Elvis Attakora-Amaniampong Real Estate & Land Management Department FPLM-UDS, Wa

Orientation for Third Trimester Field Practical TrainingMethodologies, for Sampling, Collection and Analysis of Quantitative and Qualitative Data Elvis Attakora-Amaniampong Real Estate & Land Management Department FPLM-UDS, Wa 12th February, 2012

Objectives • Key Concepts: Differentiate between quantitative and qualitative data • Identify the tools and techniques for data collection • Use the identified tools and techniques to collect data in the field • Identify the tools and techniques for data analysis • Carry out simple quantitative and qualitative data analysis • Present the results of analysed data 2

Key Concepts • Statistics: way to get information from data. • A variable is any type of observation that can take d/t values for d/t people, or for d/t times or place etc, such as marks on an exam. • Variable because marks obtained in each exam will vary from student to student • Values of a variable are the possible observations of the variable, such as values of exam thus, any integer between 0 and 100. • Data are the observed values of a variable. For eg: the marks of 10 students, which are 76 79 45 33 67 74 86 91 90 77 3

Categories/Sources of Scientific Data 1) Primary data • Survey Data: (Censuses & Sample Survey types) • Experimental data 2) Secondary data • Routine Records These categories are not all mutually exclusive. 4

Types of Variables/Data Variables / Data Qualitative Quantitative Continuous e.g. height and mass Discrete e.g. number of students Nominal e.g. colour Ordinal e.g. ranks and grades

Ratio Applies to quantitative data only & has all the properties of interval scale, has meaningful zero starting point and a meaningful ratio b/n 2 numbers Interval Values are real numbers All calculations are valid Data may be treated as ordinal or nominal Ordinal Values must represent the ranked order of the data Calculation based on ordering process are valid Data may be treated as nominal but not as interval Nominal Values are the arbitrary numbers that represent categories Only calculations based on the frequencies of occurrence are valid Data may not be treated as ordinal or interval Hierarchy of Data/Scale of Measurement

Sampling • Taking information based on a small set of the population • Can be planned; preference for random surveys Sampling Types • Probability sampling - the selection of sampling units is according to a probability (random & non-random) scheme. • Non-probability sampling - selection of samples not objectively made, but influenced a great deal by the sampler. Example – haphazard, purposive, snowball, and convenience. • Preference is for probability sampling, but situation may determine otherwise 7

SYSTEMATIC SAMPLING PROCEDURE • Sampling units are selected according to a pre-determined pattern. • Advantages of Systematic Sampling • Easy to set-up • Relative speed in data collection • Total coverage of population assured • Good base for future designs, as position of characters can easily be mapped (with known coordinates) • Demarcation of units not necessary, as sampling units are defined by first unit. 8

Disadvantages of Systematic Sampling • With only one random observation, sampling error not valid • Unknown trend(s) in population can influence results adversely [Examples: topography, season of sampling interval] Avoiding the disadvantages • The first major disadvantage on sampling error can be rectified by introducing several multiple random starts through stratification of the population • The second problem of trend is more difficult but simply relates to the choice of the sampling interval. 9

SIMPLE/UNRESTRICTED RANDOM SAMPLING • Unlike the systematic sampling, sampling units need not be equally spaced. Types • STRATIFIED RANDOM SAMPLING • CLUSTER SAMPLING 10

STRATIFIED RANDOM SAMPLING • Requires dividing the population into non-overlapping homogeneous units, which we are called STRATA. • SRS is then applied to each stratum, hence stratified random sampling (STRS). • Examples of strata types or criteria are ages of plantation, species types, aspect, topography/ altitude, farm types, habitat • Dividing the population into such homogeneous units usually leads to better estimates of the desired population parameters 11

CLUSTER SAMPLING • This is similar to SRS, except that the sampling unit is cluster • Unlike STRS, all units within selected clusters are observed. • Advantages • natural aggregation automatically defines sampling unit. • provides information on both clusters and unit constituting clusters. • variation within clusters either eliminated or reduced as all units within selected clusters are observed. 12

Disadvantages • requires knowledge of total number of cluster, (to establish the frame) • variation in clusters and clusters sizes could affect estimation of cluster-based parameters. • complex computational formulae for unequal or naturally occurring cluster. • impossible to observe all units if the cluster sizes are too large. 13

ANALYSING QUALITATIVE DATA • Qualitative data are essentially labels of a categorical variable • Statistical Analyses involve totals, percentages and conversion to pie-charts and bar charts (bar-graphs). • Sophisticated analyses include categorical modelling 14

QUALITATIVE TOOLS AND TECHNIQUES Tools and techniques of Participatory Rural Appraisal (PRA) Broadly categorised into 3 1. Interviews and discussions 2. Diagrams 3. Observation 15

Interviews and Discussions Interview type: semi-structured. • A guided interviewing or conversation with some predetermined topics or questions. • Instrument thus described as checklist but not formal questionnaire and new questions not on the checklist can be asked during the interview. • Questions are open-ended. They should not be ‘yes’ or ‘no’ questions. 16

Types of semi-structured interview • 1. Individual interview • 2. Key informant interview; e.g. model farmers/innovators, educators/school teachers, government officials, religious leaders, women’s leaders, etc. • 3. Group interviews: group size of 20-25. • 4. Focus group interviews: 6-12 knowledgeable or interested people. 17

Hints for carrying out semi-structured interview • Be sensitive and respectful • Use same language or interpreter • Use dialogue. Be interested in what is important to the respondent. • Observe non-verbal indicators, e.g. body language, facial expression, tone of voice and eye contact. • Start questions with who, what, whom, when, where, why, how, etc. 18

Hints for carrying out semi-structured interview-cont. • Ask one clear, short and unambiguous question at a time. • Ask no leading questions, e. g. do you plant cassava? • Do not conclude statements for respondents. • Listen, don’t lecture. • Probe for more details. Use any of the following. A. Nod your head or say ‘yes’. B. Repeat questions in slightly different ways. C. Use questions such as: “could you tell me more about that?”, “could you give me an example?”, “could you explain that to me?” and Use ‘why’ sparingly. 19

Diagram • Pictorial and symbolic representation of information. Very useful when dealing with illiterate and semi-literate people • Provide focus for attention during discussions. • Stimulate discussions. • Represent complex issues or processes simply. • Used in crosschecking thus provoking effective group work. • Stimulates memory about past and present situations. • Reinforce spoken or written word. • Assist in decision making and monitoring. 20

Commonly used diagrams • Local histories/historical profiles/time line • Transects • Trend analysis • Seasonal calendars • Social/resource maps • Ranking • preference ranking • pairwise ranking • matrix ranking 21

Time lines • For recall of important historical events in the community. • Help to gather information on causes of problems. How? • Organise elders/leaders into groups. • Explain process to them. • Draw vertical line and record events with dates. Example of a time line • 2001----This drought resulted in purchasing maize for 5,000 cedis per bowl for the first time • 1999----The drought led to the invasion of army worms that destroyed all the crops thus resulting in severe hunger. • 1997/98--People travelled to Burkina Faso to purchase grains. 22

Transect • Diagram provides information on land use and compares features (topography, vegetation, trees, soils, drainage) resources, problems, opportunities, etc. How to do a transect walk • Find knowledgeable and willing community members. • Discuss what is to be included in the transect with them. • Choose the starting point and route with the people. • Walk the transect • Observe and ask questions for clarification. 23

How to do a transect walk-cont. • Listen and take down notes. • Discuss problems and opportunities, • Identify the main natural and agricultural zones and sketch the distinguishing features • For each zone describe the soils, crops, livestock, topography, cropping pattern, drainage, socio-economic indicators, problems, etc. • Draw the transect on paper at the end of the walk and cross-check with the community members. 24

Trend analysis • Used to trace changes that have occurred in resources, diseases, nutritional status, etc. • The period of evaluation spans childhood to the future. How to do trend analysis • Draw a table with 5 columns and 2 rows. • In the columns indicate the resources(s), childhood days, today, expected future and desired future. (Symbols can be used for these based on preference) • List the resources or use symbols to represent them in the resource column. • Let them indicate the changes pictorially for the different periods. 25

Seasonal calendar • It shows the main activities, problems, rainfall pattern, labour availability, periods of diseases, pest infestation, opportunities, birth (livestock and human), weaning, type of food consumed, prices of goods, etc. in the community. There can be one or a series of diagrams on a single sheet of paper. How to prepare a seasonal calendar • Draw the scale of the months of the year at the top of a sheet of paper or on the ground. Allow the community members to use their local scale of months. (To make it more practicable let them use symbols to represent the months) • Allow the participants to indicate the information on the scale • Local materials like stones, seeds, etc. can be used to indicate the intensities of activities and occurrences on the calendar. Put more stones where activity is very intensive and less stones where less intensive. • Copy calendar on a sheet of paper. 26

Social and resource maps • A map indicating resources and where they are in the community. E. g. hills, valleys, wells, rivers, roads, places of worship, schools, markets, clinics, herbalists, priests, etc. • Let the members of the community draw an outline of the community and insert the desired features. Use a benchmark if available 27

Ranking • Pairwise ranking: compare resources or problems with each other to determine which is more important or serious so as to rank and prioritise them. How to do pairwise ranking • List resources, problems, etc. • Make a table and divide into rows and columns according to number of items. • Write items horizontally in the top row and vertically in the first column. • Compare 2 items at a time and ask respondents to indicate the preferred one. • Count the number of time each item appears and record then rank. 28

Matrix ranking • Comparing resources with established criteria in a tabular form. • Make a table with resources in rows and criteria in columns. • Compare each resource to criteria to determine their importance. Use stones. • Count the number of stones against each resource and record. 29

Resource flow diagrams • Show how resources are used in the community. Draw arrows from one resource to another to indicate which resource is used for what purpose. Observation • Directly observe objects, events, processes, relationships, etc. in the field and record them mentally and in notes or diagrams. 30

ANALYSING QUANTITATIVE DATA • Basic analyses involve determining the CENTRE and SPREAD of data. • Inferential, probability and non-probability based on measuring central tendencies 31

Numerical Description of Data Measures of Center and Location Mean, median, mode, geometric mean, midrange Other measures of Location Weighted mean, percentiles, quartiles Measures of Variation Range, interquartile range, variance and standard deviation, coefficient of variation

Summary Measures • Describing Data Numerically • Center and Location • Other Measures of Location • Variation • Mean • Range • Percentiles • Median • Interquartile Range • Quartiles • Mode • Variance • Weighted Mean • Standard Deviation • Coefficient of Variation

Measures of Center and Location • Overview • Center and Location • Mode • Weighted Mean • Mean • Median

Mean (Arithmetic Average) Sample mean Population mean • n = Sample Size • N = Population Size

Mean (Arithmetic Average) The most common measure of central tendency Mean = sum of values divided by the number of values Affected by extreme values (outliers) • (continued) • 0 1 2 3 4 5 6 7 8 9 10 • 0 1 2 3 4 5 6 7 8 9 10 • Mean = 3 • Mean = 4

Median Not affected by extreme values In an ordered array, the median is the “middle” number If n or N is odd, the median is the middle number If n or N is even, the median is the average of the two middle numbers • 0 1 2 3 4 5 6 7 8 9 10 • 0 1 2 3 4 5 6 7 8 9 10 • Median = 3 • Median = 3

Mode A measure of central tendency Value that occurs most often Not affected by extreme values Used for either numerical or categorical data There may be no mode There may be several modes • 0 1 2 3 4 5 6 • 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 • Mode = 6 • No Mode

Review Example Five houses on a hill by the beach • House Prices: ¢2,000,000 500,000 300,000 100,000 100,000

Summary Statistics Mean: ($3,000,000/5) = $600,000 Median: middle value of ranked data = $300,000 Mode: most frequent value = $100 ,000 . • House Prices: $2,000,000 • 500,000 300,000 100,000 100,000 • Sum 3,000,000

Measures of Variation/Spread • Variation • Range • Variance • Standard Deviation • Coefficient of Variation • Population • Standard • Deviation • Interquartile • Range • Population • Variance MSAD MSAD • Sample • Variance • Sample • Standard • Deviation SE • Mean of the sum of the absolute deviation • Standard error

Measures of variation give information on the spread or variability of the data values. Variation • Same center, • different variation

Range Simplest measure of variation Difference between the largest and the smallest observations: • Range = xmaximum – xminimum • Example: • 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 • Range = 14 - 1 = 13

Ignores the way in which data are distributed Sensitive to outliers Disadvantages of the Range • 7 8 9 10 11 12 • 7 8 9 10 11 12 • Range = 12 - 7 = 5 • Range = 12 - 7 = 5 • 1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,5 • Range = 5 - 1 = 4 • 1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,120 • Range = 120 - 1 = 119

Interquartile Range Can eliminate some outlier problems by using the interquartile range Eliminate some high-and low-valued observations and calculate the range from the remaining values. Interquartile range = 3rd quartile – 1st quartile

Interquartile Range • Example: • Median • (Q2) • X • X • Q1 • Q3 • maximum • minimum • 25% 25% 25% 25% • 12 30 45 57 70 • Interquartile range • = 57 – 30 = 27

Average of squared deviations of values from the mean Sample variance: Population variance: Variance

Standard Deviation Most commonly used measure of variation Shows variation about the mean Has the same units as the original data Sample standard deviation: Population standard deviation:

Calculation Example:Sample Standard Deviation • Sample Data (Xi) : 10 12 14 15 17 18 18 24 • n = 8 Mean = x = 16

Measures of association • Chi-square • Correlation analysis (Pearson’s rho) • Linear regression Presentation of Results • Tables • Summary of counts, frequencies with % • Diagrams/graphs • Pie Chart (in only percentages), Histogram • Line graph , bar graph( count or percentages) 50

Elvis Attakora-Amaniampong Real Estate & Land Management Department FPLM-UDS, Wa