Category Archives: Methodology

Sampling

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A colleague recently asked, “How many people need to be sampled (interviewed) to make the study valid?”

Interesting question.  Takes me to the sampling books.  And the interview books.

To answer that question, I need to know if you are gathering qualitative or quantitative data; if you are conducting two measurements on one group (like a pretest/posttest or a post-then-pre); if you are conducting one measurement on two groups; or any number of other conditions that affect sampling (like site, population size, phenomenon studied, etc).

So here is the easy answer.

If you are conducting two observations on one group, you will need a minimum of 30 participants with complete responses.

If you are conducting one observation on two groups, you will need at least 30 participants in each group with complete responses.

If you are conducting focus groups, you will need 10 -12 participants in each group and you will need to conduct groups until you reach saturation, that is, the responses are being repeated and you are not getting any new information (some folks say reaching saturation takes 3 – 4 groups).

If you are conducting exploratory qualitative research, you will need…it all depends on your research question.

If you are conducting confirmatory qualitative research, you will need…it all depends on your research question.

If you are conducting individual interviews, you will need…and here my easy answer fails me…so let me tell you some other information that may be helpful.

Dillman has a little chart on page 57 (Figure 3.1) that lists the sample size you will need for various populations sizes and three sizes of margin of error.  For example, if your population is 100 (a good size) and you want a margin of error of 5% (that means that the results will be accurate within + or – 5%, 95% of the time), you will need 49 participants with complete data sets if you think that for a yes/no question, participants will be split 50 yes/50 no (the most conservative assumption that can be made) You will need only 38 participants with complete data sets if you think that the responses will be unevenly split (the usual case).

This chart assumes random selection.  It takes into consideration variation in sample (the greater the variation, the larger the sample size needed).  It assumes maximum heterogeneity on a proportion of the population from which the sample is drawn.

Marshall and Rossman say very clearly, “One cannot study the universe…”  So you need to make selections of sites, samples of times, places, people and things to study.  The how many depends…sometimes it is one person; sometimes it is one organization;  sometimes it is more.  They say one to four respondents for case studies and mixed-methods studies; 10 groups was the average number of focus groups; 16 – 24 months was the average for observational fieldwork;  and one set of interviews they cite involved 92 participants.  It all depends on the research purpose–an unknown culture could be studied with a single in depth case study;  a study of mother’s receptivity of breast-feeding could have a huge sample or a small sample could provide thick description while a large sample would enhance transferability.  Credibility and trustworthiness of the findings must be considered.

The best answer, then, is…it all depends.

Process Evaluation

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I was putting together a reading list for an evaluation capacity building program I’ll be leading come September and was reminded about process evaluation.  Nancy Ellen Kiernan has a one page handout on the topic.  It is a good place to start.  Like everything in evaluation, there is so much more to say.  Let’s see what I can say in 440 words or less.

When I first started doing evaluation (back when we beat on hollow logs), I developed a simple approach (call it a model) so I could talk to stakeholders about what I did and what they wanted done.  I called it the P3 model–Process, Progress, Product.  This is a simple approach that answers the following evaluative questions:

  • How did I do what I did? (Process)
  • Did I do what I did in a timely manner? (Progress)
  • Did I get the outcome I wanted (Product)

It is the “how” question I’m going to talk about today.

Scriven, in the 4th ed of the Evaluation Thesaurus, says that a process evaluation “focuses entirely on the variables between input and output”.  It may include input variables.  Knowing this helps you know what the evaluative question is for the input and output parts of a logic model (remember there are evaluative questions/activities for each part of a logic model).

When considering evaluating a program, process evaluation is not sufficient; it may be necessary and still not be sufficient.  An outcome evaluation must accompany a process evaluation.  Evaluating process components of a program involves looking at internal and external communications (think memos, emails, letters, reports, etc.); interface with stakeholders (think meeting minutes); the formative evaluation system of a program (think participant satisfaction); and infrastructure effectiveness (think administrative patterns, implementation steps, corporate responsiveness; instructor availability, etc.).

Scriven provides these examples that suggest the need for program improvement: “…program’s receptionists are rude to most of a random selection of callers; the telephonists are incompetent; the senior staff is unhelpful to evaluators called in by the program to improve it; workers are ignorant about the reasons for procedures that are intrusive to their work patterns;  or the quality control system lacks the power to call a halt to the process when it discerns an emergency.”  Other examples which demonstrate program success are administrators are transparent about organizational structure; program implementation is inclusive; or participants are encouraged to provide ongoing feedback to program managers.  We could then say that a process evaluation assesses the development and actual implementation of a program to determine whether the program was  implemented as planned and whether expected output was actually produced.

Gathering data regarding the program as actually implemented assists program planners in identifying what worked and what did not. Some of the components included in a process evaluation are descriptions of program environment, program design, and program implementation plan.  Data on any changes to the program or program operations and on any intervening events that may have affected the program should also be included.

Quite likely, these data will be qualitative in nature and will need to be coded using one of the many qualitative data analysis methods.

TIMELY TOPIC: PRE-POST or POST THEN PRE?

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Hi everybody–it is time for another TIMELY TOPIC.  This week’s topic is about using pretest/posttest evaluation or a post-then-pre evaluation.

There are many considerations for using these designs.  You have to look at the end result and decide what is most appropriate for your program.  Some of the key considerations include:

  • the length of your program;
  • the information you want to measure;
  • the factors influencing participants response; and
  • available resources.

Before explaining the above four factors, let me urge you to read on this topic.  There are a couple of resources (yes, print…) I want to pass your way.

  1. Campbell, D. T. & Stanley, J. C. (1963).  Experimental and quasi-experimental designs for research.  Houghton Mifflin Company:  Boston, MA.  (The classic book on research and evaluation designs.)
  2. Rockwell, S. K., & Kohn, H. (1989). Post-then-pre evaluation. Journal of Extension [On-line]. 27(2). Available at: http://www.joe.org/joe/1989summer/a5.htm (A seminal JoE paper explaining post-then-pre test.)
  3. Nimon, K. Zigarami, D. & Allen, J. (2011).  Measures of program effectiveness based on retrospective pretest data:  Are all created equal? American Journal of Evaluation, 32, 8 – 28.  (A 2011 publication with an extensive bibliography.)

Let’s talk about considerations.

Length of program.

For pre/post test, you want a program that is long.  More than a day.  Otherwise you risk introducing a desired response bias and the threats to internal validity that  Campbell and Stanley identify.  Specifically the threats called history, maturation, testing, and instrumentation,  also a possible regression to the mean threat, though that is on a possible source of concern.  These threats to internal validity assume no randomization and a one group design, typical for Extension programs and other educational programs.  Post-then-pre works well for short programs, a day or less, and  tend to control for response shift and desired response bias.  There may still be threats to internal validity.

Information you want to measure.

If you want to know a participants specific knowledge, a post-then-pre cannot provide you with that information because you can not test something you cannot unknow.  The traditional pre/post can focus on specific knowledge, e.g., what food is the highest in Vitamin C in a list that includes apricot, tomato, strawberry cantaloupe. (Answer:  strawberry)  If you are wanting agreement/disagreement with general knowledge (e.g., I know what the key components of strategic planning are), the post-pre works well.  Confidence, behaviors, skills, and attitudes can all be easily measured with a post-then-pre.

Factors influencing participants response.

I mentioned threats to internal validity above.  These factors all influence participants responses.  If there is a long time between the pretest and the post test, participants can be affected by history (a tornado prevents attendance to the program); maturation (especially true with programs with children–they grow up); testing (having taken the pretest, the post test scores will be better);  and instrumentation (the person administering the posttest administers it differently than the pretest was administered).  Participants desire to please the program leader/evaluator, called desired response bias, also affects participants response.

Available resources.

Extension programs (as well as many other educational programs) are affected by the availability of resources (time, money, personnel, venue, etc.).  If you only have a certain amount of time, or a certain number of people who can administer the evaluation, or a set amount of money, you will need to consider which approach to evaluation you will use.

The idea is to get usable, meaningful data that accurately reflects the work that went into the program.

Conference evaluation

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We recently held Professional Development Days for the Division of Outreach and Engagement.  This is an annual opportunity for faculty and staff in the Division to build capacity in a variety of topics.  The question this training posed was evaluative:

How do we provide meaningful feedback?

Evaluating a conference or a multi-day, multi-session training is no easy task.  Gathering meaningful data is a challenge.  What can you do?  Before you hold the conference (I’m using the word conference to mean any multi-day, multi-session training), decide on the following:

  • Are you going to evaluate the conference?
  • What is the focus of the evaluation?
  • How are you going to use the results?

The answer to the first question is easy:  YES.  If the conference is an annual event (or a regular event), you will want to have participants’ feedback of their experience, so, yes, you will evaluate the conference. Look at a Penn State Tip Sheet 16 for some suggestions.  (If this is a one time event, you may not; though as an evaluator, I wouldn’t recommend ignoring evaluation.)

The second question is more critical.  I’ve mentioned in previous blogs the need to prioritize your evaluation.  Evaluating a conference can be all consuming and result in useless data UNLESS the evaluation is FOCUSED.  Sit down with the planners and ask them what they expect to happen as a result of the conference.  Ask them if there is one particular aspect of the conference that is new this year.  Ask them if feedback in previous years has given them any ideas about what is important to evaluate this year.

This year, the planners wanted to provide specific feedback to the instructors.  The instructors had asked for feedback in previous years.  This is problematic if planning evaluative activities for individual sessions is not done before the conference.  Nancy Ellen Kiernan, a colleague at Penn State, suggests a qualitative approach called a Listening Post.  This approach will elicit feedback from participants at the time of the conference.  This method involves volunteers who attended the sessions and may take more persons than a survey.  To use the Listening Post, you must plan ahead of time to gather these data.  Otherwise, you will need to do a survey after the conference is over and this raises other problems.

The third question is also very important.  If the results are just given to the supervisor, the likelihood of them being used by individuals for session improvement or by organizers for overall change is slim.  Making the data usable for instructors means summarizing the data in a meaningful way, often visually.  There are several way to visually present survey data including graphs, tables, or charts.  More on that another time.  Words often get lost, especially if words dominate the report.

There is a lot of information in the training and development literature that might also be helpful.  Kirkpatrick has done a lot of work in this area.  I’ve mentioned their work in previous blogs.

There is no one best way to gather feedback from conference participants.  My advice:  KISS–keep it simple and straightforward.

Needs Assessment

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I’ve talked about how each phase of a logic model has evaluative activities.  I’ve probably even alluded to the fact that needs assessment is the evaluative activity for that phase called situation (see the turquoise area on the left end of the image below.)

What I haven’t done is talk about is the why, what,  and how of needs assessment (NA).  I also haven’t talked about the utilization of the findings of a needs assessment–what makes meaning of the needs assessment.

OK.  So why is a NA conducted?  And what is a NA?

Jim Altschuld is my go-to person when it comes to questions about needs assessment.  He recently edited a series of books on the topic.

Although Jim is my go-to person, Belle Ruth Witkin (a colleague, friend, and collaborator of Jim Altschuld) says in the preface to the co-authored volume (Witkin and Altschuld, 1995–see below),  that the most effective way to decide the best way to divide the (often scarce) resources among the demands (read programs) is to conduct a needs assessment when the planning for the use of those resources begins.

Book 1 of the kit discusses an overview.  In that volume, Jim defines what a needs assessment is: “Needs assessment is the process of identifying needs, prioritizing them, making needs-based decisions, allocating resources, and implementing actions in organizations to resolve problems underlying important needs (pg.20).”  Altschuld states that there are many models for assessing needs and provides citations for those models.  I think the most important aspect of this first volume is the presentation of the phased model developed by Belle Ruth Witkin in 1984 and revised by Altschuld and Witkin in their 1995 and 2000 volumes.Those phases are preassessment, assessment, and postassessment.  They divide those three phases into three levels, primary, secondary, and tertiary, each level targeting a different group of stakeholders.  This volume also discusses the why and the how.  Subsequent volumes go into more detail–volume 2 discusses phase 1 (getting started); volume 3 discusses phase II (collecting data); volume 4 discusses analysis and priorities; and volume 5 discusses phase III (taking action).

Laurie Stevahn and Jean A. King are the authors of this volume. In chapter 3, they discuss strategies for the action plan using facilitation procedures that promote positive relationships, develop shared understanding, prioritize decisions, and assess progress.  They warn of interpersonal conflict and caution against roadblocks that impede change efforts.  They also promote the development of evaluation activities at the onset of the NA because that helps ensure the use of the findings.

Needs assessment is a political experience.  Some one (or ones) will feel disenfranchised, loose resources, have programs ended.  These activities create hard feelings and resentments.  These considerations need to be identified and discussed at the beginning of the process.  It is like the elephant and the blind people–everyone has an image of what the creature is, there may or may not be consensus, yet for the NA to be successful, consensus is important.  Without it, the data will sit on someone’s shelf or in someone’s computer.  Not useful.

Did you know…

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…that there is a difference between a Likert item and a Likert scale?**

Did you know that a Likert item was developed by Rensis Likert, a psychometrician and an educator? 

And that the item was developed to have the individual respond to the level of agreement or disagreement with a specific phenomenon?

And did you know that most of the studies on Likert items use a five- or seven-points on the item? (Although sometimes a four- or six-point scale is used and that is called a forced-choice approach–because you really want an opinion, not a middle ground, also called a neutral ground.)

And that the choices in an odd-number choice usually include some variation on the following theme, “Strongly disagree”, “Disagree”, “Neither agree or disagree”, “Agree”, “Strongly Agree”?

And if you did, why do you still write scales, and call them Likert, asking for information using a scale that goes from “Not at all” to “A little extent” to “Some extent” to “Great extent?  Responses that are not even remotely equidistant (that is, have equal intervals with respect to the response options) from each other–a key property of a Likert item.

And why aren’t you using a visual analog scale to get at the degree of whatever the phenomenon is being measured instead of an item for which the points on the scale are NOT equidistant? (For more information on a visual analog scale see a brief description here or Dillman’s book.)

I sure hope Rensis Likert isn’t rolling over in his grave (he died in 1981 at the age of 78).

Extension professionals use survey as the primary method for data gathering.  The choice of survey is a defensible one.  However, the format of the survey, the question content, and the question construction must also be defensible.  Even though psychometric properties (including internal consistency, validity, and other statistics) may have been computed, if the basic underlying assumptions are violated, no psychometric properties will compensate for a poorly designed instrument, an instrument that is not defensible.

All Extension professionals who choose to use survey to evaluate their target audiences need to have scale development as a personal competency.  So take it upon yourself to learn about guidelines for scale development (yes, there are books written on the subject!).

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**Likert scale is the SUM of of responses on several Likert items.  A Likert item is just one 4 -, 5-, 6, or 7-point single statement asking for an opinion.

Reference:  Devellis, R. F. (1991).  Scale development:  Theory and applications. Newbury Park: Sage Publications. Note:  there is a newer edition.

Dillman, D. A, Smyth, J. D., & Christian, L. M. (2009).  Internet, mail, and mixed-mode surveys:  The tailored design method. (3rd ed.). Hoboken, NJ: John Wiley& Sons, Inc.

More resources

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Last week, I spoke about how to questions  and applying them  to program planning, evaluation design, evaluation implementation, data gathering, data analysis, report writing, and dissemination.  I only covered the first four of those topics.  This week, I’ll give you my favorite resources for data analysis.

This list is more difficult to assemble.  This is typically where the knowledge links break down and interest is lost.  The thinking goes something like this.  I’ve conducted my program, I’ve implemented the evaluation, now what do I do?  I know my program is a good program so why do I need to do anything else?

YOU  need to understand your findings.  YOU need to be able to look at the data and be able to rigorously defend your program to stakeholders.  Stakeholders need to get the story of your success in short clear messages.  And YOU need to be able to use the findings in ways that will benefit your program in the long run.

Remember the list from last week?  The RESOURCES for EVALUATION list?  The one that says:

1.  Contact your evaluation specialist.

2.  Listen to stakeholders–that means including them in the planning.

3.  Read

Good.  This list still applies, especially the read part.  Here are the readings for data analysis.

First, it is important to know that there are two kinds of data–qualitative (words) and quantitative (numbers).  (As an aside, many folks think words that describe are quantitative data–they are still words even if you give them numbers for coding purposes, so treat them like words, not numbers).

  • Qualitative data analysis. When I needed to learn about what to do with qualitative data, I was given Miles and Huberman’s book.  (Sadly, both authors are deceased so there will not be a forthcoming revision of their 2nd edition, although the book is still available.)

Citation: Miles, M. B., & Huberman, A. Michael. (1994). Qualitative data analysis: An expanded source book. Thousand Oaks, CA: Sage Publications.

Fortunately, there are newer options, which may be as good.  I will confess, I haven’t read them cover to cover at this point (although they are on my to-be-read pile).

Citation:  Saldana, J.  (2009). The coding manual for qualitative researchers. Los Angeles, CA: Sage.

Bernard, H. R. & Ryan, G. W. (2010).  Analyzing qualitative data. Los Angeles, CA: Sage.

If you don’t feel like tackling one of these resources, Ellen Taylor-Powell has written a short piece  (12 pages in PDF format) on qualitative data analysis.

There are software programs for qualitative data analysis that may be helpful (Ethnograph, Nud*ist, others).  Most people I know prefer to code manually; even if you use a soft ware program, you will need to do a lot of coding manually first.

  • Quantitative data analysis. Quantitative data analysis is just as complicated as qualitative data analysis.  There are numerous statistical books which explain what analyses need to be conducted.  My current favorite is a book by Neil Salkind.

Citation: Salkind, N. J. (2004).  Statistics for people who (think they) hate statistics. (2nd ed. ). Thousand Oaks, CA: Sage Publications.

NOTE:  there is a 4th ed.  with a 2011 copyright available. He also has a version of this text that features Excel 2007.  I like Chapter 20 (The Ten Commandments of Data Collection) a lot.  He doesn’t talk about the methodology, he talks about logistics.  Considering the logistics of data collection is really important.

Also, you need to become familiar with a quantitative data analysis software program–like SPSS, SAS, or even Excel.  One copy goes a long way–you can share the cost and share the program–as long as only one person is using it at a time.  Excel is a program that comes with Microsoft Office.  Each of these has tutorials to help you.

How do you find the answer?

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A part of my position is to build evaluation capacity.  This has many facets–individual, team, institutional.

One way I’ve always seen as building capacity is knowing where to find the answer to the how to questions.  Those how to questions apply to program planning, evaluation design, evaluation implementation, data gathering, data analysis, report writing, and dissemination.  Today I want to give you resources to build your tool box.  These resources build capacity only if you use them.

RESOURCES for EVALUATION

1.  Contact your evaluation specialist.

2.  Listen to stakeholders–that means including them in the planning.

3.  Read.

If you don’t know what to read to give you information about a particular part of your evaluation, see resource Number 1 above.  For those of you who do not have the luxury of an evaluation specialist, I’m providing some reading resources below (some of which I’ve mentioned in previous blogs).

1.  For program planning (aka program development):  Ellen Taylor-Powell’s web site at the University of Wisconsin Extension.  Her web site is rich with information about program planning, program development, and logic models.

2.  For evaluation design and implementation:  Jody Fitzpatrick”s book.

Citation:  Fitzpatrick, J. L., Sanders, J. R., & Worthen, B. R. (2004). Program evaluation: Alternative approaches and practical guidelines.  (3rd ed.).  Boston: Pearson Education, Inc.

3.  For evaluation methods, that depends on the method you want to use for data gathering; it doesn’t cover the discussion of evaluation design, though.

  • For needs assessment, the books by Altschuld and Witkin (there are two).

(Yes, needs assessment is an evaluation activity).

Citation:  Witkin, B. R. & Altschuld, J. W. (1995).  Planning and conducting needs assessments: A practical guide. Thousand Oaks, CA:  Sage Publications.

Citation:  Altschuld, J. W. & Witkin B. R. (2000).  From needs assessment to action: Transforming needs into solution strategies. Thousand Oaks, CA:  Sage Publications, Inc.

  • For survey design:     Don Dillman’s book.

Citation:  Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009).  Internet, mail, and mixed-mode surveys:  The tailored design method.  (3rd. ed.).  Hoboken, New Jersey: John Wiley & Son, Inc.

  • For focus groups:  Dick Krueger’s book.

Citation:  Krueger, R. A. & Casey, M. A. (2000).  Focus groups:  A practical guide for applied research. (3rd. ed.).  Thousand Oaks, CA: Sage Publications, Inc.

  • For case study:  Robert Yin’s classic OR

Bob Brinkerhoff’s book. 

Citation:  Yin, R. K. (2009). Case study research: Design and methods. (4th ed.). Thousand Oaks, CA: Sage, Inc.

Citation:  Brinkerhoff, R. O. (2003).  the success case method:  Find out quickly what’s working and what’s not. San Francisco:  Berrett-Koehler Publishers, Inc.

  • For multiple case studies:  Bob Stake’s book.

Citation:  Stake, R. E. (2006).  Multiple case study analysis. New York: The Guilford Press.

Since this post is about capacity building, a resource for evaluation capacity building:

Hallie Preskill and Darlene Russ-Eft’s book .

Citation:  Preskill, H. & Russ-Eft, D. (2005).  Building Evaluation Capacity: 72 Activities for teaching and training. Thousand Oaks, CA: Sage Publications.

I’ll cover reading resources for data analysis, report writing, and dissemination another time.

Demographics

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Although I have been learning about and doing evaluation for a long time, this week I’ve been searching for a topic to talk about.  A student recently asked me about the politics of evaluation–there is a lot that can be said on that topic, which I will save for another day.  Another student asked me about when to do an impact study and how to bound that study.  Certainly a good topic, too, though one that can wait for another post.  Something I read in another blog got me thinking about today’s post.  So, today I want to talk about gathering demographics.

Last week, I mentioned in my TIMELY TOPIC post about the AEA Guiding Principles. Those Principles along with the Program Evaluation Standards make significant contributions in assisting evaluators in making ethical decisions.  Evaluators make ethical decisions with every evaluation.  They are guided by these professional standards of conduct.  There are five Guiding Principles and five Evaluation Standards.  And although these are not proscriptive, they go along way to ensuring ethical evaluations.  That is a long introduction into gathering demographics.

The guiding principle, Integrity/Honesty states that “Evaluators display honesty and integrity in their own behavior, and attempt to ensure the honesty and integrity of the entire evaluation process.”  When we look at the entire evaluation process, as evaluators, we must strive constantly to maintain both personal and professional integrity in our decision making.  One decision we must make involves deciding what we need/want to know about our respondents.  As I’ve mentioned before, knowing what your sample looks like is important to reviewers, readers, and other stakeholders.  Yet, if we gather these data in a manner that is intrusive, are we being ethical?

Joe Heimlich, in a recent AEA365 post, says that asking demographic questions “…all carry with them ethical questions about use, need, confidentiality…”  He goes on to say that there are “…two major conditions shaping the decision to include – or to omit intentionally – questions on sexual or gender identity…”:

  1. When such data would further our understanding of the effect or the impact of a program, treatment, or event.
  2. When asking for such data would benefit the individual and/or their engagement in the evaluation process.

The first point relates to gender role issues–for example are gay men more like or more different from other gender categories?  And what gender categories did you include in your survey?  The second point relates to allowing an individual’s voice to be heard clearly and completely and have categories on our forms reflect their full participation in the evaluation.  For example, does marital status ask for domestic partnerships as well as traditional categories and are all those traditional categories necessary to hear your participants?

The next time you develop a questionnaire that includes demographic questions, take a second look at the wording–in an ethical manner.

Participant and program characteristics

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Sure, you want to know the outcomes resulting from your program.  Sure, you want to know if your program is effective.  Perhaps, you will even attempt to answer the question, “So What?” when you program is effective on some previously identified outcome.  All that is important.

My topic today is something that is often over looked when developing an evaluation–the participant and program characteristics.

Do you know what your participants look like?

Do you know what your program looks like?

Knowing these characteristics may seem unimportant at the outset of the implementation.  As you get to the end, questions will arise–How many females?  How many Asians?  How many over 60?

Demographers typically ask demographic questions as part of the data collection.

Those questions often include the following categories:

  • Gender
  • Age
  • Race/ethnicity
  • Marital status
  • Household income
  • Educational level

Some of those may not be relevant to your program and you may want to include other general characteristic questions instead.  For example, in a long term evaluation of a forestry program where the target audience was individuals with wood lots, asking how many acres were owned was important and marital status did not seem relevant.

Sometimes asking some questions may seem intrusive–for example, household income or age.  In all demographic cases, giving the participant an option to not respond is appropriate.  When these data are reported, report the number of participants who chose not to respond.

When characterizing your program, it is sometimes important to know characteristics of the geographic area where the program is being implemented–rural, suburban, urban, ?  This is especially true when the program is a multisite program.   Local introduces an unanticipated variable that is often not recognized or remembered.

Any variation in the implementation–number of contact hours, for example, or the number of training modules.  The type of intervention is important as well–was the program delivered as a group intervention or individually. The time of the year that the program is implemented may also be important to document.  The time of the year may inadvertently introduce a history bias into the study–what is happening in September is different than what is happening in December.

Documenting these characteristics  and then defining them when reporting the findings helps to understand the circumstances surrounding the program implementation.  If the target audience is large, documenting these characteristics can provide comparison groups–did males do something differently than females?  Did participants over 50 do something different than participants 49 or under?

Keep in mind when collecting participant and program characteristic data, that these data help you and the audience to whom you disseminate the findings understand your outcomes and the effect of your program.