About This Book
The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.In her bestselling manual, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic through to advanced statistical techniques. She outlines each technique clearly, providing step by step procedures for performing your analysis, a detailed guide to interpreting data output and examples of how to present your results in a report.For both beginners and experienced users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential text. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing.This seventh edition is fully revised and updated to accommodate changes to IBM SPSS Statistics procedures, screens and output. 'An excellent introduction to using SPSS for data analysis. It provides a self-contained resource itself, with more than simply (detailed and clear) step-by-step descriptions of statistical procedures in SPSS. There is also a wealth of tips and advice, and for each statistical technique a brief, but consistently reliable, explanation is provided.' - Associate Professor George Dunbar, University of Warwick 'This book is recommended as ESSENTIAL to all students completing research projects - minor and major.' - Dr John Roodenburg, Monash University A website with support materials for students and lecturers is available at www.spss.allenandunwin.com
Planning the Study
- ➢Consider what type of research design (e.g. experiment, survey, observation) is the best way to address your research question. There are advantages and disadvantages to all types of research approaches; choose the most appropriate approach for your particular research question. Have a good understanding of the research that has already been conducted in your topic area.
- ➢ If you choose to use an experiment, decide whether a between-groups design (different cases in each experimental condition) or a repeated measures design (same cases tested under all conditions) is the more appropriate for your research question. There are advantages and disadvantages to each approach, so weigh up each approach carefully.
- ➢ In experimental studies, make sure you include enough levels in your independent variable. Using only two levels (or groups) means fewer participants are required, but it limits the conclusions that you can draw. Is a control group necessary or desirable? Will the lack of control group limit the conclusions that you can draw?
- ➢Always select more participants than you need, particularly if you are using a sample of humans. People are notoriously unreliable—they don't turn up when they are supposed to, and they get sick, drop out and don't fill out questionnaires properly! So plan accordingly. Err on the side of pessimism rather than optimism.
- ➢ In experimental studies, check that you have enough participants in each of your groups (and try to keep them equal when possible). With small groups, it is difficult to detect statistically significant differences between groups (an issue of power, discussed in the introduction to Part Five). There are calculations you can perform to determine the sample size that you need. See, e.g. Stangor (2006).
- ➢ Wherever possible, randomly assign participants to each of your experimental conditions, rather than using existing groups. This reduces the problem associated with non-equivalent groups in between-groups designs. Also worth considering is taking additional measurements of the groups to ensure that they don't differ substantially from one another. You may be able to statistically control for differences that you identify (e.g. using analysis of covariance).
- x27A2; Choose appropriate dependent variables that are valid and reliable (see discussion on this point later in this chapter). It is a good idea to include a variety of measures—some measures are more sensitive than others. Don't put all your eggs in one basket.
- ➢ Try to anticipate the possible influence of extraneous or confounding variables. These are variables that could provide an alternative explanation for your results. Sometimes, they are hard to spot when you are immersed in designing the study yourself. Always have someone else (e.g. supervisor, fellow researcher) check over your design before conducting the study. Do whatever you can to control for these potential confounding variables. Knowing your topic area well can also help you identify possible confounding variables. If there are additional variables that you cannot control, can you measure them? By measuring them, you may be able to control for them statistically (e.g. using analysis of covariance).
- ➢ If you are distributing a survey, pilot-test it first to ensure that the instructions, questions and scale items are clear. Wherever possible, pilot-test on the same types of people who will be used in the main study (e.g. adolescents, unemployed youth, prison inmates). You need to ensure that your respondents can understand the survey or questionnaire items and respond appropriately. Pilottesting should also pick up any questions or items that may offend potential respondents.
- ➢ If you are conducting an experiment, it is a good idea to have a full dress rehearsal and to pilot-test both the experimental manipulation and the dependent measures you intend to use. If you are using equipment, make sure it works properly. If you are using different experimenters or interviewers, make sure they are properly trained and know what to do. If different observers are required to rate behaviours, make sure they know how to appropriately code what they see. Have a practice run and check for inter-rater reliability (i.e. how consistent scores are from different raters). Pilot-testing of the procedures and measures helps you identify anything that might go wrong on the day and any additional contaminating factors that might influence the results. Some of these you may not be able to predict (e.g. workers doing noisy construction work just outside the lab's window), but try to control those factors that you can.