According to Mcmillan What Are Some Specific Purposes of Reviewing Literature

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Educational
Research
Fundamentals for
the Consumer
2nd EDITION

JAMES H. MCMILLAN
Virginia Republic University

HarperCollins College Publishers

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Educational Research: Fundamentals for the Consumer, Second Edition

Copyright C 1996 by James H. McMillan

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Library of Congress Cataloging-in-Publication Information

McMillan, James H.
Educational research: fundamentals for the consumer /James H.
McMillan. - 2d ed. Ed.
p.cm.
Includes bibliographical references and index.
ISBN 0-673-99864-9
1. Education-Research,1. Title.
LB1028.M2815 1996
370'.78-dc20 95-16506 CIP 95 96 97 98 9 8 7 6 5 four iii ii 1

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Contents

To the Instructor	xv
To the Student	xix

1.	Introduction to Research in Educational activityi
	SOURCES OF Knowledgetwo Personal Experience2Traditionthree AuthorityiiiThe Scientific Approach4
	THE NATURE OF SCIENTIFIC Enquiry4 The Purpose of Scientific Inquiry4 Characteristics of Scientific Inquiry5 The Purpose of Theories6
	APPLYING SCIENTIFIC INQUIRY TO EDUCATIONseven
	TYPES OF EDUCATIONAL RESEARCHnine Two Traditions of Research: Quantitative and Qualitative9Basic Inquiry10 Practical InquirytenActiveness Research12 Evaluation Research12Nonexperimental Enquiry12Experimental Enquiry13
	FORMAT TO REPORT EDUCATIONAL RESEARCH14 Championship and Author(south)16Abstract16 Introduction16Review of Literaturesixteen Specific Inquiry Question or Hypothesis17 Method and Design17Results17 Discussion17Conclusions18 References18

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viCONTENTS

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	ANATOMY OF A RESEARCH ARTICLE18 OUTLINE SUMMARY18 STUDY QUESTIONS27 SAMPLE TEST QUESTIONS28
two.	Variables, Inquiry Problems, and Hypotheses31
	VARIABLES IN EDUCATIONAL RESEARCH32 Constitutive and Operational Definitions32 Types of Variables33
	Inquiry Bug36 Sources for Inquiry Problems39
	CONSUMER TIPS: CRITERIA FOR EVALUATING Enquiry Problems42
	HYPOTHESES46 Why Researchers Use Hypotheses46 Types of Hypotheses47
	CONSUMER TIPS: CRITERIA FOR EVALUATING RESEARCH HYPOTHESES49
	OUTLINE SUMMARY51
	Written report QUESTIONS52
	SAMPLE Examination QUESTIONS53
3.	Locating and Reviewing Related Literature55
	THE PURPOSE OF REVIEWING RELATED LITERATURE56 Refining the Enquiry Problem56 Developing Significance for the Research56 Identifying Methodological Techniques57 Identifying Contradictory Findings57 Developing Research Hypotheses57 Learning About New Information57

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CONTENTSvii

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	STEPS TO REVIEW RELATED LITERATURE58 Pace One: Locate Existing Reviews and Other Information in Secondary Sources58 Footstep Ii: Place Key Terms65 Step Three: Place the Appropriate Journal Indexes and Abstracts67 Pace Four: Search Indexes for Chief Sources68 Step Five: Summarize and Analyze Primary Source Data74 Stride Half-dozen: Organize the Review76
	CONSUMER TIPS: CRITERIA FOR EVALUATING THE REVIEW OF LITERATURE77
	OUTLINE SUMMARYfourscore
	Report QUESTIONS81
	SAMPLE TEST QUESTIONS82
4.	Subjects and Sampling84
	INTRODUCTION TO SAMPLING85 What Is a Subject field?85 What Is a Population?85 What Is a Sample?86
	TYPES OF SAMPLING PROCEDURES86 Probability Sampling86 Nonprobability Sampling91
	HOW SUBJECTS AND SAMPLING Touch Enquiry94 Knowledge of Sampling Procedures94 Volunteer Samples94 Sample Size96 Field of study Motivation97 Sample Bias98
	CONSUMER TIPS: CRITERIA FOR EVALUATING SUBJECTS SECTIONS OF REPORTS AND SAMPLING PROCEDURES98
	OUTLINE SUMMARY100
	STUDY QUESTIONS101
	SAMPLE TEST QUESTIONS101

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viiiCONTENTS

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5.	Foundations of Educational Measurement104
	INTRODUCTION TO MEASUREMENT105 Definition of Measurement105 The Purpose of Measurement for Research106 Scales of Measurement106
	FUNDAMENTAL PRINCIPLES OF DESCRIPTIVE STATISTICS FOR UNDERSTANDING MEASUREMENT108 Frequency Distributions109Measures of Central Tendency112Measures of Variability113Correlation115
	VALIDITY OF EDUCATIONAL MEASURES118 Definition of Validity118Types of Evidence for Judging Validity119Consequence of Validity on Research122
	RELIABILITY OF EDUCATIONAL MEASURES123 Types of Reliability124Effect of Reliability on Research127
	OUTLINE SUMMARY129
	STUDY QUESTIONS130
	SAMPLE TEST QUESTIONS131
6.	Types of Educational Measures134
	CLASSIFYING EDUCATIONAL MEASURES135
	TESTS136 Norm- and Criterion-Referenced Tests136 Standardized Tests137Interpreting Test Scores141
	PERSONALITY Assessment143

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CONTENTS9

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	Attitude, VALUE, AND INTEREST INVENTORIES144 Types of Inventories145Problems in Measuring Noncognitive Traits148
	OBSERVATIONS150 Inference150 Laboratory Ascertainment151 Structured Field Observations152 Observer Effects153
	INTERVIEWS154 Types of Interview Questions155 Interviewer Furnishings155
	LOCATING AND EVALUATING EDUCATIONAL MEASURES157
	CONSUMER TIPS: CRITERIA FOR EVALUATING INSTRUMENTATION158
	OUTLINE SUMMARY162
	Report QUESTIONS164
	SAMPLE TEST QUESTIONS165
vii.	Descriptive, Correlational, and Causal-Comparative Enquiry 167
	THE PURPOSE OF NON-EXPERIMENTAL RESEARCH168
	DESCRIPTIVE STUDIES168 Characteristics of Descriptive Studies168
	CONSUMER TIPS: CRITERIA FOR EVALUATING DESCRIPTIVE STUDIES170
	RELATIONSHIP STUDIES171 Human relationship Determined past Differences171 Simple Correlational Studies172 Prediction Studies176
	CONSUMER TIPS: CRITERIA FOR EVALUATING CORRELATIONAL STUDIES178

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tenCONTENTS

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	USING SURVEYS IN DESCRIPTIVE AND Relationship STUDIES182 Cross-Sectional Surveys182 Longitudinal Surveys183
	CAUSAL-COMPARATIVE STUDIES184 Ex Mail Facto Enquiry184Correlational Causal-Comparative Inquiry186
	CONSUMER TIPS: CRITERIA FOR EVALUATING CAUSAL-COMPARATIVE Enquiry186
	OUTLINE SUMMARY187
	STUDY QUESTIONS188
	SAMPLE Examination QUESTIONS189
8.	Experimental and Single-Subject Research192
	CHARACTERISTICS OF EXPERIMENTAL RESEARCH193
	EXPERIMENTAL VALIDITY194 History195Selection196 Maturation196Pretesting197 Instrumentation197Treatment Replications198Subject Attrition198 Statistical Regression198 Diffusion of Treatment199Experimenter Effects199Bailiwick Effects200
	TYPES OF EXPERIMENTAL DESIGNS201 Single-Group Posttest-Simply Pattern201 Single-Group Pretest-Posttest Design202 Nonequivalent-Groups Posttest Only Blueprint203 Nonequivalent-Groups Pretest-Posttest Design204 Random ized-Grou ps Posttest-Just Design206 Randomized-Groups Pretest-Posttest Design207 Factorial Experimental Designs209
	CONSUMER TIPS: CRITERIA FOR EVALUATING EXPERIMENTAL Research210

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CONTENTSxi

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	Unmarried-SUBJECT Research212 Characteristics of Single-Subject Inquiry212 Types of Single-Bailiwick Designs213
	CONSUMER TIPS: CRITERIA FOR EVALUATING Single,Subject field RESEARCH215
	OUTLINE SUMMARY217
	STUDY QUESTIONS218
	SAMPLE Examination QUESTIONS219
9.	Analyzing Statistical Inferences221
	THE PURPOSE AND NATURE OF INFERENTIAL STATISTICS221 Degree of Certainty222Estimating Errors in Sampling and Measurement222The Zero Hypothesis223
	INTERPRETING RESULTS OF INFERENTIAL TESTS225 The t-Test226Simple Analysis of Variance227Factorial Analysis of Variance228Analysis of Covariance229 Multivariate Statistics230Chi-Square230
	CONSUMER TIPS: CRITERIA FOR EVALUATING INFERENTIAL STATISTICS232
	OUTLINE SUMMARY234
	Written report QUESTIONS234
	SAMPLE TEST QUESTIONS235
10.	Qualitative and Historical Research238
	QUALITATIVE Research239 Characteristics of Qualitative Research239 Qualitative Enquiry Problems241

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xiiCONTENTS

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	Entering the Research Site243Selecting Participants243Obtaining Qualitative Data244Analyzing Qualitative Data248Brownie of Qualitative Research250
	CONSUMER TIPS: CRITERIA FOR EVALUATING QUALITATIVE Inquiry253
	HISTORICAL Inquiry254 The Historical Method255
	CONSUMER TIPS: CRITERIA FOR EVALUATING HISTORICAL RESEARCH258
	OUTLINE SUMMARY259
	Study QUESTIONS261
	SAMPLE Test QUESTIONS262
xi.	Analyzing Discussion and Conclusions265
	PURPOSE AND NATURE OF THE DISCUSSION265
	INTERPRETATION OF THE RESULTS266 Interpretation Related to the Problem and/or Hypothesis266Interpretation Related to Methodology266 Interpretation Based on Statistical Procedures269Interpretation Related to Previous Research270
	CONCLUSIONS272 Limitations273Recommendations and Implications276
	CONSUMER TIPS: CRITERIA FOR EVALUATING DISCUSSION SECTIONS277
	OUTLINE SUMMARY279
	STUDY QUESTIONS279
	SAMPLE Test QUESTIONS280

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CONTENTSxiii

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12.	The Intelligent Consumer: Putting It All Together282
	QUESTIONS FOR QUANTITATIVE STUDIES283
	QUESTIONS FOR QUALITATIVE STUDIES286
	QUESTIONS FOR HISTORICAL STUDIES287
	EXAMPLES OF RESEARCH ARTICLES288 Article 1: A Study of Bookish Fourth dimension-on-Job in the Simple School288Evaluation of Article one298Article 2: Reducing Teacher Stress304Evaluation of Article two316 Commodity 3: Kindergarten Readiness and Retention: A Qualitative Study of Teachers' Beliefs and Practices322 Evaluation of Commodity three349

Appendix: Answers to Sample Test Questions	352
References	353
Acknowledgments	358
Index	360

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To the Student

It was not as well long ago that I sat, somewhat nervously, in a university auditorium waiting for my first class in educational research. Perhaps you accept had, or will have, a similar experience. I distinctly remember thinking, given what I had heard near "research," that I needed to learn only enough to pass the course and would not have to worry nigh it again! Information technology was another hurdle that I was forced to jump to graduate. I was dandy in mathematics but my interest was in working with people, not numbers. Information technology was incomprehensible that I would someday teach and write about educational research. But something happened to me equally I grudgingly struggled through the course. What I discovered was that research is a way of thinking, a tool that I could use to ameliorate the work I do with other people. My hope is that this book tin can instill a like disposition in y'all, providing cognition, skills, and attitudes to improve your life and the welfare of others. Although learning the content and skills needed to go an intelligent consumer of inquiry is not easy, my feel in teaching hundreds of students is that yous will ameliorate yourself, professionally and otherwise, through your efforts. In the beginning, specially every bit you read research articles, not everything will make sense. But as your feel in being an informed consumer increases, so will your understanding. Expert luck and all-time wishes, and please write to me if you have suggestions for improving the book.

James H. McMillan

xix

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		CHAPTER
Subjects and Sampling		four

The third major part of research reports is the methodology or methods section. Every bit noted in Chapter i, the offset subsection of the methodology section unremarkably describes the subjects from whom data are nerveless. The fashion in which subjects are selected has important implications for identifying factors that touch on subject operation and for
84 SUBJECTS AND SAMPLING85
generalizing the results. Hence it is necessary to sympathize who the subjects are and how they were selected. INTRODUCTION TO SAMPLING What Is a Discipline?
A field of study is an individual who participates in a inquiry study or is someone from whom data are nerveless. In experiments, for example, each person who is given a treatment and whose behavior is measured is considered to be a subject. The term subject may likewise identify individuals whose behavior, by or present, is used as data, without their involvement in some type of treatment or intervention. For instance, a researcher might use last year's fourth-grade test scores equally data, and each 4th-grader included is considered to be a subject, In qualitative research individuals are identified as participants rather than subjects.		Subject: Person from whom information are collected.
What Is a Population? A population is a group of elements or cases, whether individuals, objects, or events, that conform to specific criteria and to which we intend to generalize the results of the research. This group is also referred to as the target Population or universe. The specification of the population begins with the research problem and review of literature, through which a population is described conceptually or in broad terms, for case, seventh-grade students, commencement teachers, principals, special education teachers, and so forth. A more specific definition is and then needed, based on demographic characteristics. These characteristics are sometimes referred to as delimiting variables. For example, in a study of first grade minority students, in that location are 3 delimiting characteristics: students, first grade (age), and minority. Further delimiting variables should be added to provide equally precise a definition as possible. What about geographic region, socioeconomic condition, gender, type of community, and types of schools? Are both public and individual students included? How is "minority" defined? It is also important to distinguish the target population from a listing of elements from which a group of subjects is selected, which is termed the survey population or sampling frame. In a written report of beginning teachers, the target population may be outset teachers across the The states, in all types of schools. The survey population may be a list of start teachers that was obtained from 4 states. Although the intent may be all beginning teachers, the results are express, or delimited, to beginning teachers in the iv states. Thus, generalization from subjects to populations should be based on the survey population.		Population: Persons to whom results tin can exist generalized.
86CHAPTER 4
What Is a Sample? The sample is the group of elements, or a single element, from which data are obtained. Although the phrase "the sample included . . ." is used to indicate the characteristics of the people or events in the sample, the nature of the sampling procedure is ordinarily described by i or more adjectives, such equally random sampling or stratified random sampling. These types of sampling procedures are defined, with illustrations from bodily studies, in the following section. It is of import for the researcher to define equally specifically as possible both the sampling procedure and the characteristics of the sample used in the report. Hither is an instance of a proficient description of the sample.		Sample: Group of subjects from whom data are nerveless.
Example: Clarification of a Sample The sample for this study consisted of nine seventh-form mathematics teachers and their students. All teachers had volunteered for the study and each teacher received a $100 stipend. The teachers taught in 4 public schools in a medium-sized Western urban center, a low-middle to middle class community with a minor proportion of minorities. According to a school district brochure, the commune had approximately 32, 000 students in 42 elementary schools, nine middle schools, and 9 loftier schools during the schoolhouse year the study was conducted. The class sizes ranged from 16 to 34. At that place were v female'and 4 male teachers. The teachers had an average of approximately 11 years, of pedagogy experience (range = 2-26). Of these 11 years, viii were equally math teachers (range ii = 22) and vi were as middle-school math teachers (range = ii-10). All had secondary certification, and 4 had Master's degrees in Administration. Ane of the teachers had a math major in college, 6 a math minor, and two had no special training in math." (Burns and Lash, 1986, p 395)
TYPES OF SAMPLING PROCEDURES The purpose of sampling is to obtain a group of subjects who will be representative of the larger population or will provide specific information needed. The degree of representativeness is based on the sampling technique employed. I will get-go describe different sampling procedures and then consider the strengths and weaknesses of each in obtaining a representative sample. Probability Sampling In social science and educational research information technology is usually impractical and unnecessary to measure all the elements in the population of involvement. Typically, a relatively minor number of subjects or cases is selected from
SUBJECTS AND SAMPLING87
the larger population. The goal is to select a sample that volition fairly represent the population, so that what is described in the sample volition also exist true of the population. The best procedure for selecting such a sample is to apply probability sampling, a method of sampling in which the subjects are selected randomly in such a way that the researcher knows the probability of selecting each member of the population. Random option implies that each member of the population every bit a whole or of subgroups of the population has an equal chance of being selected. As long equally the number of cases selected is large enough, it is probable that a very small percentage of the population, represented by the sample, will provide an accurate description of the unabridged population. It should be noted, still, that there is always some caste of error in sampling, and that error must be considered in interpreting the results of the sample. In probability sampling this calculation can exist made very precisely with some statistical procedures. Consider a population of i,000 third-graders, from which you will select randomly 5 percent, or 50, to judge the attitudes of all the 3rd-graders toward schoolhouse. If the mental attitude score was 75 for the sample of 50 subjects, 75 tin exist used to approximate the value for the entire population of third-graders. All the same, if some other sample of 50 students is selected, their score might be a niggling different, say 73. Which one is more than correct? Since all one,000 students accept not been tested to obtain the issue we do not know for certain, but the results can exist used to gauge the fault in sampling. This is basically the technique that political polls follow when it is reported that the vote is 45 per centum � 3. The plus or minus three is the estimate of error in sampling. There are many types of probability sampling procedures. You lot will probably encounter iv types in educational enquiry: simple random, systematic, stratified, and cluster.		Probability sampling: Known probability of selection from the population.
Simple Random Sampling In unproblematic random sampling every fellow member of the population has an equal and independent run a risk of being selected for the sample. This method is often used with a modest number in the population, for instance, putting the names or numbers of all population members in a lid and cartoon some out as the sample. If every member of the population tin be assigned a dissimilar number, a table of random numbers can identify the population members that will make upwardly the sample. This approach is not convenient if the population is large and not numbered. The most common manner of selecting a random sample from a large population is by computer. There are computer programs that will assign numbers to each element in the population, generate the sample numbers randomly, and then print out the names of the people corresponding to the numbers.		Unproblematic random sampling: Each member of the population has the same probability of being selected.
88CHAPTER four
Elementary random sampling is illustrated in the post-obit study of mothers' strategies for influencing their children's schooling.
Case: Simple Random Sampling "We interviewed a sample of 41 mothers of eighth graders from one middle school. These mothers were randomly selected from a list of 129 mothers provided by the principal of the school."(Baker and, Stevenson, 1986, p. 157)
Systematic Sampling In systematic sampling every nth chemical element is selected from a list of all elements in the population, showtime with a randomly selected element. Thus, if there is a demand to select 100 subjects from a population of fifty,000, every nth element would stand for to every 500th subject field. The first element is selected randomly. In this example that would exist some number betwixt I and 500. Suppose 240 were randomly selected equally a starting point. The first subject called for the sample would be the 240th name on a listing, the adjacent subject area would exist the 740th, then the one,240th, then on until 100 subjects were selected. Systematic sampling is virtually the aforementioned as simple random sampling. It is certainly much more convenient. There is a possible weakness in systematic sampling if the list of cases in the population is arranged in a systematic pattern. For instance, if a list of fourth-graders in a school division is arranged by classroom and students in the classrooms are listed from high to low ability, there is a cyclical blueprint in the list (referred to equally periodicity). If every nth field of study that is selected corresponds to the blueprint, the sample would stand for only a certain level of ability and would non exist representative of the population. Alphabetical lists exercise not usually create periodicity and are suitable for choosing subjects systematically.		Systematic sampling:Every due northth member of the population is selected.
Stratified Sampling A modification of either simple random or systematic sampling is offset to divide the population into homogeneous subgroups and then select subjects from each subgroup, using elementary random or systematic procedures, rather than the population every bit a whole. This is termed stratified sampling. The strata are the subgroups. Stratified sampling is used primarily for two reasons. First, as long as the subgroups are identified by a variable related to the dependent variable in the enquiry (e.g., socioeconomic status in a study of achievement) and results in more homogeneous groups, the sample will exist more representative of the population than if taken from the population as a whole. This result reduces error and means that a smaller sample can be called.		Stratified sampling: Subjects are selected from strata or groups of the population.
SUBJECTS AND SAMPLING89
2nd, stratified sampling is used to ensure that an acceptable number of subjects is selected from different subgroups. For case, if a researcher is studying beginning simple schoolhouse teachers and believes that at that place may be important differences between male and female person teachers, using unproblematic random or systematic sampling would probably not upshot in a sufficient number of male teachers to study the differences. It would exist necessary in this situation start to stratify the population of teachers into male and female teachers and then to select subjects from each subgroup. The samples can be selected in one of 2 ways. A proportional stratified sample, or proportional allocation, is used when the number of subjects selected from each stratum is based on the percentage of subjects in the population that have the feature used to form the stratum. Thus, in the previous example, if v percent of the population of elementary teachers is male, 5 percent of the sample would also be male teachers.		Proportional stratified sampling: Reflects proportion of stratum in population.
A 2nd arroyo is to take the same number of subjects from each stratum, regardless of the per centum of subjects from each stratum in the population. This method is used often because information technology ensures that a sufficient number of subjects will be selected from each stratum. For case, if only 10 percent of a population of 200 elementary teachers are male, a proportional sample of 40 would include merely iv male teachers. To report male teachers it would be better to include all 20 male person teachers in the population for the sample and randomly select twenty female teachers. This sampling procedure is referred to as disproportional because the number of subjects in the sample from each subgroup is non proportional to the percentage of the subgroups in the population. Disproportional stratified sampling is not express to taking the aforementioned number of subjects from each subgroup. When disproportional sampling is used the results of each stratum need to be weighted to estimate values for the population as a whole. In the following case disproportional stratified sampling ensures that the same number of commencement and third graders are selected randomly.		Disproportional stratified sampling: Number of subjects in each strata does non reflect proportion in population.
Example: Disproportional Stratified Sampling "From a puddle of all children who returned a parental permission class (more 80% return rate) 24 first graders (x girls, 14 boys; mean half-dozen years 6 months), and 24 third graders (thirteen girls, eleven boys; mean age, 8 years, 8 months) were randomly selected." (Clements and Nastasi, 1988, p. 93)
Stratified random sampling is illustrated in Figure 4.1. In this case the population is divided first into 3 unlike historic period groups,
90Affiliate four
Effigy 4.1 Instance of stratified sampling with two strata.
then by gender. One time the groups are stratified past gender, random samples are selected from each of the half dozen subgroups. Cluster Sampling When it is impossible or impractical to sample individual elements from the population every bit a whole, usually when at that place is no exhaustive listing of all the elements, cluster sampling is used. Cluster sampling involves the random selection of naturally occurring groups or areas and and so the selection of private elements from the chosen groups or areas. Examples of naturally occurring groups would be universities, schools, school divisions, classrooms, city blocks, and households. For instance, if in that location is a demand to survey a state for the tv viewing habits of heart school students, it would exist cumbersome and difficult to select children at random from the land population of all middle-schoolers. A clustering procedure could exist employed by first list all the school divisions in the land and so randomly selecting thirty schoolhouse divisions from the listing. One heart school could and then exist selected from each partitioning, and students selected randomly from each school. This is a multistage clustering procedure. Although cluster sampling saves time and money, the results are less authentic than other random sampling techniques.		Cluster sampling: Naturally occurring groups are selected.
SUBJECTS AND SAMPLING91
Nonprobability Sampling In many research designs it is either unfeasible or unnecessary to obtain a probability sample. In these situations a nonprobability sample is used. A nonprobability sample is one in which the probability of including population elements is unknown. Unremarkably, not every element in the population has a chance of being selected. It is also quite common for the population to be the same as the sample, in which case there is no immediate need to generalize to a larger population. In fact you lot will find that much of the educational inquiry reported in journals, especially experimental studies, uses a grouping of subjects that has non been selected from a larger population.		Nonprobability sample: Probability of selection not known.
Convenience Sampling A convenience sample is a group of subjects selected because of availability, for example, a university grade of a professor conducting some enquiry on college students, classrooms of teachers enrolled in a graduate class, schools of principals in a workshop, people who decide to go to the mall on Saturday, or people who respond to an advertisement for subjects. There is no precise way of generalizing from a convenience sample to a population. Too, the nature of the convenience sample may bias the results. For instance, if the available sample for studying the impact of higher is the grouping of alumni who return on alumni 24-hour interval, their responses would probably be quite dissimilar from those of all alumni. Similarly, research on effective instruction that depends on teachers in a particular geographic surface area, because they are available, may event in different findings than research done in other geographic areas. Although we demand to be very wary of convenience samples, often this is the merely blazon of sampling possible, and the principal purpose of the research may not be to generalize simply to better understand relationships that may exist. Suppose a researcher is investigating the relationship betwixt inventiveness and intelligence, and the just available sample is a single unproblematic school. The written report is completed, and the results point a moderate relationship: Children who have higher intelligence tend to be more creative than children with lower intelligence. Because in that location was no probability sampling, should we ignore the findings or advise that the results are not valid or credible? That determination seems overly harsh. Information technology is more than reasonable to interpret the results equally valid for children like to those studied. For example, if the school serves a low socio-economic area, the results will not be every bit useful every bit those from a schoolhouse that serves all socioeconomic levels. The decision is not to dismiss the findings but to limit them to the type of subjects in the sample. Equally more and more than enquiry accumulates with different convenience samples, the overall credibility of the results is enhanced.		Convenience sample: Nonprobability available sample.
92Affiliate iv
Although it is non common for a researcher to land explicitly that a convenience sample was used, information technology will be obvious from the subjects subsection of the article. If some type of probability sampling procedure was used it volition be described. Thus, in the absenteeism of such particulars you can assume that the sample was an bachelor one. The following examples are typical.
Examples: Convenience Samples "Participants in the study were sixth grade students enrolled in four classes at a public school in a suburb north of Minneapolis, Minnesota. Of the total number, 65 students were boys and 56 were girls. From the pool of 121 subjects, 7 were not included in the final analysis for various reasons, leaving 114 subjects." (Carrier and Williams, 1988, pp. 291-292) "Twelve volunteer third-grade teachers and their students participated in the report. The teachers were employed in x public schools located in three school districts in suburban areas of northern Califomia." (Mitman, 1985, p. 151) "The initial group of subjects in this study was composed of 42 undergraduate secondary education students majoring in a variety of disciplines. They were almost to be placed in classrooms to educatee teach for their first semester. 30-v of these students also participated in the terminal part of the experiment at the end of the semester, following a 10-week student teaching feel." (Tiene and Buck, 1987, p. 262) "The report was conducted in a school system of approximately 2,800 unproblematic school students attending 6 schools. All kindergarten and first grade teachers using intraclassroom ability grouping were asked toparticipate. Of 22 teachers invited to participate, twenty agreed and were later on observed." (Haskins, Walden, and Ramey, 1983, pp. 867-868)
Purposive Sampling In purposive sampling (sometimes referred to every bit purposeful, judgment or judgmental sampling) the researcher selects detail elements from the population that will be representative or informative about the topic. Based on the researcher'south knowledge of the population, ajudgment is made about which cases should be selected to provide the best information to address the purpose of the inquiry. For example, in research on constructive teaching information technology may be most informative to observe "expert" or "master" teachers rather than all teachers. To study effective schools it may be most informative to interview cardinal personnel, such as the principal and teachers who accept been in the school a number of years. The utilize of "selected precincts" for political polls is a type of purposive sampling.		Purposive sampling: Pick of particularly informative or useful subjects.
SUBJECTS AND SAMPLING93
Purposive sampling is non widely used in quantitative studies. In qualitative research, on the other paw, some blazon of purposive sampling is almost always used. Purposive sampling is illustrated by the following excerpts. Further discussion of sampling for qualitative studies is included in Chapter 10.
Examples: Purposive Sampling "Introductory psychology students (North 210) volunteered to take the Dogmatism Scale (Form Due east) for experimental credit. From the upper and lower quartiles on the Dogmatism Scale, 44 high and 44 low dogmatic subjects were selected for the experiment." (Rickards and Slife, 1987, pp. 636-637) Notice also that this is a convenience sample. "Four second-grade and 2 first-form teachers from public schools in the San Francisco Bay Area participated in the study. All were women with at to the lowest degree 10 years of didactics experience at the elementary level. Teachers were recruited to include as wide a range of backgrounds and approaches in the teaching of mathematics every bit possible. Some were recommended by their principals as being strong mathematics teachers who had been involved in diverse inservice and curriculum evolution activities. Others agreed to participate in the study because they were interested but did not consider themselves to exist particularly outstanding mathematics teachers." (Putnam, 1987, pp. 17-18) "Half dozen schools were selected from the 26 in the commune. Selection was governed by the need to capture the variability of retention practices within the commune. For example, two schools with loftier-retaining and three with low-retaining kindergartens were selected, along with one school that had a developmental kindergarten and a transition (between kindergarten and first grade) class." (Smith and Shepard, 1988, p. 311)
Quota Sampling Quota sampling is used when the researcher is unable to have a probability sample merely still wants a sample that is representative of the entire population. Different blended profiles of major groups in the population are identified, and then subjects are selected, nonrandomly, to correspond each group. A blazon of quota sampling that is common in educational research is conducted to represent geographic areas or types of communities, such as urban, rural, and suburban. Typically, a state is divided into distinct geographic areas, and cases are selected to represent each area. Every bit in availability and purposive sampling, in that location is a heavy reliance on the decisions of the researcher in selecting the sample, and appropriate circumspection should exist used in interpreting the results.		Quota sampling: Nonrandom sampling representative of a target population.
94Chapter 4
HOW SUBJECTS AND SAMPLING Touch Research In reading and interpreting research you volition need to be conscious of how the sampling procedures might have affected the results and how the characteristics of the subjects bear on the usefulness and the generalizability of the results. Knowledge of Sampling Procedures To understand how sampling may affect research it is essential to know the characteristics of dissimilar sampling procedures. This noesis volition help you interpret the sample that is used. You should first be able to identify the sampling procedure and then evaluate its capability in addressing the research problem and in supporting the conclusions. It volition exist helpful to know the strengths and weaknesses of each sampling process, as summarized in Table four. one. Volunteer Samples A continuing problem in educational research, as well as in most social science research, is the use of volunteers as subjects. Information technology is well documented that volunteers differ from nonvolunteers in important ways. Volunteers tend to be amend educated, college socioeconomically, more than intelligent, more in need of social approval, more sociable, more unconventional, less authoritarian, and less conforming than nonvolunteers. Obviously, volunteer samples may answer differently than nonvolunteers because of these characteristics. One way volunteers are used is in survey inquiry. The researcher typically sends questionnaires to a sample of individuals and tabulates the responses of those who return them. Oft the percentage of the sample returning the questionnaire will exist l to 60 percentage or fifty-fifty lower. In this circumstance the sample is said to be biased in that the results may not be representative of the population. Thus, the nature of the results depends on the types of persons who respond, and generalizability to the target population is compromised. The specific upshot that a biased sample has on the results depends on the nature of the study. For example, a written report of the relationship between educational level and occupational success would exist probable to show only a small human relationship if only those who are most successful reply. Without some subjects who are non successful in the sample, success cannot be accurately related to the level of didactics. If a survey of teachers is conducted to ascertain their general knowledge and reading and writing skills, the results would probably be college than the truthful example because of the trend of volunteers to be improve educated.
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Table iv.1 STRENGTHS AND WEAKNESSES OF SAMPLING METHODS Method of sampling Strengths Weaknesses Probability Simple random Unremarkably representative of the population Easy to clarify and interpret results Like shooting fish in a barrel to understand Requires numbering each element in the population Larger sampling error than in stratified sampling Systematic 1, 2, and 3 higher up Simplicity of drawing sample Periodicity in list of population elements Proportional stratified one, ii, and 3 of uncomplicated random Allows subgroup comparisons Usually more representative than unproblematic random or systematic Fewer subjects needed Results represent population without weighting Requires subgroup identification of each population element Requires noesis of the proportion of each subgroup in the population May be plush and difficult to set up lists of population elements in each subgroup. Disproportional stratified ane, 2, iii, and 4 of proportional stratified Assures adequate numbers of elements in each subgroup 1, ii, and iii of proportional stratified Requires proper weighting of subgroup to correspond population Less efficient for estimating population characteristics Cluster Low cost Requires lists of elements Efficient with large populations Less accurate than elementary random, systematic, or stratified May be difficult to collect data from all elements in each cluster Requires that each population element be assigned to only one cluster (continued)
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Table 4.1 (continued)
Method of sampling Strengths Weaknesses Nonprobability Convenience Less costly Less fourth dimension-consuming Ease of assistants Commonly assures high participation rate Generalization possible to similar subjects Hard to generalize to other subjects Less representative of an identified population Results dependent on unique characteristics of the sample Purposive 1, 2, three, 4, and 5 of convenience Adds credibility to qualitative research Assures receipt of needed information 1, 2, and 3 of convenience Quota 1, 2, 3, iv, and 5 of convenience More representative of population than convenience or purposive 1, 2, and 3 of convenience Ordinarily more time consuming than convenience or purposive
Volunteers are unremarkably used in inquiry because the availability of subjects is often express past time and resources. There have been thousands of studies with teachers who volunteer their classes for research. Much inquiry on schoolhouse-age children requires written permission from parents, and this necessity can result in a biased sample. Suppose a researcher needed parents' permission to study their interest in the education of their children. Chances are good that parents who are relatively involved would be virtually likely to agree to exist in the written report, affecting a description of the nature of parental involvement for "all" students. Sample Size An of import consideration in judging the credibility of research is the size of the sample. In most studies there are restrictions that limit the number of subjects, although it is difficult to know when the sample is
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too small. Most researchers use general rules of thumb in their studies, such as having at least 30 subjects for correlational research, and at least 15 subjects in each grouping in an experiment. In surveys that sample a population, often a very small percentage of the population must be sampled, for example, less than five or even I percent. Of course if the survey sample is too small, it is likely that the results obtained cannot narrate the population. Formal statistical techniques can be practical to make up one's mind the number of subjects needed, but in nigh educational studies these techniques are not used. In educational research a major consideration with sample size is concluding that a study with a relatively modest sample that found no deviation or no relationship is true. For example, suppose that yous are studying the relationship between creativity and intelligence and, with a sample of xx students, found that in that location was no relationship. Is it reasonable to conclude that in reality there is no relationship? Probably non, since a probable reason for not finding a relationship is because such a small sample was used. In addition to the small number of subjects, it is likely that there may non be many differences in either creativity or intelligence, and without such differences it is impossible to discover that the 2 variables are related. That is, with a larger sample that has unlike creativity and intelligence scores, a human relationship may exist. This problem, interpreting results that show no difference or relationship with small samples, is subtle just very important in educational enquiry since so many studies accept pocket-sized samples. As we will run into in Chapter 9, it is also possible to misinterpret what is reported every bit a "significant" divergence or relationship with a very large sample. Also, a sample that is not properly drawn from the population is misleading, no affair what the size. Subject field Motivation Sometimes subjects will be motivated to respond in certain ways. Clues for this phenomenon volition be institute in the clarification of how the subjects were selected. For example, if a researcher was interested in studying the effectiveness of computer simulations in teaching scientific discipline, one arroyo to the problem would exist to interview teachers who used calculator simulations. The researcher might even want to select only those science teachers who had used the simulations more than two years. Information technology is non hard to sympathize that the selected teachers, because they had been using the simulations, would exist motivated to respond favorably toward them. The response would be consequent with the teachers' decision to use simulations. Psychology students may be motivated to give inaccurate responses in studies conducted past their psychology professor if
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they exercise not like the professor, or they may answer more favorably if they desire to help a professor they similar. Sampling Bias
In selecting a sample from a population at that place is e'er some caste of sampling fault. This mistake is the discrepancy betwixt the true value of a variable for the population and the value that is calculated from the sample, and it is expected and precisely estimated equally function of sampling. A unlike type of fault is due to sampling bias, a blazon of sampling fault that is controlled or influenced by the researcher to result in misleading findings. Occasionally researchers will deliberately skew the sampling. The virtually obvious deliberate bias is selecting only those subjects that volition respond in a particular way to support a point or result. For instance, if a researcher is measuring the values of college students and wants to evidence that the students are concerned most helping others and being involved in community service, bias would consequence if the researcher deliberately selected students in pedagogy or social work and ignored majors that might not exist and so altruistically oriented. Selecting friends or colleagues may besides effect in a biased sample. An fifty-fifty more flagrant type of bias occurs when a researcher discards some subjects considering they have not responded equally planned or keeps adding subjects until the desired result is obtained. Sampling bias too occurs nondeliberately, often considering of inadequate knowledge of what is required to obtain an unbiased sample and the motivation to "bear witness" a desired result or point of view. In qualitative studies the researcher needs to be particularly careful about possible unintended bias if sampling changes during the written report. Bias tin besides result from selecting subjects from different populations and assigning them to different groups for an experiment or comparison. Suppose a researcher used graduate sociology students to receive a treatment in an experiment and graduate psychology students equally a command group. Even if the samples were selected randomly from each population, differences in the populations, and consequently samples, in attitudes, values, knowledge, and other variables could explain why sure results were obtained. CONSUMER TIPS: CRITERIA FOR EVALUATING SUBJECTS SECTIONS OF REPORTS AND SAMPLING PROCEDURES ane. The subjects in the written report should be clearly described, and the description should be specific and detailed. Demographic characteris-		Sampling bias: Sampling error acquired by the researcher.
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tics, such as age, gender, socioeconomic status, power, and grade level, should be indicated, as well as any unique characteristics, for example, gifted students, students enrolled in a psychology class, or volunteers. 2. The population should be clearly defined. It is especially important to provide a specific definition of the population in studies using probability sampling. Vague descriptions, such as "retired workers" or "high-ability students," should exist avoided. The characteristics of each stratum in a stratified sampling procedure should likewise be included. 3. The method of sampling should be conspicuously described. The specific blazon of sampling procedure, such as simple random, stratified, cluster, or convenience, should be explicitly indicated in sufficient detail to enable other researchers to replicate the study. 4. The return rate should be indicated and analyzed. In studies that survey a population, the return charge per unit of questionnaires should exist indicated. If the render rate is less than 60 percent, the researcher should analyze the implications of excluding a meaning portion of the population. This step is accomplished past comparison the nonrespondents to those who returned the questionnaires to decide if there are significant differences between the groups. v. The selection of subjects should be complimentary of bias. The procedures and criteria for selecting subjects should not upshot in systematic error. Bias is more likely when a researcher is "proving" something to be true, with convenience samples, and when volunteers are used every bit subjects. 6. Selection procedures should exist appropriate for the problem existence investigated. If the problem is to investigate science attitudes of heart school students, it would be inappropriate to use high school students as subjects. If the trouble is to written report the characteristics of effective educational activity, the piece of work of student teachers would probably not exist very representative of effective teaching behaviors. 7. There should be an adequate number of subjects. If the sample is selected from a population, the sample size must be large enough to represent the population accurately. In that location must as well be a sufficient number of subjects in each subgroup that is analyzed. Studies with small samples that report no differences or no relationships should be viewed with caution since a higher number or a ameliorate choice of subjects may result in meaningful differences or relationships. Studies that have a very large number of subjects may report "pregnant" differences or relationships that are of little practical utility. viii. Qualitative studies should take informative and knowledgeable subjects. Since the purpose of qualitative research is to understand a miracle in depth, it is important to select subjects that will provide the richest data. The researcher should indicate the criteria used to select subjects, the reasons why these particular individuals were selected, and the strategies used for selecting subjects during the study.
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OUTLINE SUMMARY Subject selection. Participants from whom information are gathered. Population. Group to whom results are generalized. Described past delimiting variables. Sample. Procedures for selecting subjects. Probability sampling. Subjects selected from a larger population. Ever some mistake in sampling. Simple random sampling. Every member of the population has the aforementioned take chances of beingness selected. Every member of the population must be numbered. Systematic random sampling. Subjects are selected without numbering each member of the population. Periodicity may cause bias in the result. Stratified random sampling. Divides population into groups before sample selection. Often provides a more accurate sample. Desirable for comparing subgroups. Proportional or disproportional selection. Cluster sampling. Naturally occurring groups of subjects are selected at random. Usually less accurate. Nonprobability sampling. Very mutual and over fourth dimension results in generalizable conclusions. Convenience samples. Purposive samples. Quota sampling. Subjects and sampling procedures bear on research in several means. Volunteer subjects. Sample size. Subject motivation. Sampling bias. Criteria for evaluating subjects sections of reports and sampling. Clearly defined subjects, population, and sampling design. Acceptable and/or analyzed return rate. Selection should exist gratuitous of bias.
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Selection should be advisable to the trouble. Sample size should be adequate. Qualitative enquiry should employ the most knowledgeable and informative subjects. STUDY QUESTIONS What is a sample and a population? Why is it important to define the population as specifically as possible? What is the difference between probability and nonprobability sampling? When should a researcher use stratified random sampling? How is cluster sampling different from stratified sampling? Why should readers of research be cautious of studies that use a convenience sample? What are some strengths and weaknesses of various types of sampling? How tin can volunteer subjects cause bias in a study? Why is sample size an important consideration in research that fails to discover a "significant" difference or relationship? In what ways can sampling be biased? Give an example of a study that used both stratified and systematic sampling. What is the difference between a convenience and a purposive sample? What criteria should be used in judging the adequacy of a subjects section in a report or sampling procedure? SAMPLE TEST QUESTIONS Answers are provided in Appendix A. The sampling frame is near similar to the population. sample. participants. elements. Nosotros use the results obtained from a sample to
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generalize to the population. stratify the sample. select convenience samples. place the subjects used in the study. It is important to have a complete description of the sample to exist able to stratify the sample. describe the population. generalize the results. select informative subjects. Probability sampling is to systematic sampling as nonprobability sampling is to stratified sampling. proportional sampling. disproportional sampling. purposive sampling. Systematic sampling is preferred when stratified sampling is not possible. certain subjects demand to be selected because of their position or special knowledge. information technology is not possible to number all members of the population. there is periodicity in a list of the population. A researcher decides to select a sample by taking elementary random samples from three subgroups that accept been identified from the population. What type of sampling was used? Proportional. Cluster. Convenience. Stratified. In qualitative research the sampling process is most likely to be purposive. cluster. quota. systematic. If your subjects have volunteered to participate in your study, what will you need to be careful about and so that the enquiry is apparent? Sample size that is inadequate.
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Sampling bias. Whether the sampling was proportional or disproportional. Whether the sampling was systematic. When a study has a small number of subjects and finds no relationship among the variables studied, information technology can typically be concluded that there is no relationship among the variables. there is a human relationship among the variables. it is not possible to conclude that there is no relationship amid the variables. the study is non very apparent. Each of the following about the subjects should be indicated in a enquiry report EXCEPT render charge per unit of surveys. method of sampling. a clear description of the subjects. names of the subjects.

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