Intermediate SPSS. StatLab Workshop February 21, Sherlock Campbell & Oriana Aragón

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1 Intermediate SPSS StatLab Wrkshp February 21, 2013 Sherlck Campbell & Oriana Aragón SPSS Syntax This sectin prvides an intrductin and sampling f basic syntax in SPSS. All illustratins and details are intended t apply t SPSS v19 fr Windws (and mst Macs). Mst f the infrmatin will be the same fr ther versins, but there may be discrepancies. Syntax refers t the cmputer language SPSS uses t cmplete analyses. While mst cmmnly used cmmands are available thrugh the menu system f SPSS (pint & click) many mre ptins and functinalities are available using syntax. Why Use Syntax? Using syntax can save a great deal f time when running repetitive analyses. It is als an easy way t dcument yur wrk. It allws yu t instantly duplicate that wrk with a new (r updated) data set. It allws yu t 'tweak' yur analysis in ways nt available thrugh dialg bxes If yu'd like t fllw alng, please pen the sample data file satisf.sav' It shuld be lcated in C:\Prgram Files\IBM\SPSS\Statistics\19\Samples\English\satisf.sav. Creating and Saving a Syntax File: Syntax files in SPSS are plain text files with an extensin f '.sps'. 1) Duble click and it will pen t the data file- variable view. 2) Next pen a new syntax file: File > New > Syntax 3) T save the file File >Save > (Dialgue bx will pp up. Name and designate lcatin) Archiving Prcedure and Describing Data 1

2 1) Syntax files in SPSS are plain text files with an extensin f '.sps'. Yu can create syntax several ways. Prbably the easiest way t start using syntax is by using the 'paste' buttn available in mst dialg bxes. It is seen belw in the Descriptives dialg bx. First select the ptins yu wish frm the dialg bx, then, instead f clicking 'OK', click 'Paste'. If yu d nt have a syntax windw pen already, this will pen a new syntax windw cntaining the cmmands yu selected in the dialg bx as seen belw. If yu already have a syntax windw pen, the cmmands will be pasted at the bttm f the currently active syntax windw. The Syntax Editr allws yu t edit a plain text file and submit selected cmmands t SPSS directly. Hit the green arrw (run cmmand) and yu will see n yur utput viewer the descriptive statistics fr these data. 2

3 Next, let s say that we want every variable in the data set. Likewise if yu wuld nly like the mean and standard deviatin, yu can eliminate the MIN and MAX frm the /STATISTICS = Yu can add ntes (using *), cut, paste and edit just as with any ther text file. Structure f Cmmands in SPSS syntax Cmmands in SPSS begin with a keywrd that is the name f the cmmand fllwed by any subcmmands and user specificatins. The end f the cmmand is marked by a perid/full stp. In SPSS syntax files, cmmands must always be placed in the first clumn. Refer t the Cmmand Syntax Reference fr a discussin f available cmmands and ptins. It can be fund in the menus under Help > Cmmand Syntax Reference. Example: the FREQUENCIES cmmand: FREQUENCIES prduces tables f frequency cunts and percentages f the values f individual variables. FREQUENCIES is used t btain frequencies and statistics fr categrical variables and t btain statistics and graphical displays fr cntinuus variables. By default, SPSS will paste syntax with cmmands and specificatins in all caps, and will display variables as yu have entered them. Cmmands and specificatins d nt have t be entered in all caps, but I will cntinue t display them that way t help differentiate them frm variables. In the syntax belw, just as pasted frm the dialg bx, 'sex' and 'race' are 3

4 the variables that I selected. NOTE: Variable names in SPSS are generally separated by spaces. FREQUENCIES VARIABLES=gender regular /ORDER= ANALYSIS. In the syntax windw there is a very useful tlbar buttn called 'Syntax Help'. It is cntext sensitive, meaning that it will display a syntax chart fr the cmmand where the cursr is currently lcated. Clicking n the 'Syntax Help' buttn prvides the fllwing infrmatin abut the FREQUENCIES cmmand. Dn't let the lng list intimidate yu. Many peple are surprised t learn that FREQUENCIES has s many subcmmands and specificatins! Subcmmands and specificatins in square brackets ([ ]) are ptinal, and thse in braces ({ }) indicate a chice between elements. Lk clsely, there are nly tw wrds that are NOT in brackets, FREQUENCIES and 'varlist'. This means that yu can run frequencies n the tw variables 'sex' and 'race' by typing the fllwing int a syntax editr windw: FREQUENCIES gender regular. There are many abbreviatins allwed in syntax (generally the first 3 r 4 letters f a cmmand/specificatin will suffice.) S the fllwing syntax will prvide the same utput: FREQ gender regular. Dn't frget the perid at the end f the cmmand. The 'Syntax Help' buttn prvides the skeletn f the cmmand yu are using, but des nt prvide a detailed explanatin f each pssible subcmmand and/r specificatin. That infrmatin is fund in the Cmmand Syntax Reference (use the menus: Help >Cmmand Syntax Reference). 4

5 Nw lets add a little mre t the syntax. The fllwing will prvide descriptive statistics and barcharts fr ur tw variables: FREQUENCIES gender regular /STATISTICS=STDDEV MINIMUM MAXIMUM MEAN MEDIAN MODE /BARCHART. If yu want t add dcumentatin t yur syntax file, indicate the start f a cmment with an asterisk (*). Everything between that asterisk and the next perid will by ignred by SPSS. Remember nt t add ther perids in yur dcumentatin if yu use this methd, since SPSS will try t interpret everything after the perid as cmmands. Anther methd is t use /* and */ t set ff a cmment. That is, start with /*, insert yur cmment f as many r as few wrds and lines as yu want then end the cmment with */. Remember nt t include any perids in the cmment using this technique either. Missing Values, Variable and Value Labels Missing Values Creating a well dcumented data file can be quite tedius. Syntax statements can make the prcess a little less painful. The MISSING VALUES cmmand declares values fr variables as user-missing. User-missing values are then treated the same as the system-missing values. (That is, they are usually ignred.) Multiple missing values are separated by cmmas, and a range f missing values may be declared using the keywrds LO, LOWEST, HI, HIGHEST, and THRU. If the variable(s) are strings, enclse the missing values in single qutes. Still using the 'dem' file frm abve; the fllwing cmmand sets values f the variable 'age' higher than 99 (including 99) and values f 'regin' equal t 999 t user-missing. Dn't frget the perid at the end. MISSING VALUES age (99 THRU HIGHEST) regin (999). T cancel previusly declared missing values, simply reassign the missing values t blank (use () in the previus statement.) T remve all missing values settings at nce, use the fllwing cde: MISSING VALUES ALL (). 5

6 Variable and Value Labels VARIABLE LABELS assigns descriptive labels t variables in the data file. Yu can assign a label t ne variable r t a lng list at the same time. The fllwing syntax assigns labels t tw variables in the active file. NOTE: Each variable label can be up t 120 characters lng, althugh mst prcedures will nly print fewer than the 120 characters. All statistical prcedures display at least 40 characters. VARIABLE LABELS cntact 'Cntact with Emplyee' regular Shpping Frequency. In general, syntax will ignre spaces and lines within cmmands and subcmmands. It is ften easier t read a syntax file if yu add spaces and start new lines t create clumns, as belw. VARIABLE LABELS cntact 'Cntact with Emplyee'. The VALUE LABELS cmmand assigns descriptive labels t values f variables in the data file. Many peple cnfuse variable and value labels when they are new t them. Variable labels describe the variables and value labels allw yu t assign descriptins t particular values f a variable. In the 'dem' file, cntact is either 0 r 1. Value labels help yu t remember whether 0 means married r nt married. VALUE LABELS cntact 0 'n' 1 'yes'. NOTE: The VALUE LABELS cmmand deletes all existing value labels fr the specified variable(s) and assigns new value labels. The ADD VALUE LABELS cmmand can be used t add new labels r t alter labels fr specified values withut deleting existing labels. T create value labels fr additinal variables just list the next variable after the last value label f the previus, fllwed by the value labels in single qutes. Remember t put a perid nly at the very end f the cmmand. 6

7 Data Management: COMPUTE, RECODE, SPLIT FILE, and FILTER In this sectin we will g ver creating new variables (COMPUTE), recding the values f existing variables (RECODE), running the same analysis n subgrups (SPLIT FILE) and using filters t select subsectins f yur data (FILTER). The COMPUTE cmmand The COMPUTE cmmand creates new numeric variables r mdifies the values f existing string r numeric variables. Yu may be familiar with the dialg bx, accessed by clicking Transfrm >Cmpute BUT, It is ften mre efficient t write COMPUTE statements in syntax, instead f 'pinting and clicking' yur way t a new variable. While the dialg bx is especially useful fr functins yu are nt familiar with, I find it faster t cde cmmn frmulas directly in syntax. The examples belw illustrate why cmputing with syntax might save yu sme time. Nte the differences between the 4 different cmputatins f the Satisfactin. 7

8 Nte: Syntax will nt warn yu if yu are abut t rewrite an existing variable. But yu will have recrd f what yu have dne. Ntice the EXECUTE statement at the end. SPSS requires this at the end f COMPUTE cmmands, unless a prcedural cmmand fllws it (i.e. FREQUENCIES, r ther statistical analyses.) If yu frget and run the syntax withut it, the Data Editr windw will display "Transfrmatins Pending" in the bttm display area. Either add the EXECUTE statement t the syntax, highlight it and run it, r g t the menus and click Transfrm >Run Pending Transfrms t cmplete the cmmand. EXECUTE can be shrtened t EXE. The RECODE cmmand The RECODE functin allws yu change the values f a variable. Fr example, it is smetimes useful t reverse cde respnses t a survey (change the highest respnse t the lwest and vice versa.) Yu may als need t cllapse categries fr an analysis. In general, it is safer t recde int a new variable, rather than change an existing ne, s I will nt address that ptin here. Here is an example frm ur data set. We wuld like t change distance frm stre int a binary variable where peple have traveled either mre r less than 10 miles t shp: Ntice, at this time it makes sense t apply desired variable and value labels. The RECODE cmmand needs a list f variables t act upn (yes, yu can recde many variables at nce by listing them after the RECODE statement). The variable(s) are fllwed by a list f recdes each enclsed in parentheses. Here is an example, splitting the satisfactin variables int lw and high grups. 8

9 The keywrds MISSING and SYSMIS bth refer t missing values. MISSING includes bth systemmissing values (n value entered in the data set) and user-missing (values entered but set t missing by the user.) SYSMIS refers nly t system-missing values. Since I included MISSING, I did nt need t include SYSMIS. RECODE price (1=0) (2=0) (3=0) (ELSE=SYSMIS) INTO price_lw. EXECUTE. Remember that yu can get reference infrmatin by clicking n the 'Syntax Help' buttn in the Syntax Editr, r by lking in the Cmmand Reference. Anther great recde feature that is nt available thrugh the dialgue bxes is the ability t recde given particular If statements. Let s say that we want t create a variable that tells us if the custmer wh answered the survey was an lder wman r nt, because we are curius if the lder female custmers were getting the same attentin as the ther shppers by the sales assciates. With syntax we can create a variable with tw (r mre) cnditinal statements. Here is what that wuld lk like. COMPUTE FemOlder = 0. EXECUTE. If (Gender = 1) and (agecat > 4) FemOlder = 1. EXECUTE. Nw we have a variable that we can crss with Cntact t check if ut ur ideas. The SPLIT FILE cmmand If yu are interested in running the same analysis n a set f subgrups in yur data yu can use SPLIT FILE t accmplish this. Using the survey data, let's see if the distributin f educatin is different fr males and females. We'll use SPLIT FILE and FREQUENCIES t get the utput we're lking fr. SPLIT FILE requires that the data be srted by the variable(s) we want t split by. The SORT CASES cmmand will srt by the variables listed after the cmmand. The default is t srt ascending. If yu want t srt descending, add (D) after that variable. SORT CASES BY cntact. SPLIT FILE SEPARATE BY cntact. FREQUENCIES Satisfactin /HISTOGRAM. 9

10 SORT CASES BY cntact. SPLIT FILE LAYERED BY cntact. FREQUENCIES Satisfactin /HISTOGRAM. SPLIT FILE OFF. By default, SPLIT FILE will prduce utput with the values f the split variable as the utermst clumn entries f a table (the LAYERED BY ptin.) If yu want split file grups t display in separate tables, use SEPARATE BY instead. It is a gd practice t 'turn ff' splits as sn as yu cmplete the analysis. SPLIT FILE will be verridden by a later SPLIT FILE cmmand. Until SPLIT FILE OFF is entered, all analyses will be carried ut n a split file. The FILTER and SELECT IF cmmand Filter variables (als knwn as indicatr variables and dummy variables) are used t identify cases that meet the criteria yu have specified. Fr example, let's create a variable t identify all the wmen in the wh have had cntact with a sales emplyee and live within 10 miles f the stre that they visited. T accmplish this enter: USE ALL. COMPUTE filter_$=(gender=1 & cntact=1 & distance >2). VARIABLE LABELS filter_$ 'gender=1 & cntact=1 & distance >2 (FILTER)'. VALUE LABELS filter_$ 0 'Nt Selected' 1 'Selected'. FORMATS filter_$ (f1.0). FILTER BY filter_$. EXECUTE. The USE ALL cmmand is added by the dialgue bx t ensure that n ther filters are active when creating the new variable The COMPUTE cmmand creates a new variable 'filter_$'. This is the default SPSS name fr filters. If yu used the dialgue bxes t create anther filter, it wuld als be named 'filter_$' which wuld verwrite the previus filter. Yu culd, f curse, rename the filter after using the dialgue bxes, but it is surprisingly easy t frget. Using syntax, yu can name the variable smething mre useful as yu create it. The next three lines f syntax (VARIABLE LABEL, VALUE LABEL, and FORMAT) are nt abslutely necessary but are very helpful fr dcumentatin sake, nt t mentin ease f use f the data set. Remember, if yu change the name f the filter variable, yu need t change 'filter_$' t the new name in each f the next fur lines as well. The next line f the syntax applies the filter t yur data. FILTER BY filters ut all cases where the filter variable is 0. In ther wrds, applying a filter selects nly thse cases where the filter variable equals 1 r greater. The SELECT IF statement is anther way t select subsets f cases. Instead f using a filter variable, the lgical expressin (r frmula) t select cases is specified in the cmmand. The majr difference is that this cmmand permanently deletes nn-selected cases. S, SELECT IF can be used if yu need t create a new data set that is a subset f existing data.fr example, the fllwing syntax will prvide a data set that includes nly females: 10

11 SELECT IF (gender = 1). SELECT IF will evaluate the expressin yu enter between the parentheses as either True, False, r Missing; all the False and Missing cases are drpped frm the active data set. Using the TEMPORARY cmmand will allw yu t return t the riginal data set nce a cmmand that reads the data is run. That is, temprary transfrmatins nly apply until the next cmmand that reads data and are n lnger in effect nce that cmmand has run. S, the fllwing syntax will temprarily filter ut males, and then run DESCRIPTIVES fr the variables 'happy' and 'life'. Since the DESCRIPTIVES cmmand reads the data, it turns ff the TEMPORARY cmmand. Repeating the same DESCRIPTIVES cmmand will then act upn the entire data set, nt just females. TEMPORARY. SELECT IF (gender=1). DESCRIPTIVES distance verall. Which is better; SELECT IF r creating a filter? That depends n hw yu prefer t wrk in SPSS. If yu are writing a syntax file t dcument an analysis, it may be easier t fllw if yu use SELECT IF, particularly if yu need t run nly ne cmmand n a subset f data. If yu need t run multiple cmmands n a subset f data, it may be easier t use filters t subset yur data, depending upn hw yu need t split the data. If yu are in the midst f an analysis and are switching back and frth frm syntax t pint & click, it is useful t have permanent filter variables instead f re-creating them each time yu want t use them. Preparing Data fr Analysis Syntax can als be helpful in keeping a gd recrd f the preparatin that has gne int a data set BEFORE hypthesis testing begins. Fr example let s cnsider ur data set. Let s say that we want t test ut custmer satisfactin. We have 6 different variables regarding custmer satisfactin. The ideal is t test these items fr nrmality, t cnfirm a single latent factr in these items (f satisfactin), and t cnfirm this idea with reliability analysis. Syntax can serve as a gd recrd f due diligence in data preparatin. T run frequencies: FREQUENCIES VARIABLES=price numitems rg service quality verall /STATISTICS=STDDEV MEAN MEDIAN /HISTOGRAM NORMAL /ORDER=ANALYSIS. T identify latent factrs: We can use the dialgue bx under Analyze > Dimensin Reductin > Factr 11

12 Next Click n Extractin. In this case, we will allw fr 3 factrs t emerge. Under Extract click Fixed number f factrs and then specify 3 in the dialgue bx. Press Cntinue. Once back t the main dialgue bx, click PASTE. Here is the resulting syntax. FACTOR /VARIABLES price numitems rg service quality verall /MISSING LISTWISE /ANALYSIS price numitems rg service quality verall /PRINT INITIAL EXTRACTION ROTATION /CRITERIA FACTORS(3) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX 12

13 /METHOD=CORRELATION. The Output : Rtated Cmpnent Matrix a Cmpnent Price satisfactin Variety satisfactin Organizatin satisfactin Service satisfactin Item quality satisfactin Overall satisfactin Extractin Methd: Principal Cmpnent Analysis. Rtatin Methd: Varimax with Kaiser Nrmalizatin. a. Rtatin cnverged in 4 iteratins. The Rtatin Matrix lets us knw that indeed nt all items lad nt nly ne factr. Indeed it appears that what seems t lad with Overall satisfactin are the variables f Price, Variety, and Service. The items f Organizatin satisfactin and the Item quality satisfactin seem t have different riginatins and pssibly shuld nt be cnsidered in the same scale. But we d nt need t guess abut this, we can nw run reliability analysis that will prvide fr us inter-item crrelatins s that we may verify the internal cnsistency f this dependent variable f satisfactin. T run reliability: G t Analyze > Scale > Reliability Analysis Yu will see a dialgue bx such as this: 13

14 Press the Statistics Buttn and check ff item, scale, scale if item deleted, means, variances and then hit cntinue. Once back t the main dialgue bx, enter the variables f interest Then hit Paste. Yu will see a syntax like this 14

15 RELIABILITY /VARIABLES=price numitems rg service quality verall /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL MEANS VARIANCE. Let s run it and make sme decisins abut ur measure f satisfactin. The analysis tells us that althugh Item quality satisfactin is nt really hurting us, Organizatin satisfactin actually is. Scale Mean if Item Deleted Item-Ttal Statistics Scale Variance if Item Deleted Crrected Item-Ttal Crrelatin Squared Multiple Crrelatin Crnbach's Alpha if Item Deleted Price satisfactin Variety satisfactin Organizatin satisfactin Service satisfactin Item quality satisfactin Overall satisfactin Here is ne f the beauties f syntax. Nw we simply make a nte abut what we see in ur syntax (i.e. *alpha at.828, but shws that measure culd be imprved by the remval f Organizatin), cpy and paste the first reliability cmmand, remve the Organizatin satisfactin variable and run it again. Nw we have alpha at.867 and the fllwing utput: Scale Mean if Item Deleted Item-Ttal Statistics Scale Variance if Item Deleted Crrected Item-Ttal Crrelatin Squared Multiple Crrelatin Crnbach's Alpha if Item Deleted Price satisfactin Variety satisfactin Service satisfactin Item quality satisfactin Overall satisfactin

16 Creating ur new variable. Nw we knw hw t d this! Let s cmpute ur new variable f satisfactin. COMPUTE Satisfactin=SUM(price,numitems,service,quality,verall)/ NVALID (price,numitems,service,quality,verall). EXECUTE. VARIABLE LABELS Satisfactin 'Measure f Satisfactin'. These prcedures can be mdified and used whenever data preparatin is necessary BEFORE that all-imprtant analysis is dne. It is crucial t understand the distributins and the nature f yur measures befre yu begin hypthesis testing. Using syntax is a very useful way t keep track fr yurself and thers, f yur wrk. 16