1. Research Methods: Final Year Project Toolkit – Level 6 Laura Lake

2. The research process Selecting research area Formulating research questions / hypotheses Selecting a research strategy Collecting data Analysing the data Report writing

3. Research questions – product of theories drawn from: literature review discussions with professionals or experts personal interest hunches Research questions

4. Deductive – aims to deduce meaning for particular individuals / cases based on broader principles. Can be reductionist –it may break a particular theory or problem into its many component parts and test each element separately. Objective – the phenomenon under investigation must be observable and verifiable. operationalising concepts detailed within hypotheses. Inductive: developing theory from findings. The research process

5. Deductive & inductive Deductive =Theory Observations / findings Inductive = Observations / findings Theory

6. What is a hypothesis? statement or proposal about relationships between variables of differences between groups that can be tested. Example: is socio-economic group associated with occupation? Used mainly in quantitative rather than qualitative research. Hypotheses

7. Operationalising concepts – can the hypotheses be measured and what is the most appropriate way of doing so? Example: relationship between age and fear of crime. How are the variables ‘age’ and ‘fear of crime’ defined and how can they be measured? A variable is a unit of data collection whose value can vary. Cases are individuals (people), organisations, communities, schools, countries. Variables – testing relationships

8.  Independent variable (x) is a characteristic of the case under study e.g. sex, age, social class, marital status Dependent variable (y) is the variable that is causally influenced by x. Income (y) (Dependent) Age (x) Independent)

9. Variables can be defined into types according to the level of mathematical scaling that can be carried out on the data. There are four types of variable: Variables –levels of measurement 1. Nominal2. Ordinal 3. Interval4. Ratio

10. Nominal or categorical: variable measured using categories that cannot be rank ordered. No order in categories so no judgement possible about the relative size or distance from one category to another. What does this mean? No mathematical operations can be performed on the variables relative to each other. Nominal variables reflect qualitative differences rather than quantitative ones. Nominal variables

11. Examples: Nominal variables What is your gender? (please tick) Male Female Did you enjoy the film? (please tick) Yes No

12. Ordinal : variables that comprises of categories that can be rank ordered. Distance between each category cannot be calculated but the categories can be ranked above or below each other. What does this mean? Can make statistical judgements and perform limited maths. Ordinal variables

13. Example: Ordinal variables How satisfied are you with the level of service you have received? (please tick) Very satisfied Somewhat satisfied Neutral Somewhat dissatisfied Very dissatisfied

14. Both interval and ratio data are examples of scale variables. Scale variables: numeric format (£50, £100, £150) can be measured on a continuous scale distance between each can be observed and as a result measured can be placed in rank order. Interval and ratio variables

15. Measured on a continuous scale and has no true zero point. Examples: Time – moves along a continuous measure or seconds, minutes and so on and is without a zero point of time. Temperature – moves along a continuous measure of degrees and is without a true zero. Interval variables

16. Measured on a continuous scale and does have a true zero point. Examples: Age Weight Height Ratio variables

17. These levels of measurement can be placed in hierarchical order. Hierarchical variable order Ratio Interval Ordinal Nominal

18. Example: salary data for is often recorded as interval data (i.e. just a number). Why? Because it can then be analysed in many ways: Any mathematical operation e.g. average salary reformatted into ordinal or nominal data e.g. salary bands (£10,000 to £14,999, £15,000 to £19,999) Hierarchical variable order

19. Salary collected as an ordinal variable i.e. in salary bands? Cannot perform mathematical operations e.g. finding average salary. So, if possible capture this kind of data in a scale variable This should be thought about at the research design stage! Hierarchical variable order

20. Bryman, A. (2008) Social Research Methods. 3rd Ed. Oxford: Oxford University Press. David, M. and Sutton, C. (2011) Social Research : An Introduction. 2nd ed. London: Sage. References

21. This resource was created by the University of Plymouth, Learning from WOeRk project. This project is funded by HEFCE as part of the HEA/JISC OER release programme.Learning from WOeRk This resource is licensed under the terms of the Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales license (http://creativecommons.org/licenses/by-nc-sa/2.0/uk/).http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ The resource, where specified below, contains other 3 rd party materials under their own licenses. The licenses and attributions are outlined below: 1.The name of the University of Plymouth and its logos are unregistered trade marks of the University. The University reserves all rights to these items beyond their inclusion in these CC resources. 2.The JISC logo, the and the logo of the Higher Education Academy are licensed under the terms of the Creative Commons Attribution -non-commercial-No Derivative Works 2.0 UK England & Wales license. All reproductions must comply with the terms of that license. Author Laura Lake Research Assistant InstituteUniversity of Plymouth Title Final Year Research Project Toolkit Description How to develop research questions and hypotheses Date Created July 2011 Educational Level Undergraduate (Level 6) Keywords UKOER, Learning from WOeRK, LFWOER, UOPCPDRM, Work-Based Learning, WBL, Continuous Professional Development, CPD, Deductive, reductionist, objective, inductive, operationalising, variable, nominal, ordinal, interval and ratio variables. Creative Commons License Back page originally developed by the OER phase 1 C-Change project ©University of Plymouth, 2010, some rights reserved