EAA492/6: FINAL YEAR PROJECT DEVELOPING QUESTIONNAIRES PART 2 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


 Gladys Howard
 1 years ago
 Views:
Transcription
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) CriterionRelated 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 CriterionRelated 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 CriterionRelated 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 CriterionRelated 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, criterionrelated 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 selfesteem say Rosenberg s selfesteem scale. Theoretically, Rosenberg has argued that a student s level of selfesteem is positively related to participation in school activities. Determine correlation between Rosenberg s selfesteem 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 selfesteem 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 alternativeform method, the splithalves 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 splithalf 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 splithalf 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 splithalf method The SpearmanBrown 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 splithalf
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 subpopulations (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 onestage 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. Twoprong approach. (1) Sampling must be representative of population (2) Correct conclusions made of population
51 Descriptive and Inferential Statistics 51 Inferential statistics  ttests  Analysis of variance tests  Chisquare 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
60 THANK YOU 60
Describe what is meant by a placebo Contrast the doubleblind procedure with the singleblind 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
More informationChapter 8: Quantitative Sampling
Chapter 8: Quantitative Sampling I. Introduction to Sampling a. The primary goal of sampling is to get a representative sample, or a small collection of units or cases from a much larger collection or
More information1) Overview 2) Sample or Census 3) The Sampling Design Process i. Define the Target Population ii. Determine the Sampling Frame iii.
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
More informationGCSE Statistics Revision notes
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
More informationBusiness 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, McGrawHill/Irwin, 2008, ISBN: 9780073319889. Required Computing
More informationCourse 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, McGrawHill/Irwin, 2010, ISBN: 9780077384470 [This
More information4. Introduction to Statistics
Statistics for Engineers 41 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
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationExperimental data and survey data
Experimental data and survey data An experiment involves the collection of measurements or observations about populations that are treated or controlled by the experimenter. A survey is an examination
More informationSAMPLING METHODS. Chapter 5
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
More informationGCSE HIGHER Statistics Key Facts
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
More informationThe Big 50 Revision Guidelines for S1
The Big 50 Revision Guidelines for S1 If you can understand all of these you ll do very well 1. Know what is meant by a statistical model and the Modelling cycle of continuous refinement 2. Understand
More informationNumerical Summarization of Data OPRE 6301
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
More informationLEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016
UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe
More informationDr Roy Ramphal (UNISA)
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
More informationDescriptive statistics parameters: Measures of centrality
Descriptive statistics parameters: Measures of centrality Contents Definitions... 3 Classification of descriptive statistics parameters... 4 More about central tendency estimators... 5 Relationship between
More informationSTA201TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance
Principles of Statistics STA201TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis
More informationRESEARCH METHODS IN I/O PSYCHOLOGY
RESEARCH METHODS IN I/O PSYCHOLOGY Objectives Understand Empirical Research Cycle Knowledge of Research Methods Conceptual Understanding of Basic Statistics PSYC 353 11A rsch methods 01/17/11 [Arthur]
More informationDescriptive Methods Ch. 6 and 7
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
More informationAnswer keys for Assignment 10: Measurement of study variables
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,
More informationGuided Reading 9 th Edition. informed consent, protection from harm, deception, confidentiality, and anonymity.
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
More informationBUSINESS OCR LEVEL 3 CAMBRIDGE TECHNICAL. Cambridge TECHNICALS MARKET RESEARCH IN BUSINESS CERTIFICATE/DIPLOMA IN H/502/5427 LEVEL 3 UNIT 10
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
More informationFairfield Public Schools
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
More information430 Statistics and Financial Mathematics for Business
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
More information2. 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
More informationSelecting Research Participants
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
More informationInferential Statistics
Inferential Statistics Sampling and the normal distribution Zscores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are
More informationVariables 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
More informationUnivariate Descriptive Statistics
Univariate Descriptive Statistics Displays: pie charts, bar graphs, box plots, histograms, density estimates, dot plots, stemleaf plots, tables, lists. Example: sea urchin sizes Boxplot Histogram Urchin
More informationGlossary of Terms Ability Accommodation Adjusted validity/reliability coefficient Alternate forms Analysis of work Assessment Battery Bias
Glossary of Terms Ability A defined domain of cognitive, perceptual, psychomotor, or physical functioning. Accommodation A change in the content, format, and/or administration of a selection procedure
More informationWhat is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data
Introduction What is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data What is Statistics? Statistics is divided into two areas: descriptive statistics and
More informationTYPES OF SAMPLING. 2. Types of Nonprobability Sample: There are the following four types of nonprobability PROBABILITY SAMPLING
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
More informationAnalyzing and interpreting data Evaluation resources from Wilder Research
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
More informationSampling: Design and Procedures
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
More informationQuantitative Methods for Finance
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
More informationSAMPLING & INFERENTIAL STATISTICS. Sampling is necessary to make inferences about a population.
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
More informationA Correlation of. to the. South Carolina Data Analysis and Probability Standards
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
More informationPOLI 300 Handout #2 N. R. Miller RANDOM SAMPLING. Key Definitions Pertaining to Sampling
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
More informationStatistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013
Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.11.6) Objectives
More informationSampling & Sample Size Estimation
Sampling & Sample Size Estimation Moazzam Ali MD, PhD, MPH Department of Reproductive Health and Research World Health Organization Geneva, Switzerland Presented at: GFMER September 16, 2014 Topics to
More informationBasic Biostatistics for Clinical Research. Ramses F Sadek, PhD GRU Cancer Center
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
More informationManual. How large a Sample do we need SRS STRAT.xls. Guido Lüchters September 2006
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
More informationSampling: Design and Procedures
Chapter Eleven Sampling: Design and Procedures 111 Brand name change and reposition Coke to New Coke Legend to Lenovo (2003) Panasonic, National, Technic all to Panasonic Vegemite to isnack 2.0 (social
More informationCurriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 20092010
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 20092010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different
More informationSchools Valueadded Information System Technical Manual
Schools Valueadded Information System Technical Manual Quality Assurance & Schoolbased Support Division Education Bureau 2015 Contents Unit 1 Overview... 1 Unit 2 The Concept of VA... 2 Unit 3 Control
More informationNonrandom/nonprobability sampling designs in quantitative research
206 RESEARCH MET HODOLOGY Nonrandom/nonprobability sampling designs in quantitative research N onprobability sampling designs do not follow the theory of probability in the choice of elements from the
More informationBasic Research Progress
Mgt 540 Research Methods Sampling Issues 1 Basic Research Progress Explorative  Descriptive (Qualitative) 1. Framework / Domain extant knowledge for reference 2. Research Design 3. Data collection / presentation
More informationMathematics. Probability and Statistics Curriculum Guide. Revised 2010
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
More informationChapter 15 Multiple Choice Questions (The answers are provided after the last question.)
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
More informationDr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101
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
More informationElementary Statistics
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,
More informationStatistics revision. Dr. Inna Namestnikova. Statistics revision p. 1/8
Statistics revision Dr. Inna Namestnikova inna.namestnikova@brunel.ac.uk Statistics revision p. 1/8 Introduction Statistics is the science of collecting, analyzing and drawing conclusions from data. Statistics
More informationX = T + E. Reliability. Reliability. Classical Test Theory 7/18/2012. Refers to the consistency or stability of scores
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
More informationChapter 1: Using Graphs to Describe Data
Department of Mathematics Izmir University of Economics Week 1 20142015 Introduction In this chapter we will focus on the definitions of population, sample, parameter, and statistic, the classification
More informationSurvey Process White Paper Series Five Steps in Creating a Survey Sampling Plan
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
More informationSampling Techniques Surveys and samples Source: http://www.deakin.edu.au/~agoodman/sci101/chap7.html
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
More informationSurvey Sampling. Know How No 9 guidance for research and evaluation in Fife. What this is about? Who is it for? What do you need to know?
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
More informationMAT 12O ELEMENTARY STATISTICS I
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 PreRequisite:
More informationInstitute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
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
More informationBusiness Research Methods
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
More informationSurvey Research: Choice of Instrument, Sample. Lynda Burton, ScD Johns Hopkins University
This work is licensed under a Creative Commons AttributionNonCommercialShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationIntroduction to Sampling. Dr. Safaa R. Amer. Overview. for NonStatisticians. Part II. Part I. Sample Size. Introduction.
Introduction to Sampling for NonStatisticians Dr. Safaa R. Amer Overview Part I Part II Introduction Census or Sample Sampling Frame Probability or nonprobability sample Sampling with or without replacement
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 15 scale to 0100 scores When you look at your report, you will notice that the scores are reported on a 0100 scale, even though respondents
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationFoundation of Quantitative Data Analysis
Foundation of Quantitative Data Analysis Part 1: Data manipulation and descriptive statistics with SPSS/Excel HSRS #10  October 17, 2013 Reference : A. Aczel, Complete Business Statistics. Chapters 1
More informationResearch 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.
More informationDetermining Sample Size 1
Fact Sheet PEOD6 November 1992 Determining Sample Size 1 Glenn D. Israel 2 Perhaps the most frequently asked question concerning sampling is, "What size sample do I need?" The answer to this question
More informationStatistics, Research, & SPSS: The Basics
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
More informationLecture 2: Descriptive Statistics and Exploratory Data Analysis
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
More informationAP 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.
More informationWhat is Psychology? chapter 1
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?
More informationCHAPTER 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
More informationPRINCIPLES OF HIGH QUALITY ASSESSMENT. Allan M. Canonigo
PRINCIPLES OF HIGH QUALITY ASSESSMENT Allan M. Canonigo http://love4mathed.com PRINCIPLES OF HIGH QUALITY ASSESSMENT 1. Clarity of learning targets 2. (knowledge, reasoning, skills, products, affects)
More informationSample size determination
Sample size determination Review Articles Influencing factors and calculation strategies for survey research Ali A. AlSubaihi, PhD. ABSTRACT The paper reviews both the influencing factors and calculation
More informationSampling Procedures Y520. Strategies for Educational Inquiry. Robert S Michael
Sampling Procedures Y520 Strategies for Educational Inquiry Robert S Michael RSMichael 21 Terms Population (or universe) The group to which inferences are made based on a sample drawn from the population.
More informationTheoretic perspectives & empirical insights from the European Social Survey. August 25th
Theoretic perspectives & empirical insights from the European Social Survey Research Centre for Survey Research and Methodology Mannheim, Germany ganninger@zumamannheim.de August 25th Overview 1 2 3 4
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 informationSheffield Hallam University. Faculty of Health and Wellbeing Professional Development 1 Quantitative Analysis. Glossary
Sheffield Hallam University Faculty of Health and Wellbeing Professional Development 1 Quantitative Analysis Glossary 2 Using the Glossary This does not set out to tell you everything about the topics
More informationChapter Eight: Quantitative Methods
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
More informationOnline International Interdisciplinary Research Journal, {BiMonthly}, ISSN22499598, VolumeII, IssueVI, NovDec 2012
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,
More informationCH.6 Random Sampling and Descriptive Statistics
CH.6 Random Sampling and Descriptive Statistics Population vs Sample Random sampling Numerical summaries : sample mean, sample variance, sample range StemandLeaf Diagrams Median, quartiles, percentiles,
More informationSession 1.6 Measures of Central Tendency
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
More informationGeostatistics Exploratory Analysis
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Exploratory Analysis Carlos Alberto Felgueiras cfelgueiras@isegi.unl.pt
More informationThe main objective of the study was to establish the brand equity of the. provincial, regional and national rugby teams of South Africa.
Chapter 5 RESEARCH METHODOLOGY 5.1 INTRODUCTION The main objective of the study was to establish the brand equity of the provincial, regional and national rugby teams of South Africa. Primary research
More informationMARKETING RESEARCH AND MARKET INTELLIGENCE (MRM711S) FEEDBACK TUTORIAL LETTER SEMESTER `1 OF 2016. Dear Student
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
More informationII. 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,
More informationTask force on quality of BCS data. Analysis of sample size in consumer surveys
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
More informationVALIDITY FOR TEACHERS A N O V E R V I E W
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
More informationIntroduction 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.
More informationSurvey Process White Paper Series The Six Steps in Conducting Quantitative Marketing Research
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
More informationAP Statistics 1998 Scoring Guidelines
AP Statistics 1998 Scoring Guidelines These materials are intended for noncommercial use by AP teachers for course and exam preparation; permission for any other use must be sought from the Advanced Placement
More informationSemester 2 Statistics Short courses
Semester 2 Statistics Short courses Course: STAA0001  Basic Statistics Blackboard Site: STAA0001 Dates: Sat 10 th Sept and 22 Oct 2016 (9 am 5 pm) Room EN409 Assumed Knowledge: None Day 1: Exploratory
More informationNorthumberland Knowledge
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
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationMEASURES OF VARIATION
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
More informationMinitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab StepByStep
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
More information4.1 Exploratory Analysis: Once the data is collected and entered, the first question is: "What do the data look like?"
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
More informationStandard Deviation Estimator
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
More informationHawaii State Standards correlated to Merit Software Math Programs
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
More information