1. Lecture 8: Descriptive Statistics and Statistical Inference on SPSS
Research Methods I
Lecture 8: Descriptive Statistics and Statistical Inference on SPSS

2. Introduction This lecture has several purposes:
To reinforce notions of statistical analysis encountered elsewhere
To highlight conceptual issues in descriptive statistics in particular
To introduce you to some basic features and functions of the SPSS package
Lecture: conceptual and practical

3. Conceptual issues Descriptive statistics: describing the sample
Statistical inference: making (probabilistic) claims about the population, based on the sample
Inference from samples is always a gamble (Bryman and Cramer: 110) but it is one which is usually taken!

4. Remainder of the lecture
The remainder of the lecture material here will be lists of issues you should be familiar with
However, the lecture time will be spent demonstrating how SPSS can assist in dealing with these concepts
You should practise with SPSS in order to get familiar with it. SPSS is a good package used widely in social science

5. Types of data There are four types of data:
Nominal or categorical data: e.g. gender
Ordinal: e.g. ranking preference but not on a scale
Interval: measured on a scale with equal intervals: e.g. 4>3 = 10>9
Ratio: same as interval but has a true zero
Type of data constrains type of test

6. Univariate frequency distribution
Discrete categories required
Interval/ratio data and midpoints
Relative frequency: pie, bar, histogram
Cumulative frequency
Frequency distribution
Normal distribution
Skewness and kurtosis

7. Measures of location and dispersion
Mode
Median
Percentiles and quartiles
Mean (including grouped and frequency)
Measure of qualitative variation
Range
Interquartile range
Variance, SD and coefficient of variation

8. Parametric data Conditions for parametric data: Normality
Homogeneity of variance
Independence of observations
Interval/ratio data
NB non-parametric statistics
Most tests economists use are parametric

9. Hypothesis testing in data
Arbitrary nature of statistical testing
Covariance
Correlation
Partial correlation
Testing between two means
Regression will be dealt with in lecture 10

10. Methodology and Ho testing
Positivists generally regard hypothesis testing as crucial to science and advocate statistical testing (with a range of methods); inference crucial
Interpretivists: generally do not engage in inferential statistical work
Critical Realism: testing involves ‘closure’; but many CR have used stats, including non-parametrics