Análisis e interpretación de resultados de la encuesta aplicada a los

There are many kinds of the presentation and instruments. The basic principles for choosing such presentation instruments depend on the purpose of presentation, the appropriateness of selected tools, the content needs to be presented and the research results. The presentation approaches and instruments might vary from a very simple tool (that more easy to understand), such as using verbal or narrative technique, descriptive statistic and graph (e.g. line graph, bar graph, pie graph and etc.) up to some sophisticated tools that are more difficult to understand, especially, when using statistic inferential techniques such as bivariate and multivariate analysis.

Aside from basic principles mentioned earlier, in practical process, the selection of tools or instruments for presentation of research findings that were obtained from project monitoring and evaluation activities depends largely on knowledge, experiences and the way in which evaluators want to indicate, explain and present in the monitoring and evaluation reports. The example in Box 1 and Box 2 shows examples of the commonly used descriptive statistics and the frequently used descriptive analysis.

Box 1

Example of the Commonly used Descriptive Statistics Frequencies (numbers, a count of how many)

• 245 farmers finished training program provided by the AD project Percent/Proportion Distribution

• 35 percent of farmers who participates with the AD project are Lisu Means (average)

• The average age of farmers that participates with the AD project are 45 years of age.

Medians (mid-point)

• The ages of farmers who participate with the AD project ranged from 35 to 55, with the mid point at 38

Modes (the most frequent value)

• The most frequently reported age was 40 Money (costs, revenues and expenses)

• total amount or average amount of production cost is 4,650 Baht Percent Change over Two Points in Time (or the rate of change)

• Income level of farmers who participate with the AD project increase by 15 percent as compared with last year

Ratio (number of one thing per number of something)

• Ratio between the farmers and field extension workers is 20:1 Comparisons (could be numbers, percents and means)

• The average income of farmers who participate with the AD project was 35 percent higher than the farmers who not participate with the AD project

Box 2

Frequently Used Descriptive Analysis Frequency Distributions (number and percent)

Describing Parts of a Whole (100%):

• Percent: parts of a whole expressed as a percent;

e.g. good seed introduced by the AD project has rate of germination only 65 percent.

• Proportion: parts of a whole expressed as a decimal, not as a percent;

e.g. proportion of good seed that introduced by the AD project is only 0.65.

Rates: number of occurrences that are standardized; allows for comparison.

• e.g. crop yield per rai (or other area unit)

• e.g. 35 percent of farmers who has participated with the AD project are Lisu and the rest are Karen.

Ratio: another way to show the relationship between two numerical variables

• farmers to field workers ratio of the AD project is 20:1

Rate of Change or Percentage change: shows change over time between two items and can be calculated in the percentage by using the following formula: 1 100 New Time Old Time  _{ − ×}   _{ }   

e.g. The rate of change of alternative cash crop from 1994 to 1995 is calculated as follow: 520,000 1 100 15.5% 450,000  _{ − × =} _ _   

Therefore the area (in rai) of alternative HYV cash crop increased 20 percent from 1990 to 1995

Table shows rate of change from previous year of alternative HYV cash crop That introduced by AD project during 1990-1995

Year Cultivation area of alternative HYV cash crop Rate of change

1990 100,000 Baseline 1991 120,000 20% 1992 190,000 58% 1993 280,000 47% 1994 450,000 58% 1995 520,000 15%

5.8 REFERENCES

Benus, J. and L. Orr ,2000., Study of Alternative Quantitative Evaluation Methodologies. Working Paper. ABT Associates, Washington D.C.. Provides an overview of the importance of conducting evaluations, evaluation techniques and who should conduct evaluations.

Burdge, Rabel J., Editor. 1994., A Conceptual Approach to Social Impact Assessment. Middleton, WI: Social Ecology Press.

Casley, Dennis J. and Krishna Kumar, 1993.,The Collection, Analysis and Use of Monitoring and Evaluation Data. Baltimore, MD: The John Hopkins University Press for the World Bank, 1987.

Casley, Dennis J. and Krishna Kumar, 1997., Project Monitoring and Evaluation in Agriculture. Washington, D.C.: World Bank.

Donald F. Morrison., 1990., Multivariate Statistical Mthods, Third Edition, McGraw Hill Series in Probability and Statistics. New York, McGraw Hill Publishing Company.

Denzin, N. and Lincoln, Y, Editor, 1996., Handbook of Qualitative Research, Second Edition, Thousand Oaks, CA: Sage Publication.

Glass, G. and Hopkins, K, 1996., Statistical Methods in Education and Psychology, Third Edition, Boston: Allyn and Bacon.

G.S. Maddala. 1987., Limited-dependent and Qualitative Variables in

Econometrics. The Press Syndicate of the University of Cambridge. NY Joseph F. Hair, Jr, Rolph E. Anderson, Ronald L. Tatham and Willium C.

Black.1984., Multivariate Data Analysis With Readings, Fourth Edition. New Jersey. Prentice Hall, Inc.

Lane, D. M. Hyperstat Online Textbook.

Online: http://www. davidmlane.com/hyperstat/index.html

Operations Evaluation Department, 1994., World Bank, Building Evaluation Capacity, Lessons & Practices No. 4, November 1994.

Partton, Michael Quinn, 1990., Qualitative Evaluation and Research Methods, Newbury Park, CA: Sage Publication.

Partton, Michael Quinn, 1987., How to Use Qualitative Methods in Evaluation, Newbury Park, CA: Sage Publication.

Rossi P.H. and Freeman H.E. 1993., Evaluation: A Systematic Approach. Newbury Park: Sage Publication.

StatSoft, Inc, 2001., Electronic Statistics Textbook. Tulsa, OK: StatSoft. Online: http://www. Statsoft.com/textbook/stathome.html

Sidney Sieget and N.John Castellan, Jr. 1988., Non Parametric Statistics for Behavioral Sciences, Second Edition, McGraw Hill. Toronto.

Taro Yamane, 1979., Statistics: An Introductory Analysis, Third Edition. Harper& Row Publishing, Inc, New York.

The World Bank Group, International Training Program for Development Evaluation; Building skills to Evaluate Development Interventions. Module 8: Data Analysis and Interpretation.

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