1. Lecture 1.2 Field work (lab work). Analysis of data.

2. Short field work –ex. “Rapid rural appraisal”. –Seeks a sketch of local conditions rather than an in-depth study. –The emphasis is on highly visual techniques that community members carry out amongst themselves. –This analysis of the data is carried out in the community. –Measurements are qualitative rather than quantitative Field work (lab work)

3. Long field work involves at least three field work periods. Allow an in-depth analysis of a situation. Time is available for quantitative analysis. Allow a broader scope of the study. Time is available for voucher collections.

4. Prepare for field work (applies for both short field work and long field work). –Obtain secondary information – maps, floras, census statistics etc. –Obtain permission of local authorities before starting fieldwork. – Review existing literature, also local fora. The first field season. –Describe the field site, including geographical location and map, population size and distribution, languages spoken etc. –Identify main informants /study units. –Produce some brief results.

5. Main field work. Remember locating and obtaining permission to use materials. Make sure to do everything systematically.  Data should be stored for 10 years!!  Stick to the same units, tools and scales throughout the study.  A unique identification number for each collection. While in the field, it is helpful to make some initial analyses of the data.

6. Last field work period. Before this last field period, perform initial analysis of the data. Look for what’s missing in the data or what you would like to explore in greater detail. Do not forget to say good bye and thank you to the community in which you have been working, leaving it in a good manner.

7. Analysis of data The purpose of analysing the data is explore different, interesting characteristics inherent in the results. Characteristics that the study units have in common. Characteristics that the study units are different from. Categorization is the way that something is divided up into a set off of different classes.

8. Analysis, categorization Selecting categories. Categorization of the data into different levels, how many levels? Value can be assigned to different categories Beware of units and scales, need to be the same.

9. Analysing quantitative forms of data. The number of individuals and species. The structure of the data set. Quantitative data in social sciences: how many inhabitants, different age classes etc.

10. Analysis of data Graphical presentation –Tables and figures; permits us to present a simplified version of the results. –Can be used to report precise numbers or to illustrate a trend. –A trend might be better illustrated with a figure. –Graphs, typically relate two dimensions such as quantity of time. –Graphs show trends or movements over time. –Use the program “excel”, free and relatively simple.

11. Analysis of data An important tool for analyzing data is statistics, a mathematical way of summarizing and interpreting quantifiable research results. Do your study allow for statistics? –Must be considered when designing the study. It is important to understand when to apply each statistical tool, and how to interpret the results.

12. Statistical analysis of data Everything varies, if you measure two things twice they will be different. P value -the power of a test is the probability of rejecting the null hypothesis when it is false. The null hypothesis, nothing happens. The alternative hypothesis there is significant divergence or pattern in the data

13. Statistical analysis of data Various measures of central tendency describe important properties of a population of study units. –Calculation of the mode, –Calculation of the median, –Calculation of the mean. The measure of variability/the analysis of variation about means. –Used for establishing measures of unreliability. –Used for testing hypothesis.

14. Regression The statistical model when explanatory variables is continuous. To check relationship between two variables. A number of assumptions, the most important normally distributed errors. Can you think of data where analysis of regression could be useful?

15. Analysis of variance (anova) Analysis which involves a range of discrete levels of categories. Fundamental question are there differences between means. Several assumptions underlying the analysis, the most important equal variances. Can you think of data where analysis of anova could be useful?

16. Other common statistics are the chi-square test, correlation and ordination analysis. –Analysis of co-variance –Nested design, different treatments are applied to plots of different size. –Correlation analysis, to check how variables vary together.

17. Qualitative analysis A general analysis strategy advanced by three qualitative authors (Bogdan & Biklen 1992, Huberman & Miles 1994, Wolcott 1994). 1. A general review of all information. 2. The process of reducing the data begins.

18. Qualitative analysis To analyse qualitative data the researcher engages in the process of moving in analytic circles.

19. Qualitative analysis Computer programs, ex. NUD· IST may help in the analysis phase. –The program provides an organized storage “file” system. –Helps locating material easily –However, programs should not take the place of careful analysis of the material.