Spss survival manual 3rd edition website

From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the 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. It covers new SPSS tools for generating graphs and non-parametric statistics, importing data, and calculating dates. How will you tell when it is cooked? Once it is cooked, how should you serve it so that it looks appetising? The same questions apply equally well to the process of analysing your data.

You need to plan your experiment or survey so that it provides the information you need, in the correct format. You must prepare your data file properly and enter your data carefully.

You should have a clear idea of your research questions and how you might go about addressing them. You need to know what statistical techniques are available, what sort of data are suitable and what are not. You must be able to perform your chosen statistical technique e. Structure of this book This SPSS Survival Manual consists of 21 chapters, covering the research process from designing a study through to the analysis of the data and presentation of the results.

It is broken into five main parts. Part One Getting started covers the preliminaries: designing a study, preparing a codebook, and becoming familiar with SPSS. In Part Two Preparing the data file you will be shown how to prepare a data file, enter your data, and check for errors.

Preliminary analyses are covered in Part Three, which includes chapters on the use of descriptive statistics and graphs; the manipulation of the data; and the procedures for checking the reliability of scales.

You will also be guided, step by step, through the sometimes difficult task of choosing which statistical technique is suitable for your data. In Part Four the major statistical techniques that can be used to explore relationships are presented e. These chapters summarise the purpose of each technique, the underlying assumptions, how to obtain results, how to interpret the output, and how to present these results in your thesis or report.

Part Five discusses the statistical techniques that can be used to compare groups. These include t-tests, analysis of variance, multivariate analysis of variance and analysis of covariance. A chapter on non-parametric techniques is also included. Using this book To use this book effectively as a guide to the use of SPSS you need some basic computer skills. In the instructions and examples provided throughout the text I assume that you are already familiar with using a personal computer, particularly the Windows functions.

I have listed below some of the skills you will need. Seek help if you have difficulty with any of these operations. It is assumed that you have been exposed to the fundamentals of statistics and have access to a statistics text.

SPSS is an enormously powerful data analysis package that can handle very complex statistical procedures. This manual does not attempt to cover all the different statistical techniques available in the program.

Only the most commonly used statistics are covered. It is designed to get you started and to develop your confidence in using the program. Depending on your research questions and your data, it may be necessary to tackle some of the more complex analyses available in SPSS. There are many good books available covering the various statistical techniques available with SPSS in more detail.

Read as widely as you can. Browse the shelves in your library, look for books that explain statistics in a language that you understand well, at least some of it anyway!

You might find that some authors explain concepts in a way that you can understand easily. Collect this material together to form a resource to be used throughout your statistics classes and your research project. It is also useful to collect examples of journal articles where statistical analyses are explained and results are presented. You can use these as models for your final write-up.

The SPSS Survival Manual is suitable for use as both an in-class text, where you have an instructor taking you through the various aspects of the research process, and as a self-instruction book for those conducting an individual research project.

If you are teaching yourself, be sure to actually practise using SPSS by analysing the data that is included on the website accompanying this book see p.

The best way to learn is by actually doing, rather than just reading. This will improve your confidence and also allow you to check that you are performing the analyses correctly. Sometimes you may find that the output you obtain is different from that presented in the book.

SPSS is updated regularly, which is great in terms of improving the program, but it can lead to confusion for students who find that what is on the screen differs from what is in the book. Usually the difference is not too dramatic, so stay calm and play detective. The information may be there but just in a different form. Research tips If you are using this book to guide you through your own research project there are a few additional tips I would like to recommend.

Draw on existing theories and research to guide the design of your project. Know what you are trying to achieve and why. Think ahead. Anticipate potential problems and hiccups—every project has them! Know what statistics you intend to use and use this information to guide the formulation of data collection materials. Make sure that you will have the right sort of data to use when you are ready to do your statistical analyses.

Get organised. Keep careful notes of all relevant research, references etc. Work out an effective filing system for the mountain of journal articles you will acquire and, later on, the output from SPSS.

It is easy to become disorganised, overwhelmed and confused. Keep good records. When using SPSS to conduct your analyses, keep careful records of what you do. I recommend to all my students that they buy a spiral bound exercise book to record every session they spend on SPSS. You should record the date, new variables you create, all analyses you perform and also the names of the files where you have saved the SPSS output.

If you have a problem, or something goes horribly wrong with your data file, this information can be used by your supervisor to help rescue you! Stay calm! If this is your first exposure to SPSS and data analysis there may be times when you feel yourself becoming overwhelmed.

Take some deep breaths and use some positive self-talk. Just take things step by step—give yourself permission to make mistakes and become confused sometimes. If it all gets too much, then stop, take a walk and clear your head before you tackle it again.

Most students find SPSS quite easy to use, once they get the hang of it. Like learning any new skill, you just need to get past that first feeling of confusion and lack of confidence. Give yourself plenty of time. The research process, particularly the data entry and data analysis stages, always takes longer than expected, so allow plenty of time for this.

Work with a friend. Make use of other students for emotional and practical support during the data analysis process.

Social support is a great buffer against stress! The References relating to each chapter appear at the end of the chapter. Further reading and resource material can be found in the Recommended references at the end of the book.

Before you can use SPSS to analyse your data there are a number of things that need to happen. First, you have to design your study and choose appropriate data collection instruments. Each of these steps is discussed in Part One. Chapter 1 provides some tips and suggestions for designing a study, with the aim of obtaining good quality data. Chapter 2 covers the preparation of a codebook to translate the information obtained from your study into a format suitable for SPSS.

Finally, in Chapter 3 you are taken on a guided tour of SPSS and some of the basic skills that you will need are discussed. If this is your first time using SPSS, it is important that you read the material presented in Chapter 3, before attempting any of the analyses presented later in the book. The data you enter into SPSS must come from somewhere—responses to a questionnaire, information collected from interviews, coded observations of actual behaviour, or objective measurements of output or performance.

The data are only as good as the instrument that you used to collect them and the research framework that guided their collection. In this chapter a number of aspects of the research process that impact on the potential quality of the data are discussed. First, the overall design of the study is considered, followed by a discussion of some of the issues to consider when choosing scales and measures and finally, some guidelines for preparing a questionnaire are presented.

Planning the study Good research depends on the careful planning and execution of the study. There are many excellent books written on the topic of research design to help you with this process—from a review of the literature, formulation of hypotheses, choice of study design, selection and allocation of subjects, recording of observations and collection of data. Decisions made at each of these stages can impact on the quality of the data you have to analyse and the way you address your research questions.

In designing your own study I would recommend that you take your time working through the design process to make it the best study that you can do. Reading a variety of texts on the topic will help. A couple of good, easy-to-follow titles are Stangor and Goodwin 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. There are advantages and disadvantages to each approach see Stangor, , pp. In experimental studies make sure you include enough levels in your independent variable. Using only two levels or groups means fewer subjects are required, however 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 subjects than you need, particularly if you are using a sample of human subjects.

So plan accordingly. Err on the side of being pessimistic, rather than optimistic. In experimental studies, check that you have enough subjects in each of your groups 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 will need. See for example, Stangor , p. Wherever possible, randomly assign subjects to each of your experimental conditions, rather than use existing groups.

This reduces the problem associated with non-equivalent groups in between-groups designs. You may be able to statistically control for differences that you identify e. 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 number of different measures—some measures are more sensitive than others. Try to anticipate the possible influence of extraneous or confounding variables. These are variables that could provide an alternative explanation for your results. Sometimes these are hard to spot when you are too immersed in designing the study yourself. Always have someone else supervisor, fellow researcher check over your design before conducting the study.

Do whatever you can to control 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.

If you are distributing a survey, pilot test it first to ensure that the instructions, questions, and scale items are clear. You need to ensure that your respondents can understand the survey or questionnaire items, and respond appropriately. Pilot testing 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 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. This might involve measuring output or performance on some objective criteria, or rating behaviour according to a set of specified criteria. There are many thousands of validated scales that can be used in research.

Finding the right one for your purpose can sometimes be difficult. A thorough review of the literature in your topic area is the first place to start. What measures have been used by other researchers in the area? Sometimes the actual items that make up the scales are included in the appendix of journal articles, otherwise you may need to trace back to the original article describing the design and validation of the scale you are interested in.

It is very important however to properly acknowledge each of the scales you use, giving full reference details. In choosing appropriate scales there are two characteristics that you need to be aware of: reliability and validity.

Both of these factors can influence the quality of the data you obtain. When reviewing possible scales to use you should collect information on the reliability and validity of each of the scales.

No matter how good the reports are concerning the reliability and validity of your scales, it is important to pilot test them with your intended sample. Sometimes scales can be reliable with some groups e. Reliability The reliability of a scale indicates how free it is from random error. The test-retest reliability of a scale is assessed by administering it to the same people, on two different occasions, and calculating the correlation between the two scores obtained.

High test-retest correlations indicate a more reliable scale. You need to take into account the nature of the construct that the scale is measuring when considering this type of reliability. A scale designed to measure current mood states is not likely to remain stable over a period of a few weeks. The test-retest reliability of a mood scale, therefore is likely to be low.

You would, however, hope that measures of stable personality characteristics would stay much the same, showing quite high test-retest correlations. The second aspect of reliability that can be assessed is internal consistency. Internal consistency can be measured a number of ways. This statistic provides an indication of the average correlation among all of the items that make up the scale. Values range from 0 to 1, with higher values indicating greater reliability. While different levels of reliability are required, depending on the nature and purpose of the scale, Nunnally recommends a minimum level of.

Cronbach alpha values are dependent on the number of items in the scale. When there are a small number of items in the scale less than ten Cronbach alpha values can be quite small. In this situation it may be better to calculate and report the mean inter-item correlation for the items. Optimal mean inter-item correlation values range from. Validity The validity of a scale refers to the degree to which it measures what it is supposed to measure.

The main types of validity you will see discussed are content validity, criterion validity and construct validity. Content validity refers to the adequacy with which a measure or scale has sampled from the intended universe or domain of content.

Criterion validity concerns the relationship between scale scores and some specified, measurable criterion. Construct validity involves testing a scale, not against a single criterion, but in terms of theoretically derived hypotheses concerning the nature of the underlying variable or construct. The construct validity is explored by investigating its relationship with other constructs, both related convergent validity and unrelated discriminant validity.

An easy to follow summary of the various types of validity is provided in Chapter 5 of Stangor There are many good books and articles that can help with the selection of appropriate scales. Some of these are also useful if you need to design a scale yourself. See the References at the end of the chapter. Preparing a questionnaire In many studies it is necessary to collect information from your subjects or respondents. This may involve obtaining demographic information from subjects prior to exposing them to some experimental manipulation.

Alternatively, it may involve the design of an extensive survey to be distributed to a selected sample of the population. A poorly planned and designed questionnaire will not give good data with which to address your research questions. In preparing a questionnaire, you must consider how you intend to use the information; you must know what statistics you intend to use. Depending on the statistical technique you have in mind, you may need to ask the question in a particular way, or provide different response formats.

Some of the factors you need to consider in the design and construction of a questionnaire are outlined in the sections that follow. This section only briefly skims the surface of the questionnaire design, therefore I would suggest that you read further on the topic if you are designing your own study. A good book for this purpose is Oppenheim Question types Most questions can be classified into two groups: closed or open-ended.

A closed question involves offering respondents a number of defined response choices. They are asked to mark their response using a tick, cross, or circle etc. For example Yes can be coded as a 1, No can be coded as a 2; Males as 1, Females as 2.

In the education question shown above, the number corresponding to the response ticked by the respondent would be entered. For example, if they ticked University undergraduate then this would be coded as a 5. Numbering each of the possible responses helps with the coding process. For data entry purposes, decide on a convention for the numbering e. Sometimes you cannot guess all the possible responses that respondents might make—it is therefore necessary to use open-ended questions.

The advantage here is that respondents have the freedom to respond in their own way, not restricted to the choices provided by the researcher. What is the major source of stress in your life at the moment?

Responses to open-ended questions can be summarised into a number of different categories for entry into SPSS. These categories are usually identified after looking through the range of responses actually received from the respondents. Some possibilities could also be raised from an understanding of previous research in the area.

Each of these response categories is assigned a number e. More details on this are provided in the section on preparing a codebook in Chapter 2. Sometimes a combination of both closed and open-ended questions works best.

This involves providing respondents with a number of defined responses, and also an additional category other that they can tick if the response they wish to give is not listed.

A line or two is provided so that they can write the response they wish to give. This combination of closed and open-ended questions is particularly useful in the early stages of research in an area, as it gives an indication of whether the defined response categories adequately cover all the responses that respondents wish to give. The type of response format you choose can have implications when you come to do your statistical analysis.

Some analyses e. If you had asked respondents to indicate their age by giving them a category to tick less than 30, between 31 and 50 and over 50 , these data would not be suitable to use in a correlational analysis.

So, if you intend to explore the correlation between age and say, self-esteem, you would need to ensure that you asked respondents for their actual age in years. Try to provide as wide a choice of responses to your questions as possible. You can always condense things later if you need to see Chapter 8.

You will need to make a decision concerning the number of response steps e. DeVellis has a good discussion concerning the advantages and disadvantages of different response scales.

Whatever type of response format you choose, you must provide clear instructions. Do you want your respondents to tick a box, circle a number, make a mark on a line?

For many respondents this may be the first questionnaire that they have completed. Give clear instructions, provide an example if appropriate, and always pilot test on the type of people that will make up your sample. Iron out any sources of confusion before distributing hundreds of your questionnaires. In designing your questions always consider how a respondent might interpret the question and all the possible responses a person might want to make.

For example, you may want to know if people smoke or not. You might ask the question: Do you smoke? Is knowing whether they smoke enough? The message here is to consider each of your questions, what information they will give you and what information might be missing.

Wording the questions There is a real art to designing clear, well-written questionnaire items. Unfortunately there are no clear cut rules that can guide this process, however there are some things you can do to improve the quality of your questions, and therefore your data.

Oppenheim suggests a number of things that you should avoid when formulating your questions. For further suggestions on writing questions, see Oppenheim , pp. References Planning the study Goodwin, C. Research in psychology: Methods and design 2nd edition. New York: John Wiley. Stangor, C. Research methods for the behavioral sciences. Boston: Houghton Mifflin. Selection of appropriate scales Briggs, S. The role of factor analysis in the development and evaluation of personality scales.

Journal of Personality, 54, — Dawis, R. Scale construction. Journal of Counseling Psychology, 34, — DeVellis, R. Scale development: Theory and applications. Newbury, California: Sage. Nunnally, J. Psychometric theory. New York: McGraw-Hill. Oppenheim, A. Questionnaire design, interviewing and attitude measurement.

London: St Martins Press. Robinson, J. Criteria for scale selection and evaluation. Robinson, P. Hillsdale, NJ: Academic Press. Streiner, D. Health measurement scales: A practical guide to their development and use 2nd edition. Oxford: Oxford University Press. This is a summary of the instructions you will use to convert the information obtained from each subject or case into a format that SPSS can understand.

The steps involved will be demonstrated in this chapter using a data file that was developed by a group of my Graduate Diploma students. A copy of the questionnaire, and the codebook that was developed for this questionnaire, can be found in the Appendix.

The data file is provided on the website that accompanies this book see p. The provision of this material allows you to see the whole process from questionnaire development, through to the creation of the final data file ready for analysis. Although I have used a questionnaire to illustrate the steps involved in the development of a codebook, a similar process is also necessary in experimental studies.

All this information should be recorded in a book or computer file. In your codebook you should list all of the variables in your questionnaire, the abbreviated variable names that you will use in SPSS and the way in which you will code the responses. In this chapter simplified examples are given to illustrate the various steps. In the first column of Table 2. In the second column you write the abbreviated name for that variable that will appear in SPSS see conventions below , and in the third column you detail how you will code each of the responses obtained.

Variable names Each question or item in your questionnaire must have a unique variable name. Some of these names will clearly identify the information e.

There are a number of conventions you must follow in assigning names to your variables in SPSS. The first variable in any data set should be ID, that is a unique number that identifies each case. Before beginning the data entry process, go through and assign a number to each of the questionnaires or data records.

Write the number clearly on the front cover. Later, if you find an error in the data set, having the questionnaires or data records numbered allows you to check back and find where the error occurred. Some of the information will already be in this format e. If not, decide on a convention and stick to it. For example, code the first listed response as 1, the second as 2 and so on across the page. What is your current marital status?

Coding open-ended questions For open-ended questions where respondents can provide their own answers , coding is slightly more complicated. Take for example the question: What is the major source of stress in your life at the moment? To code responses to this you will need to scan through the questionnaires and look for common themes. You might notice a lot of respondents listing their source of stress as related to work, finances, relationships, health or lack of time.

When entering the data for each respondent you compare their response with those listed in the codebook and enter the appropriate number into the data set under the variable, stress.

Once you have drawn up your codebook, you are almost ready to enter your data. There are two things you need to do first: 1. In Chapter 3 the basic structure and conventions of SPSS are covered, followed in Chapter 4 by the procedures needed to set up a data file and to enter data. Before you can access these windows you need to either open an existing data file, or create one of your own. So, in this chapter, we will cover how to open and close SPSS; how to open and close existing data files; and how to create a data file from scratch.

We will then go on to look at the different windows SPSS uses. Place your cursor on the icon and double click. Whichever method you use to start SPSS, you should get a screen resembling the one displayed in Figure 3. If this is the case, you should get a blank spreadsheet, just waiting for you to either enter data or open a data file.

To open a file from the spreadsheet screen, click on File, and then Open, from the menu displayed at the top of the screen. Working with data files SPSS will only allow you to have one data file open at any one time.

You can however, change data files during an SPSS session. If you try to, SPSS just closes the whole program down. Instead, you ask SPSS to open a second file and it automatically closes the first one.

If you have made any changes to the first file, SPSS will ask if you would like to save the file before closing. This will allow you to search through the various directories on your computer to find where your data file is stored. Find the file you wish to use and click on Open. Remember all SPSS data files have a. The data file will open in front of you in what is labelled the Data Editor window more on this window later.

Hint If your data file is on a floppy disk it is much faster, easier and safer if you transfer your data file from the A: drive onto the hard drive usually the C: drive using Windows Explorer, before starting your SPSS session. Do your data entry or analyses on the hard drive and then, at the end of your session, copy the files back onto your floppy disk.

If you are working in a computer lab it may be necessary to check with your lecturer or lab supervisor concerning this process. Saving a data file When you first create a data file, or make changes to an existing data file e. This does not happen automatically, as in some word processing programs. So save yourself the heartache and save regularly. If you are entering data, this may need to be as frequently as every ten minutes or after every five or ten questionnaires.

To save a file you are working on, go to the File menu top left hand corner and choose Save. Or if you prefer, you can also click on the icon that looks like a floppy disk which appears on the toolbar at the top, left of your screen. Please note: although this icon looks like a floppy disk, clicking on it will save your file to whichever drive you are currently working on.

This may be either the hard drive if that is where your file is located or the A: floppy drive. When you first save a new data file, you will be asked to specify a name for the file and to indicate a directory and a folder that it will be stored in. Choose the directory and then type in a file name. SPSS will automatically give all data file names the extension. This is so that it can recognise it as a SPSS data file. Opening a different data file If you finish working on a data file and wish to open another one, just click on File and then Open, and find the directory where your second file is stored.

Click on the desired file and then click the Open button. Unlike a word processor, you cannot close one data file and then open another. You must have a data file open at all times. This allows you to set up a new data file. Click on this option and SPSS will give you a blank spreadsheet where you can name your variables and enter your data more about this later. If you are already in SPSS and have been using other data sets, but wish to start a new one of your own, click on File, and then from the drop-down menu, click on New and then Data.

Before you can start entering data and analysing it, you need to understand a little about the windows and dialogue boxes that SPSS uses. These are discussed in the next section. These windows are summarised here, but are discussed in more detail in later sections of this book.

When you begin to analyse your data you will have a number of these windows open at the same time. Some students find this idea very confusing.

Once you get the hang of it, it is really quite simple. You will always have the Data Editor open because this contains the data file that you are analysing. Once you start to do some analyses you will have the Viewer window open because this is where the results of all your analyses are displayed, listed in the order in which you performed them. The different windows are like pieces of paper on your desk—you can shuffle them around so that, sometimes one is on top and at other times, others.

Each of the windows you have open will be listed along the bottom of your screen. To change windows just click on whichever window you would like to have on top. You can also click on Window on the top menu bar. This will list all the open windows and allow you to choose which you would like to display on the screen. Sometimes the windows SPSS displays do not initially fill the full screen.

It is much easier to have the Viewer window where your results are displayed enlarged on top, filling the entire screen. To do this look on the top right hand area of your screen. There should be three little buttons or icons. If you wish to shrink it down again, just click on this middle icon again.

Data Editor window The Data Editor window displays the contents of your data file, and it is this window that you use to open, save and close existing data files; create a new data file; enter data; make changes to the existing data file; and run statistical analyses.

If you want to make any changes to your data file, or to save it, you must have this window open and on the screen in front of you. I would welcome and appreciate your assistance in arranging a compromise.

Haynes ManualsThe Haynes Author : Julie Pallant """Description:Praise for previous editions: ""This book really is a life saver If the mere thought of statistics gives you a headache, then this is the book for you. So, gold star and thanks. To browse Academia. Skip to main content.

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