• Summary
  • Contents
  • Subject index

"This guide will serve well as a handbook for undergraduate psychology students working on senior projects or theses. Clear, concise, and well organized, the book instructs the student from the beginning of the project to the final draft and offers advice both specific and general."—J. Bailey, CHOICEAnxious about your final year Psychology Project? Having trouble getting started? Your Psychology Project: The Essential Guide for Success clearly maps out all the requirements of a project in psychology. Acting as a definitive survival manual it guides students through every aspect of a psychology project from conception of an idea, to writing up the final draft. It will help students think through the whole research process by bridging the relationship between the research question, the design, and the use of statistical and qualitative analyses. By using clear practical examples this book provides an invaluable insight into applying theory to practice and will equip students with the knowledge, skills and abilty to carry out and write up their thesis project. Written in a clear and engaging manner Your Psychology Project: The Essential Guide for Success should be essential reading for all students undertaking a psychology research project.

Analysing Quantitative Data
Analysing quantitative data

On reading this chapter you should:

  • understand the two major trends involved in quantitative data analysis;.
  • be aware of the steps involved in hypothesis testing using SPSS as a means of answering your research question;
  • understand the important role played by exploratory data analysis;
  • understand the importance of testing the assumptions of your planned inferential analysis;
  • know what steps to take if your data is not normally distributed; for example, transforming your data;
  • understand the importance of computing confidence intervals, effect sizes/measures of association, and tests of power to overcome the limitations of the null hypothesis test procedure;
  • be able to deal with results that are highly significant, or nearly significant, and understand the meaning of significance; and
  • be aware of the limitations of statistical computer software.

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