a questionnaire These questions are split into two equal halves each having N/2 questions. 28 Split can be done arbitrarily. Example: Can choose the first questions and the other being the last questions or we can choose the even numbered questions for the first half and the odd numbered questions as the second half. The value of the reliability measures will be different for different splits.
29 The split-half method The Spearman-Brown prophecy formula for measuring reliability is given by : xx 2 xx 1 xx 29 xx is the reliability for the whole sample xx is the correlation between the two split-half
30 Internal consistency method 30 This method require only a single test on the sample and is usually known as the internal consistency method. The most popular of the internal consistency method is the Cronbach s alpha method which is given by N [ 1 ( N 1)] N is equal to the number of questions is the mean correlation between each questions
31 CONTENTS Reliability And Validity Sample Size Determination Sampling Designs Descriptive And Inferential Statistics Pilot Study 31
32 Sample Size Determination 32 Population Sample Sample is chosen at random from a population
33 Sample Size Determination Sample size depends on the budget and degree of confidence required. Smaller samples are more likely to be different from the population than larger ones. So smaller samples have more sampling error and lower reliability. Sample Size 33 Sampling error Sample Reliability
34 Sample Size Determination Krejcie, R.V. and Morgan, D.W. (1970), Determining Sample Size For Research Activities, Educational And Psychological Measurement, 30, The following Table 1 is from this paper. 34
35 Sample Size Determination 35 N S N S N S N is the population size; S is sample size
36 CONTENTS Reliability And Validity Sample Size Determination Sampling Designs Descriptive And Inferential Statistics Pilot Study 36
37 SAMPLING Foundation of a good sample survey is the sample. A sample is some part of a larger body specially selected to represent the population. Sampling is the process by which it is done. Samples must be representative of the population. 37
38 PROBABILITY AND NONPROBABILITY SAMPLING 38 Probability sampling is a process of sample selection in which elements are chosen by chance procedures and with known probabilities of selection. Nonprobability sampling includes all methods in which units are not selected by chance procedures or with known probabilities of selection.
39 NONPROBABILITY SAMPLING 39 Haphazard sampling: Samples are made up of individuals casually met or conveniently available such as students enrolled in a class or people passing by on a street corner. Cannot make generalization beyond the collections themselves and are seldom of scientific interest. Also known as convenience sampling. Judgmental or purposive sampling: Sample elements are chosen from the population by interviewers using their own discretion about which informants are typical or representative. Results of such sampling procedure can be very good, if the interviewers intuition or judgment is sound.
40 NONPROBABILITY SAMPLING 40 Quota sampling: Process of selection in which the element are chosen by interviewers using prearranged categories of sample elements to obtain a predetermined number of cases in each category. Expert sampling: Elements are chosen on the basis of informed opinion that they are representative of the population in question. Example: A specialist on secondary education may decide that four schools across the country adequately represent the range of variation seen in teaching methods.
41 PROBABILITY SAMPLING 41 Simple Random Sampling (srs): Each population member has the same probability of appearing in the sample. Sample size: Depends on the objective of survey. Assume that we need to estimate the population mean, by using a srs mean and restricting to an acceptable level the probability that the absolute difference between the population mean and the sample mean is greater than some specified value.
42 Simple Random Sampling 42 Then we have for some given d and α Thus, where
43 Determination of S 2 in SRS 43 From pilot studies From previous surveys From a preliminary sample
44 PROBABILITY SAMPLING 44 Systematic sampling: Method of selecting units from a list through the application of a selection interval, I, so that every I th unit on the list, following a random start, is included in the sample. Sample size: Depends on the objective of survey. Assume that we need to estimate the population mean, by using a sample mean restricting to an acceptable level the probability that the absolute difference between the population mean and the sample mean is greater than some specified value.
45 Systematic Sampling 45 Then we have for some given d and α Thus, where
46 PROBABILITY SAMPLING 46 Stratified (simple) random sampling: Technique where a population can be conveniently partitioned into a set of sub-populations (strata). Such a population is said to be stratified. Within a strata, simple random sampling method is used to determine the sample. Cluster sampling: Sometimes a finite population may consist of a large number of groups of individuals, e.g. of households in a city. This is a special form of stratification (many strata of rather small size) and is referred as clusters. Draw a cluster sample as a srs of the clusters. If all the members of the sampled clusters are obtained, this is known as one-stage cluster sampling.
47 CONTENTS Reliability And Validity Sample Size Determination Sampling Designs Descriptive And Inferential Statistics Pilot Study 47
48 Descriptive and Inferential Statistics Descriptive Statistics- used to describe the data where data are presented in the form of tables, charts or summarization by means of percentiles and standard deviation Measures of locations: mean, median, mode 48 Measures of spread: standard deviation, variance, range. Plots such bar chart, pie chart, histogram, Box and Whiskers plot. Not enough just to do this in your FYP!!!
49 Descriptive and Inferential Statistics 49 Population Sample Sample is chosen at random from a population
50 Descriptive and Inferential Statistics 50 Inferential statistics - Process of drawing information from sampled observations of a population and making conclusions about the population. -Two-prong approach. (1) Sampling must be representative of population (2) Correct conclusions made of population
51 Descriptive and Inferential Statistics 51 Inferential statistics - t-tests - Analysis of variance tests - Chi-square tests - Regression models Students must do some inferential statistics.
52 CONTENTS Reliability And Validity Sample Size Determination Sampling Designs Descriptive And Inferential Statistics Pilot Study 52
53 Pilot Study 53 Last major stage of survey work before data collection stage. Designed to find any problems with the data collection process such as: - Poor introduction and instructions to questionnaire - Unclear or undefined terms - Unclear or ambiguous response task - Too many don t know responses - Biased or offensive questions - and so on. Check reliability
54 Pilot Study 54 Choose potential respondents to complete a questionnaire Other approaches are: - Behaviour coding: Investigator watches the respondent and/or interviewer complete the questionnaire or observes the behaviour after it has been recorded on tape. - Cognitive interview: Respondents are asked to think aloud while completing the survey and to describe everything that comes to mind while arriving at an answer.
55 Pilot Study 55 Interviewer evaluation: Interviewers are asked to code question characteristics and respondent behaviour. Respondent evaluation: Respondents are asked to rate and/or comment about the questions. Expert panels: Experts in survey research can be asked to review a questionnaire and identify potential problems.
56 Guidelines 56 Sample size: At least 25 samples. For behaviour and cognitive interview, sample size can be reduced to about 12 samples. Sample composition: Should be similar to that of the survey. Number of pretests: One pretest is adequate but not always recommended. Data collection time: For interviews, can allow 50% longer than the projected interview. Statistical analysis: Can be done if data is more than 25. Number of identified problems: Will definitely find problems. Measure the reliability of the questionnaire
57 SOME COMMENTS Before the pilot study, the following must be followed: 57 (1) For questionnaires that are adopted and adapted (with less than 20% change), content validity need not be checked. (2) For NEW and adapted (more than 20%) questionnaires, content validity must be done with at least three experts.
58 SOME COMMENTS 58 During the pilot study, for NEW questionnaires, (1)the number of sample size must be more than 100 (2)factor analysis must be carried out.
59 SUMMARY FYP report must include (1) Validity Content Validity (2) Reliability (3) Pilot Study (4) Descriptive Statistics (5) Inferential Statistics 59
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
1) Overview 2) Sample or Census 3) The Sampling Design Process i. Define the Target Population ii. Determine the Sampling Frame iii. Select a Sampling Technique iv. Determine the Sample Size v. Execute
GCSE Statistics Revision notes Collecting data Sample This is when data is collected from part of the population. There are different methods for sampling Random sampling, Stratified sampling, Systematic
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
Statistics for Engineers 4-1 4. Introduction to Statistics Descriptive Statistics Types of data A variate or random variable is a quantity or attribute whose value may vary from one unit of investigation
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
SAMPLING METHODS Chapter 5 1 LEARNING OBJECTIVES Reasons for sampling Different sampling methods Probability & non probability sampling Advantages & disadvantages of each sampling method 2 SAMPLING A sample
GCSE HIGHER Statistics Key Facts Collecting Data When writing questions for questionnaires, always ensure that: 1. the question is worded so that it will allow the recipient to give you the information
Numerical Summarization of Data OPRE 6301 Motivation... In the previous session, we used graphical techniques to describe data. For example: While this histogram provides useful insight, other interesting
Dr Roy Ramphal (UNISA) It is a process that involves obtaining scientific knowledge by means of various objective methods and procedures. Objective means that these methods and procedures do not rely on
Descriptive statistics parameters: Measures of centrality Contents Definitions... 3 Classification of descriptive statistics parameters... 4 More about central tendency estimators... 5 Relationship between
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
Descriptive Methods Ch. 6 and 7 Purpose of Descriptive Research Purely descriptive research describes the characteristics or behaviors of a given population in a systematic and accurate fashion. Correlational
Answer keys for Assignment 10: Measurement of study variables (The correct answer is underlined in bold text) 1. In a study, participants are asked to indicate the type of pet they have at home (ex: dog,
Guided Reading Educational Research: Competencies for Analysis and Applications 9th Edition EDFS 635: Educational Research Chapter 1: Introduction to Educational Research 1. List and briefly describe the
Cambridge TECHNICALS OCR LEVEL 3 CAMBRIDGE TECHNICAL CERTIFICATE/DIPLOMA IN BUSINESS MARKET RESEARCH IN BUSINESS H/502/5427 LEVEL 3 UNIT 10 GUIDED LEARNING HOURS: 60 UNIT CREDIT VALUE: 10 MARKET RESEARCH
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions
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 H A P T E R 6 Selecting Research Participants OBJECTIVES After studying this chapter, students should be able to Define the term sampling frame Describe the difference between random sampling and random
Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are
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
TYPES OF SAMPLING 1. Types or Techniques Probability Sampling: There are a number of techniques of taking Probability sample. But here only six important techniques have been discussed as follows: 1. Simple
Wilder Research Analyzing and interpreting data Evaluation resources from Wilder Research Once data are collected, the next step is to analyze the data. A plan for analyzing your data should be developed
MBACATÓLICA JAN/APRIL 2006 Marketing Research Fernando S. Machado Week 6 Sampling: Design and Procedures Sampling: Sample Size Determination Data Preparation 1 Sampling: Design and Procedures The Sampling
Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain
SAMPLING & INFERENTIAL STATISTICS Sampling is necessary to make inferences about a population. SAMPLING The group that you observe or collect data from is the sample. The group that you make generalizations
A Correlation of to the South Carolina Data Analysis and Probability Standards INTRODUCTION This document demonstrates how Stats in Your World 2012 meets the indicators of the South Carolina Academic Standards
POLI 300 Handout #2 N. R. Miller Key Definitions Pertaining to Sampling RANDOM SAMPLING 1. Population: the set of units (in survey research, usually individuals or households), N in number, that are to
Basic Biostatistics for Clinical Research Ramses F Sadek, PhD GRU Cancer Center 1 1. Basic Concepts 2. Data & Their Presentation Part One 2 1. Basic Concepts Statistics Biostatistics Populations and samples
Manual How large a Sample do we need SRS STRAT.xls Guido Lüchters September 2006 File: How large a Sample do we need SRS STRAT.doc Last save: Friday, 8. September 2006 How large a Sample do we need SRS
Chapter Eleven Sampling: Design and Procedures 11-1 Brand name change and re-position Coke to New Coke Legend to Lenovo (2003) Panasonic, National, Technic all to Panasonic Vegemite to isnack 2.0 (social
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different
Schools Value-added Information System Technical Manual Quality Assurance & School-based Support Division Education Bureau 2015 Contents Unit 1 Overview... 1 Unit 2 The Concept of VA... 2 Unit 3 Control
206 RESEARCH MET HODOLOGY Non-random/non-probability sampling designs in quantitative research N on-probability sampling designs do not follow the theory of probability in the choice of elements from the
Mathematics Probability and Statistics Curriculum Guide Revised 2010 This page is intentionally left blank. Introduction The Mathematics Curriculum Guide serves as a guide for teachers when planning instruction
Chapter 15 Multiple Choice Questions (The answers are provided after the last question.) 1. What is the median of the following set of scores? 18, 6, 12, 10, 14? a. 10 b. 14 c. 18 d. 12 2. Approximately
Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam Software Profiling Seminar, 2013 Statistics 101 Descriptive Statistics Population Object Object Object Sample numerical description Object
Elementary Statistics Chapter 1 Dr. Ghamsary Page 1 Elementary Statistics M. Ghamsary, Ph.D. Chap 01 1 Elementary Statistics Chapter 1 Dr. Ghamsary Page 2 Statistics: Statistics is the science of collecting,
Statistics revision Dr. Inna Namestnikova email@example.com Statistics revision p. 1/8 Introduction Statistics is the science of collecting, analyzing and drawing conclusions from data. Statistics
Reliability It is the user who must take responsibility for determining whether or not scores are sufficiently trustworthy to justify anticipated uses and interpretations. (AERA et al., 1999) Reliability
Department of Mathematics Izmir University of Economics Week 1 2014-2015 Introduction In this chapter we will focus on the definitions of population, sample, parameter, and statistic, the classification
Survey Process White Paper Series Five Steps in Creating a Survey Sampling Plan POLARIS MARKETING RESEARCH, INC. 1455 LINCOLN PARKWAY, SUITE 320 ATLANTA, GEORGIA 30346 404.816.0353 www.polarismr.com ii
Sampling Techniques Surveys and samples Source: http://www.deakin.edu.au/~agoodman/sci101/chap7.html In this section you'll learn how sample surveys can be organised, and how samples can be chosen in such
guidance for research and evaluation in Fife What this is about? Sampling allows you to draw conclusions about a particular population by examining a part of it. When carrying out a survey, it is not usually
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 12O ELEMENTARY STATISTICS I 3 Lecture Hours, 1 Lab Hour, 3 Credits Pre-Requisite:
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
ESSENTIALS OF Business Research Methods SECOND EDITION Joseph F. Hair Jr. Mary Wolfinbarger Celsi Arthur H. Money Phillip Samouel Michael J. Page am.e.sharpe Armonk, New York London, England Detailed Table
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
Introduction to Sampling for Non-Statisticians Dr. Safaa R. Amer Overview Part I Part II Introduction Census or Sample Sampling Frame Probability or non-probability sample Sampling with or without replacement
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
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.
Statistics, Research, & SPSS: The Basics SPSS (Statistical Package for the Social Sciences) is a software program that makes the calculation and presentation of statistics relatively easy. It is an incredibly
Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals
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.
What is Psychology? chapter 1 Overview! The science of psychology! What psychologists do! Critical and scientific thinking! Correlational studies! The experiment! Evaluating findings What is psychology?
Sample size determination Review Articles Influencing factors and calculation strategies for survey research Ali A. Al-Subaihi, PhD. ABSTRACT The paper reviews both the influencing factors and calculation
Sampling Procedures Y520 Strategies for Educational Inquiry Robert S Michael RSMichael 2-1 Terms Population (or universe) The group to which inferences are made based on a sample drawn from the population.
Theoretic perspectives & empirical insights from the European Social Survey Research Centre for Survey Research and Methodology Mannheim, Germany firstname.lastname@example.org August 25th Overview 1 2 3 4
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
Chapter Eight: Quantitative Methods RESEARCH DESIGN Qualitative, Quantitative, and Mixed Methods Approaches Third Edition John W. Creswell Chapter Outline Defining Surveys and Experiments Components of
Methods of Sampling Design in the Legal Research: Advantages and Disadvantages Kalpana V. Jawale Assistant Professor, Post Graduate Teaching Department of Law, Sant Gadge Baba Amravati University, Amravati,
CH.6 Random Sampling and Descriptive Statistics Population vs Sample Random sampling Numerical summaries : sample mean, sample variance, sample range Stem-and-Leaf Diagrams Median, quartiles, percentiles,
Session 1.6 Measures of Central Tendency Measures of location (Indices of central tendency) These indices locate the center of the frequency distribution curve. The mode, median, and mean are three indices
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Exploratory Analysis Carlos Alberto Felgueiras email@example.com
MARKETING RESEARCH AND MARKET INTELLIGENCE (MRM711S) FEEDBACK TUTORIAL LETTER SEMESTER `1 OF 2016 Dear Student Assignment 1 has been marked and this serves as feedback on the assignment. I have included
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,
Task force on quality of BCS data Analysis of sample size in consumer surveys theoretical considerations and factors determining minimum necessary sample sizes, link between country size and sample size
VALIDITY FOR TEACHERS A N O V E R V I E W DEFINITION Validity refers to how appropriate the interpretations of a test score are for the purpose intended. For instance, does a test that is supposed to measure
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.
Survey Process White Paper Series The Six Steps in Conducting Quantitative Marketing Research POLARIS MARKETING RESEARCH, INC. 1455 LINCOLN PARKWAY, SUITE 320 ATLANTA, GEORGIA 30346 404.816.0353 www.polarismr.com
AP Statistics 1998 Scoring Guidelines These materials are intended for non-commercial use by AP teachers for course and exam preparation; permission for any other use must be sought from the Advanced Placement
Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about
NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are
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
Data Analysis Plan The appropriate methods of data analysis are determined by your data types and variables of interest, the actual distribution of the variables, and the number of cases. Different analyses
CSS.com Chapter 905 Standard Deviation Estimator Introduction Even though it is not of primary interest, an estimate of the standard deviation (SD) is needed when calculating the power or sample size of
Hawaii State Standards correlated to Merit Software Math Programs The Language Arts Content Standards (1999) emphasize reading, writing, oral communication, and the study of literature and language from
Your consent to our cookies if you continue to use this website.