October 2003
Volume 41 Number 5

Tools of the Trade

Transformative Explanations: Writing to Overcome Counterintuitive Ideas

Joye C. Gordon
Assistant Professor
A.Q. Miller School of Journalism and Mass Communication
Kansas State University
Manhattan, Kansas
Internet Address: gordon@ksu.edu

Introduction

Perhaps the most daunting challenge faced by Extension communicators occurs when the message opposes pre-existing, intuitive understandings of the reader. How do writers explain that one does not see objects, only the light reflected from them? How does one explain that vehicle passengers would be incapable of retaining a held child during a collision? The world is replete with phenomena for which scientific interpretations contradict common lay theories and intuitions.

Several mechanisms can promote clarity of Extension messages. Readability formulas, for example, can help create passages using short words and simple sentences. Functional design and layout can make passages more readable and structurally organized. But these mechanisms, alone, are inadequate when readers are psychologically predisposed to reject counterintuitive messages and to retain pre-existing lay theories.

Lay Theories as Barriers to Effective Messages

Lay theories are functional and appear reasonable, and individuals are naturally motivated to retain existing beliefs. Whether I think I see objects or perceive the light reflected from them doesn't matter when I stub my toe in the dark. The intuition that I see objects themselves serves my everyday needs and has been confirmed by experience.

The psychological predisposition is to protect belief structures. Self-perceptions are confirmed when beliefs are affirmed. Also, people of similar cultural, social, and educational backgrounds often share lay theories. I want to believe that I understand my environment and see the world consistently with my peers. I am motivated to retain the notion that I see the objects themselves.

Lay theories are relatively stable, perhaps even obdurate, cognitive structures held by ordinary people that are used to generate explanations, descriptions, and/or predictions regarding a phenomenon. Lay theories may or may not conform to orthodox scientific interpretations and may relate to physical or social phenomena. Science educators and health communicators have approached the subject out of concerns about scientific illiteracy and public health. They find that lay theories represent significant barriers to gaining perceptual compliance.

Extension personnel should address lay theories because such theories often lead to rejection of messages, preventing the perceptual compliance that is often the first step to achieving communication objectives. People who are confident that they would be strong enough to hold a child in their lap in a car accident, for example, are much less likely to use child-safety seats. However, when people realize that the momentum of a 20-pound child at 30 miles per hour exceeds any human's strength, they are more likely to adopt child-safety seats. Perceptual compliance is a key component in promoting scientific understanding, achieving behavioral change, and making Extension messages more effective

Writing to Overcome Lay Theories: A Five-Part Technique

Katherine Rowan has developed a contemporary theory of explanatory writing. She labels discourse designed to supplant erroneous lay theories as "transformative explanations" (1988, p. 37). They are transformative because such messages, if successful, must transform an inadequate, counterproductive lay theory to a more explicit, adequate one.

Effective transformative explanations contain five key elements according to Rowan (1991, p. 376). They should:

1. State the counterproductive lay theory
2. Acknowledge the counterproductive lay theory's apparent plausibility
3. Demonstrate the counterproductive lay theory's inadequacy
4. Convey orthodox scientific understanding
5. Establish greater adequacy of the advocated theory

The transformative explanation must state the existing lay theory that is obstructing understanding. Lay theories are often implicit, and one may not recognize that they hold a particular understanding of some phenomenon. To overcome a counterproductive lay theory, the reader must first acknowledge that it exists.

Good transformative explanations must acknowledge the counterproductive lay theory's apparent plausibility. Acknowledging readers' logical motivations for holding the lay theory is a critical step. Simply stating that their intuitions are ridiculous places readers in a defensive position, jeopardizing the transformative objective of the message.

Perhaps the most critical element is that transformative explanations must demonstrate the inadequacy of the counterproductive lay theory. Simply stating inadequacy is unlikely to make the reader dissatisfied and may, again, promote defensiveness. Readers must become dissatisfied with naïve theories before abandoning them. This may be accomplished by helping the reader recall everyday experiences that their lay theory cannot explain.

Good transformative explanations must convey the theories being advocated. Finally, good transformative explanations must demonstrate the superiority of orthodox scientific theories over pre-existing intuitions. Effective transformative explanations demonstrate how their advocated theories account for everyday phenomena explained by counterproductive theories; they can also explain phenomena that preexisting theories cannot.

An Example of a Transformative Explanation

The following passage effectively incorporates the five key elements of a transformative explanation.

Many people believe that plants grow because they consume water and soil nutrients. We know that plants provided with the proper soil and adequate water will thrive and grow better than those in weak soil or with inadequate water supplies.

However, such as understanding of plant growth can't explain why plants provided with rich soil and proper water supplies will die if denied sunlight. Scientists say that plants don't simply convert water and soil to grow. Plants grow or make more plant tissue by creating their own tissue through a process of photosynthesis. Photosynthesis is the production of organic substances, especially sugars, from carbon dioxide and water by the action of light on the chlorophyll in green plant cells.

When we understand that plants create tissue and grow, not from eating soil and water, but from using light energy to synthesize tissue, we understand why plants will die without sunlight. Plants provided with adequate water and rich soil thrive because water and soil help plants create tissue through the photosynthesis process.

Summary

Overcoming erroneous lay theories represents a significant challenge for those developing Extension messages. Readers are psychologically motivated to retain their everyday understandings. Explanations that help readers overcome inadequate naïve theories are called "transformative explanations" because, if the explanation is successful, inadequate lay theories are transformed into adequate ones. By using the plant growth example, this article demonstrates that the five-part transformative explanation is a useful technique when pre-existing, counterproductive lay theories are barriers to message acceptance.

References

Rowan, K. (1988). A contemporary theory of explanatory writing. Written Communication, 5(1), 23-56.

Rowan, K. E. (1991). When simple language fails: Presenting difficult science to the public. Journal of Technical Writing and Communication, 21(4), 369-382.

The Community Activeness--Consciousness Matrix

Gene L. Theodori
Departments of Rural Sociology and Recreation, Park, & Tourism Sciences
Texas Cooperative Extension
The Texas A&M University System
College Station, Texas
Internet Address: g-theodori@tamu.edu

More times than not, Extension faculty and other professionals who work in the area of rural community development will agree with the following statement: "When you've seen one rural community, you've seen one rural community." By no means am I asserting that we cannot make generalizations about rural communities. We, in fact, can and often do.

Instead, my claim is that every rural community has certain social, economic, and/or environmental issues/problems that are unique to that particular community. Furthermore, the way in which each rural community attempts to address its insistent issues and solve its pressing problems oftentimes is unparalleled. With that said, I now offer one generalization about rural communities and present a tool for Extension faculty to use when delivering programs relating to community development.

A Generalization: Activeness and Consciousness        

One generalization that I have made while working in the area of rural community development is that all rural communities differ along the dimensions of "activeness" and "consciousness." Activeness refers to the degree of interaction at the community level. Community-level interaction can be defined as the behaviors of local residents working together to address and solve specific locale-oriented needs and problems. Community-level interactions include activities such as participating in a community improvement project or working with other members of the community to try and solve local problems. Action at the community level varies widely across communities and within the same community over time.

Consciousness refers to a community's level of awareness of its current social, economic, and environmental situations in real time. Consciousness, like activeness, varies across and within communities. When a community is truly conscious, it is fully aware and knowledgeable of its current social, economic, and environmental conditions. Moreover, the community understands the history of its people, economy, and environment, and it also has a well-defined vision for the future.

A Tool: The Activeness--Consciousness Matrix

A tool that Extension faculty might find useful when initially delivering a community development-related program is something that I have termed the "activeness--consciousness matrix" (Figure 1). The activeness--consciousness matrix allows Extension personnel to quickly assess the levels of activeness and consciousness of a community as viewed from the local residents' perspective.

Figure 1.
The Activeness--Consciousness Matrix

As shown in Figure 1, I have dichotomized the activeness dimension into two groups, "active" and "not active." I have also collapsed the consciousness concept into two categories, "conscious" and "not conscious."

Using the Activeness--Consciousness Matrix

First, draw the activeness--consciousness matrix on a posterboard; then project the matrix using an overhead projector, or simply distribute handouts of the matrix.

Next, explain what each cell of the matrix represents to those attending the community development meeting.

Cell 1 represents communities that are both conscious and active. Communities of this nature characteristically are comprised of local citizens who are fully aware and knowledgeable of the current social, economic, and environmental conditions of their community. The local residents understand their community's history, and they have a well-defined vision of how they want their community to look, feel, and operate in the future. At the same time, citizens are actively working together to positively improve their community and purposively accomplish their vision.

Cell 2 represents communities that are conscious but not active. Communities of this nature characteristically are comprised of local citizens who are fully aware and knowledgeable of the current social, economic, and environmental conditions of their community. The local residents understand their community's history, and they have a well-defined vision of how they want their community to look, feel, and function in the future. However, citizens are not actively working together to positively improve their community or purposively accomplish their vision.

Cell 3 represents communities that are not conscious and not active. Communities of this nature characteristically are comprised of local citizens who are not fully aware or knowledgeable of the current social, economic, and environmental conditions of their community. The local residents do not understand their community's history, and they do not have a well-defined vision of how they want their community to look, feel, and operate in the future. At the same time, citizens are not actively working together to positively improve or purposively develop their community.

Cell 4 represents communities that are not conscious but active. Communities of this nature characteristically are comprised of local citizens who are not fully aware or knowledgeable of the current social, economic, and environmental conditions of their community. The local residents do not understand their community's history, and they do not have a well-defined vision of how they want their community to look, feel, and function in the future. However, citizens are actively working together to positively improve or purposively develop their community. Due to the lack of a common, well-defined vision, efforts at community development are generally uncoordinated and piecemeal in nature and produce, at best, transitory results in the community.

Last, ask program participants to state in which cell they would place their community. Also, ask the individuals to put forward the reason(s) why they chose to place their community in that particular cell.

Concluding Comment

The activeness--consciousness matrix can provide Extension personnel with a speedy assessment of the levels of activeness and consciousness of a community as viewed from the local citizens' perspective. Extension faculty who use the activeness--consciousness matrix will find that cell placement of the community and the reason(s) behind such placement may vary, sometimes widely, amongst individuals.

In actuality, the utilitarian value of the matrix increases with variation in responses. As residents put forth their justifications as to why the community should be placed in one particular cell as opposed to the others, critical dialogue emerges about the views and actions of various sectors of the community. And, as practice and research suggest, communication among individuals and groups within a community is one crucial element in the overall process of community development.

A Tool for Developing Questionnaire Content

Kathlene Larson
CD-DIAL Research Coordinator
Sociology Extension
Internet Address: katelar@iastate.edu

Vern Ryan
Professor, CD-DIAL Director
Department of Sociology
Internet Address: vryan@iastate.edu

Iowa State University
Ames, Iowa

Identifying the content of data collection instruments can be one of the most challenging aspects of conducting a community survey or needs assessment. This article describes a tool used by CD-DIAL (Community Development--Data Information and Analysis Laboratory). CD-DIAL is an Iowa State University Extension unit that provides technical assistance and training to non-profit and government organizations attempting to collect primary data from their constituents, clients, or organization members.

The Goal

In our work with organizations, our primary goal is to assist the organization in collecting valid, reliable information. Our second goal is that the information will be used to make decisions leading to enhanced community or organizational capacity. As we begin new projects, we often see examples of past surveys and focus groups results that were never used for program or community improvement. Completion of the survey became the end of the process, instead of the first step in community or organizational improvement.

Because we work with communities and organizations on a variety of topics in which we do not necessarily have expertise, we rely on the knowledge and expertise of local members. In order to build on this local expertise, we facilitate a dialogue with a team of community/organization members using three statements that provide the information needed to advise the community/organization on survey methodology.

The Setting

The community or organization brings together a team of 10 to 20 persons. Members of the team are from organizations requesting the assistance and often include others such as clients, users of services, and citizen representatives to provide a broader range of perceptions and concerns. The meeting room is comfortable; chairs are arranged around a table so that participants can see each other and the facilitator.

The facilitator introduces the process and provides each participant with a worksheet containing three statements. Team members are given approximately 5 minutes to complete each statement, and responses are written on a flip chart. The atmosphere is open and nonjudgmental. Participants are encouraged to explain and discuss their statements without criticizing or evaluating others because the primary purpose of the process is to understand the range of issues and concerns that need to be addressed.

The Statements

Three statements are used so that the team and facilitator can reach a shared understanding of the team's vision for the future, perceptions of how the information will be used, and a set of predictions about data collection results (Figures 1, 2, and 3).

Figure 1.
Statement Eliciting Team's View of Where the Community Will Be in the Future

In this first statement, we explore a basic understanding of the team's view on where the community should be at some point in the future. Note that the statement is written in present tense, as though the date has arrived and the condition exists. An example of statements we have seen generated from this first exercise is "In the year 2005, Pottawattamie County children have increased access to quality early childhood education programming and are ready to learn when they enter school."

Figure 2.
Statement Eliciting Team's View of How They Will Achieve the Ideal Condition

The second statement assists the team in exploring potential strategies that could lead to community or organizational change. Note that "conducting a survey" is not a satisfactory answer to this statement. As participants share their phrases, discussion often ensues regarding methods to be explored and challenges to existing notions about boundaries on personal and organizational change. In response to this statement, we have seen phrases such as "Our team will create the ideal condition for residents by breaking down the barriers to accessible and understandable services, or teaching residents about a healthy lifestyle."

Figure 3.
Statement Eliciting Team's View of What the Survey Will Show

Much like the popular television show that asks teams of participants to compete in predicting survey results, this statement provides a list of predictions or hypotheses that drive the development of survey instruments and help to clarify the target population for the data collection process. Some examples of phrases resulting from this statement are "Seniors have transportation problems," and "Residents are not aware of county conservation facilities."

These statements also are useful in the summaries we provide to organizations when data collection is complete. We use survey results to either confirm or disprove beliefs about the target population.

In our work with community teams, we frequently find examples of professional bias, often when team members work exclusively with certain populations. Responses to this third statement allow identification and exploration of those biases, leading to better question construction. For example, in one session a participant predicted the survey would show that single parents who work did not really want to be at home, raising their children. This participant believed that single parents should be home doing exactly that. The process allowed members of the team to discuss the participant's perceptions and clarify the team's goals for program development.

The Outcome

In our work with over 100 communities and organizations, asking teams to complete these statements has typically resulted in several pages of phrases. Phrases are compiled and shared with the team as part of the survey development process. The exercise has assisted in clarifying the target population for the survey and team member training regarding sound question construction and survey methodology. Use of the exercise has consistently provided our clients and our staff with a basic framework for building and implementing a well-designed survey process that results in useful data and often results in positive community or organizational change.

A Simple Method to Determine Consumer Preference

Jo Ann Robbins
Extension Educator
University of Idaho, Jerome County Extension
Jerome, Idaho
Internet Address: jrobbins@uidaho.edu

Introduction

Extension field trials often involve consumer preference. This may be the look of a turf grass; the feel of a textile; the taste of a cooked, raw, or processed food product; or the smell of a product.

Statistical analysis of consumer preference often requires a trained consumer panel to show significant results. Even then, simple statistical procedures, such as analysis of variance, can be inappropriate for this type of data due to panelist variation: e.g., sensory preferences, personality differences, and variation in the use of the rating scale. Use of a 1 to 10 preference scale often varies, even among trained panelists. Some panelists use the lower range, some the higher, and some rate all choices somewhere around the middle of the scale.

Thus, analysis of preference data often is complex, using techniques such as multiplicative mixed models (Smith, 2003) or principle components analysis (M. McDaniel, personal communication, August 27, 2003). These advanced statistical methods make analysis more precise by factoring out much of the panelist variation interfering with analysis of the data using a simple analysis of variance (M. McDaniel, personal communication, August 27, 2003).

In Extension field trials, trained test panels are not available. Advanced statistical methods require help from statisticians, often located some distance from the field site. A simple, practical consumer preference technique is needed to evaluate field data.

Consumers at field tours, local Master Gardeners, farmers' market shoppers, 4-H members, parents and leaders, and commodity producers are available to extension personnel, and willingly volunteer their services to rate products. Using these consumers, statistically valid tests for preferences can be conducted using the method described here.

Collection of Data

Prepare and present samples to be evaluated in an identical manner. Provide evaluation forms and pencils for the consumers to record their preferences. Tables and chairs arranged around the central distribution area can make the evaluation process comfortable for the consumers.

Collection Example

An example uses specialty potato cultivars sliced and boiled in an identical manner. Cultivars were identified by number and placed on paper plates for sampling by consumers (shoppers at a local farmers' market). Evaluation form instructions read, "On a scale from 1 to 10 (10 being the best, 1 being the worst), rate the following potato cultivars. Please take a sip of water between samples." Paper cups and cold water were provided.

Analysis of Data

This procedure uses the SAS 1999 statistical software for analysis. This is easily installed on an Extension office computer.

Enter raw rating data into a spreadsheet such as Excel. Number each consumer (replication), and record the rating for each product attribute (texture, flavor, and appearance in the potato example) for each treatment (each cultivar in the potato example). This data must be saved as a text file to be read by the SAS program.

Use a nonparametric statistical technique to analyze the mean ratings for each treatment. The rank of each rating is computed. By ranking the 1 to 10 ratings of the evaluators, the variation due to differences in use of the rating scale is minimized.

Next, employ a signed rank test to assess statistical differences between treatments. A chi-square test will indicate the level of significance of the data. This will tell you if the mean ranks for each attribute of the treatments are significantly different.

To perform mean comparisons on significantly different attributes, use the standard error of the mean ranks for each attribute. The mean rank, plus or minus two standard errors, approximates a 95% confidence interval. Therefore, any two means differing by more than two standard errors are implied to be significantly different. You can then state which treatments are statistically significantly different. In the potato example, after data analysis, such statements as the following can be made:

• "At the Twin Falls (Idaho) Farmers' Market, consumers preferred the texture of 'Caribe' and 'Huckleberry' potatoes over all other cultivars tested."
• "Taste test results indicate that 'Caribe,' 'German Butterball,' "Yukon Gold,' 'Viking Red,' and 'NorDonna' rank high"(Olsen, 2003).

Analysis Example

Using SAS, the following nonparametric procedure was applied to the specialty potato data. Where:

• Location is either 1 or 2 since the test was performed at two separate farmers' markets.
• Cultivar is the specialty potato type (these are the treatments)
• Texture is the mouth feel of the boiled sample
• Flavor is how the boiled sample tasted
• Appearance is how the boiled sample looked
• Rep is each individual consumer completing the entire evaluation
• A:\Potatotaste00.txt is the location and the name of the text data file
• The procedure NPAR1WAY calculates the mean ranks

The SAS program is as follows.

Data Pottaste2;
Infile'A:\Potatotaste00.txt'  delimiter='09'x;
Input location rep cultivar texture flavor appearance;
Proc sort;   
    by location cultivar texture flavor appearance rep

Proc MEANS;
    by location cultivar;

Proc NPAR1WAY WILCOXON;
    by location;
    Class cultivar;
       OUTPUT OUT=DataT Wilcoxon;
   VAR texture flavor appearance;

Proc rank out=rankdata data=pottaste2;
var texture flavor appearance;
rankst f a;
by location;

Proc sort data=rankdata;
by location cultivar;

Proc means mean stderr;
var t f a;
by location cultivar;

run;

The results of this SAS program provide, for each attribute at each location for each treatment:

1. The rating mean--the raw score averaged for each attribute
2. The rank mean--the rank scores averaged for each attribute

Also provided are:

1. The chi-square for the ranked means. This will tell you if there are significant differences between the rank means.
2. The standard error for the rank means. If the chi-square shows significance, the standard error will tell you which rank means are different from each other.

This consumer preference technique and statistical method allows Extension professionals in remote field situations to measure sensory attributes of products using available consumer clientele and statistical analysis possible on an office computer.

Acknowledgement

Thanks to William Price, Statistician, Statistical Programs, College of Agriculture and Life Sciences, University of Idaho, Moscow, Idaho for help with SAS programming and interpretation.

References

Olsen, N., Robbins, J., Brandt, T., Lanting, R., Parr, J., Jayo, C., & Falen, C. (2003). Specialty potato production and marketing in southern Idaho. University of Idaho College of Agricultural and Life Sciences CIS 1110.

SAS Institute Inc., 1999. SAS OnlineDoc®, Version 8, Cary, NC.

Smith, A.,Cullis, B., Brockhoff, P., & Thompson, R. (2003). Multiplicative mixed models for the analysis of sensory evaluation data. Food Quality and Preference. Vol. 14 (5/6), 387-395.

Estimation of Attendance at a Large Outdoor Event

Anne M. Streich
Extension Educator
Internet Address: astreich2@unl.edu

David B. Marx
Professor of Biometry
Internet Address: dmarx@unl.edu

Jeanette M. Stafford
Graduate Student

Steven N. Rodie
Extension Landscape Specialist
Internet Address: snrodie@unomaha.edu

Kim W. Todd
Extension Landscape Specialist
Internet Address: ktodd2@unl.edu

University of Nebraska
Lincoln, Nebraska

Introduction

Documenting the impact and accountability of educational programming has become an integral part of Extension/outreach programs. This documentation ensures that programs are benefiting clientele and that limited funding resources are being directed to address critical educational needs. Impact reports typically include data relating to social, environmental, and economic program impacts.

High program attendance does not necessarily correlate to high program impact, but attendance, if accurately measured, can be an important factor in accountability reporting. In large settings, it may be difficult to assess the number of people that attend a program. This can be due to one or more of the following factors:

1. There are numerous event entrances and no distinct "main" entrance,
2. The event encompasses a large area, making it impractical to count everyone at one time,
3. Attendees are not required to register, and
4. Attendees come and go at various times during the event.

The following methodology and case study outline one approach to estimate event attendance and calculate the accuracy of this estimate.

Basic Methodology

Because it is impractical to survey everyone at a large event, a proportion of the attendees should be sampled. The basic assumption behind this estimation method is that an accurate "hard count" of people who complete an event-related action is available. Definable actions may include the number of people who buy a drink or snack, the number of people who pick up a schedule or map, or the number of people who attend a particular activity, such as a talk.

The second piece of required information is attained by sampling a proportion of those people leaving the event. A question relating back to the definable action must be asked. For example, if the total number of drinks sold was the definable action, the appropriate question to ask would be, "Did you buy a drink, and if so, how many did you buy?" This question should be repeated to a manageable percentage of people leaving the event (one out of every three, for example). By asking this question, the average number of times that a person did some action while at that event can be calculated. The number of people attending the event can then be estimated by taking the "hard count" of those buying a drink (X) and dividing it by the average number of drinks per person estimated by the exit sample (Y). This is shown in Equation 1.

Equation (1): TOTAL = X / Y

In addition to estimating the total number of attendees, it is also important to estimate the variance (sum of the squared deviations divided by the total observations) of the total number of people attending the event. The total variance is calculated as shown in Equation 2.

Equation (2): V(TOTAL) = {V(X)/Y²} + {[X² *V(Y)]/Y³}

Where:

• V(X) = variance of the total "hard count"
• V(Y) = variance of the survey data adjusted for a finite population correction
• V(Y) = (1 - p) * (variance of survey values) / n

Where:

• p = proportion of attendees sampled in the survey
• n = number of attendees surveyed

The usual confidence limits of the total can be computed by adding and subtracting twice the standard deviation (positive square root of the variance total, as calculated above) from the total count. This allows an accurate estimate of the total attendance figure to be given with some measure of how good this estimate is.

Case Study

On September 14, 2002, a University of Nebraska Cooperative Extension outdoor event titled "Landscape Connections" was held on East Campus at the University of Nebraska - Lincoln. Thirty-five scheduled talks were given throughout the day. At the beginning of each talk, the number of people attending the talk was recorded. This represented the "hard count." About half of the talks had two people recording the "hard count." The additional counts were used to estimate the variance for the "hard count."

Seven locations were identified where a majority of the attendees would be expected to exit. Every third person exiting through one of these locations was asked "How many talks were you in attendance at the start of the talk?" In this example, it was important to designate "at the start of the talk" because counts were conducted at the beginning of each talk; participants joining the talk after the initial count was completed were not included. An example of the form used to record the number of people surveyed and the number of talks they attend is shown in Figure 1. This form was used for six time periods throughout the day.

Statistics were then put into the formula above to calculate a confidence interval for the number of attendees for Landscape Connections. The results are shown in Table 1.

Table 1.
Confidence Interval for Number of Attendees

Description	Value
X	1884
Y	1.25912
P	1/3
N	274
V(X)	64.2722
Variance of survey data (at seven locations)	2.09012

Therefore:

• TOTAL = 1884/1.25912 = 1496.28
• V(Y) = (1-1/3)*2.09012/274 = 0.005085
• V(TOTAL) = 64.27/1.25912²+(1884²*0.005085)/1.25912³ = 9084.94
• Confidence interval = square root of 9084.94*2 = 190.63
• Approximate 95% confidence interval for the total number of attendees = 1306 to 1687

Conclusion

Attendance figures are only a small portion of many accountability reports, but still remain an important factor. In many instances, it is impossible or impractical to count participants as they arrive or leave a program conducted in a large area. This simple estimation method, which combines a count and a measure of people's involvement in the activity, can be used to generate an estimate of the total number of people attending the event, as well as a statement of how accurate that estimate is.

Acknowledgement

Contribution no.1011 of the Nebraska Cooperative Extension Division, Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln.

Developing a Phosphorus Fertilizer Training Program for Golf Course Personnel

B. P. Horgan
Assistant Professor and Turfgrass Extension Specialist
Internet Address: bphorgan@umn.edu

P. Bierman
Assistant Extension Educator
Internet Address: pbierman@soils.umn.edu

C. Rosen
Extension Specialist
Internet Address: crosen@soils.umn.edu

University of Minnesota
St. Paul, Minnesota

Introduction

Minnesota recently passed legislation (SF 1555) restricting the use of all phosphorus (P) fertilizers applied to turfgrass in the Twin Cities Metropolitan Area (TCMA is comprised of 7-counties) and fertilizers with more than three percent P in non-TCMA counties. The law takes effect on January 1, 2004. Exceptions to the law include:

1. When a soil or tissue test performed within the last three years indicates that the levels of available P in the soil are not sufficient to support healthy turf growth;
   
   
2. When first establishing turf via seed or sod, but only during the first growing season;
   
   
3. When fertilizer containing P is used on a golf course under the direction of a person licensed, certified or approved by an organization with an ongoing training program approved by the Commissioner of the Minnesota Department of Agriculture.

The Turfgrass Science Program at the University of Minnesota was accepted by the Commissioner of the Minnesota Department of Agriculture (MDA) to be the "approved organization with the ongoing training program" that will "license or certify" the golf course personnel.

The objectives of developing the Extension program for golf course personnel were to:

1. Create a baseline understanding of the P fertilizer law, soil science and plant nutrition and
2. Provide detailed information on P chemistry, soil and plant testing, and fertilizer management.

Moreover, the program was designed so that other turfgrass organizations within Minnesota and other states that are considering such legislation may adopt a portion or all of the materials used.

Methods

A committee from the Minnesota Golf Course Superintendent's Association (MGCSA) and turfgrass/soil scientists from the University of Minnesota was formed to help guide the creation of the Phosphorus Fertilizer Training Program. Throughout the entire process, the committee felt it vital to keep the MDA informed as to the progress and major steps toward completion. Ultimately, the MDA is in charge of enforcement and evaluating the effectiveness of the P Fertilizer Training Program.

Finally, a curriculum was developed and a final meeting with all interested parties from the University of Minnesota, MDA, and the MGCSA were included for comment. The curriculum included the following:

1. Understanding the Law
   
   
2. Basic Soil Science - This includes soil formation, texture, structure, organic matter, essential nutrients for plant growth, pH, and cation exchange capacity.
   
   
3. Phosphorus Chemistry - This includes soil phosphorus cycling, factors affecting phosphorus losses and best turfgrass management practices to reduce P loss.
   
   
4. Soil and Tissue Testing - This includes soil sampling and analysis, plant tissue sampling and analysis, and interpreting soil and plant tissue results.
   
   
5. Fertilizer Management - This includes evaluating the mineral composition of plants and soils, cycling of essential nutrients in the soil and factors that affect plant uptake, sources of fertilizers, fertility programs, and application timing.

In addition to supplying printed slides and notes on the previously described curriculum in notebook form, supporting materials are also provided that include Extension publications on water quality, P cycling and fate, soil and nutrient interactions, fertilizer recommendations, eutrophication, and a copy of the P law.

The legislation also stated that this training must be "ongoing." Therefore, every 2 years, golf course personnel previously trained are required to attend one of three MGCSA sponsored educational events where a review of the P law and the current literature on P nutrition and environmental fate issues will be presented.

One judge of training program effectiveness is the number of attendees or success in reaching the target audience. Another measure is whether participants considered the program informative and useful. Survey data were collected to help evaluate program objectives and the comprehensive P resource notebook.

Results

Phosphorus fertilizer training participants were asked if the 4-hour seminar was very useful, useful, or not useful. Table 1 indicates the percentage of respondents that answered that the seminar was either useful or very useful. The data presented indicate that respondents found the program either useful or very useful with respect to the day-to-day management of P fertilizers applied to turf.

An estimated 475 people in Minnesota will need training prior to applying P fertilizer in the 2004 growing season. To accommodate this large number, four training sites were proposed that were strategically located across Minnesota. Each site represented a different training date. At each training session, continuing education credits were approved by the Golf Course Superintendent's Association of America. As of July 24, 2003, three of the four training sessions have been completed, and approximately 230 people have attended. The fourth training session will occur at the Minnesota Green Expo, which is the largest educational program for turf and nursery professionals. (Last year's attendance was 7100 people).

Conclusions

The Turfgrass Science Program at the University of Minnesota works closely with the MGCSA to develop and deliver educational programs. These programs help members of the MGCSA obtain continuing education credits used for both state certification and licensure, and for national membership requirements in the Golf Course Superintendent's Association of America.

The P Fertilizer Training Program is meeting the defined objectives set forth by the committee representatives and the MDA. This program is proving to be beneficial to golf course personnel and can easily be adapted for other professional turfgrass managers both within Minnesota and around the country.

Although Minnesota is the first state to restrict the use of a fertilizer applied to turfgrass, currently other states across the country are considering similar action.

Using a Computer Simulation Game to Teach Agri-Business Management

Freddie L. Barnard
Professor and Extension Economist
Department of Agricultural Economics
Purdue University
West Lafayette, Indiana
Internet Address: barnardf@purdue.edu

Introduction

A computer simulation game is used to teach agri-business management to undergraduate students, agri-business managers, and agricultural lenders. The Purdue Farm Supply Game (Akridge, Erickson, & Babb) is an effective way to teach agri-business management tools, including:

• Cash flow budgeting,
• Breakeven analysis, and
• Profitability analysis.

The model provides enough business activity to illustrate the teaching points, while avoiding the trap of being so complex it overwhelms students and Extension clientele.

Purdue Farm Supply Game

The model represents a hypothetical farm supply store that sells four products:

• Complete feed,
• Concentrate feed,
• Commercial grade fertilizer, and
• Custom blend fertilizer.

The game is played a total of six quarters and decisions are made each quarter. Participants are divided into teams of four, and each participant chooses one of four available jobs:

• General manager,
• Inventory manager,
• Labor manager, and
• Cash manager.

Up to six stores compete in a rural market. Normally, three are used with undergraduate students. The number of stores is dictated by the number of participants when used with Extension audiences. All teams begin the game with identical balance sheets, and all earnings remain in the firm as retained earnings.

After each decision, students receive four reports:

• Income statement,
• Balance sheet,
• Cash flow statement, and
• Market share report.

They can then assess their results and prepare to make the next decision.

Cash Flow Budget

Participants prepare a projected cash flow budget as one of three written assignments. This requires them to project sales and expenses, as well as make several management decisions, such as:

• Change credit policies,
• Purchase storage facilities and/or trucks,
• Make extra payments on loans, and
• Contribute to savings.

Because sales vary from quarter to quarter, it is essential that participants accurately project cash inflows and outflows. Otherwise, a firm can end the quarter with excess cash or an emergency loan, which is charged an interest rate of 18%.

The importance of cash flow management is clearly illustrated to participants, because a failure to accurately prepare a projected cash flow budget becomes apparent with the next printout. Teams with an adverse cash position must then determine how they will rectify the situation. Consequently, participants are convinced of the usefulness of a projected cash flow budget through experience rather than by a lecture.

Breakeven Analysis

The income statement provided each quarter reports not only total amounts of revenues and costs for both the quarter and previous 12 months, but also reports revenues and costs on a per ton basis and as a proportion of sales. For the second written assignment, students classify costs into variable and fixed, and calculate breakeven in number of tons and total sales.

Next, students calculate the breakeven amount when considering changes in fixed costs (e.g., purchase of a storage facility, purchase of a truck, or increasing the loan amount), variable costs, and selling price of a product. Participants are often amazed at the impact of decreasing the selling price of a product. Additional sales needed to breakeven are usually much greater than expected. Again, participants are convinced through experience rather than by a lecture.

Profitability Analysis

Students evaluate management performance as their third written assignment, using the profitability linkage model. The model is a technique used to conduct a comprehensive profitability analysis. It illustrates the interrelationships that exist among three financial ratios:

• Return on sales,
• Asset turnover, and
• Financial leverage (total assets divided by owner's equity).

When multiplied together, the result is rate-of-return-on-equity.

This model is completed at the beginning of the game for October-December and then again 1 year later for October-December. The results are compared, and students must determine whether the profitability has improved or deteriorated during the past year. In either situation, students must provide at least three reasons for the resulting performance.

Also, comparative data are provided for previous classes. The results are sorted by rate-of-return-on-equity into high-profit and low-profit groups. Participants can easily see the impact of having better or worse financial ratios, such as:

• Gross margin,
• Operating expenses,
• Interest expense,
• Return on sales,
• Asset turnover, and
• Financial leverage.

They then have two remaining quarters before the game ends and the winner is determined. So their efforts are focused on areas they determine as weaknesses.

Grading and Evaluation

Quarterly printouts for undergraduate students are graded by evaluating performance in the following areas:

• Cash,
• Emergency loan,
• Unfilled orders,
• Inventory, and
• Labor utilization.

Team results are compared to criteria provided before the start of the game. Points are deducted when the results fall outside an acceptable range. Feedback is provided to Extension clientele using the same criteria.

At the conclusion of the six quarters, the undergraduate student team with the greatest owner's equity receives a 20 out of 20 quiz score, second place receives 18 out of 20, and third place receives 16 out of 20. Individuals on winning teams for Extension audiences receive a prize.

Closing Comments

The Purdue Farm Supply Game provides a simple, but effective technique for teaching agri-business management tools in a dynamic and competitive environment. Evaluations from both undergraduate students and Extension clientele praise the benefits received from active decision-making, competition, and working as a team to facilitate sharing ideas and experiences. To receive a copy of the manual or to order a copy of the program, contact the author.

References

Akridge, Jay, Steven P. Erickson, and E. M. Babb. Purdue farm supply business management game manual. Purdue Research Foundation. West Lafayette, IN: 1992.

Target-Audience-Specific Networking Groups: Could They Be Helpful in Your Work?

Debra Minar Driscoll
Extension Family and Community Education Faculty
Oregon State University Extension Service
Dallas, Oregon
Internet Address: debra.driscoll@oregonstate.edu

Those of us who are putting our efforts into outreach to underserved audiences can find plenty of challenges in connecting the people to be served with the educational programming that could benefit them. Many communities have networking groups designed to assist in connecting services providers with their intended audiences.

In western Oregon, a focus of many networking groups is outreach to migrant and resident Latino families. This article compares and contrasts three of these groups. It also shares the results of a survey that was conducted with members of a local group.

The Groups

Group A

Group A was initially formed to help service providers assist migrant workers who had come to the area when cold weather delayed the strawberry crop, making many workers temporarily unable to provide for their basic needs. This group meets monthly and is facilitated by the director of a food bank network.

The facilitator and an informal committee choose speakers for each meeting. Meetings begin with the guest speaker presentation, followed by sharing of current programs or issues by everyone present. Meetings last up to 2 hours, and attendance is normally 20 to 30 persons.

Anyone in the community may attend meetings at any time. Minutes and agendas are mailed to those on the sign-in list. Its geographic area is the northern part of a large agricultural county.

Group B

Group B was formed about 10 years ago as a way to advocate for equal access to services for Latino families. In the past year, its structure has changed. What was an informal group is now one with an executive committee, officers, bylaws, and council member registration forms. The reason for this change was to garner a greater commitment from members to work actively on the group's goals.

The group meets monthly and is planning a training event for social service workers. Meetings last 3 hours and are divided into a networking segment and a council meeting, where the emphasis is on goal setting and action. Eight to 15 participants attend. Its geographic base is from three counties.

Meetings are held in the state capital, and as a result there is a greater representation of state agency staff. The secretary of the group sends meeting notices via electronic mail. Speakers are part of the program quarterly, and are selected by the executive council, which meets monthly.

Group C

Group C was formed as a result of a lawsuit judgment against the state child welfare agency asserting that seven counties in the state were not providing Latino families equal access to services. Although no longer legally required, this countywide group meets monthly and has evolved over the past 10 years from one focused on providing a community cultural event and working toward expansion of needed services, to a networking group with monthly speakers and rotating facilitators and recorders. It has also gained a reputation in the county as a place for new staff to gather ideas on ways to reach Latino audiences.

Attendance is open to anyone, and varies from 10 to 20 participants each month. Meetings last from 1 to 2 hours. Monthly speakers are selected during a discussion at each meeting, and volunteers contact the speakers. Minutes and agendas are e-mailed to all who have electronic access and mailed to those who do not. The geographic base is one county.

Similarities and Differences

All three groups

• Have similar missions,
• Meet within the geographic communities,
• Have established sound reputations,
• Are made up of agency professionals, and
• Are governed by an executive committee or have identified leadership.

The key difference among them is the expected level of participation.

Participants in Group A

• Attend meetings,
• Listen, and
• Observe.

Participants in Group B

• Can attend networking with no additional expectations, or
• Can become council members, who are expected to participate in decision making and assume committee roles.

Participants in Group C

• Are expected to take part in problem solving, and
• Help with meeting roles and arrange for speakers.

Survey Conducted with Group C in 2002

What are service providers looking for when they attend networking groups? With the budget crunch that Oregon and other states are facing, why would someone who is already overloaded with work want to attend yet another meeting? To find out more about the thinking of the participants in Group C, a four-page written survey instrument was developed by a subcommittee. Surveys were sent in June of 2002 to 52 people who had attended at least two meetings over the previous 3 years. Twenty-three surveys were returned by early August, for a 48% rate of return.

After some preliminary questions regarding the mission statement, the survey asked, "Why do you attend, or why did you attend the [Group C] meetings? Check all that apply." Respondents were also asked to place an asterisk by the one or two most important reasons for attendance. Table 1 summarizes the responses.

A review of the three most frequently selected responses shows that participants value networking opportunities, are committed to helping the target audience, and seek to improve their effectiveness in the community. Networking and helping families connect to resources, with 11 responses each, were selected as the most important reasons to attend the meetings.

A question later in the survey asked: "What is your level of interest and time commitment available for the Council? Please choose as many as apply." Table 2 presents those results.

The majority of respondents could commit to attending and participating in meetings. Going beyond that level of commitment into subcommittee work was a little more tentative. After presenting the results of this survey at a Group C meeting, the group voted to focus the meetings on networking and drop their struggling effort of continuing to sponsor a community event.

Conclusions

Could target-audience-specific networking groups enhance your work in the local community? Effective networking groups can take many forms. I suggest jumping in with both feet and making a commitment to attend meetings for 3 months before you decide whether or not it is worth your time. You will know it's a good match if you feel comfortable in the group and feel that your needs are being met.

What if there are no existing groups, but the need for one is evident in your community? Find a few partner agencies, and start one. Be sure to consider the time limitations of those invited, and work together to set clear goals. If you are the designated coordinator, take the time to set up an electronic mail group for sending announcements of meetings. Delegate as much of the detail work as possible to build group ownership and lighten your load.

Participation in networking groups can help you:

• Increase your knowledge about the audience;
• Assess the needs of the target audience;
• Increase service providers' knowledge of Extension programming;
• Find agency personnel with goals similar to yours;
• Develop partnerships with other groups and agencies to extend services;
• Gain access to audiences served by other agencies; and
• Develop collaborative programming efforts and events.

My time spent attending networking meetings has been richly rewarded with new ideas for programming and methodology. I found new partners who gave me access to audiences I could never have reached on my own.

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