DOCSLIB.ORG

What Is Quantitative Research? 1 Foundations of Quantitative Research Methods 3 When Do We Use Quantitative Methods? 7 Summary 11 Exercises 11 Further Reading 12

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
What Is Quantitative Research? 1 Foundations of Quantitative Research Methods 3 When Do We Use Quantitative Methods? 7 Summary 11 Exercises 11 Further Reading 12 DANIEL MUIJS DOING quantitative research IN EDUCATION 9079 Prelims (i-xii) 26/2/04 3:41 pm Page i Doing Quantitative Research in Education with SPSS 9079 Prelims (i-xii) 26/2/04 3:41 pm Page ii 9079 Prelims (i-xii) 26/2/04 3:41 pm Page iii Doing Quantitative Research in Education with SPSS Daniel Muijs Sage Publications London • Thousand Oaks • New Delhi 9079 Prelims (i-xii) 26/2/04 3:41 pm Page iv © Daniel Muijs 2004 First published 2004 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1998, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Inquiries concerning reproduction outside those terms should be sent to the publishers. SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B-42, Panchsheel Enclave Post Box 4109 New Delhi 100 017 British Library Cataloguing in Publication data A catalogue record for this book is available from the British Library ISBN 0-7619-4382-X ISBN 0-7619-4383-8 (pbk) Library of Congress Control Number: 2003115358 Typeset by Pantek Arts Ltd Printed in Great Britain by Athenaeum Press Ltd, Gateshead, Tyne & Wear 9079 Prelims (i-xii) 26/2/04 3:41 pm Page v Contents List of figures viii List of tables x Preface xi 1 Introduction to quantitative research 1 What is quantitative research? 1 Foundations of quantitative research methods 3 When do we use quantitative methods? 7 Summary 11 Exercises 11 Further reading 12 2 Experimental and quasi-experimental research 13 Types of quantitative research 13 How to design an experimental study 15 Advantages and disadvantages of experimental research in education 22 Quasi-experimental designs 26 Summary 32 Exercises 33 Further reading 33 3 Designing non-experimental studies 34 Survey research 34 Observational research 51 Analysing existing datasets 57 Summary 60 Exercises 61 Appendix 3.1 Example of a descriptive form 62 Appendix 3.2 Rating the quality of interactions between teachers and pupils 63 v 9079 Prelims (i-xii) 26/2/04 3:41 pm Page vi vi ■ Contents 4Validity, reliability and generalisability 64 Validity 65 Reliability 71 Generalisability 75 Summary 82 Exercises 83 Further reading 83 5 Introduction to SPSS and the dataset 85 Introduction to SPSS 85 Summary 90 Exercises 90 6 Univariate statistics 91 Introduction 91 Frequency distributions 91 Levels of measurement 97 Measures of central tendency 99 Measures of spread 105 Summary 110 Exercises 112 Further reading 112 7 Bivariate analysis: comparing two groups 113 Introduction 113 Cross tabulation – looking at the relationship between nominal and ordinal variables 114 The t-test: comparing the means of two groups 127 Summary 139 Exercises 140 Further reading 140 8 Bivariate analysis: looking at the relationship between two variables 142 The relationship between two continuous variables: Pearson’s r correlation coefficient 142 Spearman’s rho rank-order correlation coefficient: the relationship between two ordinal variables 151 9079 Prelims (i-xii) 26/2/04 3:41 pm Page vii Contents ■ vii Summary 156 Exercises 157 Further reading 158 9 Multivariate analysis: using multiple linear regression to look at the relationship between several predictors and one dependent variable 159 Introduction 159 What is multiple linear regression? 160 Doing regression analysis in SPSS 163 Using ordinal and nominal variables as predictors 169 Diagnostics in regression 176 Summary 183 Exercises 184 Further reading 184 10 Using analysis of variance to compare more than two groups 185 Want is ANOVA? 185 Doing ANOVA in SPSS 187 The effect size measure 194 Using more than one independent variable 196 Summary 200 Exercises 201 Further reading 201 11 One step beyond: introduction to multilevel modelling and structural equation modelling 202 Multilevel modelling 202 Structural equation modelling 209 Summary 217 Exercises 218 Further reading 219 References 220 Index 222 9079 Prelims (i-xii) 26/2/04 3:41 pm Page viii List of figures 4.1 Shavelson’s multifaceted, hierarchical self-concept model 69 4.2 Type I and type II errors 76 5.1 SPSS opening screen 86 5.2 The ‘Data View’ screen with our file opened 87 5.3 The ‘Variable View’ 88 6.1 Producing a frequency table: steps 1–3 92 6.2 Producing a frequency table: steps 4–6 93 6.3 ‘Frequencies’ output 94 6.4 Getting a chart in ‘Frequencies’ 95 6.5 A bar chart 96 6.6 Measures of central tendency in SPSS 103 6.7 Measures of central tendency output 104 6.8 Measures of spread 108 6.9 Output measures of spread 109 6.10 Describing single variables 111 7.1 Producing a cross tabulation table: steps 1–3 116 7.2 Producing a cross tabulation table: steps 4 and 5 117 7.3 Producing a cross tabulation table: steps 6 and 7 118 7.4 ‘Crosstabs’ output 119 7.5 Obtaining expected values in ‘Crosstabs’: steps 8–10 120 7.6 ‘Crosstabs’ output with expected values 121 7.7 Obtaining the chi square test in ‘Crosstabs’: steps 11–13 123 7.8 Chi square text output 124 7.9 Selecting cases: steps 1 and 2 128 7.10 Selecting cases: steps 3 and 4 129 7.11 Selecting cases: steps 5–7 130 7.12 The t-test: steps 1–3 132 7.13 The t-test: steps 4–6 133 7.14 The t-test: step 7 134 7.15 T-test: output 135 7.16 Summary of bivariate data 140 viii 9079 Prelims (i-xii) 26/2/04 3:41 pm Page ix List of figures ■ ix 8.1 Correlations: steps 1–3 148 8.2 Correlations: step 4 149 8.3 Pearson’s r output 150 8.4 Spearman’s correlation: step 5 154 8.5 Output of Spearman’s rho 155 8.6 Summary of bivariate relationships 156 9.1 Scatter plot of English and maths scores 161 9.2 Multiple linear regression: steps 1–3 164 9.3 Multiple regression: steps 4 and 5 165 9.4 Regression output: part 1 166 9.5 Regression output: part 2 167 9.6 Output including two ordinal variables 170 9.7 Transforming variables: steps 1–3 172 9.8 Recoding variables: steps 4–7 173 9.9 Recoding variables: steps 8–10 174 9.10 Output including dummy variables 175 9.11 Diagnostics: part 1 177 9.12 Diagnostics: part 2 178 9.13 Outliers casewise diagnostics output 179 9.14 Collinearity diagnostics 180 9.15 Collinearity diagnostics output 181 10.1 ANOVA: steps 1–3 188 10.2 ANOVA: steps 4–6 189 10.3 ANOVA: steps 7 and 8 190 10.4 ANOVA output: part 1 191 10.5 ANOVA output: part 2 – multiple comparisons 192 10.6 ANOVA output: part 3 – homogeneous subgroups 193 10.7 ANOVA: producing effect size measures – part 1 194 10.8 ANOVA: producing effect size measures – part 2 195 10.9 Effect size output 196 10.10 Multiple predictors and interaction effects: output 197 11.1 Predictors of achievement: a regression model 210 11.2 A more complex model 211 11.3 Four manifest variables determined by a latent maths self-concept 212 11.4 Year 5 structural equation model 215 9079 Prelims (i-xii) 26/2/04 3:41 pm Page x List of tables 6.1 Height, gender and whether respondent likes their job 100 6.2 Height, gender and whether respondent likes their job ordered by height 101 6.3 Wages in an organisation 102 6.4 Test scores in two schools 105 6.5 Calculating the interquartile range 107 7.1 Cross tabulation of gender and ethnicity (1) 114 7.2 Cross tabulation of gender and ethnicity (2) 115 8.1 Actual responses 152 8.2 Ranking of actual responses 152 11.1 Multilevel model: end-of-year test scores predicted by beginning-of-year test scores 206 11.2 Multilevel model: end-of-year test scores predicted by beginning-of-year test scores and pupil variables 207 11.3 Multilevel model: end-of-year test scores predicted by beginning-of-year test scores, pupil, school and classroom variables 208 x 9079 Prelims (i-xii) 26/2/04 3:41 pm Page xi Preface In this book we will be looking at quantitative research methods in edu- cation. The book is structured to start with chapters on conceptual issues and designing quantitative research studies before going on to data analysis. While each chapter can be studied separately, a better under- standing will be reached by reading the book sequentially. This book is intended as a non-mathematical introduction, and a soft- ware package will be used to analyse the data. This package is SPSS, the most commonly used statistical software package in the social sciences. A dataset from which all examples are taken can be downloaded from the accompanying website (www.sagepub.co.uk/resources/muijs.htm). The website also contains the answers to the exercises at the end of each chapter, additional teaching resources (to be added over time), and a facility to address questions and feedback to the author. I hope you find this book useful, and above all that it will give you the confidence to conduct and interpret the results of your own quantitative inquiries in education.
Load more

Recommended publications
  • Education Quarterly Reviews
    Education Quarterly Reviews Allanson, Patricia E., and Notar, Charles E. (2020), Statistics as Measurement: 4 Scales/Levels of Measurement. In: Education Quarterly Reviews, Vol.3, No.3, 375-385. ISSN 2621-5799 DOI: 10.31014/aior.1993.03.03.146 The online version of this article can be found at: https://www.asianinstituteofresearch.org/ Published by: The Asian Institute of Research The Education Quarterly Reviews is an Open Access publication. It May be read, copied, and distributed free of charge according to the conditions of the Creative ComMons Attribution 4.0 International license. The Asian Institute of Research Education Quarterly Reviews is a peer-reviewed International Journal. The journal covers scholarly articles in the fields of education, linguistics, literature, educational theory, research, and methodologies, curriculum, elementary and secondary education, higher education, foreign language education, teaching and learning, teacher education, education of special groups, and other fields of study related to education. As the journal is Open Access, it ensures high visibility and the increase of citations for all research articles published. The Education Quarterly Reviews aiMs to facilitate scholarly work on recent theoretical and practical aspects of education. The Asian Institute of Research Education Quarterly Reviews Vol.3, No.3, 2020: 375-385 ISSN 2621-5799 Copyright © The Author(s). All Rights Reserved DOI: 10.31014/aior.1993.03.03.146 Statistics as Measurement: 4 Scales/Levels of Measurement 1 2 Patricia E. Allanson , Charles E. Notar 1 Liberty University 2 Jacksonville State University. EMail: [email protected] (EMeritus) Abstract This article discusses the basics of the “4 scales of MeasureMent” and how they are applicable to research or everyday tools of life.
    [Show full text]
  • Levels of Measurement and Types of Variables
    01-Warner-45165.qxd 8/13/2007 4:52 PM Page 6 6—— CHAPTER 1 evaluate the potential generalizability of research results based on convenience samples. It is possible to imagine a hypothetical population—that is, a larger group of people that is similar in many ways to the participants who were included in the convenience sample—and to make cautious inferences about this hypothetical population based on the responses of the sample. Campbell suggested that researchers evaluate the degree of similarity between a sample and hypothetical populations of interest and limit general- izations to hypothetical populations that are similar to the sample of participants actually included in the study. If the convenience sample consists of 50 Corinth College students who are between the ages of 18 and 22 and mostly of Northern European family back- ground, it might be reasonable to argue (cautiously, of course) that the results of this study potentially apply to a hypothetical broader population of 18- to 22-year-old U.S.col- lege students who come from similar ethnic or cultural backgrounds. This hypothetical population—all U.S.college students between 18 and 22 years from a Northern European family background—has a composition fairly similar to the composition of the conve- nience sample. It would be questionable to generalize about response to caffeine for pop- ulations that have drastically different characteristics from the members of the sample (such as persons who are older than age 50 or who have health problems that members of the convenience sample do not have). Generalization of results beyond a sample to make inferences about a broader popula- tion is always risky, so researchers should be cautious in making generalizations.
    [Show full text]
  • Chapter 1 Getting Started Section 1.1
    Chapter 1 Getting Started Section 1.1 1. (a) The variable is the response regarding frequency of eating at fast-food restaurants. (b) The variable is qualitative. The categories are the number of times one eats in fast-food restaurants. (c) The implied population is responses for all adults in the U.S. 3. (a) The variable is student fees. (b) The variable is quantitative because arithmetic operations can be applied to the fee values. (c) The implied population is student fees at all colleges and universities in the U.S. 5. (a) The variable is the time interval between check arrival and clearance. (b) The variable is quantitative because arithmetic operations can be applied to the time intervals. (c) The implied population is the time interval between check arrival and clearance for all companies in the five-state region. 7. (a) Length of time to complete an exam is a ratio level of measurement. The data may be arranged in order, differences and ratios are meaningful, and a time of 0 is the starting point for all measurements. (b) Time of first class is an interval level of measurement. The data may be arranged in order and differences are meaningful. (c) Class categories is a nominal level of measurement. The data consists of names only. (d) Course evaluation scale is an ordinal level of measurement. The data may be arranged in order. (e) Score on last exam is a ratio level of measurement. The data may be arranged in order, differences and ratios are meaningful, and a score of 0 is the starting point for all measurements.
    [Show full text]
  • Appropriate Statistical Analysis for Two Independent Groups of Likert
    APPROPRIATE STATISTICAL ANALYSIS FOR TWO INDEPENDENT GROUPS OF LIKERT-TYPE DATA By Boonyasit Warachan Submitted to the Faculty of the College of Arts and Sciences of American University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy In Mathematics Education Chair: Mary Gray, Ph.D. Monica Jackson, Ph.D. Jun Lu, Ph.D. Dean of the College of Arts and Sciences Date 2011 American University Washington, D.C. 20016 © COPYRIGHT by Boonyasit Warachan 2011 ALL RIGHTS RESERVED DEDICATION This dissertation is dedicated to my beloved grandfather, Sighto Chankomkhai. APPROPRIATE STATISTICAL ANALYSIS FOR TWO INDEPENDENT GROUPS OF LIKERT-TYPE DATA BY Boonyasit Warachan ABSTRACT The objective of this research was to determine the robustness and statistical power of three different methods for testing the hypothesis that ordinal samples of five and seven Likert categories come from equal populations. The three methods are the two sample t-test with equal variances, the Mann-Whitney test, and the Kolmogorov-Smirnov test. In additional, these methods were employed over a wide range of scenarios with respect to sample size, significance level, effect size, population distribution, and the number of categories of response scale. The data simulations and statistical analyses were performed by using R programming language version 2.13.2. To assess the robustness and power, samples were generated from known distributions and compared. According to returned p-values at different nominal significance levels, empirical error rates and power were computed from the rejection of null hypotheses. Results indicate that the two sample t-test and the Mann-Whitney test were robust for Likert-type data.
    [Show full text]
  • To Get SPSS to Conduct a One-Way Chi-Square Test on Your Data
    Biomeasurement 4e Dawn Hawkins SPSS24 HELP SHEET: Mann-Whitney U test (using legacy dialogs) CONTENTS 1. How to enter data to do a Mann-Whitney U test test. 2. How to do a Mann-Whitney U test test. 1. How to enter data to do a Mann-Whitney U test test. For general advice on data entry see the “How to enter data into SPSS” help sheet. Mann-Whitney U test tests are used on unrelated data: Data for the dependent variable go in one column and data for the independent variable goes in another. In this example, the dependent variable is BMD and the independent variable is SEX. BMD is bone-density measurement measured in grams per square centimetre of the neck of the femur which is a scale level of measurement). SEX is measured at the nominal level: either 1 (value label = female) or 2 (value label = male). Variable View Data View Data View (View – Value Labels off) (View – Value Labels on) Page 1 of 2 Dawn Hawkins: Anglia Ruskin University, January 2019 Biomeasurement 4e Dawn Hawkins 2. How to do a Mann-Whitney U test test… To get SPSS to conduct a Mann-Whitney U test test : Open your data file. Select: Analyze – Nonparametric Tests – Legacy Dialogs - 2 Independent Samples... This will bring up the Two-Independent-Samples Tests window. Select the variable that you want to analyse, and send it to the Test Variable List box (in the example above this is Bone Density Measurement). Select the independent variable, and send it to the Grouping Variable box (in the example above this is Sex).
    [Show full text]
  • Levels of Measurement
    8/16/18, 223 PM LO1-5 Distinguish between nominal, ordinal, interval, and ratio levels of measurement. LEVELS OF MEASUREMENT Data can be classified according to levels of measurement. The level of measurement determines how data should be summarized and presented. It also will indicate the type of statistical analysis that can be performed. Here are two examples of the relationship between measurement and how we apply statistics. There are six colors of candies in a bag of M&Ms. Suppose we assign brown a value of 1, yellow 2, blue 3, orange 4, green 5, and red 6. What kind of variable is the color of an M&M? It is a qualitative variable. Suppose someone summarizes M&M color by adding the assigned color values, divides the sum by the number of M&Ms, and reports that the mean color is 3.56. How do we interpret this statistic? You are correct in concluding that it has no meaning as a measure of M&M color. As a qualitative variable, we can only report the count and percentage of each color in a bag of M&Ms. As a second example, in a high school track meet there are eight competitors in the 400-meter run. We report the order of finish and that the mean finish is 4.5. What does the mean finish tell us? Nothing! In both of these instances, we have not used the appropriate statistics for the level of measurement. © Ron Buskirk/Alamy Stock Photo There are four levels of measurement: nominal, ordinal, interval, and ratio.
    [Show full text]
  • Statistics Lesson 1.2.Notebook January 07, 2016
    Statistics Lesson 1.2.notebook January 07, 2016 DATA CLASSIFICATION Data sets can consist of two types of data: Qualitative Data ­ data consisting of attributes, labels, or nonnumerical entries. Quantitative Data ­ data consisting of numerical measurements or counts. Suggested retail prices of several Honda vehicles. Model Suggested retail price Accord Sedan $21,680 Civic Hybrid $24,200 Civic Sedan $18,165 Crosstour $27,230 CR­V $22,795 Fit $15,425 Odyssey $28,675 Pilot $29,520 Ridgeline $29,450 a. Identify the two data sets. b. Decide whether each data set consists of numerical or nonnumerical entries. c. Specify the qualitative data and the quantitative data. 1 Statistics Lesson 1.2.notebook January 07, 2016 Another characteristic of data is its level of measurement. The level of measurement determines which statistical calculations are meaningful. The four levels of measurement, in order from lowest to highest, are nominal, ordinal, interval, and ratio. Nominal Level of Measurement ­ qualitative only. Data at this level are categorized using names, labels, or qualities. No mathematical computation are mad at this level. Ordinal Level of Measurement ­ Data is qualitative or quantitative. Data at this level can be arranged in order, or ranked, but differences between entries are not meaningful. EXAMPLE 2: Page 10 The two data sets shown are the top 5 grossing movies of 2012 and movie genres. The first data set lists the ranks of five movies. The set consists of ranks1, 2, 3, 4, and 5. These can be ranked in order so this data is at the ordinal level. The ranks have no mathematical meaning.
    [Show full text]
  • Scales of Measurement
    SCALES OF MEASUREMENT Level of measurement or scale of measure is a classification that describes the nature of information within the numbers assigned to variables. Psychologist Stanley Smith Stevens developed the best known classification with four levels or scales of measurement: nominal, ordinal, interval, and ratio. Stevens proposed his typology in a 1946 Science article titled "On the theory of scales of measurement". In this article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal" "ordinal" "interval" and "ratio" unifying both "qualitative" (which are described by his "nominal" type) and "quantitative" (all the rest of his scales). The concept of scale types later received the mathematical rigor that it lacked at its inception with the work of mathematical psychologists Theodore Alper (1985, 1987), Louis Narens (1981a, b), and R. Duncan Luce (1986, 1987, 2001). As Luce (1997, p. 395) wrote; S. S. Stevens (1946, 1951, and 1975) claimed that what counted was having an interval or ratio scale. Subsequent research has given meaning to this assertion, but given his attempts to invoke scale type ideas it is doubtful if he understood it himself ... no measurement theorist I know accepts Stevens’ broad definition of measurement ... in our view, the only sensible meaning for 'rule' is empirically testable laws about the attribute. 1. Nominal Scale: A nominal scale of measurement deals with variables that are non-numeric or where the numbers have no value. The lowest measurement level you can use, from a statistical point of view, is a nominal scale. A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure.
    [Show full text]
  • Binary Logistic Regression
    Binary Logistic Regression The coefficients of the multiple regression model are estimated using sample data with k independent variables Estimated Estimated Estimated slope coefficients (or predicted) intercept value of Y ˆ Yi = b0 + b1 X 1i + b2 X 2i ++ bk X ki • Interpretation of the Slopes: (referred to as a Net Regression Coefficient) – b1=The change in the mean of Y per unit change in X1, taking into account the effect of X2 (or net of X2) 2 – b0 Y intercept. It is the same as simple regression. Binary Logistic Regression • Binary logistic regression is a type of regression analysis where the dependent variable is a dummy variable (coded 0, 1) • Why not just use ordinary least squares? ^ Y = a + bx – You would typically get the correct answers in terms of the sign and significance of coefficients – However, there are three problems 3 Binary Logistic Regression OLS on a dichotomous dependent variable: Yes = 1 No = 0 Y = Support Privatizing Social Security 1 10 X = Income 4 Binary Logistic Regression – However, there are three problems 1. The error terms are heteroskedastic (variance of the dependent variable is different with different values of the independent variables 2. The error terms are not normally distributed 3. And most importantly, for purpose of interpretation, the predicted probabilities can be greater than 1 or less than 0, which can be a problem for subsequent analysis. 5 Binary Logistic Regression • The “logit” model solves these problems: – ln[p/(1-p)] = a + BX or – p/(1-p) = ea + BX – p/(1-p) = ea (eB)X Where: “ln” is the natural logarithm, logexp, where e=2.71828 “p” is the probability that Y for cases equals 1, p (Y=1) “1-p” is the probability that Y for cases equals 0, 1 – p(Y=1) “p/(1-p)” is the odds ln[p/1-p] is the log odds, or “logit” 6 Binary Logistic Regression • Logistic Distribution P (Y=1) x • Transformed, however, the “log odds” are linear.
    [Show full text]
  • Examples of Scales of Measurement in Psychology
    Examples Of Scales Of Measurement In Psychology Pacific and expiscatory Roman infuscate so lethally that Mike interwound his moves. Unchastisable Tirrell always dilacerated his anaesthesiology if Tanner is coprolitic or lustrates disputably. Brocaded Roderigo escalates, his keepsake muffs sympathised senselessly. They are commonly used in psychology and education research. Thank you ride good explanation and giving some idea of scales. Cola has a lot of sugar in it. You could collect ordinal data by asking participants to select from four age brackets, the very best way to assure yourself that your measure has clear instructions, or Ratio. Measurement models represent an unobservable construct by formally incorporating a measurement theory into the measurement process. And Behaviors Interview Development reliability and validity in an enormous sample. Net, and, each cell represents the intersection of two variables. As for the findings in the current study, they find the mean or average of the data point. For example persons 1 and 4 are equally happy based on the third and. Measurement theory Frequently asked questions SAS. The scale in psychology work experience. What needs of such as indicating the scale and measuring growth of conceptual from work and clinical data set to. The same person is a nominal level determine what we sometimes causes that differences in posting a continuum and sushi second and. To respond, all type our data analysis. Deliver the best one our CX management software. One way to do noise is to parameterize the model so own any permissible transformation can be inverted by a corresponding change move the parameter estimates.
    [Show full text]
  • Study Designs and Their Outcomes
    © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC CHAPTERNOT FOR SALE 3 OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © JonesStudy & Bartlett Designs Learning, LLC and Their Outcomes© Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION “Natural selection is a mechanism for generating an exceedingly high degree of improbability.” —Sir Ronald Aylmer Fisher Peter Wludyka © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION OBJECTIVES ______________________________________________________________________________ • Define research design, research study, and research protocol. • Identify the major features of a research study. • Identify© Jonesthe four types& Bartlett of designs Learning,discussed in this LLC chapter. © Jones & Bartlett Learning, LLC • DescribeNOT nonexperimental FOR SALE designs, OR DISTRIBUTIONincluding cohort, case-control, and cross-sectionalNOT studies. FOR SALE OR DISTRIBUTION • Describe the types of epidemiological parameters that can be estimated with exposed cohort, case-control, and cross-sectional studies along with the role, appropriateness, and interpreta- tion of relative risk and odds ratios in the context of design choice. • Define true experimental design and describe its role in assessing cause-and-effect relation- © Jones &ships Bartlett along with Learning, definitions LLCof and discussion of the role of ©internal Jones and &external Bartlett validity Learning, in LLC NOT FOR evaluatingSALE OR designs. DISTRIBUTION NOT FOR SALE OR DISTRIBUTION • Describe commonly used experimental designs, including randomized controlled trials (RCTs), after-only (post-test only) designs, the Solomon four-group design, crossover designs, and factorial designs.
    [Show full text]
  • Quantitative Research Methods Distribute Student Learning Objectives Or
    7 Quantitative Research Methods distribute Student Learning Objectives or After studying Chapter 7, students will be able to do the following: 1. Describe the defining characteristics of quantitative research studies 2. List and describe the basic steps in conducting quantitative research studies 3. Identify and differentiate among variouspost, approaches to conducting quantitative research studies 4. List and describe the steps and procedures in conducting survey, correlational, causal-comparative, quasi-experimental, experimental, and single-subject research 5. Identify and discusscopy, the strengths and limitations of various approaches to conducting quantitative research 6. Identify and explain possible threats to both internal and external validity 7. Design quantitativenot research studies for a topic of interest Do his chapter focuses on research designs commonly used when conducting quantitative research studies. The general purpose of quantitative research is to T investigate a particular topic or activity through the measurement of variables in quantifiable terms. Quantitative approaches to conducting educational research differ in numerous ways from the qualitative methods we discussed in Chapter 6. You will learn about these characteristics, the quantitative research process, and the specifics of several different approaches to conducting quantitative research. Copyright ©2016 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. 108 Part II • Designing A Research Study Characteristics of Quantitative Research Quantitative research relies on the collection and analysis of numerical data to describe, explain, predict, or control variables and phenomena of interest (Gay, Mills, & Airasian, 2009). One of the underlying tenets of quantitative research is a philosophical belief that our world is relatively stable and uniform, such that we can measure and understand it as well as make broad generalizations about it.
    [Show full text]