SPSS统计分析软件基础教程(英文有图示)

An Introduction to SPSSOr PASWThe two laboratory sessions created for this course introduce students to the use of SPSS software.Section OneTo Should be completed by all studentsSection ThreeSection Four Further statistical analysis for you to tryIt is expected that students should complete the exercises up to and including Section Three within class time, if this is not achieved students should complete the exercises in their own time.Introduction to SPSSSection OneIntroductionSection One introduces the various screens and displays as well as explaining how to inputyour survey and your data.SPSS is one of the most popular statistical analysis packages in use today and has been around for well over 20 years. The latest version with the w indow‟s interface is particularly easy to use. The windows environment also facilitates the import and export of data, for example importing data from a spreadsheet and exporting results to a word document.The University holds a license that allows students to have a copy of SPSS on their own computers. The CD for installing the latest version of SPSS can be borrowed from the Library. Starting SPSSPASW Version 18 is the latest version. It can befound from Start(Bottom left hand corner),Programmes, SPSS Inc, PASW 18. The openingdisplay asks you to select one of a number ofoptions. At the moment click on the Red Cross toclose the box.The Opening DisplayThe window displayed iscalled the Data Editor; this isused for entering, editing andselecting data. The DataEditor has two views: theVariable View and the DataView you can flip from oneview to the other by using thetab at the bottom of the page. SPSS has a number of other windows including Output, Help and Tutorial.Help SystemThe SPSS Help System is very comprehensive; from the mainmenu select Help: a sub menu appears containing:Help Topics -.Arranged in three ways:1.Contents give help by general function.2. Index,gives an alphabetical listing of help topics. Enter a topic, then select amore specific topic from the list displayed (by highlighting it), click Display. Thisgives general information on the topic. SPSS is a very powerful programme andhas a variety of application, so don‟t worry about the various topics which will bemeaningless to you at the moment!3. Search, will find a given word or set of words in help.Tutorial - step by step tutorials are available on a number of topics.Statistics CoachA data set must be loaded in the Data Editor before this option can be used. Thiscould be data collected for your assignment or from questionnaires used in your dissertation. The statistics coach provides step by step guidance on the analyses that are permissible on the open data set.Entering DataThe best introduction to entering data in SPSS is to go through the SPSS tutorial on entering data. To do this select: Help, Topic, Entering Data.Data EditorVariable View contains descriptions of the attributes of each variable in the data file. In Variable View:• Rows are variables.• Columns are variable attributes.(For example: if you have produced a questionnaire each row represents a question or part of a question from your questionnaire.)1 2 3 4 5 6 7 8 9 10 11You can add or delete variables and modify attributes of variables, including the following attributes:1. Name-This is the name you allocate to the variableVariable names:∙Must be unique∙Can only have eight or less characters∙Must begin with a letter (not a number)∙Cannot include full stops, blanks or other characters (!, ?, @, etc); and∙Cannot include word used as commands by SPSS (all, ne, eq, to, le, It, by, or, gt, and, ge, with)NB The first variable in any data set should be ID, which is a unique number that identifies each case. Before beginning that data entry process, go through and assign a number to each of your 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 will allow you to check back and find where the error occurred.2. Type-the variable type defaults as numeric, leave it as this unless changing tocurrency (see later).3. Width- The number of characters displayed. The default here is 8, it is normallynot necessary to change it.4. Decimals-The number of decimal places displayed, the default here is two.5. Label-Allows for a more detailed description of the Name.6. Values-Values are code numbers applied to a stringed response7. Missing Values- defines specified data values as user-missing. For example,you might want to distinguish between data that are missing because arespondent refused to answer and data that are missing because the questiondidn't apply to that respondent. Data values that are specified as user-missing areflagged for special treatment and are excluded from most calculations.8. Columns- Simply the width of the columns: defaults at 8.9. Align- The position of text within the column: defaults to the right.10. Measure- You can specify the level of measurement as scale (numeric data onan interval or ratio scale), ordinal, or nominal.•Nominal. A variable can be treated as nominal when its values representcategories with no intrinsic ranking (for example, the department of thecompany in which an employee works). Examples of nominal variablesinclude region, zip code, and religious affiliation.•Ordinal.A variable can be treated as ordinal when its values representcategories with some intrinsic ranking (for example, levels of servicesatisfaction from highly dissatisfied to highly satisfied). Examples of ordinalvariables include attitude scores representing degree of satisfaction orconfidence and preference rating scores.•Scale. A variable can be treated as scale when its values represent orderedcategories with a meaningful metric, so that distance comparisons betweenvalues are appropriate. Examples of scale variables include age in years andincome in thousands of pounds.11. Role -Some dialogs support predefined roles that can be used to pre-selectvariables for analysis. When you open one of these dialogs, variables that meetthe role requirements will be automatically displayed in the destination list(s).Available roles are:Input. The variable will be used as an input (e.g., predictor, independent variable).Target. The variable will be used as an output or target (e.g., dependent variable).Both. The variable will be used as both input and output.None. The variable has no role assignment.Partition. The variable will be used to partition the data into separate samples fortraining, testing, and validation.By default, all variables are assigned the Input role.All of these attributes are saved when you save the data file.Data ViewThe variables defined in Variable View as rows are transferred in the Data View as columns. Data View is where you input your responses to your survey etc. Each row represents one respondent.The screen above depicts the variable attributes as values; thiscan be changed to value labels by selecting View, and ValueLabels.Preparing a CodebookBefore you can enter the information from a questionnaire into SPSS you need to prepare a …codebook‟. This is in effect a summary of the instructions you will use to convert the information obtained from each questionnaire response into a format that SPSS can understand. Preparing a codebook involves deciding (and documenting) how you will go about:∙Defining and labelling each of the responses to the questions in your questionnaire (variables); and∙Assigning numbers to each of the possible responses.All this information should be recorded in a book or computer file. Keep this somewhere safe, there is nothing worse than coming back to a data file, which you may have not used for a while, and wondering what the abbreviations and numbers refer to. In your code book you should list all of the variables used in your questionnaire, the abbreviated variable names that you will use in SPSS and the way in which you will code the responses.Coding ResponsesEach response must be assigned a numerical code before it can be entered into SPSS. Variable responses which are already in numerical format i.e. age in years, do not require to be coded, other variables such as gender will need to be converted to numbers (1 = males, 2 = females). If you have used numbers in your questions to label your responses this is relatively straightforward. If not decide on a convention and stick to it. For example, code the first response as 1, the second as 2 and so on.To code responses to the question above: if a person ticked single they would be coded as 1; if in a relationship, they would be coded 2; if married, 3; and if divorced, 4.Table 1 below indicates an example of a possible code book for a questionnaire.Coding Open-ended QuestionsFor 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 questionnaire and look for common themes, (e.g.) work, finance, relationships, health or lack of time might be highlighted a number of times. In your codebook you would list these major groups of responses under the variable stress, and assign a number to each (work = 1, finance = 2, relationships = 3 and so on). You may also need to add another numerical code for responses that did not fall into these listed categories (other = 9). When entering that data for each respondent you compare their response with those listed in your codebook and enter the appropriate number into the data set under the variable stress.Job Survey Parts 1 and 2 ExercisesThroughout the SPSS sessions we will be referring to the Job Survey. In Part 1 the questionnaire targets employees of a local organisation; in Part 2 the questionnaire targets supervisors of the same employees.Exercise 1.1: Coding the SurveyOn the Job Survey questionnaire sheet (See Appendix 1.1), in the allocated space, assign codes to the possible responses.Exercise 1.2: Entering the Survey into a Blank Data SheetIf restarting SPSS, select Type in Data and ensure the Data Editor is showing Variable View.Enter each question from the Survey intoData Editor.Start by allocating an Identification Number toeach respondent, enter the variable Name asID, change the Decimals to0, and leave allother columns at the default settings. SeeAppendix 1.2 for variable namesContinue on to row 2 with Question 1 in the survey; the variable name will be Ethnicity, change the Decimals to 0. Add the coded values by clicking in the values cell (on the appropriate row) and click on the grey box which will appear. Type 1 for the value and White/European for the value label, and then click ADD, continue until all variable attributes are completed, click OK. Finally, change Measure to Nominal as this is categorical data but is not ranked.Continue to enter all of thequestions from thequestionnaire.Exercise 1.3: To Change the FormatCurrency FormatQuestion 3 in the survey asks about salary. We can change the format of this data to currencyformat i.e. all data relating to the information willbe prefixed with the £ sign. To define currency inthe Type column click on the grey box, selectCustom Currency, CCA, and change to 0decimal places.Continue to enter all of the questions from thequestionnaire.Exercise 1.4: To Input Responses into the Data EditorTo input the responses to the questionnaire ensure the Data Editor is showing Data View. To allow you to practice doing this refer to Appendix 1.3. Appendix 1.3 is an extract of responses given to the Job Survey up to Question 6d. Input these responses into Data View.Missing ValuesOne of the most common problems encountered with using questionnaires is that some questions are not fully answered. These gaps in your questionnaire are called missing values or missing data. If this occurs you have to think why the questions have not been answered and what you can do about it. There are four possibilities why this might happen: ∙The question did not apply to the respondent∙The respondent refused to answer this particular question∙The respondent did not know the answer∙The respondent simply forgot to answer the questionIf whilst inputting your data you find you have missing values, go back to Variable Viewand at the appropriate variable, click on the box to the left of the Missing Value cell and select as below.For the purposes of this survey all missing values willbe coded as 0, with the exception of Absence whichwill be coded 99.Remember to SAVE your work to My Documents.Introduction to SPSSSection TwoIntroductionSection Two explains how to view, manipulate, display and describe your data.Exercise 2.1: To load SPSSStart, Programs, SPSS.Click Cancel on the dialogue box to remove it, you are then left with the Data Editor.Data Editor has a menu bar with many options, as shown below, these include:File: Used to access any files whether you want to Open an existing SPSS file or read data from another application such as Excel or dBase, or start a New file. It is also the menu option you choose to Save files.Edit: Can be used to alter data or text in the Data View or the Variable View.View: Used to alter the way your screen looks. Please leave this on the default settings. Data: Used to define variables and make changes to the data file you are using. Transform: Used to make changes to selected variable(s) in the data file you are using. This can include recode(ing) existing variables and compute(ing) new variables.Analyze:Used to undertake a variety of analyses such as producing Reports, calculating Descriptive Statistics such as Frequencies and Crosstabs(cross tabulations) and associated summary statistics, as well as various statistical procedures such as Regression and Correlation.Graphs: Used to create a variety of graphs and charts such as Bar, Line and Pie charts. Utilities:Is for more general housekeeping such as changing display options and fonts, displaying information on variables.Add-ons: Includes services available such as statistical help.Window: Operates in the same way as other Windows packages.Help:A context sensitive help feature which operates the same way as other Windows packages.Exercise 2.2: To load a previously created SPSS for Windows data fileSPSS for Windows saves data files using a filename of up to 8 characters and the file extension .SAV, for example job survey.savIn the Menu Bar click on the following:File (main menu bar), Open (in File menu), exchange on Wide (X:), Data, IB04A dialogue box similar to that below will appear:Open the file by clicking on JOB SURVEY and then on the button (or double click on the file name).You will now see the file appear in the Data View and the filename above the menu bar change to JOB SURVEY.SAV SAVE this file to H:drive and open the file from H:drive for use in today‟s session. (Exchange on Wide is a shared network; therefore you can not change and save to this network).Exercise 2.3: To undertake a frequency distributionIt is always a good idea to “eyeball” your data; this can be done using Frequencies.∙Click on Analyse, Descriptive Statistics, Frequencies∙Opens the Frequencies dialogue box.∙Highlight ethnicity in the left hand box by clicking on it∙Click on the button to move ethnicity into the Variable(s) box(Note how the arrow button changes direction and the cursor moves to the Variable(s) box. This is to allow you to reverse your decision if you wish.)∙Click on OKYou will now see a series of tables displayed in the SPSS Output Viewer. Note how SPSS first tells you if there are any missing cases. For this variable there are no missing cases.∙Use the and arrows to scroll down and across to view the actual frequencies table. Note how SPSS lets you know if there are any missing casesand calculates the valid percent appropriately. The valid percentages takeaccount of any missing values. If they were none then the percent and validpercent would be the same.∙Repeat this process using Analyze, Descriptive Statistics, Frequencies for Income and Age. You can do this by pointing and clicking on the menucommands which are visible at the top of your screen.Whilst you are doing this explore the effect of thebutton on your output.∙To remove the variables from the right Variable(s)box within the dialogue box either:click on the buttonor highlight the variable in the right Variable(s) box and click on the button ∙To quit this analysis (for example if you make a mistake) click on the buttonYou may (or may not!) have noticed that each of the tasks you have performed in SPSS have been automatically appended to the SPSS Output Viewer. You can see this by scrolling through your output window using the up and down arrows on the right of the window.You can edit the SPSS Output Viewer and save it, or parts of it, to a file which can subsequently be read into a word processor. SPSS output files are suffixed with .spv. Alternatively you can print it out directly.TIP∙To delete some output in the SPSS Output Viewer:click on the area you want to delete, a line will appear around it.press the delete key on the keyboardTo delete all the output in the SPSS Output Viewer:Ensure that the SPSS Output Viewer window is maximisedIn the SPSS Output Viewer click on Edit, Select, All, press the delete key on the keyboardExercise 2.4: To save the OutputTo save the contents of the SPSS Output Viewer to a fileEnsure that the SPSS Output Viewer window is maximisedClick on File, Save asType in the filename you wish to save it to in the File name box, making sure the file type is *.spvEnsure that the file is being saved to the correct drive and directory (i.e. H:drive)NB Donot close without saving or you will loose all of your output. The Output viewer works like a scroll, each new analysis is added to the end of the previous analysis.Exercise 2.5: To produce charts∙Click on Graphs, Chart Builder (If a dialogue box appears click okay).∙Select Simple bar graph by clicking on it and dragging to the canvas.∙Then click on and grab ethnicity from the column of variables, onto the x-axis∙Click OKYour graph will now appear in the Output Viewer.∙Double click on the bar chart and a smaller box appears entitled chart editor.∙Click on one of the bars, all of the bars should now be highlighted with a blue line.∙From the Properties box select Fill and Border; change the colour of your bars.∙Click on Elements, then show data labels.∙In the Properties box, you can add/remove things from the area marked display from the box marked not displayed which are then added to your graphic byusing the red cross to remove and the green arrow to add. Add percentages toyour data labels.Exercise 2.6: To produce a clustered bar chartIt may make more sense to compare two groups, forexample men and woman. A clustered bar chart will show this.∙Click on Graphs∙Click on Chart Builder.∙Click Bar and Drag the Cluster bar chart icon to the canvas∙Drag Ethnicity from the Variables list to the X-axis and Gender to Cluster on X: set colour∙As the two groups male and female are not of equal numbers, 39 men: 31 women, it would give a more accurate comparison to change Count to Percentage. In the Element Properties box, under Statistic, use the arrow to find percentage.∙Click Apply∙Click Ok∙In Chart Editor show the Percentage Data Labels∙Under Number Format, change the decimal places to 0Exercise 2.7: Produce a Stacked Bar∙Produce a Stacked Bar variation of the above chartExercise 2.8: To produce a pie chart∙Click on Graphs∙Click on Chart Builder.∙From Gallery select Pie/Polar∙Drag the Pie chart icon onto the canvas∙Drag Ethnicity to the x-axis from the variables list∙Change Count to Percentage on the y-axis (Element Properties, Statistics)∙Click Apply∙Click OKYour Pie chart will now appear on the Output Viewer.Exercise 2.9: To present your graph in Word∙Open Microsoft Word.∙Select one of the graphs you have produced in the Output Viewer and give it a title.∙Click on the icon circled below or Options, Title∙Give your graph a suitable title∙Close Chart EditorYour chart with a title should have a box around it, if not click on your chart.∙Right click on your chart and select Copy.∙Paste your chart into your open Word document∙Give your chart a numbered title by selecting Insert, Reference, Caption, decide on position and click OK.∙Immediately after the Figure number, title your chart again.∙Adjust the size of your chart by clicking on it and manipulating the black boxes at the corners.Figure 1 Ethnicity of Respondents by GenderExercise 2.10: To recode variablesWhen a variable is a scale measure such as at the interval/ratio level, it normally has to be grouped to be presented as a frequency table. Income from the Job Survey is such a variable. To examine the data:∙Click on Analyse, Descriptive Statistics, Frequencies, select Income and click on the Blue Arrow (income should appear in the variables box)∙Select Statistics, select Minimum and Maximum, press Continue then OKOutput Viewer displays the data for Income, you can see the minimum income is £11800 and the maximum is £21000, and there are thirty-three different categories which are too many to produce a meaningful chart or table. We must, therefore reduce the amount of categories by grouping them. Six new categories seems a useful number. To do this we must Recode the data and give the income category a new variable name called incomegp (income group).∙Transform, Recode,Variables (opensRecode into DifferentVariables box)∙Select Income andplace in the NumericVariables… box∙Type Incomegp intoName∙Select Change∙Select Old and NewValues (opens Old andNew Values box)System or UserMissing and under NewValue select SystemMissing, select Add∙Select Range:Lowestthrough and type11999, under NewValue type1, selectAdd∙Select Range and type12000 and through andtype13999, under NewValue type 2, select Add∙Repeat this until you reach 19999∙Select Range Value: through Highest and type20000, under New Value type6, select Add∙Select Continue∙Select OKYou have now created a newvariable called Incomegp, it is nownecessary to allocate value labelsto the new variable.NB this variable is no longer ascale measure as you havetransformed it to categorical; themeasure should therefore bechanged to Nominal.∙Select Graphs, Chart Builder, Bar to produce a graph of your new category.You have now produced a HistogramFrom the graph, the Income group £16000-17999 is the most common amongstemployees.Exercise 2.11: To create a table (crosstab) of one variable against anotherA useful feature of SPSS is its ability to create crosstabulations (or a table) of one variable against another.You will now create a table of the variable incomegp by the variable gender.∙Click on Analyze, Descriptive Statistics, Crosstabs, (opens dialogue box:)∙Select the Row(s)variable incomegp and the Column(s)variable Gender using the same principles as when selecting frequencies∙Once a row and a column variable have been selected you will be able to click on OKThe Case Summary and the Crosstab will appear in Output Viewer. There are two missing values. Both men and women‟s income is the most common at £16000-17999; however, only women earn more than £20000.NB Crosstabs are an appropriate way to analyse a categorical variable against another categorical variable.Exercise 2.12: To add row and column percents to a table using crosstabs∙Repeat Exercise 11 but before selecting OK∙Click on the button∙Click on Row and/or Column and/or Total in thePercentages dialogue box (see below) to obtain thedesired percentages.∙Click on Continue, OKExercise 2.13: To calculate the arithmetic mean (average), median, mode and the standard deviation∙ Click on Analyze,Descriptive Statistics, Frequencies ∙ Select the Agevariable, then click on the right arrow to put it in the Variable(s) box. ∙ Now click on .You can see from the box to the right that there are a number of choices available to you. Depending on what measure of central tendency is required you can choose from one value or all three.∙ Select the boxesshown∙ Click Continue , then the OK button.Output Viewer as well as displaying the frequencies,displays the statistics that were selected.We can see the average age of respondents is 39.19 years, the minimum age is 18 and the maximum age is 63 years.∙ Repeat this exercise to find the average income of respondents.Obviously calculating a mean makes sense here as we are working out the average age. However we have tobe careful....Categorical variables are given a code, so if your were to ask SPSS to calculate the mean of gender for example, SPSS would take the codes for male (1) and female (2), add them all up and divide by the number of observations, which would result in a mean gender of 1.44 which doesn‟t make sense! It is therefore important that you decide what statistic makes sense for the type of data you have.Your choices for the most appropriate measure of central tendency are:Statistics ageN Valid 69Missing 1 Mean 39.19Median 39.00 Mode 29Std. Deviation 12.317 Range 45Minimum 18 Maximum 63∙Mean: Normally known as the average of the data values, used to measure interval variables but can be skewed if data contains extreme values∙Median: The mid point once all the data values have been ranked: used to measure interval and ordinal data. The median is less distorted by extreme data than the mean.∙Mode: The data value that occurs most often and the measure of central tendency that is used the least. The mode can be employed for all measurement levels but is sometimes difficult to interpret.∙Repeat the above exercise for ethnicity but also check the box to produce a bar graph.AS can be seen the Median and the Mode both indicate the Code 1 which relates to White, but sometimes it is just as effective to look at your data: the graph of ethnicity clearly depicts the white ethnic group as the most common.Exercise 2.14: To select cases for separate analysesIn the above exercise we discovered the average income of respondents is£15638.24. Sometimes we need to extract sub-sets for further analysis and we dothis by selecting cases.∙Select Data, Select Cases∙Select If condition is satisfiedin the dialogue box∙Click on IF∙Select gender and moveover to box using the blue arrow∙Use the numbers andsymbols within the dialogue boxand type in=2 (this means youare only selecting females)∙Select Continue∙Select OK∙Now go to Analyse,Descriptive Statistics,Frequencies∙Select Income (ensure the previous statistics are still selected)∙Select OKThe Output Viewer will reveal the average income for women is £15554.84.What is the average income for men?NB When you have finished analysing your subsets remember to go back into Data, Select Cases and Select All Conditions, to bring all of your data back into play.Exercise 2.15: To recode the value of a variable。