Recap of data analysis and procedures Food Security Indicators Training Bangkok January 2009

Objective: To provide a quick overview of the topics covered during the previous trainings and with the pre-training material. Refresh our mind on key coefficients / procedures

Data management in SPSS Import/export data into/from SPSS Merge datasets Label variables / values Clean datasets Recode, Compute Sort and Select cases

Data analysis in SPSS Compute descriptives (mean, median, etc.) Run frequencies, crosstabulations, multiple response analysis, etc. Compare means, run T-tests and Anova Analyse associations (r, regression, Chi- square)

Basic statistics Two types of variables: 1.Continuous: assume numeric values (e.g., household size) 2.Categorical: Categories are denoted by numbers that do not have numeric value (e.g., ethnic group) oThe type of analysis depends upon the nature of the variables.

Continuous Variables: descriptives Mean: sum of all the values divided by the numbers of cases. Measure of central tendency. Median: the middle value of a set of observations ranked in order. Measure of central tendency. Mode: the value of the observation occurring most frequently. Standard deviation: it measures the average distance of the observations from the mean of the distribution. Measure of eterogeneity of the distribution

Continuous Variables: descriptives Looking at the age data from 10 individuals… What is the range? 12 to 38 What is the mean? 27.2 What is the median? 28 What is the mode?

 Simple frequencies: distribution of a variable  Cross-tabulations: distribution of a variable within the categories of another other variable. e.g., same total, but also distribution for each region Categorical Variables: descriptives

 When respondents give more than one answer (e.g., report the 3 main crops cultivated)… we can analyse responses as a set. 1.Percentages based on responses, or 2.Percentages based on cases Categorical Variables: multiple responses

 The percentage based on cases (HHs) tells us the prevalence (%) of HHs that cultivate a specific crop (disregarding the order)  Household is the denominator.  E.g., 100% of the HHs cultivate maize (3/3*100). Categorical Variables: multiple responses

 The percentage based on responses (crops) compares one crop against all the cultivated crops.  Here the denominator is all the cultivated crops.  E.g., Maize represents 30% of the cultivated crops (3/10*100) Categorical Variables: multiple responses

Significance tests Is the relationship observed by chance or because there actually is a relationship between the variables? o(independent) T-test: to see whether two means are different and if the difference is statistically significant (i.e., exist also in the population) oANOVA (and post-hoc tests): to see if there are statistically significant differences between the means computed on the 3 (or more) groups and which means statistically differ. oChi-square: to see if there is a statistically significant association between two categorical variables. It does not tell us of how strong the association is!

Independent T-tests works well if:  continuous variables  groups to compare are composed of different people  within each group, variable’s values are normally distributed  there is the same level of homogeneity in the 2 groups. T-test: assumptions

T-test: SPSS procedure  Drag the variables into the proper boxes  define values for the independent variable

T-test: SPSS output Look at the Levene’s Test …  If the Sig. value of the test is less than.05, groups have different variance. Read the row “Equal variances not assumed”  If the Sig. value of test is bigger than.05, read the row “labelled Equal variances assumed”

ANOVA: SPSS procedure 1.Analyze; compare means; one-way ANOVA 2.Drag the independent and dependent variable into proper boxes 3.Ask for the descriptive 4.Click on ok

ANOVA: SPSS output Along with the mean for each group, ANOVA produces the F-statistic. It tells us if there are differences between the means. It does not tell which means are different.  Look at the F’s value and at the Sig. level

Pairwise comparisons: SPSS output Once you have decided which post-hoc test is appropriate  Look at the column “mean difference” to know the difference between each pair  Look at the column Sig.: if the value is less than.05 then the means of the two pairs are significantly different

Chi square: SPSS output Look at the row labelled ‘Sig.’  If it is higher than 0.05 → the association is not statistically significant  If it is lower than 0.05 → the association is statistically significant

Association/causality Is there a relationship between two variables? oCorrelation: to measure the association between two continuous variables. Pearsons’ r = -1 → perfect negative relationship Pearsons’ r = 1 → perfect positive relationship Pearsons’ r = 0 → no relationship at all. oRegression analysis: to measure how the change of one unit of an independent continuous variable impacts the value of the dependent continuous variable. Regression equation: Y = a + b x

Correlation: SPSS output

Simple linear regression: SPSS output Y= FCS a= b= x= wealth index Using this output, we can use the regression equation (Y = a + b x) to measure the FCS change for each one-unit change of the wealth index.

What’s next? Having good data analysis skills is a good starting point, but... one of the objectives of the training will be to apply your data analysis skills to quantitative food security analysis.