Project management and Research skills: Quants

1. Project management and Research skills: Quants • Aims • to introduce the concept of modelling as a research application • to apply different models to real data • to consider appropriate research questions for a positivistic study • to consider areas for potential hypothesis testing

2. Modelling • Real world …….Mathematics ……Abstract world • DATA THINKING • To formulate your ideas mathematically • Then compare the outcomes with the actual data • eg What outcome does the impact of a career break scheme have on the recruitment and retention of female staff • eg How far can GCSE results be used as a factor in predicting degree classifications?

3. Nature of models • problem domainmodels Compare and restructure

4. Creating a model • …an iterative and adaptive process • A description of the problem area so that the domain can be identified ( and the constraints) • A consensus on the model requirements…A common set of requirements • The viewpoint of the model must be flexible so as to encompass the set of process governing the area…this will be critical to its usefulness

5. Model building steps • 1. Classification of the problem using internal knowledge • 2. Encoding the elements into familiar concepts ie mathematical equation or textual statement • 3. Formation into structural elements and representations…. Rules, equations,scenarios,values etc • eg linear regression, time series, or collation of values(not necessarily numerical)

6. The design problem • Which advert is more effective? • Sales have dropped for three months? • Support for a political party? • Level of activity in the economy? • Which are structured, or unstructured? • How many variables could be incorporated into appropriate models? • Eg level of activity in the economy may include the growth rate, unemployment, rate of inflation, expected future levels

7. Overview of quantitative data analysis • Need to distinguish between exploratory data analysis and decriptive statistics • Choice of technique will rely on: • exploratory data analysis to summarise, describe or display the data • confirmatory data analysis to make inferences about the population from your sample data • consider the number of variables that you wish to analyse at the same time • consider the measurement scale of your data(nominal, ordinal, interval or ratio)

8. Type of analysis by data type: exploratory data analysis • Univariate data: • Presenting frequencies • Tables,Graphical forms • Measuring location • Mean,Median,Mode • Measuring dispersion • range, IQR, standard deviation • Measuring change • Index numbers • Bivariate data • Presenting frequencies • cross tabulations,scatter diagrams, stem plots

9. Continued: confirmatory data analysis • Univariate data • Estimating from samples • confidence intervals (P) • Forecasting • time series analysis • Bivariate data • Measuring association • Pearsons correlation coefficient(P) • Spearmans rank correlation coefficient(NP) • Measuring difference • Chi squared test(NP) • Students t-test(P) where P..parametric, NP..non-parametric

10. What comes first: Theory or Research? • Theory before research: • identify relevant concepts • adjust the concepts to the problem under scrutiny • prime task is to identify relevant factors and construct explanations • Research before theory • should be a reason for this approach • ‘re-inventing the wheel’ • approach implies theory construction

11. Consultation of the 1999 review of the Index of Local deprivation • Methodology report containing the confirmed and final list of domains and indicators • Discusses the combining of the domain indices into an overall Index of Multiple deprivation • indicators for inclusion are discussed eg spatial variation of benefit take up; the number of 17-19year olds who have not successfully applied to higher education…is averaged over 2 years to offer robustness • copies can be obtained from http://index99.apsoc.ox.ac.uk