Most students and beginning researchers do not fully understand what a research proposal means, nor do they understand its importance. To put it bluntly, one's research is only as a good as one's proposal. An ill-conceived proposal dooms the project even if it somehow gets through the Thesis Supervisory Committee. A high quality proposal, on the other hand, not only promises success for the project, but also impresses your Thesis Committee about your potential as a researcher.

A research proposal is intended to convince others that you have a worthwhile research project and that you have the competence and the work-plan to complete it. Generally, a research proposal should contain all the key elements involved in the research process and include sufficient information for the readers to evaluate the proposed study.

Regardless of your research area and the methodology you choose, all research proposals must address the following questions: What you plan to accomplish, why you want to do it and how you are going to do it.

The proposal should have sufficient information to convince your readers that you have an important research idea, that you have a good grasp of the relevant literature and the major issues, and that your methodology is sound.

The quality of your research proposal depends not only on the quality of your proposed project, but also on the quality of your proposal writing. A good research project may run the risk of rejection simply because the proposal is poorly written. Therefore, it pays if your writing is coherent, clear and compelling.

This paper focuses on proposal writing rather than on the development of research ideas.

Title:

It should be concise and descriptive. For example, the phrase, "An investigation of . . ." could be omitted. Often titles are stated in terms of a functional relationship, because such titles clearly indicate the independent and dependent variables. However, if possible, think of an informative but catchy title. An effective title not only pricks the reader's interest, but also predisposes him/her favourably towards the proposal.

Abstract:

It is a brief summary of approximately 300 words. It should include the research question, the rationale for the study, the hypothesis (if any), the method and the main findings. Descriptions of the method may include the design, procedures, the sample and any instruments that will be used.

Introduction:

The main purpose of the introduction is to provide the necessary background or context for your research problem. How to frame the research problem is perhaps the biggest problem in proposal writing.

If the research problem is framed in the context of a general, rambling literature review, then the research question may appear trivial and uninteresting. However, if the same question is placed in the context of a very focused and current research area, its significance will become evident.

Unfortunately, there are no hard and fast rules on how to frame your research question just as there is no prescription on how to write an interesting and informative opening paragraph. A lot depends on your creativity, your ability to think clearly and the depth of your understanding of problem areas.

However, try to place your research question in the context of either a current "hot" area, or an older area that remains viable. Secondly, you need to provide a brief but appropriate historical backdrop. Thirdly, provide the contemporary context in which your proposed research question occupies the central stage. Finally, identify "key players" and refer to the most relevant and representative publications. In short, try to paint your research question in broad brushes and at the same time bring out its significance.

The introduction typically begins with a general statement of the problem area, with a focus on a specific research problem, to be followed by the rational or justification for the proposed study. The introduction generally covers the following elements:

1. State the research problem, which is often referred to as the purpose of the study.

2. Provide the context and set the stage for your research question in such a way as to show its necessity and importance.

3. Present the rationale of your proposed study and clearly indicate why it is worth doing. 4. Briefly describe the major issues and sub-problems to be addressed by your research. 5. Identify the key independent and dependent variables of your experiment. Alternatively, specify the

phenomenon you want to study. 6. State your hypothesis or theory, if any. For exploratory or phenomenological research, you may not have

any hypotheses. (Please do not confuse the hypothesis with the statistical null hypothesis.) 7. Set the delimitation or boundaries of your proposed research in order to provide a clear focus. 8. Provide definitions of key concepts. (This is optional.)

Literature Review:

Sometimes the literature review is incorporated into the introduction section. However, most professors prefer a separate section, which allows a more thorough review of the literature.

The literature review serves several important functions:

1. Ensures that you are not "reinventing the wheel". 2. Gives credits to those who have laid the groundwork for your research. 3. Demonstrates your knowledge of the research problem. 4. Demonstrates your understanding of the theoretical and research issues related to your research question. 5. Shows your ability to critically evaluate relevant literature information. 6. Indicates your ability to integrate and synthesize the existing literature. 7. Provides new theoretical insights or develops a new model as the conceptual framework for your research. 8. Convinces your reader that your proposed research will make a significant and substantial contribution to

the literature (i.e., resolving an important theoretical issue or filling a major gap in the literature).

Most students' literature reviews suffer from the following problems:

Lacking organization and structure Lacking focus, unity and coherence Being repetitive and verbose Failing to cite influential papers Failing to keep up with recent developments Failing to critically evaluate cited papers Citing irrelevant or trivial references Depending too much on secondary sources

Your scholarship and research competence will be questioned if any of the above applies to your proposal.

There are different ways to organize your literature review. Make use of subheadings to bring order and coherence to your review. For example, having established the importance of your research area and its current state of development, you may devote several subsections on related issues as: theoretical models, measuring instruments, cross-cultural and gender differences, etc.

It is also helpful to keep in mind that you are telling a story to an audience. Try to tell it in a stimulating and engaging manner. Do not bore them, because it may lead to rejection of your worthy proposal. (Remember: Professors and scientists are human beings too.)

Methods:

The Method section is very important because it tells your Research Committee how you plan to tackle your research problem. It will provide your work plan and describe the activities necessary for the completion of your project.

The guiding principle for writing the Method section is that it should contain sufficient information for the reader to determine whether methodology is sound. Some even argue that a good proposal should contain sufficient details for another qualified researcher to implement the study.

You need to demonstrate your knowledge of alternative methods and make the case that your approach is the most appropriate and most valid way to address your research question.

Please note that your research question may be best answered by qualitative research. However, since most mainstream psychologists are still biased against qualitative research, especially the phenomenological variety, you may need to justify your qualitative method.

Furthermore, since there are no well-established and widely accepted canons in qualitative analysis, your method section needs to be more elaborate than what is required for traditional quantitative research. More importantly, the data collection process in qualitative research has a far greater impact on the results as compared to quantitative research. That is another reason for greater care in describing how you will collect and analyze your data. (How to write the Method section for qualitative research is a topic for another paper.)

For quantitative studies, the method section typically consists of the following sections:

1. Design -Is it a questionnaire study or a laboratory experiment? What kind of design do you choose? 2. Subjects or participants - Who will take part in your study ? What kind of sampling procedure do you use? 3. Instruments - What kind of measuring instruments or questionnaires do you use? Why do you choose them?

Are they valid and reliable? 4. Procedure - How do you plan to carry out your study? What activities are involved? How long does it take?

Results:

Obviously you do not have results at the proposal stage. However, you need to have some idea about what kind of data you will be collecting, and what statistical procedures will be used in order to answer your research question or test you hypothesis.

Discussion:

It is important to convince your reader of the potential impact of your proposed research. You need to communicate a sense of enthusiasm and confidence without exaggerating the merits of your proposal. That is why you also need to mention the limitations and weaknesses of the proposed research, which may be justified by time and financial constraints as well as by the early developmental stage of your research area.

Common Mistakes in Proposal Writing

1. Failure to provide the proper context to frame the research question. 2. Failure to delimit the boundary conditions for your research. 3. Failure to cite landmark studies. 4. Failure to accurately present the theoretical and empirical contributions by other researchers. 5. Failure to stay focused on the research question. 6. Failure to develop a coherent and persuasive argument for the proposed research. 7. Too much detail on minor issues, but not enough detail on major issues. 8. Too much rambling -- going "all over the map" without a clear sense of direction. (The best proposals move

forward with ease and grace like a seamless river.) 9. Too many citation lapses and incorrect references. 10. Too long or too short. 11. Failing to follow the APA style. 12. Slopping writing.

Elements of a research proposal and report

2005 © David S. Walonick, Ph.D.

All research reports use roughly the same format. It doesn't matter whether you've done a customer satisfaction survey, an employee opinion survey, a health care survey, or a marketing research survey. All have the same basic structure and format. The rationale is that readers of research reports (i.e., decision makers, funders, etc.) will know exactly where to find the information they are looking for, regardless of the individual report.

Once you've learned the basic rules for research proposal and report writing, you can apply them to any research discipline. The same rules apply to writing a proposal, a thesis, a dissertation, or any business research report.

The Research Proposal and Report

General Style, layout, and page formatting Outline of the chapters and sections Chapter I - Introduction Chapter II - Background Chapter III - Methodology Chapter IV - Results Chapter V - Conclusions and Recommendations

 

General considerations

Research papers usually have five chapters with well-established sections in each chapter. Readers of the paper will be looking for these chapters and sections so you should not deviate from the standard format unless you are specifically requested to do so by the research sponsor.

Most research studies begin with a written proposal. Again, nearly all proposals follow the same format. In fact, the proposal is identical to the first three chapters of the final paper except that it's writtten in future tense. In the proposal, you might say something like "the researchers will secure the sample from ...", while in the final paper, it would be changed to "the researchers secured the sample from ...". Once again, with the exception of tense, the proposal becomes the first three chapters of the final research paper.

The most commonly used style for writing research reports is called "APA" and the rules are described in the Publication Manual of the American Psychological Association. Any library or bookstore will have it readily available. The style guide contains hundreds of rules for grammar, layout, and syntax. This paper will cover the most important ones.

Avoid the use of first person pronouns. Refer to yourself or the research team in third person. Instead of saying "I will ..." or "We will ...", say something like "The researcher will ..." or "The research team will ...".

A suggestion: Never present a draft (rough) copy of your proposal, thesis, dissertation, or research

paper...even if asked. A paper that looks like a draft, will interpreted as such, and you can expect extensive and liberal modifications. Take the time to put your paper in perfect APA format before showing it to anyone else. The payoff will be great since it will then be perceived as a final paper, and there will be far fewer changes.

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Style, layout, and page formatting

Title page

All text on the title page is centered vertically and horizontally. The title page has no page number and it is not counted in any page numbering.

Page layout

Left margin: 1½"Right margin: 1"Top margin: 1"Bottom margin: 1"

Page numbering

Pages are numbered at the top right. There should be 1" of white space from the top of the page number to the top of the paper. Numeric page numbering begins with the first page of Chapter 1 (although a page number is not placed on page 1).

Spacing and justification

All pages are single sided. Text is double-spaced, except for long quotations and the bibliography (which are single-spaced). There is one blank line between a section heading and the text that follows it. Do not right-justify text. Use ragged-right.

Font face and size

Any easily readable font is acceptable. The font should be 10 points or larger. Generally, the same font must be used throughout the manuscript, except 1) tables and graphs may use a different font, and 2) chapter titles and section headings may use a different font.

References

APA format should be used to cite references within the paper. If you name the author in your sentence, then follow the authors name with the year in parentheses. For example:

Jones (2004) found that...

If you do not include the authors name as part of the text, then both the author's name and year are enclosed in parentheses. For example:

One researcher (Jones, 2004) found that...

A complete bibliography is attached at the end of the paper. It is double spaced except single-spacing is used for a multiple-line reference. The first line of each reference is indented.

Examples:

     Bradburn, N. M., & Mason, W. M. (1964). The effect of question order on response. Journal of Marketing Research 1 (4), 57-61.

     Bradburn, N. M., & Miles, C. (1979). Vague quantifiers. Public Opinion Quarterly 43 (1), 92-101.

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Outline of chapters and sections

TITLE PAGE

TABLE OF CONTENTS

CHAPTER I - Introduction     Introductory paragraphs     Statement of the problem     Purpose     Significance of the study     Research questions and/or hypotheses

CHAPTER II - Background     Literature review     Definition of terms

CHAPTER III - Methodology     Restate purpose and research questions or null hypotheses     Population and sampling     Instrumentation (include copy in appendix)     Procedure and time frame     Analysis plan (state critical alpha level and type of statistical tests)     Validity and reliability     Assumptions     Scope and limitations

CHAPTER IV - Results

CHAPTER V - Conclusions and recommendations     Summary (of what you did and found)     Discussion (explanation of findings - why do you think you found what you did?)     Recommendations (based on your findings)

REFERENCES

APPENDIX

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Chapter I - Introduction

Introductory paragraphs

Chapter I begins with a few short introductory paragraphs (a couple of pages at most). The primary goal of the introductory paragraphs is to catch the attention of the readers and to get them "turned on" about the subject. It sets the stage for the paper and puts your topic in perspective. The introduction often contains dramatic and general statements about the need for the study. It uses dramatic illustrations or quotes to set the tone. When writing the introduction, put yourself in your reader's position - would you continue reading?

Statement of the Problem

The statement of the problem is the focal point of your research. It is just one sentence (with several paragraphs of elaboration).

You are looking for something wrong.     ....or something that needs close attention     ....or existing methods that no longer seem to be working.

Example of a problem statement:

"The frequency of job layoffs is creating fear, anxiety, and a loss of productivity in middle management workers."

While the problem statement itself is just one sentence, it is always accompanied by several paragraphs that elaborate on the problem. Present persuasive arguments why the problem is important enough to study. Include the opinions of others (politicians, futurists, other professionals). Explain how the problem relates to business, social or political trends by presenting data that demonstrates the scope and depth of the problem. Try to give dramatic and concrete illustrations of the problem. After writing this section, make sure you can easily identify the single sentence that is the problem statement.

Purpose

The purpose is a single statement or paragraph that explains what the study intends to accomplish. A few typical statements are:

The goal of this study is to...     ... overcome the difficulty with ...     ... discover what ...      ... understand the causes or effects of ...     ... refine our current understanding of ...     ... provide a new interpretation of ...     ... understand what makes ___ successful or unsuccessful

Significance of the Study

This section creates a perspective for looking at the problem. It points out how your study relates to

the larger issues and uses a persuasive rationale to justify the reason for your study. It makes the purpose worth pursuing. The significance of the study answers the questions:

     Why is your study important?     To whom is it important?     What benefit(s) will occur if your study is done?

Research Questions and/or Hypotheses and/or Null Hypotheses

Chapter I lists the research questions (although it is equally acceptable to present the hypotheses or null hypotheses). No elaboration is included in this section. An example would be:

The research questions for this study will be:

     1. What are the attitudes of...     2. Is there a significant difference between...     3. Is there a significant relationship between...

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Chapter II - Background

Chapter II is a review of the literature. It is important because it shows what previous researchers have discovered. It is usually quite long and primarily depends upon how much research has previously been done in the area you are planning to investigate. If you are planning to explore a relatively new area, the literature review should cite similar areas of study or studies that lead up to the current research. Never say that your area is so new that no research exists. It is one of the key elements that proposal readers look at when deciding whether or not to approve a proposal.

Chapter II should also contain a definition of terms section when appropriate. Include it if your paper uses special terms that are unique to your field of inquiry or that might not be understood by the general reader. "Operational definitions" (definitions that you have formulated for the study) should also be included. An example of an operational definition is: "For the purpose of this research, improvement is operationally defined as posttest score minus pretest score".

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Chapter III - Methodology

The methodology section describes your basic research plan. It usually begins with a few short introductory paragraphs that restate purpose and research questions. The phraseology should be identical to that used in Chapter I. Keep the wording of your research questions consistent throughout the document.

Population and sampling

The basic research paradigm is:

     1) Define the population     2) Draw a representative sample from the population     3) Do the research on the sample     4) Infer your results from the sample back to the population

As you can see, it all begins with a precise definition of the population. The whole idea of inferential research (using a sample to represent the entire population) depends upon an accurate description of the population. When you've finished your research and you make statements based on the results, who will they apply to? Usually, just one sentence is necessary to define the population. Examples are: "The population for this study is defined as all adult customers who make a purchase in our stores during the sampling time frame", or "...all home owners in the city of Minneapolis", or "...all potential consumers of our product".

While the population can usually be defined by a single statement, the sampling procedure needs to be described in extensive detail. There are numerous sampling methods from which to choose. Describe in minute detail, how you will select the sample. Use specific names, places, times, etc. Don't omit any details. This is extremely important because the reader of the paper must decide if your sample will sufficiently represent the population.

Instrumentation

If you are using a survey that was designed by someone else, state the source of the survey. Describe the theoretical constructs that the survey is attempting to measure. Include a copy of the actual survey in the appendix and state that a copy of the survey is in the appendix.

Procedure and time frame

State exactly when the research will begin and when it will end. Describe any special procedures that will be followed (e.g., instructions that will be read to participants, presentation of an informed consent form, etc.).

Analysis plan

The analysis plan should be described in detail. Each research question will usually require its own analysis. Thus, the research questions should be addressed one at a time followed by a description of the type of statistical tests that will be performed to answer that research question. Be specific. State what variables will be included in the analyses and identify the dependent and independent variables if such a relationship exists. Decision making criteria (e.g., the critical alpha level) should also be stated, as well as the computer software that will be used.

Validity and reliability

If the survey you're using was designed by someone else, then describe the previous validity and reliability assessments. When using an existing instrument, you'll want to perform the same reliability measurement as the author of the instrument. If you've developed your own survey, then you must describe the steps you took to assess its validity and a description of how you will measure its reliability.

Validity refers to the accuracy or truthfulness of a measurement. Are we measuring what we think we are? There are no statistical tests to measure validity. All assessments of validity are subjective opinions based on the judgment of the researcher. Nevertheless, there are at least three types of validity that should be addressed and you should state what steps you took to assess validity.

Face validity refers to the likelihood that a question will be misunderstood or misinterpreted. Pretesting a survey is a good way to increase the likelihood of face validity. One method of establishing face validity is described here. How to make sure your survey is valid.

Content validity refers to whether an instrument provides adequate coverage of a topic. Expert opinions, literature searches, and pretest open-ended questions help to establish content validity.

Construct validity refers to the theoretical foundations underlying a particular scale or measurement. It looks at the underlying theories or constructs that explain a phenomena. In other words, if you are using several survey items to measure a more global construct (e.g., a subscale of a survey), then you should describe why you believe the items comprise a construct. If a construct has been identified by previous researchers, then describe the criteria they used to validate the construct. A technique known as confirmatory factor analysis is often used to explore how individual survey items contribute to an overall construct measurement.

Reliability is synonymous with repeatability or stability. A measurement that yields consistent results over time is said to be reliable. When a measurement is prone to random error, it lacks reliability.

There are three basic methods to test reliability : test-retest, equivalent form, and internal consistency. Most research uses some form of internal consistency. When there is a scale of items all attempting to measure the same construct, then we would expect a large degree of coherence in the way people answer those items. Various statistical tests can measure the degree of coherence. Another way to test reliability is to ask the same question with slightly different wording in different parts of the survey. The correlation between the items is a measure of their reliability. See: How to test the reliability of a survey.

Assumptions

All research studies make assumptions. The most obvious is that the sample represents the population. Another common assumptions are that an instrument has validity and is measuring the desired constructs. Still another is that respondents will answer a survey truthfully. The important point is for the researcher to state specifically what assumptions are being made.

Scope and limitations

All research studies also have limitations and a finite scope. Limitations are often imposed by time and budget constraints. Precisely list the limitations of the study. Describe the extent to which you believe the limitations degrade the quality of the research.

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Chapter IV - Results

Description of the sample

Nearly all research collects various demographic information. It is important to report the descriptive statistics of the sample because it lets the reader decide if the sample is truly

representative of the population.

Analyses

The analyses section is cut and dry. It precisely follows the analysis plan laid out in Chapter III. Each research question addressed individually. For each research question:

     1) Restate the research question using the exact wording as in Chapter I     2) If the research question is testable, state the null hypothesis     3) State the type of statistical test(s) performed     4) Report the statistics and conclusions, followed by any appropriate table(s)

Numbers and tables are not self-evident. If you use tables or graphs, refer to them in the text and explain what they say. An example is: "Table 4 shows a strong negative relationship between delivery time and customer satisfaction (r=-.72, p=.03)". All tables and figures have a number and a descriptive heading. For example:

Table 4The relationship between delivery time and customer satisfaction.

Avoid the use of trivial tables or graphs. If a graph or table does not add new information (i.e., information not explained in the text), then don't include it.

Simply present the results. Do not attempt to explain the results in this chapter.

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Chapter V - Conclusions and recommendations

Begin the final chapter with a few paragraphs summarizing what you did and found (i.e., the conclusions from Chapter IV).

Discussion

Discuss the findings. Do your findings support existing theories? Explain why you think you found what you did. Present plausible reasons why the results might have turned out the way they did.

Recommendations

Present recommendations based on your findings. Avoid the temptation to present recommendations based on your own beliefs or biases that are not specifically supported by your data. Recommendations fall into two categories. The first is recommendations to the study sponsor. What actions do you recommend they take based upon the data. The second is recommendations to other researchers. There are almost always ways that a study could be improved or refined. What would you change if you were to do your study over again? These are the recommendations to other researchers.

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References

List references in APA format alphabetically by author's last name

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Appendix

Include a copy of any actual instruments. If used, include a copy of the informed consent form.

THE ELEMENTS OF A PROPOSALFrank Pajares

Emory University

I. Introduction and Theoretical Framework

A. “The introduction is the part of the paper that provides readers with the background information for the research reported in the paper. Its purpose is to establish a framework for the research, so that readers can understand how it is related to other research” (Wilkinson, 1991, p. 96).

B. In an introduction, the writer should

1. create reader interest in the topic,2. lay the broad foundation for the problem that leads to the study,3. place the study within the larger context of the scholarly literature, and4. reach out to a specific audience. (Creswell, 1994, p. 42)

C. If a researcher is working within a particular theoretical framework/line of inquiry, the theory or line of inquiry should be introduced and discussed early, preferably in the introduction or literature review. Remember that the theory/line of inquiry selected will inform the statement of the problem, rationale for the study, questions and hypotheses, selection of instruments, and choice of methods. Ultimately, findings will be discussed in terms of how they relate to the theory/line of inquiry that undergirds the study.

D. Theories, theoretical frameworks, and lines of inquiry may be differently handled in quantitative and qualitative endeavors.

1. “In quantitative studies, one uses theory deductively and places it toward the beginning of the plan for a study. The objective is to test or verify theory. One thus begins the study advancing a theory, collects data to test it, and reflects on whether the theory was confirmed or disconfirmed by the results in the study. The theory becomes a framework for the entire study, an organizing model for the research questions or hypotheses for the data collection procedure” (Creswell, 1994, pp. 87-88).

2. In qualitative inquiry, the use of theory and of a line of inquiry depends on the nature of the investigation. In studies aiming at “grounded theory,” for example,

theory and theoretical tenets emerge from findings. Much qualitative inquiry, however, also aims to test or verify theory, hence in these cases the theoretical framework, as in quantitative efforts, should be identified and discussed early on.

II. Statement of the Problem

A. “The problem statement describes the context for the study and it also identifies the general analysis approach” (Wiersma, 1995, p. 404).

B. “A problem might be defined as the issue that exists in the literature, theory, or practice that leads to a need for the study” (Creswell, 1994, p. 50).

C. It is important in a proposal that the problem stand out—that the reader can easily recognize it. Sometimes, obscure and poorly formulated problems are masked in an extended discussion. In such cases, reviewers and/or committee members will have difficulty recognizing the problem.

D. A problem statement should be presented within a context, and that context should be provided and briefly explained, including a discussion of the conceptual or theoretical framework in which it is embedded. Clearly and succinctly identify and explain the problem within the framework of the theory or line of inquiry that undergirds the study. This is of major importance in nearly all proposals and requires careful attention. It is a key element that associations such as AERA and APA look for in proposals. It is essential in all quantitative research and much qualitative research.

E. State the problem in terms intelligible to someone who is generally sophisticated but who is relatively uninformed in the area of your investigation.

F. Effective problem statements answer the question “Why does this research need to be conducted.” If a researcher is unable to answer this question clearly and succinctly, and without resorting to hyperspeaking (i.e., focusing on problems of macro or global proportions that certainly will not be informed or alleviated by the study), then the statement of the problem will come off as ambiguous and diffuse.

G. For conference proposals, the statement of the problem is generally incorporated into the introduction; academic proposals for theses or dissertations should have this as a separate section.

III. Purpose of the Study

A. “The purpose statement should provide a specific and accurate synopsis of the overall purpose of the study” (Locke, Spirduso, & Silverman, 1987, p. 5). If the purpose is not clear to the writer, it cannot be clear to the reader.

B. Briefly define and delimit the specific area of the research. You will revisit this in greater detail in a later section.

C. Foreshadow the hypotheses to be tested or the questions to be raised, as well as the significance of the study. These will require specific elaboration in subsequent sections.

D. The purpose statement can also incorporate the rationale for the study. Some committees prefer that the purpose and rationale be provided in separate sections, however.

E. Key points to keep in mind when preparing a purpose statement.

1. Try to incorporate a sentence that begins with “The purpose of this study is . . .” This will clarify your own mind as to the purpose and it will inform the reader directly and explicitly.

2. Clearly identify and define the central concepts or ideas of the study. Some committee Chairs prefer a separate section to this end. When defining terms, make a judicious choice between using descriptive or operational definitions.

3. Identify the specific method of inquiry to be used.4. Identify the unit of analysis in the study.

IV. Review of the Literature

A. “The review of the literature provides the background and context for the research problem. It should establish the need for the research and indicate that the writer is knowledgeable about the area” (Wiersma, 1995, p. 406).

B. The literature review accomplishes several important things.

1. It shares with the reader the results of other studies that are closely related to the study being reported (Fraenkel & Wallen, 1990).

2. It relates a study to the larger, ongoing dialogue in the literature about a topic, filling in gaps and extending prior studies (Marshall & Rossman, 1989).

3. It provides a framework for establishing the importance of the study, as well as a benchmark for comparing the results of a study with other findings.

4. It “frames” the problem earlier identified.

C. Demonstrate to the reader that you have a comprehensive grasp of the field and are aware of important recent substantive and methodological developments.

D. Delineate the “jumping-off place” for your study. How will your study refine, revise, or extend what is now known?

E. Avoid statements that imply that little has been done in the area or that what has been done is too extensive to permit easy summary. Statements of this sort are usually taken as indications that the writer is not really familiar with the literature.

F. In a proposal, the literature review is generally brief and to the point. Be judicious in your choice of exemplars—the literature selected should be pertinent and relevant (APA, 2001). Select and reference only the more appropriate citations. Make key points clearly and succinctly.

G. Committees may want a section outlining your search strategy—the procedures you used and sources you investigated (e.g., databases, journals, test banks, experts in the field) to compile your literature review. Check with your Chair.

V. Questions and/or HypothesesA. Questions are relevant to normative or census type research (How many of them are

there? Is there a relationship between them?). They are most often used in qualitative inquiry, although their use in quantitative inquiry is becoming more prominent. Hypotheses are relevant to theoretical research and are typically used only in quantitative

inquiry. When a writer states hypotheses, the reader is entitled to have an exposition of the theory that lead to them (and of the assumptions underlying the theory). Just as conclusions must be grounded in the data, hypotheses must be grounded in the theoretical framework.

B. A research question poses a relationship between two or more variables but phrases the relationship as a question; a hypothesis represents a declarative statement of the relations between two or more variables (Kerlinger, 1979; Krathwohl, 1988).

C. Deciding whether to use questions or hypotheses depends on factors such as the purpose of the study, the nature of the design and methodology, and the audience of the research (at times even the taste and preference of committee members, particularly the Chair).

D. The practice of using hypotheses was derived from using the scientific method in social science inquiry. They have philosophical advantages in statistical testing, as researchers should be and tend to be conservative and cautious in their statements of conclusions (Armstrong, 1974).

E. Hypotheses can be couched in four kinds of statements.

1. Literary null—a “no difference” form in terms of theoretical constructs. For example, “There is no relationship between support services and academic persistence of nontraditional-aged college women.” Or, “There is no difference in school achievement for high and low self-regulated students.”

2. Operational null—a “no difference” form in terms of the operation required to test the hypothesis. For example, “There is no relationship between the number of hours nontraditional-aged college women use the student union and their persistence at the college after their freshman year.” Or, “There is no difference between the mean grade point averages achieved by students in the upper and lower quartiles of the distribution of the Self-regulated Inventory.” The operational null is generally the preferred form of hypothesis-writing.

3. Literary alternative—a form that states the hypothesis you will accept if the null hypothesis is rejected, stated in terms of theoretical constructs. In other words, this is usually what you hope the results will show. For example, “The more that nontraditional-aged women use support services, the more they will persist academically.” Or, “High self-regulated students will achieve more in their classes than low self-regulated students.”

4. Operational alternative—Similar to the literary alternative except that the operations are specified. For example, “The more that nontraditional-aged college women use the student union, the more they will persist at the college after their freshman year.” Or, “Students in the upper quartile of the Self-regulated Inventory distribution achieve significantly higher grade point averages than do students in the lower quartile.”

F. In general, the null hypothesis is used if theory/literature does not suggest a hypothesized relationship between the variables under investigation; the alternative is generally reserved for situations in which theory/research suggests a relationship or directional interplay.

G. Be prepared to interpret any possible outcomes with respect to the questions or hypotheses. It will be helpful if you visualize in your mind=s eye the tables (or other

summary devices) that you expect to result from your research (Guba, 1961).

H. Questions and hypotheses are testable propositions deduced and directly derived from theory (except in grounded theory studies and similar types of qualitative inquiry).

I. Make a clear and careful distinction between the dependent and independent variables and be certain they are clear to the reader. Be excruciatingly consistent in your use of terms. If appropriate, use the same pattern of wording and word order in all hypotheses.

VI. The Design--Methods and Procedures

A. “The methods or procedures section is really the heart of the research proposal. The activities should be described with as much detail as possible, and the continuity between them should be apparent” (Wiersma, 1995, p. 409).

B. Indicate the methodological steps you will take to answer every question or to test every hypothesis illustrated in the Questions/Hypotheses section.

C. All research is plagued by the presence of confounding variables (the noise that covers up the information you would like to have). Confounding variables should be minimized by various kinds of controls or be estimated and taken into account by randomization processes (Guba, 1961). In the design section, indicate

1. the variables you propose to control and how you propose to control them, experimentally or statistically, and

2. the variables you propose to randomize, and the nature of the randomizing unit (students, grades, schools, etc.).

D. Be aware of possible sources of error to which your design exposes you. You will not produce a perfect, error free design (no one can). However, you should anticipate possible sources of error and attempt to overcome them or take them into account in your analysis. Moreover, you should disclose to the reader the sources you have identified and what efforts you have made to account for them.

E. Sampling

1. The key reason for being concerned with sampling is that of validity—the extent to which the interpretations of the results of the study follow from the study itself and the extent to which results may be generalized to other situations with other people (Shavelson, 1988).

2. Sampling is critical to external validity—the extent to which findings of a study can be generalized to people or situations other than those observed in the study. To generalize validly the findings from a sample to some defined population requires that the sample has been drawn from that population according to one of several probability sampling plans. By a probability sample is meant that the probability of inclusion in the sample of any element in the population must be given a priori. All probability samples involve the idea of random sampling at some stage (Shavelson, 1988). In experimentation, two distinct steps are involved.

Random selection—participants to be included in the sample have been chosen at random from the same population. Define the population and indicate the sampling plan in detail.

Random assignment—participants for the sample have been assigned at random to one of the experimental conditions.

3. Another reason for being concerned with sampling is that of internal validity—the extent to which the outcomes of a study result from the variables that were manipulated, measured, or selected rather than from other variables not systematically treated. Without probability sampling, error estimates cannot be constructed (Shavelson, 1988).

4. Perhaps the key word in sampling is representative. One must ask oneself, “How representative is the sample of the survey population (the group from which the sample is selected) and how representative is the survey population of the target population (the larger group to which we wish to generalize)?”

5. When a sample is drawn out of convenience (a nonprobability sample), rationale and limitations must be clearly provided.

6. If available, outline the characteristics of the sample (by gender, race/ethnicity, socioeconomic status, or other relevant group membership).

7. Detail procedures to follow to obtain informed consent and ensure anonymity and/or confidentiality.

F. Instrumentation

1. Outline the instruments you propose to use (surveys, scales, interview protocols, observation grids). If instruments have previously been used, identify previous studies and findings related to reliability and validity. If instruments have not previously been used, outline procedures you will follow to develop and test their reliability and validity. In the latter case, a pilot study is nearly essential.

2. Because selection of instruments in most cases provides the operational definition of constructs, this is a crucial step in the proposal. For example, it is at this step that a literary conception such as “self-efficacy is related to school achievement” becomes “scores on the Mathematics Self-Efficacy Scale are related to Grade Point Average.” Strictly speaking, results of your study will be directly relevant only to the instrumental or operational statements (Guba, 1961).

3. Include an appendix with a copy of the instruments to be used or the interview protocol to be followed. Also include sample items in the description of the instrument.

4. For a mailed survey, identify steps to be taken in administering and following up the survey to obtain a high response rate.

G. Data Collection1. Outline the general plan for collecting the data. This may include survey

administration procedures, interview or observation procedures. Include an explicit statement covering the field controls to be employed. If appropriate, discuss how you obtained entré.

2. Provide a general outline of the time schedule you expect to follow.

H. Data Analysis

1. Specify the procedures you will use, and label them accurately (e.g., ANOVA, MANCOVA, HLM, ethnography, case study, grounded theory). If coding procedures are to be used, describe in reasonable detail. If you triangulated, carefully explain how you went about it. Communicate your precise intentions and reasons for these intentions to the reader. This helps you and the reader evaluate the choices you made and procedures you followed.

2. Indicate briefly any analytic tools you will have available and expect to use (e.g., Ethnograph, NUDIST, AQUAD, SAS, SPSS, SYSTAT).

3. Provide a well thought-out rationale for your decision to use the design, methodology, and analyses you have selected.

VII. Limitations and Delimitations

A. A limitation identifies potential weaknesses of the study. Think about your analysis, the nature of self-report, your instruments, the sample. Think about threats to internal validity that may have been impossible to avoid or minimize—explain.

B. A delimitation addresses how a study will be narrowed in scope, that is, how it is bounded. This is the place to explain the things that you are not doing and why you have chosen not to do them—the literature you will not review (and why not), the population you are not studying (and why not), the methodological procedures you will not use (and why you will not use them). Limit your delimitations to the things that a reader might reasonably expect you to do but that you, for clearly explained reasons, have decided not to do.

VIII. Significance of the Study

A. Indicate how your research will refine, revise, or extend existing knowledge in the area under investigation. Note that such refinements, revisions, or extensions may have either substantive, theoretical, or methodological significance. Think pragmatically (i.e., cash value).

B. Most studies have two potential audiences: practitioners and professional peers. Statements relating the research to both groups are in order.

C. This can be a difficult section to write. Think about implications—how results of the study may affect scholarly research, theory, practice, educational interventions, curricula, counseling, policy.

D. When thinking about the significance of your study, ask yourself the following questions.

1. What will results mean to the theoretical framework that framed the study?2. What suggestions for subsequent research arise from the findings?

3. What will the results mean to the practicing educator?4. Will results influence programs, methods, and/or interventions?5. Will results contribute to the solution of educational problems?6. Will results influence educational policy decisions?7. What will be improved or changed as a result of the proposed research?8. How will results of the study be implemented, and what innovations will come

about?

IX. References

A. Follow APA (2001) guidelines regarding use of references in text and in the reference list. Of course, your committee or discipline may require Chicago or MLA.

B. Only references cited in the text are included in the reference list; however, exceptions can be found to this rule. For example, committees may require evidence that you are familiar with a broader spectrum of literature than that immediately relevant to your research. In such instances, the reference list may be called a bibliography.

C. Some committees require that reference lists and/or bibliographies be “annotated,” which is to say that each entry be accompanied by a brief description, or an abstract. Check with your committee Chair before the fact.

Appendixes

The need for complete documentation generally dictates the inclusion of appropriate appendixes in proposals (although this is generally not the case as regards conference proposals).

The following materials are appropriate for an appendix. Consult with your committee Chair.

Verbatim instructions to participants.Original scales or questionnaires. If an instrument is copyrighted, permission in writing

to reproduce the instrument from the copyright holder or proof of purchase of the instrument.

Interview protocols.Sample of informed consent forms.Cover letters sent to appropriate stakeholders.Official letters of permission to conduct research.

References

American Psychological Association (APA). (2001). Publication manual of the American Psychological Association (Fourth edition). Washington, DC: Author.

Armstrong, R. L. (1974). Hypotheses: Why? When? How? Phi Delta Kappan, 54, 213-214.Creswell, J. W. (1994). Research design: Qualitative & quantitative approaches. Thousand Oaks, CA:

Sage.Guba, E. G. (1961, April). Elements of a proposal. Paper presented at the UCEA meeting, Chapel Hill,

NC.Fraenkel, J. R. & Wallen, N. E. (1990). How to design and evaluate research in education. New York:

McGraw-Hill.Kerlinger, F. N. (1979). Behavioral research: A conceptual approach. New York: Holt, Rinehart, &

Winston.Krathwohl, D. R. (1988). How to prepare a research proposal: Guidelines for funding and dissertations

in the social and behavioral sciences. Syracuse, NY: Syracuse University Press.Locke, L. F., Spirduso, W. W., & Silverman, S. J. (1987). Proposals that work: A guide for planning

dissertations and grant proposals (2nd ed.). Newbury Park, CA: Sage.Marshall, C., & Rossman, G. B. (1989). Designing qualitative research: Newbury Park, CA: Sage.Shavelson, R. J. (1988). Statistical reasoning for the behavioral sciences (second edition). Boston: Allyn

and Bacon.Wiersma, W. (1995). Research methods in education: An introduction (Sixth edition). Boston: Allyn and

Bacon.Wilkinson, A. M. (1991). The scientist’s handbook for writing papers and dissertations. Englewood

Cliffs, NJ: Prentice Hall.

The Proposal in Qualitative Research1 by

Anthony W. Heath2

The Qualitative Report, Volume 3, Number 1, March, 1997(http://www.nova.edu/ssss/QR/QR3-1/heath.html)

The purpose of "qualitative" or "naturalistic" research varies according to the research paradigm, methods, and assumptions. Generally speaking, qualitative researchers attempt to describe and interpret some human phenomenon, often in the words of selected individuals (the informants). These researchers try to be clear about their biases, presuppositions, and interpretations so that others (the stakeholders) can decide what they think about it all.

Unlike conventional, positivist research, there is no single accepted outline for a qualitative research proposal or report (Morse, 1991). The generic outline that follows is suggested as a point of departure for qualitative research proposals, and it applies specifically to the research paradigm and methods that seem most applicable to the study of families and family therapy (e.g., post-positivist, phenomenological clinical observation and long interviews). The outline is intended to serve as a point of departure for researchers, who must decide how to organize their proposals (a) to best communicate their ideas to their intended audiences and (b) to satisfy the demands of the context.

I. Introduction A. Begin with something interesting, e.g., a quote or story, to capture the reader's interest.

B. Introduce you question or curiosity. What is it that you want to know or understand? How did you get interested in the topic? If your question has evolved since you have begun, describe the process.

C. Tell why there's a need for the study. Cite relevant literature that calls for the need for the research in this area, or demonstrates the lack of attention to the topic. In your own words, describe how you think this study will be useful.

D. Describe the intended audience for your research (e.g., the public, family therapists). E. Describe your research product. What form will the report take (e.g., scholarly

manuscript, magazine article for the public, script for a documentary video)? F. Conclude the introduction with an overview of your proposal.

II. Research Paradigm

This section should be included in your proposal when you expect to have readers who are not familiar with the naturalistic research paradigm. It may not be necessary in contexts where qualitative research is an accepted form of inquiry.

A. Use specific language to name and describe your research paradigm (e.g., naturalistic, post-positivist).The term "paradigm" is used here to represent the epistemological, conceptual foundation for qualitative research. See Guba (1990).

B. Describe the philosophical correlates of your research paradigm (e.g., phenomenology, hermeneutics).

C. Cite authors who have defined your research paradigm in the social sciences and suggested its application to your field of study and/or your specific topic of study. See Moon, Dillon, and Sprenkle (1990).

D. Explain the assumptions of your research paradigm. 1. Broadly speaking, describe what you intend to accomplish through this research

(e.g., expanding a knowledge base, generating hypotheses for quantitative research, developing a grounded theory, emancipating informants, establishing the trustworthiness of a theory). See Atkinson and Heath (1990a, 1990b); Lincoln and Guba (1985).

2. Explain the assumptions about the nature of knowledge and reality that underlie your research paradigm. Discuss how a formal literature review will be used.

3. Describe the major tasks of the researcher in this paradigm of research. Comment on how the tasks differ in conventional social science research.

4. Explain the type of relationship that the researcher has with the informants (e.g., unobtrusive observer, participant observer, collaborator, emancipation).

E. Suggest the appropriate criteria for evaluating the research findings, research process, and the research report. The criteria should be consistent with your research paradigm and well documented. See Atkinson, Heath, and Chenail (1991).

III. Research Method A. Identify and generally describe your research method (e.g., ethnographic field study,

single case study), and your research procedures (e.g., long interviews, observation). B. Cite the major authors who have described your research method. See Lincoln and

Guba (1985); Glaser and Strauss (1967), etc. C. Describe what you intend to do in detail, as you begin your study.

1. Explain how you will select informants and gain entry into the research context (if relevant).

2. Describe the procedures you will take to protect the rights of your informants (e.g., informed consent, human subjects approval, debriefing).

3. Describe the kind of relationship you intend to have with the informants. Will you be neutral, collaborative, objective?

4. Describe the kind of data you will collect (e.g., field notes from memory, audio tapes, video tapes, transcripts of conversations, examination of existing documents, etc.).

5. Describe your intended data collection procedures.If interviews are to be used, list your question(s) or attach as an appendix. Describe any equipment to be used.

6. Describe the procedures you will use to keep track of the research process. This will become part of your audit trail.

a. Process notes: Day to day activities, methodological notes, decision making procedures.

b. Materials relating to intentions and reactions: personal notes about motivations, experiences with informants, etc.

c. Instrument development information: revisions of interview questions, etc.

7. Describe your intended data analysis procedures (coding, sorting, etc.)? a. Data reduction: Write-ups of field notes, transcription procedures and

conventions, computer programs used, etc. b. Data reconstruction: development of categories, findings, conclusions,

connections to existing literature, integration of concepts. 8. Describe how the research design may evolve as the process unfolds. 9. Describe how you will organize, format and present your data, interpretations,

and conclusions. D. Describe how you will consider and protect "reliability" and "validity." Will you use

systematic methods and procedures, triangulation, member checking, peer debriefing, auditing?

IV. Preliminary Biases, Suppositions and Hypotheses A. Summarize and reference all of the relevant literature that you have reviewed to date. B. Describe how your review of the literature has influenced the way you are approaching

the research. C. Discuss how your previous experience with your topic has influenced the way you have

conceptualized this research. Summarize relevant personal an professional experiences, if you have not done so in the Introduction.

D. Disclose the anticipated findings, your hypotheses and your hunches. E. Describe the procedures you will use to remain "open" to unexpected information (e.g.,

peer debriefing). F. Discuss the limitations of your study in the context of the limitations of all similar

studies. V. References and Mini-Bibliography

    Atkinson, B., & Heath, A. (1990a). Further thoughts on second-order family therapy: This time it's personal. Family Process, 29(2), 145-156.

    Atkinson, B., & Heath, A. (1990b). The limits of explanation and evaluation. Family Process, 29(2), 164-168.

    Atkinson, B., Heath, A., & Chenail, R. (1991). Qualitative research and the legitimization of knowledge. Journal of Marital and Family Therapy, 17(2), 175-180.

    Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.

    Guba, E.(1990). The paradigm dialog. Beverly Hills, CA: Sage.

    Lincoln, Y., & Guba, E. (1985). Naturalistic inquiry. New York: Sage.

    Moon, S., Dillon, D., & Sprenkle, D. (1990). Family therapy and qualitative research. Journal of Martial and Family Therapy, 16(4), 357-373.

    Morse, J. (1991). On the evaluation of qualitative proposals. Qualitative Health Research, 1(2), 147-151.

Sampling In Research

Mugo Fridah W.

INTRODUCTION

This tutorial is a discussion on sampling in research it is mainly designed to eqiup beginners with knowledge on the general issues on sampling that is the purpose of sampling in research, dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample size. For a clear flow of ideas, a few definitions of the terms used are given.

What is research?

According Webster(1985), to research is to search or investigate exhaustively. It is a careful or diligent search, studious inquiry or examination especially investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts or practical application of such new or revised theories or laws, it can also be the collection of information about a particular subject.

What is a sample?

A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). When dealing with people, it can be defined as a set of respondents(people) selected from a larger population for the purpose of a survey.

A population is a group of individuals persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students.

What is sampling? Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population.

What is the purpose of sampling? To draw conclusions about populations from samples, we must use inferential statistics which enables us to determine a population`s characteristics by directly observing only a portion (or sample) of the population. We obtain a sample rather than a complete enumeration (a census ) of the population for many reasons. Obviously, it is cheaper to observe a part rather than the whole, but we should prepare ourselves to cope with the dangers of using samples. In this tutorial, we will investigate various kinds of sampling procedures. Some are better than others but all may yield samples that are inaccurate and unreliable. We will learn how to minimize these dangers, but some potential error is the price we must pay for the convenience and savings the samples provide.

There would be no need for statistical theory if a census rather than a sample was always used to obtain information about populations. But a census may not be practical and is almost never economical. There are six main reasons for sampling instead of doing a census. These are; -Economy -Timeliness -The large size of many populations -Inaccessibility of some of the population -Destructiveness of the observation -accuracy

The economic advantage of using a sample in research Obviously, taking a sample requires fewer resources than a census. For example, let us assume that you are one of the very curious students around. You have heard so much about the famous Cornell and now that you are there, you want to hear from the insiders. You want to know what all the students at Cornell think about the quality of teaching they receive, you know that all the students are different so they are likely to have different perceptions and you believe you must get all these perceptions so you decide because you want an indepth view of every student, you will conduct personal interviews with each one of them and you want the results in 20 days only, let us assume this particular time you are doing your research Cornell has only 20,000 students and those who are helping are so fast at the interviewing art that together you can interview at least 10 students per person per day in addition to your 18 credit hours of course work. You will require 100 research assistants for 20 days and since you are paying them minimum wage of $5.00 per hour for ten hours ($50.00) per person per day, you will require $100000.00 just to complete the interviews, analysis will just be impossible. You may decide to hire additional assistants to help with the analysis at another $100000.00 and so on assuming you have that amount on your account.

As unrealistic as this example is, it does illustrate the very high cost of census. For the type of information desired, a small wisely selected sample of Cornell students can serve the purpose. You don`t even have to hire a single assistant. You can complete the interviews and analysis on your own. Rarely does a circustance require a census of the population, and even more rarely does one justify the expense.

The time factor.

A sample may provide you with needed information quickly. For example, you are a Doctor and a disease has broken out in a village within your area of jurisdiction, the disease is contagious and it is killing within hours nobody knows what it is. You are required to conduct quick tests to help save the situation. If you try a census of those affected, they may be long dead when you arrive with your results. In such a case just a few of those already infected could be used to provide the required information.

The very large populations

Many populations about which inferences must be made are quite large. For example, Consider the population of high school seniors in United States of America, agroup numbering 4,000,000. The responsible agency in the government has to plan for how they will be absorbed into the differnt departments and even the private sector. The employers would like to have specific knowledge about the student`s plans in order to make compatiple plans to absorb them during the coming year. But the big size of the population makes it physically impossible to conduct a census. In such a case, selecting a representative sample may be the only way to get the information required from high school seniors.

The partly accessible populations

There are Some populations that are so difficult to get access to that only a sample can be used. Like people in prison, like crashed aeroplanes in the deep seas, presidents e.t.c. The inaccessibility may be economic or time related. Like a particular study population may be so costly to reach like the population of planets that only a sample can be used. In other cases, a population of some events may be taking too long to occur that only sample information can be relied on. For example natural disasters like a flood that occurs every 100 years or take the example of the flood that occured in Noah`s days. It has never occured again.

The destructive nature of the observation Sometimes the very act of observing the desired charecteristic of a unit of the population destroys it for the intended use. Good examples of this occur in quality control. For example to test the quality of a fuse, to determine whether it is defective, it must be destroyed. To obtain a census of the quality of a lorry load of fuses, you have to destroy all of them. This is contrary to the purpose served by quality-control testing. In this case, only a sample should be used to assess the quality of the fuses

Accuracy and sampling A sample may be more accurate than a census. A sloppily conducted census can provide less reliable information than a carefully obtained sample.

BIAS AND ERROR IN SAMPLING A sample is expected to mirror the population from which it comes, however, there is no guarantee that any sample will be precisely representative of the population from which it comes. Chance may dictate that a disproportionate number of untypical observations will be made like for the case of testing fuses, the sample of fuses may consist of more or less faulty fuses than the real population proportion of faulty cases. In practice, it is rarely known when a sample is unrepresentative and should be discarded.

Sampling error

What can make a sample unrepresentative of its population? One of the most frequent causes is sampling error.

Sampling error comprises the differences between the sample and the population that are due solely to the particular units that happen to have been selected.

For example, suppose that a sample of 100 american women are measured and are all found to be taller than six feet. It is very clear even without any statistical prove that this would be a highly unrepresentative sample leading to invalid conclusions. This is a very unlikely occurance because naturally such rare cases are widely distributed among the population. But it can occur. Luckily, this is a very obvious error and can be etected very easily.

The more dangerous error is the less obvious sampling error against which nature offers very little protection. An example would be like a sample in which the average height is overstated by only one inch or two rather than one foot which is more obvious. It is the unobvious error that is of much concern.

There are two basic causes for sampling error. One is chance: That is the error that occurs just because of bad luck. This may result in untypical choices. Unusual units in a population do exist and there is always a possibility that an abnormally large number of them will be chosen. For example, in a recent study in which I was looking at the number of trees, I selected a sample of households randomly but strange enough, the two households in the whole population, which had the highest number of trees (10,018 and 6345 ) were both selected making the sample average higher than it should be. The average with these two extremes removed was 828 trees. The main protection agaisnt this kind of error is to use a large enough sample. The second cause of sampling is sampling bias.

Sampling bias is a tendency to favour the selection of units that have paticular characteristics.

Sampling bias is usually the result of a poor sampling plan. The most notable is the bias of non response when for some reason some units have no chance of appearing in the sample. For example, take a hypothetical case where a survey was conducted recently by Cornell Graduate school to find out the level of stress that graduate students were going through. A mail questionnaire was sent to 100 randomly

selected graduate students. Only 52 responded and the results were that students were not under strees at that time when the actual case was that it was the highest time of stress for all students except those who were writing their thesis at their own pace. Apparently, this is the group that had the time to respond. The researcher who was conducting the study went back to the questionnaire to find out what the problem was and found that all those who had responded were third and fourth PhD students. Bias can be very costly and has to be gaurded against as much as possible. For this case, $2000.00 had been spent and there were no reliable results in addition, it cost the reseacher his job since his employer thought if he was qualified, he should have known that before hand and planned on how to avoid it. A means of selecting the units of analysis must be designed to avoid the more obvious forms of bias. Another example would be where you would like to know the average income of some community and you decide to use the telephone numbers to select a sample of the total population in a locality where only the rich and middle class households have telephone lines. You will end up with high average income which will lead to the wrong policy decisions.

Non sampling error (measurement error)

The other main cause of unrepresentative samples is non sampling error. This type of error can occur whether a census or a sample is being used. Like sampling error, non sampling error may either be produced by participants in the statistical study or be an innocent by product of the sampling plans and procedures.

A non sampling error is an error that results solely from the manner in which the observations are made.

The simplest example of non sampling error is inaccurate measurements due to malfuntioning instruments or poor procedures. For example, Consider the observation of human weights. If persons are asked to state their own weights themselves, no two answers will be of equal reliability. The people will have weighed themselves on different scales in various states of poor caliberation. An individual`s weight fluctuates diurnally by several pounds, so that the time of weighing will affect the answer. The scale reading will also vary with the person`s state of undress. Responses therefore will not be of comparable validity unless all persons are weighed under the same circumstances.

Biased observations due to inaccurate measurement can be innocent but very devastating. A story is told of a French astronomer who once proposed a new theory based on spectroscopic measurements of light emitted by a particular star. When his colleques discovered that the measuring instrument had been contaminated by cigarette smoke, they rejected his findings.

In surveys of personal characteristics, unintended errors may result from: -The manner in which the response is elicited -The social desirability of the persons surveyed -The purpose of the study -The personal biases of the interviewer or survey writer

The interwiers effect

No two interviewers are alike and the same person may provide different answers to different interviewers. The manner in which a question is formulated can also result in inaccurate responses. Individuals tend to provide false answers to particular questions. For example, some people want to feel younger or older for some reason known to themselves. If you ask such a person their age in years, it is easier for the idividual just to lie to you by over stating their age by one or more years than it is if you asked which year they were born since it will require a bit of quick arithmetic to give a false date and a date of birth will definitely be more accurate.

The respondent effect

Respondents might also give incorrect answers to impress the interviewer. This type of error is the most difficult to prevent because it results from out right deceit on the part of the respondee. An example of this is what I witnessed in my recent study in which I was asking farmers how much maize they harvested last year (1995). In most cases, the men tended to lie by saying a figure which is the reccomended expected yield that is 25 bags per acre. The responses from men looked so uniform that I became suspicious. I compared with the responses of the wives of the these men and their responses were all different. To decide which one was right, whenever possible I could in a tactful way verify with an older son or daughter. It is important to acknowledge that certain psychological factors induce incorrect responses and great care must be taken to design a study that minimizes their effect.

Knowing the study purpose

Knowing why a study is being conducted may create incorrect responses. A classic example is the question: What is your income? If a government agency is asking, a different figure may be provided than the respondent would give on an application for a home mortgage. One way to guard against such bias is to camouflage the study`s goals; Another remedy is to make the questions very specific, allowing no room for personal interpretation. For example, "Where are you employed?" could be followed by "What is your salary?" and "Do you have any extra jobs?" A sequence of such questions may produce more accurate information.

Induced bias

Finally, it should be noted that the personal prejudices of either the designer of the study or the data collector may tend to induce bias. In designing a questionnaire, questions may be slanted in such a way that a particular response will be obtained even though it is inacurrate. For example, an agronomist may apply fertilizer to certain key plots, knowing that they will provide more favourable yields than others. To protect against induced bias, advice of an individual trained in statistics should be sought in the design and someone else aware of search pitfalls should serve in an auditing capacity.

SELECTING THE SAMPLE

The preceding section has covered the most common problems associated with statistical studies. The desirability of a sampling procedure depends on both its vulnerability to error and its cost. However, economy and reliability are competing ends, because, to reduce error often requires an increased expenditure of resources. Of the two types of statistical errors, only sampling error can be controlled by exercising care in determining the method for choosing the sample. The previous section has shown that sampling error may be due to either bias or chance. The chance component (sometimes called random error) exists no matter how carefully the selection procedures are implemented, and the only way to minimize chance sampling errors is to select a sufficiently large sample (sample size is discussed towards the end of this tutorial). Sampling bias on the other hand may be minimized by the wise choice of a sampling procedure.

TYPES OF SAMPLES

There are three primary kinds of samples: the convenience, the judgement sample, and the random sample. They differ in the manner in which the elementary units are chosen.

The convenient sample

A convenience sample results when the more convenient elementary units are chosen from a population for observation.

The judgement sample

A judgement sample is obtained according to the discretion of someone who is familiar with the relevant characteristics of the population.

The random sample

This may be the most important type of sample. A random sample allows a known probability that each elementary unit will be chosen. For this reason, it is sometimes referred to as a probability sample. This is the type of sampling that is used in lotteries and raffles. For example, if you want to select 10 players randomly from a population of 100, you can write their names, fold them up, mix them thoroughly then pick ten. In this case, every name had any equal chance of being picked. Random numbers can also be used (see Lapin page 81).

TYPES OF RANDOM SAMPLES

A simple random sample

A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly. Instead of using a least of random numbers, data collection can be simplified by selecting say every 10th or 100th unit after the first unit has been chosen randomly as discussed below. such a procedure is called systematic random sampling.

A systematic random sample

A systematic random sample is obtained by selecting one unit on a random basis and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained. For example, there are 100 students in your class. You want a sample of 20 from these 100 and you have their names listed on a piece of paper may be in an alphabetical order. If you choose to use systematic random sampling, divide 100 by 20, you will get 5. Randomly select any number between 1 and five. Suppose the number you have picked is 4, that will be your starting number. So student number 4 has been selected. From there you will select every 5th name until you reach the last one, number one hundred. You will end up with 20 selected students.

A stratified sample

A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable like income of education. Like any body with ten years of education will be in group A, between 10 and 20 group B and between 20 and 30 group C. These groups are referred to as strata. You can then randomly select from each stratum a given number of units which may be based on proportion like if group A has 100 persons while group B has 50, and C has 30 you may decide you will take 10% of each. So you end up with 10 from group A, 5 from group B and 3 from group C.

A cluster sample

A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. For example, a cluster may be some thing like a village or a school, a state. So you decide all the elementary schools in Newyork State are clusters. You want 20 schools selected. You can use simple or systematic random sampling to select the schools, then every school selected becomes a cluster. If you interest is to interview teachers on thei opinion of some new program which has been introduced, then all the teachers in a cluster must be interviewed. Though very economical cluster sampling is very susceptible to sampling bias. Like for the above case, you are likely to get similar responses from teachers in one school due to the fact that they interact with one another.

PURPOSEFUL SAMPLING

Purposeful sampling selects information rich cases for indepth study. Size and specific cases depend on the study purpose.

There are about 16 different types of purposeful sampling. They are briefly described below for you to be aware of them. The details can be found in Patton(1990)Pg 169-186.

Extreme and deviant case sampling This involves learning from highly unusual manifestations of the phenomenon of interest, suchas outstanding successes, notable failures, top of the class, dropouts, exotic events, crises.

Intensity sampling This is information rich cases that manifest the phenomenon intensely, but not extremely, such as good students,poor students, above average/below average.

Maximum variation sampling This involves purposefully picking a wide range of variation on dimentions of interest. This documents unique or diverse variations that have emerged in adapting to different conditions. It also identifies important common patterns that cut across variations. Like in the example of interviewing Cornell students, you may want to get students of different nationalities, professional backgrounds, cultures, work experience and the like.

Homogenious sampling This one reduces variation, simplifies analysis, facilitates group interviewing. Like instead of having the maximum number of nationalities as in the above case of maximum variation, it may focus on one nationality say Americans only.

Typical case sampling It involves taking a sample of what one would call typical, normal or average for a particular phenomenon,

Stratified purposeful sampling This illustrates charecteristics of particular subgroups of interest and facilitates comparisons between the different groups.

Critical case sampling> This permits logical generalization and maximum application of information to other cases like "If it is true for this one case, it is likely to be true of all other cases. You must have heard statements like if it happenned to so and so then it can happen to anybody. Or if so and so passed that exam, then anybody can pass.

Snowball or chain sampling This particular one identifies, cases of interest from people who know people who know what cases are information rich, that is good examples for study, good interview subjects. This is commonly used in studies that may be looking at issues like the homeless households. What you do is to get hold of one and he/she will tell you where the others are or can be found. When you find those others they will tell you where you can get more others and the chain continues.

Criterion sampling Here, you set a criteria and pick all cases that meet that criteria for example, all ladies six feet tall, all white cars, all farmers that have planted onions. This method of sampling is very strong in quality assurance.

Theory based or operational construct sampling. Finding manifestations of a theoretical construct of interest so as to elaborate and examine the construct.

Confirming and disconfirming cases Elaborating and deepening initial analysis like if you had already started some study, you are seeking further information or confirming some emerging issues which are not clear, seeking exceptions and testing variation.

Opportunistic Sampling This involves following new leads during field work, taking advantage of the unexpected flexibility.

Random purposeful sampling This adds credibility when the purposeful sample is larger than one can handle. Reduces judgement within a purposeful category. But it is not for generalizations or representativeness.

Sampling politically important cases This type of sampling attracts or avoids attracting attention undesired attention by purposisefully eliminating from the sample political cases. These may be individuals, or localities.

Convenience sampling It is useful in getting general ideas about the phenomenon of interest. For example you decide you will interview the first ten people you meet tomorrow morning. It saves time, money and effort. It is the poorest way of getting samples, has the lowest credibility and yields information-poor cases.

Combination or mixed purposeful sampling This combines various sampling strategies to achieve the desired sample. This helps in triangulation, allows for flexibility, and meets multiple interests and needs. When selecting a sampling strategy it is necessary that it fits the purpose of the study, the resources available, the question being asked and the constraints being faced. This holds true for sampling strategy as well as sample size.

SAMPLE SIZE

Before deciding how large a sample should be, you have to define your study population. For example, all children below age three in Tomkin`s County. Then determine your sampling frame which could be a list of all the chidren below three as recorded by Tomkin`s County. You can then struggle with the sample size.

The question of how large a sample should be is a difficult one. Sample size can be determined by various constraints. For example, the available funding may prespecify the sample size. When research costs are fixed, a useful rule of thumb is to spent about one half of the total amount for data collection and the other half for data analysis. This constraint influences the sample size as well as sample design and data collection procedures.

In general, sample size depends on the nature of the analysis to be performed, the desired precision of the estimates one wishes to achieve, the kind and number of comparisons that will be made, the number of variables that have to be examined simultaneously and how heterogenous a universe is sampled. For example, if the key analysis of a randomized experiment consists of computing averages for experimentals and controls in a project and comparing differences, then a sample under 100 might be adequate, assuming that other statistical assumptions hold.

In non-experimental research, most often, relevant variables have to be controlled statistically because groups differ by factors other than chance.

More technical considerations suggest that the required sample size is a function of the precision of the estimates one wishes to achieve, the variability or variance, one expects to find in the population and the statistical level of confidence one wishes to use. The sample size N required to estimate a population mean (average) with a given level of precision is:

The square root of N=(1.96)*(&)/precision Where & is the population standard deviation of the for the variable whose mean one is interested in estimating. Precision refers to width of the interval one is willing to tolerate and 1.96 reflects the confidence level. For details on this please see Salant and Dillman (1994).

For example, to estimate mean earnings in a population with an accuracy of $100 per year, using a 95% confidence interval and assuming that the standard deviation of earnings in the population is $1600.0, the required sample size is 983:[(1.96)(1600/100)] squared.

Deciding on a sample size for qualitative inquiry can be even more difficult than quantitative because there are no definite rules to be followed. It will depend on what you want to know, the purpose of the inquiry, what is at stake, what will be usefull, what will have credibility and what can be done with available time and resources. With fixed resources which is always the case, you can choose to study one specific phenomenon in depth with a smaller sample size or a bigger sample size when seeking breadth. In purposeful sampling, the sample should be judged on the basis of the purpose and rationale for each study and the sampling strategy used to achieve the studies purpose. The validity, meangfulness, and insights generated from qualitative inquiry have more to do with the information-richness of the cases selected and the observational/analytical capabilities of the researcher than with sample size.

CONCLUSION

In conclusion, it can be said that using a sample in research saves mainly on money and time, if a suitable sampling strategy is used, appropriate sample size selected and necessary precautions taken to reduce on sampling and measurement errors, then a sample should yield valid and reliable information. Details on sampling can be obtained from the references included below and many other books on statistics or qualitative research which can be found in libraries.

References

Webster, M. (1985). Webster`s nith new collegiate dictionary. Meriam - Webster Inc.

Salant, P. and D. A. Dillman (1994). How to conduct your own survey. John Wiley & Sons, Inc.

Patton, M.Q.(1990). Qualitative evaluation and research methods. SAGE Publications. Newbury Park London New Delhi.

Lapin, L. L. (1987). Statistics for mordern business decisions. Harcourt Brace Jovanovich, Inc.

Sampling is the process of selecting a sufficient number of elements from the population. So that a study of the sample and an understanding of its properties or characteristics would make it possible for us to generalize such properties or character tics to the population elements. The characteristics of the population such as mean, standard deviations and the population variance are referred to as its parameters. The central tendencies the dispersion and other statistics in the sample of interest to the research are treated as approximation of the central tendencies, dispersions and other parameters of the population. As such, all conclusions drawn about the sample under study are generalized to the population. In other words the sample statistics are used as estimates of the population parameters.

The reasons for using a sample rather than collecting data from the entire population are self-

evident. In research investigations involving several hundreds and even thousands of elements, it would be practically impossible to collect data from or test or examine every element.  Even if it were possible it would be prohibitive in terms of time, cost and other human resources. Study of sample rather than the entire population is also sometimes likely to produce more reliable results.

ABSTRACT

The cost of studying an entire population to answer a specific question is usually prohibitive in terms of time, money and resources. Therefore, a subset of subjects representative of a given population must be selected; this is called sampling. The concepts involved in selecting subjects to represent the larger population are presented. Sampling errors and associated determining factors are reviewed.

Definitions of the research populations, including target and accessible groups, are given. The inclusion and exclusion criteria required to refine the accessible population to a researchable subgroup are explained, and an example is provided. The two types of sampling methods, probability and nonprobability, are defined and presented with their respective types. Probability sampling includes simple random sampling, systematic sampling, stratified sampling, cluster sampling and disproportional sampling. Nonprobability sampling includes convenience sampling, consecutive sampling, judgmental sampling, quota sampling and snowball sampling.

The goals and concepts related to recruitment are reviewed with application to survey and experimental research. Three steps are suggested for obtaining an appropriate research sample: (1) clearly define the target population, (2) define the accessible population, and (3) define the steps and effort that will be employed to recruit subjects for study.

Introduction

The first two questions most researchers ask once a research project has been defined are, "How many subjects will I need to complete my study, and how will I select them?" This article, "Part I," will attempt to address the issues related to selecting subjects for a research project. "Part II," which will be published in the Fall JPO, will present in detail the factors relevant to determining sample size.

In clinical research it would be ideal to include the entire population when conducting a study; this enables a generalization to be made about the results to the population as a whole. In some cases this has been possible, such as when the 1976 Philadelphia Legionnaire's disease epidemic was studied. However, in most cases, the population in question is too large or too spread out over time and distance to allow for measuring or evaluating each member of the population.

Researchers have developed a number of techniques where only a small portion of the total population is sampled, and attempts to generalize the results and conclusions for the entire population are made. There are some distinct advantages and disadvantages in using samples. Advantages include that sampling involves a smaller number of subjects and is more time efficient, less costly and potentially more accurate (since it is more feasible to maintain control over a smaller number of subjects). Disadvantages include potential bias in the selection of subjects, which may lead to error in interpretation of results and decrease in ability to generalize the results beyond the subjects actually studied (1-3).

Cox and West describe a population as a well-defined group of people or objects that share common characteristics (1). All immigrants from Germany or all patients with left hemiplegia are examples of well-defined groups with common characteristics. A population in a research study is a group about which

some information is sought. Most researchers cannot include all members of the population in their studies and must resort to limiting the number of subjects to only a sample from the population.

A sample is a small subset of the population that has been chosen to be studied (1,2). The sample should represent the population and have sufficient size so a given innovative orthosis or prosthesis can be subjected to a fair statistical analysis. Unfortunately, all samples deviate from the true nature of the overall population by a certain amount due to chance variations in drawing the sample's few cases from the population's many possible members. This is called sampling error and is distinguished from non-chance variations due to determining factors. Determining factors include items such as biased sampling procedures, effects of independent variables, research conditions and other causal agents or circumstances (2).

One of the most famous cases of biased sampling was the 1936 Literary Digest poll before the U.S. presidential election of 1936 (2,3). Two million ballots were mailed out, received back and tabulated; the results confidently predicted the easy election of Landon (57 percent) over Roosevelt. Unfortunately, the names on the mailing list were taken from telephone directories and lists of automobile owners. At that time, only people of certain wealth had telephones and/or drove cars, and there was a strong correlation between those with wealth and a preference for Landon. The larger mass of people without cars or telephones voted for Roosevelt, giving him the largest margin of victory in history at that time. This large error in prediction is a prime example of the consequences that biased sampling can produce.

Many clinical studies do not achieve their intended purposes because the researcher is unable to enroll enough subjects. Therefore, at some point in planning a study consideration should be given to sample size. While the number of subjects studied is important, even more important in a study is that the subjects accurately represent the larger population. In contrast to the previous example where more than two million ballots gave a biased and erroneous result, polls taken by Gallup and Harris in 1968, in which only 2,000 voters were sampled, predicted a victory by Richard Nixon of 41 and 43 percent, respectively. Nixon won by 42.9 percent (2).

Sampling Concepts

Sampling

target populationexternal validity

Since it will not be practical to recruit every human with spasticity for this study, it is necessary to define an accessible population. The accessible population is a subset of the target population that reflects specific characteristics with respect to age, gender, diagnosis, etc., and who are accessible for study (4).

Therefore, in the AFO footplate example, it is critical to define or characterize the target population before a sample of subjects can be defined. For example, will all patients with spasticity be included? Is the question to be limited to children with diplegia, secondary to cerebral palsy, adults post CVA or adults and children post brain injury? This narrowing and refining of the research question is useful since it more clearly defines the target as well as accessible populations and has direct impact on the external validity of the inferences to be drawn at the conclusion of the study.

Once the specific clinical and demographic characteristics of the accessible population are defined, it is important to consider the geographic and time constraints with which both the researcher and potential subjects will have to contend.

Will the study intervention require more than one visit to the clinic, laboratory or office? If so, how far can subjects be expected to travel, and what means of transport are required to get them there? If the

research is to be conducted in a large metropolitan area with good public transportation, repeated trips and distance may not be a problem. Transportation logistics can be an insurmountable problem if not planned for and resolved in designing the research plan.

In the case of the AFO footplate study, one constraint that might be placed on the accessible subjects is that they live within 30 minutes' travel by car and are able to commit to four visits within a one-month period of time. This leads to the next major consideration in the sampling process, defining inclusion and exclusion criteria of the accessible population.

Inclusion Criteria

In the above example of specific AFOs for patients with spasticity, it is important to consider the research question and include factors that will enable a homogeneous selection of subjects, e.g., age, gender, diagnosis, degree of spasticity, muscle groups affected, etc. It may be determined that the specific variables under study (footplate contours and spasticity) are more likely to show an effect in the growing child than in the adult. Therefore, one inclusion criterion may be a specific population of children whose ages encompass the growing years.

Since patterns of tone are different depending on diagnosis, it may be desirable to specify cerebral palsy and not include other diagnoses. Also, since loads and deforming forces on feet are different if the subject has bilateral versus unilateral involvement, it may be desirable to include only those subjects with hemiplegia. Therefore, the inclusion criteria for this study may be children between the ages of 2 and 15 (growth with walking years) diagnosed as cerebral palsy with spastic hemiplegia. Also, subjects who live within a specific distance, have convenient and affordable transportation and are able to commit to a specific number of visits may be included. A final inclusion criterion may be parental consent and support.

Exclusion Criteria

Exclusion criteria are applied to subjects who generally meet the inclusion criteria but must be excluded because they cannot complete the study or possess unique characteristics that may confound the results. For example, it may be necessary to exclude subjects with spastic hemiplegia secondary to cerebral palsy who were premature at birth or who have additional medical problems that may affect their outcomes. A child with epilepsy may be taking medication that can also affect his/her muscle tone, which could confound study results. If the ability to walk is an important dependent variable of the study, then subjects who do not walk or who have been walking for less than one year may be excluded. Subjects who may have unreliable sources of transportation or noncompliant parents also may need to be excluded.

An important ethical consideration is the willingness of the subject to participate. In the instance of a study of spastic children, parental permission must be obtained or the subject must be excluded. Also, withholding one treatment to evaluate another may pose a difficult ethical consideration. The exclusion criteria, considering all of the above, may result in sampling guidelines that exclude children who are less than 2 or more than 15 years of age, are not on medication that affects muscle tone, do not walk or have walked less than one year, have inadequate transportation and/or whose parents will agree to participate (see Table 1 ).

Sampling Methods

The process of defining a representative subpopulation to study is called sampling. There are two main categories of sampling, probability and nonprobability.

Probability Sampling

The first potential problem in any system of selection is bias. Bias can occur easily as previously described in the Roosevelt and Landon election of 1936, and it also may be related to researcher preference. Patient volunteers can introduce bias since they tend to be healthier and produce results different from subjects chosen randomly. To avoid selection bias it is important to guarantee that each of the candidates for inclusion in the study has an equal opportunity for selection, That guarantee requires subjects to be selected at random, or that randomization be employed. Randomization is important for two reasons: First, it provides a sample that is not biased, and second, it meets the requirements for statistical validity (2). Several methods exist that can be used to randomly select subjects.

Simple random sampling can be accomplished using an array of random numbers (1) (see Table 2 ). In this table the numbers are grouped into series of five digits. This grouping method is for ease of presentation only; the same numbers could be grouped in twos or threes. For grouping by threes, the first column would contain 104,803,757,042, etc. How the random numbers are organized depends on the size of the population to be studied. Once the random numbers are organized into columns and rows, the researcher must decide where to start in the table and in what direction to proceed.

Suppose it has been decided that there are 900 patients (i.e., the accessible population) with spasticity from which to draw a sample for the AFO footplate study. From this accessible population it is desired to randomly select 90 subjects for the study. The first step is to assign a number from 1 to 900 to each member of the accessible population. Next. the starting number in the table must be selected. (An easy way to do this is to close your eyes and place the point of a pencil on the table.)

Assume that the number selected is 88974 (column 3, row 3), and it has been decided to use the last three digits to determine which subject is selected first. In this case, the last three digits are 974. However, the accessible population numbers range from 1 to 900, and 974 cannot be used. Arbitrarily proceeding downward, the next random number is 48237; therefore, patient 237 will become the first subject selected for the study. The next subjects would be numbers 306, 301,802, 308, etc., until the entire sample of 90 subjects is selected. Obviously, a larger random number table would be required to select 90 subjects.

It is also possible to use all digits in the random number table. Beginning again with the five digit number 88974 and progressing downward, the first subjects selected would be 889, 744, 823, 725, 306, 012, 802, etc., until again all 90 subjects are selected for the sample.

Systematic random sampling is a method frequently chosen for its simplicity because it is a periodic process (1,2,46). This method could be carried out by selecting the first subject randomly as described above and then selecting every second or third subject who comes to the office and meets the inclusion/exclusion criteria. This method, however, is problematic in that other staff who know of the method can manipulate patient appointments to assure inclusion. There is no advantage to this method over simple random sampling (5).

Stratified sampling is a method by which subjects are grouped according to strata such as age, gender or diagnosis (left hemi vs. right hemi), etc. (1,2,4-6). Using this method, subgroups of interest can be defined and equal numbers of subjects sampled for each group. For example, if there was interest in the functional outcomes for use of a certain type of AFO footplate in patients post brain injury, then it would be useful to define age as a subgrouping since age often relates to the functional challenge imposed on various orthoses. For example, a young child may engage in a lot of crawling, jumping, running, etc., when wearing an AFO whereas a senior citizen is more likely to walk cautiously. This permits comparison of the subgroups, such as children (5 to 12), teens (13 to 19), adults (20 to 55) and seniors (56 and up). Using this method, subjects would be recruited randomly for each subgroup, and, although each subgroup would have a different age range, the general inclusion/exclusion criteria would apply to each of the subgroups.

Cluster sampling is a method used to enable random sampling to occur while limiting the time and costs that would otherwise be required to sample from either a very large population or one that is geographically diverse (1,2,4,5). An example of how this might be used is as follows.

To obtain as many subjects as possible and to eliminate any potential bias inherent in selecting subjects from one specific clinic, the researcher may wish to select subjects from all of the hospitals and outpatient clinics within a given area. However, this would be too costly and time-consuming. Therefore, use of the cluster approach is appropriate. Using this method, a one- or two-level randomization process is used. First, each hospital and outpatient clinic that meets the inclusion criteria is identified. Second, one of the selection methods described above is used to randomly select a portion of those facilities. All of the available subjects from the randomly selected facilities could be included, or subjects from each of the randomly selected facilities could themselves be randomly selected. The important element in this process is that each of the facilities and each of the subjects have an equal opportunity to be chosen, with no researcher or facility bias.

Disproportional sampling is a method that facilitates the difficulty encountered with stratified samples of unequal size (2). Suppose, for example, it is desired to conduct a survey of the members of the American Academy of Orthotists and Prosthetists. Also suppose an educational grant has been secured that will support study of only 200 members (subjects) and that the available population in the Academy is 2,000 individuals, in the available population of 2,000 there are 1,700 males and 300 females. Since the 200 subjects needed for the study comprise only 10 percent of the available population, then how many of each gender are required? Simple proportioning suggests that 17/20 (85 percent) of the 200 be males and 3/20 (15 percent) be females. This would result in approximately 170 males and 30 females. The small number of females probably would not provide adequate representation for drawing conclusions about the entire membership.

One way of dealing with this situation is to use a simple random sample and leave the proportional representation to chance; however, unless the sample is unusually large, the differential effect of gender will probably not be controlled (6). A disproportional sampling design will permit random selection of Academy members of adequate size from each category. For example, 100 males and 100 females may be selected. This sample of 200 cannot be considered random because each female has a much greater chance (higher probability) of being chosen.

This approach creates an adequate sample size, but it presents problems for data analysis because the characteristics of one group (in this case, the females) will be overrepresented in the sample. Fortunately, this effect can be controlled by weighting the data so the males receive a proportionally larger mathematical representation in the analysis of scores than the females.

Calculating proportional weights involves determining the probability that any one male or female Academician will be selected. Selecting 100 male Academicians involves a probability of 100 out of 1,700, or 1 of 17 (1/17). The probability of any one female Academician being selected is 100 out of 300, or 1 of 3 (1/3). Therefore, each female has a probability of selection more than five times that of any male.

Next, the assigned weights are determined by taking the inverse of these probabilities. The weight for male scores is 17/1 17, and that for females is 3/1 = 3. This means that when the data are analyzed, each male's score will be multiplied by 17, and each female's score will be multiplied by 3. In any mathematical manipulation of the data, the total of the males' scores would be larger than the total of the females' scores. Therefore, the proportion of each group is differentiated in the total data set.

Because all Academy members in a group will have the same weight, the average scores for that group will not be affected; however, the relative contribution of these scores to overall data interpretation will be controlled.

Nonprobability Sampling

In the real world of clinical research true random sampling is very difficult to achieve. Time, cost and ethical considerations often prohibit researchers from making the necessary arrangements and securing the necessary clearances, for example, to obtain subjects from other facilities or professional practices to test a hypothesis. Therefore, it is often necessary to use other sampling techniques. These techniques produce nonprobability samples in that the sampling technique is not random (2,5).

With nonprobability sampling it is unlikely that the population selected will have the correct proportions because all members of the population do not have an equal chance of being selected. Therefore, it may not be assumed that the sample fully represents the target, and any statement generalizing the results beyond the actual sample tested must be stated with qualification.

Because the validity of statistical testing methods is based on random selection of subjects, it is important when using nonprobability sampling that random techniques be employed to the maximum extent possible. Five nonprobability sampling techniques have evolved: convenience sampling, consecutive sampling, judgmental sampling, quota sampling and snowball sampling.

Convenience sampling is probably the most commonly used technique in clinical research today (1,2,4,5). With convenience sampling, subjects are selected because of their convenient accessibility to the researcher. These subjects are chosen simply because they are the easiest to obtain for the study. This technique is easy, fast and usually the least expensive and troublesome. The famous sample description of "10 healthy young men" is assuredly either 10 male medical, prosthetic/orthotic or therapist students who have volunteered to be subjects for a study. The criticism of this technique is that bias is introduced into the sample. Volunteers always are suspect because they tend to be the healthiest, strongest, fastest, most skilled, etc. (7).They often volunteer because they like to show off or are competitive in nature and like to be tested. Volunteers may not be representative of the larger overall population.

Another common example of a convenience sample occurs when subjects are selected from the clinic, facility or educational institution at which the researcher is employed. Bias is likely to be introduced using this sampling technique because of the methods, styles and preferences of treatment employed at a given facility.

Consecutive sampling is a strict version of convenience sampling where every available subject is selected, i.e., the complete accessible population is studied. This is the best choice of the nonprobability sampling techniques since by studying everybody available, a good representation of the overall population is possible in a reasonable period of time (5).

Even though consecutive sampling does not allow randomization of the original subject pool to be studied, every effort should be made to randomize at all other levels. For example, assume it is desirable to test two different prosthetic feet. Once the study pool of subjects is defined, the assignment of prosthetic feet to subjects should be random. If all subjects will be tested with each of the feet, the order of testing should be randomized to remove as much bias as possible in the testing procedures.

If every subject is tested wearing foot A first and foot B second, foot B may prove to be the best foot, due to the learning effect. The learning effect gives an advantage to the subsequent items (prosthetic feet in this case) tested because the subjects become more familiar with the procedures and protocol and develop experimental skill. If foot B were truly superior and the testing was not random, its beneficial effect would be vulnerable to challenge because of the learning effect. The subjects become comfortable with the testing procedure with foot A and simply perform better the second time around with foot B. The results and generalizability would be flawed.

Judgmental sampling, also called purposive sampling, is another form of convenience sampling where subjects are handpicked from the accessible population (2). This technique leaves much to be desired

because of its inherent bias. Subjects usually are selected using judgmental sampling because the researcher believes that certain subjects are likely to benefit or be more compliant. For example, in the study of prosthetic feet athletic subject amputees might be selected for the more athletic foot because they are more likely than a sedentary or geriatric patient to benefit from that foot.

Quota sampling is a nonprobability technique used to ensure equal representation of subjects in each layer of a stratified sample grouping (2). For example, in the study of the orthotic impact on spasticity using different footplate contour designs, assume there are four different designs, and it is desired that randomization be applied as to which subject gets which footplate to test.

Using Table 3 , one method would be to assign subjects consecutively to footplate designs I to IV for the first four subjects (Round 1). The next round would assign subject 1 to footplate IV, subject 2 to footplate I, subject 3 to footplate II and subject 4 to footplate III, etc. In this manner there are equal numbers of subjects for each insert tested, and bias is managed as long as the subjects are assigned consecutively with no manipulation by anyone familiar or involved with the study. This allows control over the distribution of subjects across test situations and provides some protection from bias even though the original set of subjects was not randomly selected.

Snowball sampling is a technique used to identify potential subjects when appropriate candidates for study are hard to locate (2). For example, if locating an adequate number of amputees becomes difficult, an amputee belonging to a local support group could be recruited to assist in locating subjects willing to participate in a study. In other words, it is possible to have assistance from patients to help identify people with similar disabilities or conditions to assist in identifying subjects for study. This process is known as snowballing or chain referral (2).

Recruitment

Once the decision to use a certain sampling approach has been made, subjects must be recruited. The goal of recruitment is to obtain a sample large enough to enable valid statistical analysis and allow subjects to be selected in such a manner as to avoid bias (4). Errors or problems in either of these areas can be prevented with a research design that employs controls and a carefully planned sampling technique.

The chosen method of recruitment usually is based on the type of study; for example, survey data collected via questionnaire may be obtained by a direct person-to-person interview, telephone or mailed form. Experimental research, such as for the AFO footplate study, requires that subjects be able to commit time and transportation to come to the study site and repeat this effort more than once.

There often is an inverse relationship between the ease of recruitment effort and the success in obtaining data. In survey research, for example, direct personal effort in recruitment often is not employed; the recruitment method frequently is comprised of obtaining a mailing list and submitting questionnaires to the accessible population via the mail. A frequent drawback in this type of recruitment effort is a very low response rate of 50 to 60 percent (7). Another disadvantage is that the researcher loses all control over the actual data gathering. If the low return is anticipated and an adequate number of questionnaires is sent, then problems caused by inadequate data may be avoided; however, this does not help the loss of control.

Alternately, subjects are more difficult to recruit when more effort on their part is requested. For example, when multiple visits are required, such as in the AFO footplate example, recruiting subjects is a bigger chore for the researcher since subjects are asked to travel to the test site and do so on more than one occasion. However, because the researcher applies test conditions directly to the subject, not only is it probable that all necessary data will be obtained, but control over the experiment and the data acquisition is maintained.

Once the accessible population has been defined, every effort should be made to obtain subjects in the manner planned. If a systematic random sampling method has been chosen and a large proportion of the accessible subjects refuses to participate, then a bias error is introduced into the study. In the case of subject refusal, bias is introduced since the reason for their refusal is often universal. For example, several subjects may refuse because the study seems physically too difficult; when this occurs, the researcher is left with only subjects who do not think the effort requested of them is too difficult. This implies that the remaining subjects may be more fit or healthy than those who refused. This is a threat to external validity and affects the researcher's ability to generalize the results to the original target population (3).

Recruitment techniques may include personal contact, follow-up phone calls, incentives (such as paying subjects for their time or parking), etc. Some researchers even make home visits to potential subjects to explain the research and its importance; others mail advertising brochures to make participating seem exciting and important.

Language also may present a potential difficulty with recruitment. Therefore, a brochure in the appropriate foreign language or a staff or volunteer who can translate or interpret the foreign language may be required. Subjects may be recruited from the facility in which the researcher works or is familiar, or special efforts may have to be made to contact other similar facilities to engage their permission to approach their patients.

Sometimes advance work can be done to assist the recruitment process once the study is ready to begin. Community groups, such as local churches, YMCA, youth organizations, patient support groups and local business groups such as Kiwanis and Elks, may be contacted for support in identifying potential subjects. Depending on the community impact, these groups may even invite a researcher to address their membership to explain the importance of their project to gain acceptance and willingness to participate.

Summary

The goals of sampling are to decrease time and money costs, to increase the amount of data and detail that can be obtained, and to increase accuracy of data collection by preventing errors.

To accomplish these goals it is necessary to follow these steps:

Clearly define the target population to which the results will be generalized. For example, the AFO footplate study could be targeted to children in the growing years with flexible deformities or to adults with fixed deformities. Very specific inclusion criteria that outline the desired demographic and clinical characteristics of the desired target population are necessary.

An accessible population representative of the target must be defined by additional inclusion criteria with specific characteristics regarding the geographic, social and time frames required for this subpopulation. For example, having transportation available, being English-speaking and not being Christian Scientists could be inclusion criteria. Also, exclusion criteria are developed in this step to avoid any ethical problems and eliminate characteristics that may invalidate the results. For example, if an ethical problem may arise in denying treatment to one of the groups, an exclusion criterion might include excluding anyone already on a treatment protocol for the clinical problem under study.

The sampling process must be defined well ahead of subject selection whether it be a random (probability) or nonrandom (nonprobability) approach, and the researchers must adhere to a specific technique for recruitment appropriate for that approach. The recruitment effort must be vigorous enough to assure a large enough sample to enable statistical validity and must minimize probability of error and bias of selection.

   

Research paradigms or perspectives have developed their own cultures of inquiry

that describe different research processes used to observe, describe, and understand

phenomena. Action, participative, and participatory action research are relatively new

types of social research methods which coincide with the move from the Newtonian

world to an era when quantum theory has deeply challenged the Cartesian-based

philosophy in science. The rise of a post mechanistic view within the scientific

disciplines, one where the observer affects and is affected by the observed, has signified

the transition from the industrial age to the age of cybernetic theory and systems thinking.

These three types of research are a part of a continuum of action-oriented research

processes that combine inquiry with creating direct social change and is not limited to

just explanation of information or data (Boga, 2004). Each reflects a different level of

commitment and influence of those being studied on and in the research process. Each

also has a different purpose. The following briefly describes each research process and

explores the similarities and difference between them based on the goals of the research

model, the frameworks of the research including any assumptions that are made at the

base level, and the level of commitment, involvement and influence of participants.

Action Research

Action research (AR) is a paradigm of inquiry where the researcher�s primary

purpose is to improve the capacity and subsequent practices of the researcher rather than

to produce theoretical knowledge (Elliott, 1991). Improving practice means that the

quality of the outcome of the process and products together are enhanced. A defining

characteristic of AR is that the researcher initiates change based on a feeling that

something needs to change to create a better human situation. The researcher provides

direction toward realization and transformation of values through the process. Ends are

not defined as specific goals or objectives before hand.

The researcher may act as an individual or with a team of colleagues as the

facilitator of clients. The researcher improves skills and co-learns with the clients during

the process. The researcher leads the process of identifying the problem, drawing facts

and opinions from the clients, and leads the group to identify gaps in understanding.

There is a unified conception, but there is not a rigid division of specialized tasks or roles.

The researcher and the group identify actions to take and jointly analyze results, reflect

on these actions and results, and propose new courses of action. The researcher and the

clients act together to create or actualize satisfying results for change. The researcher

leads the group through identifying the course of actions for diffusion, but does not

necessarily engage in these actions. (Boga, 2004).

This continuing process of reflection on the part of the researcher and clients

develops the researcher�s capacity to discern the right course of action and to make

ethical judgments in future situations involving complex, human relationships. This

resulting practical wisdom is grounded in the researcher�s experience in real cases. A

wholistic appreciation of the situation to inform the narrative of the case at hand is

greater than any analytical or theoretical contributions.

Several disparate processes are unified such as the development of the individual

researcher, the design of the process, and the action-reflection cycle for both the

researcher as an individual and with the clients. Although this method is primarily

researcher led, collaborative reflection is imperative to encompass the experience and

perceptions of the clients to make modifications to other change efforts based on shared

feedback from collaborative members of the group (Elliott, 1991).

Participative Research

Participative research (PR) is a method where the primary goal is to create an

environment and process where context-bound knowledge emerges to develop �local

theory� that is understandable and actionable. PR is initiated by the organization of

interest. The researcher and participants collaborate actively in a loosely defined group

process to study and change their social reality. (Whyte, 1989)

All members of the organization can participate. Participants must have the will

and resources to participate and take on active roles and directly influence defining the

problem, choose the methods used to gather the data, analyze the data, prepare the

findings, and create action. (Boga, 2004) (Elden, 1981, 258). The wholistic process is

group led and self-organized, and adapts to changes as needed. Results are jointly

prepared, and reported to those affected. The group decides when the group is finished.

Participants treat each other as colleagues. Through the give and take of a dialogic

process, the researcher and participants learn together. The researcher�s role as one of

many �co-learners� in not as an expert, but as a �co-producer of learning.� The

researcher is dependent on where and how the data comes, has less control over the

research design process itself, and has to be flexible to the perspectives and definitions of

the participants. The researcher is not merely a bystander but needs to contribute toward

the creation and discovery of a process that can stand on its own. A participative

researcher needs to develop a context-sensitive framework, be flexible to changes in the

framework based on the local knowledge from participants in their own terms, and solve

problems. The result of this type of collaboration is very context-oriented to create new

shared understandings. (Reason & Rowan, 1981).

As Sohng (1995) comments, participatory research is a collaborative and

empowering process because it (a) brings isolated people together around common needs

and problems; (b) validates their experiences as the foundation for understanding and

critical reflection; (c) presents the knowledge and experiences of the researchers as

additional resources upon which to critically reflect; and (d) contextualises what might

have previously felt like personal, individual problems or weaknesses. The primary

strength of an action-oriented or participatory approach to research is therefore not about

description but about trying things out. It is a research approach that sees its function as

one of giving us different ways of relating to natural and social environments.

Researchers need to be aware of how members of a group perceive and speak about their

lives. This means they must endeavour to find out everything that can be found out about

the community being researched. Ideally, the researcher already lives in the community,

partakes in its affairs and has an ongoing relationship with the community.

Participatory Action Research

Participatory action research (PAR) combines both the goals of improved capacity

and practice of researchers, as in AR, and of achieving practical objectives and changing

social reality, as in PR, through group participation. Those affected by a problem

participate in planning, carrying out, analyzing and applying the results of the research.

The growth and development of the participants are also an important part of the desired

outcome. This method is initiated by the organization of interest and engages researchers

that share control of the social process design with participants in the organization.

The research approach is jointly designed through discussions between

professional researchers and active participation by some members of the organization.

PAR acknowledges that people affected by a problem are in the best position to

understand and suggest solutions. Local and experiential knowledge are valued.

Participants carry out the data collection and analyze the results. The researcher cannot

have tight control or an agenda in terms of research topic or design, but do need to be in a

situation where the problem is relevant and important to participants, and uses credible

methods.

Specifically when situations are complex with no clear line of inquiry to follow,

PAR can contribute to advancing theory and knowledge along with achieving practical

results. As a participant-centered approach, PAR is grounded in first-hand knowledge and

participation by the participants affected. This enables researchers to gain relevant

knowledge during the process which encourages creative surprises. This leads to new

understandings by integrating ideas across disciplines that are typically isolated from

each other to solve problems. These advances can contribute to major organizational

changes along with advancing theoretical understandings across multiple disciplines.

Similarities between Methods

The primary similarities in the three methods are active participation, open-ended

objectives, and high levels of commitment from the researcher and the participants to the

research problem and active learning.

The first similarity between these three methods of research is that

individuals/employees and not only researchers/leadership from an organization

collaboratively design and actively participate in the research process. In AR, although

the researchers are studying themselves in the context of a working with an organization,

it can also be a collaborative effort when the whole group or organization is being

supported by an action research process. PR requires the input and involvement of

employees, including leadership, in designing the process with researchers as a group

through implementing the results. PAR involves those most affected by a problem and

engages them in planning, carrying out, and applying the results of the research.

The second similarity in that each of these methods is that the end objectives are

not directly specified in the beginning and the process results in solving real problems in

organizations. AR is geared toward creating a more capable individual so that person is

equipped to deal with the complexity of today�s work issues. PR allows employees to

influence and create solutions to a business problem. PAR creates new knowledge

through the process of solving real business or organizational problems while also

improving the capacity of individuals in the organization.

Third, these research models are similar in the high level of commitment and

involvement required from the organization, the employees, and the researcher about the

importance of the problem and to the learning that results. The organization is central to

the success of the research because participants are empowered to change their reality in

all three methods. The researcher guides the process to varying degrees in each method,

but in all cases contributes to framing a process that is wholistic, flexible, and enhances

shared learning. Isolated people, groups, disciplines and disparate processes are unified

through dialogue. The result is context-oriented new understandings about individuals

and the organization as a whole.

Differences between Methods

The differences between the three types of research lie in the methods used to

reach the goal of problem solving but are also primarily in the specific goal of each type

of research. As Elden points out:

The cutting edge difference is the immediate goal of the research. Where the goal is to

develop change capacity so that workers can solve their own problems and keep solving

them (self-maintained learning.) the general knowledge research design seems to be of

limited utility. (1981, 259)

Action research focuses on the idea that improving the process improves the

organization. Elliot explains:

The fundamental aim of action research is to improve practice rather than to produce

knowledge. The production and utilization of knowledge is subordinate to, and

conditioned by, this fundamental aim. (Elliott, 1991)

AR requires the most personal commitment and involvement of these three research

methods. In effect, this method requires ongoing practice and growth and is therefore a

long-term commitment.

Participative research utilizes the tacit knowledge and experience of employees and

leadership in the process, requires group level commitment as well as researcher

commitment for the term of the project while the team addresses and solves a relevant

problem. In participative research, the long-term skills of the participants to �solve their

own problems and keep solving them� (Elden, 1981, 259) is an outcome that extends

beyond the research project itself. The focus in participative research is on the inclusion

of the participants and their organizations within the process and the practical outcome,

rather removing the process from its context. The researcher is not a facilitator of the

process as in action research, but a �co-producer of learning.� As Elden makes clear:

Research is participatory when those directly affected by it influence each of these four

[problem definition, methods choice, data analysis & use of findings] decisions and help

carry them out. (1981, 258)

In contrast, PAR requires both researchers in their own group, organizational

members in their own group and both groups collaboratively to commit to the research

process for both a scientific goal of furthering the research method and a tangible

problem solving goal such as whether or not to close a manufacturing plant. PAR has

implications for the participants as participant within their larger environment. The

participants and researchers are processing significant theoretical issues together.

We can rekindle the intellectual excitement in our field if we are willing to leave the

mainstream to involve ourselves with practitioners and struggle with them to solve

important practical problems – which also have important theoretical implications

(Whyte, 1989)

PAR relies on reflective practice of the researchers in action and unlike action research

does not wait to apply new understandings to the next situation, but incorporates them

into the ongoing process. This reflective practice transforms views of structural problems

and their values about the systems under study in the process and leads to more creative

�surprises� and solutions. The result of participatory action research is the opportunity

for researchers and participants to link enhanced capacity and wisdom from action

research with the �local theory� from group participants in participative research to be

agents of major social changes at the organizational level.

Conclusion

In comparing basic, applied and participative research, Elden makes the point that

his examination is not to exclude any specific paradigm, but to highlight the relative

utility of each for specific purposes. Elden states,

No one of these types, of course, is intrinsically right or wrong. The question is useful for

what? Regardless of what one is aiming at, researcher role must be consistent with the

research goal. (1981, 261)

The three types of research discussed are a part of a continuum of naturalistic,

post-positivist, systemic research methodology. All three have frameworks for the

research method used but allow for modification as new observations and conclusions are

made. Knowledge regarding a particular problem is best determined by groups of people

affected. By arriving at a consensus and using qualitative methods of research rather than

drawing conclusions purely through observation, measurement and quantitative analysis

as is done in rationalistic research greater creativity and problem solving can emerge.

Appendix I – Types of Research

 Action Participative Participatory ActionPost-Positivist Post-positivist Post-positivistResearcher achieves learning, and larger group may also learn

Researcher and select participants learn about larger group

Participants (and researcher) achieve learning within larger group 

The researcher facilitates the process, and collaborates with clients to create or actualize change. Researcher typically does not engage in change actions.

Participants make essential decisions in research project by which they are affected

Actions taken through process – action is incorporated into research itself

Researcher collaborates with �clients�

Researcher works with �participants�

Researcher works with �participants�

Researcher and clients engage in self-reflection

Researcher works with select participants / No Expert

Participant issues, actions and learning highlighted / No Expert

3rd party researcher engages in change as expert

Group works to change self with researcher not as expert

3rd party group works to change self and larger groups

Subjective Subjective WholisticEmergent property: improved capacity and wisdom

Emergent property: self-knowledge

Emergent property: creativity