spss file for binding

8/11/2019 SPSS File for Binding

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PASW Statistics 17 (SPSS 17)UGANDAMANAGEMENTINSTITUTE(UMI

Mr. Wagema Samuel

Version 1.0

Table of Contents

Introduction Part 1...............................................................................................4

Downloading the Data Files....................................................................................4

Starting PASW Statistics........................................................................................4

The PASW Statistics Window.................................................................................5

Data View.................................................................................................................5

Variable View...........................................................................................................

!reating a Data File................................................................................................

De"ining Variables...................................................................................................

Data #ntr$................................................................................................................%

Descri&ti'e Statistics...............................................................................................(

Fre)uenc$ Anal$sis..................................................................................................(

!rosstabs................................................................................................................11

Data *ani&ulation................................................................................................1+

Select !ases............................................................................................................1+

S&litting a File........................................................................................................14

Find and ,e&lace...................................................................................................15

,e&orting................................................................................................................1

A&&endi-................................................................................................................1

Introduction Part +.............................................................................................1%

Downloading the Data Files..................................................................................1%

/ull 0$&othesis......................................................................................................1%

Statistical Tests.......................................................................................................1(

Tests o" Signi"icance..............................................................................................1(

For additional SSS !el"# $isit!tt"%&&'''.outu)e.*om&m*sula
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!orrelations............................................................................................................1(

PairedSa2&les T Test...........................................................................................+3

Inde&endentSa2&les T Test................................................................................++

*ulti&le ,es&onse Sets.........................................................................................+

*ulti&le ,es&onse Fre)uencies............................................................................+4

*ulti&le ,es&onse !rosstabs................................................................................+5

Data *ani&ulation................................................................................................+

!o&$ing and Pasting Variable Pro&erties...........................................................+

Inserting Variables and !ases..............................................................................+(

Deleting Variables and !ases................................................................................3

*erging Data Files................................................................................................3

!reating the Data File "or *erging.....................................................................3

In&utting the Data in Variable View....................................................................3

*erging the Data Files..........................................................................................+

A&&endi-................................................................................................................5

Introduction Part .............................................................................................

Downloading the Data Files..................................................................................

Si2&le ,egression..................................................................................................

Scatter Plot.............................................................................................................

Predicting Values o" De&endent Variables..........................................................(

Predicting This ear6s Sales with Si2&le ,egression *odel.............................41

*ulti&le ,egression...............................................................................................4

Predicting Values o" De&endent Variables..........................................................4

Predicting This ear6s Sales with *ulti&le ,egression *odel..........................45

Data Trans"or2ation.............................................................................................4

!o2&uting..............................................................................................................4

Pol$no2ial ,egression..........................................................................................4

ASW Statisti*s 1+ (SSS 1+,# art 1 -


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,egression Anal$sis...............................................................................................4%

Anal$7ing the ,esults............................................................................................4%

!hart #diting.........................................................................................................4(

Adding a 8ine to the Scatter Plot.........................................................................4(

*ani&ulating the Scales on 9 and a-es..........................................................53

Adding a Title to the !hart...................................................................................5+

Adding !olors to the !hart..................................................................................5

Filling a :ac;ground !olor..................................................................................54

Introduction Part 4.............................................................................................55

Downloading the Data Files..................................................................................55

!hiS)uare.............................................................................................................55

!hiS)uare Test "or #-&orting *icroso"t #-cel and PowerPoint.....................................%

?sing Scri&ting "or ,edundant Statistical Anal$ses...........................................1

ASW Statisti*s 1+ (SSS 1+,# art 1


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Introduction Part 1PASWstands for Predicti'e Anal$tics So"tware. This program can be used to analyze datacollected from surveys, tests, observations, etc.It can perform a variety of data analyses andpresentation functions, including statistical analysis and graphical presentation of data. Amongits features are modules for statistical data analysis. These include 1) descriptive statistics, suchas frequencies, central tendency, plots, charts, and lists; and ) sophisticated inferential andmultivariate statistical procedures, such as analysis of variance !A"#$A), factor analysis, clusteranalysis, and categorical data analysis. %A&' &tatistics is particularly (ellsuited for surveyresearch, though by no means is it limited to *ust this topic of e+ploration.

This handout !escriptive &tatistics) introduces basic s-ills necessary to run %A&' &tatistics. Itincludes ho( to create a data fileand run descriptive statistics. It is especially tailored to ans(erthree research questions formulated in the sample survey questionnaire, eventually giving usersan overvie( of ho( %A&' &tatistics can be used for survey research. The three researchquestions formulated in the sample survey are as follo(s

1. 'hat -ind of computer do people prefer to o(n/. 'hat color do people prefer for their computer/0. Is computer color preference different bet(een genders/

Downloading the Data ilesThis handout includes sample data files that can be used for handson practice. The data files arestored in a selfe+tracting archive. The archive must be do(nloaded and e+ecuted in order toe+tract the data files. The data files used (ith this handout are available for do(nload at

http(((.calstatela.eduitstrainingdatafilespas(12p1.e+e. Instructions on ho( to do(nload and e+tract the data files are available at

http(((.calstatela.eduitsdocsdo(nload.php.

Starting PASW StatisticsThe follo(ing steps are for starting %A&'&tatistics 12 using the computers in the #penAccess 3abs !#A3s). The steps for startingthe program at home or on other computersmay be slightly different.

To start %A&' &tatistics 121. 4lic- the Startbutton, point to All

Progra2s, point to !ourse Wor;,

point to SPSS Inc, point to PASWStatistics 1, and select PASWStatistics 1. ThePASW Statistics 17dialog bo+ opens !see 5igure 1@.

. 4lic- the !ancelbutton to create ane( data file.

igure 1 ! PASW Statistics 17 Dialog "o#

ASW Statisti*s 1+ (SSS 1+,# art 1 /
http://www.calstatela.edu/its/training/datafiles/pasw17p1.exehttp://www.calstatela.edu/its/docs/download.phphttp://www.calstatela.edu/its/training/datafiles/pasw17p1.exehttp://www.calstatela.edu/its/docs/download.php


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The PASW Statistics WindowThe Data #ditor (indo( opens (ith t(o vie( tabsData ViewandVariable View. TheDataViewis used for data input, and the VariableViewis used for adding variables and definingvariable properties !e.g., modifying attributes of variables). As displayed in 5igure ,the Data#ditor(indo( includes several components. The Titlebar displays the name of the current file

and the application. The *enubar allo(s you to access various commands that are groupedaccording to function. The Toolbarprovides shortcuts to commonly used menu commands.

igure $ ! PASW Statistics Data %ditor Window

DATA&I%W'hen %A&' &tatistics is launched, the Data #ditor(indo( opens inData View, (hich loo-ssimilar to a *icroso"t#-celspreadsheet !(hich is *ust an array of ro(s and columns). Thedifference is that the ro(s and columns inData Vieware referred to as cases and variables,respectively !see Table 1).

Table 1 ! %le'ents in Data &iew

#le2ent Descri&tion

$ariable 6ach column represents a variable. Any survey questionnaire item or testitem can be a variable. 4ommonly defined variable types are numeric orstring. 'hen defining variables as numeric, users need to specify decimalplaces. $ariable names can be up to 78 characters long and must start(ith a letter. 9a-e variable names meaningful and easily recognizable.

4ase 6ach ro( represents a case. The participants in the study can be cases. 5ore+ample, if 1:: participants are involved in your study, then 1:: cases !orro(s) of information should be generated. esponses to the question itemsshould be entered consistently from left to right for each participant.



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#le2ent Descri&tion

4ell A cell is an intersection bet(een cases and variables. 6ach response to asurvey question should be entered in a cell for each participant accordingto the defined variable data types.

&AIA"%&I%WVariable Viewis (here variables are defined by assigning variable names and specifying the

attributes, such as data type !


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igure , ! &ariable &iew Tab

igure - ! &ariable T./e Dialog "o#

8. Type


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0. epeat steps : for values +B through5Band label them as sho(n inTable 0!youmay also refer bac- to the sample questionnaire). &ee 5igure 8for the results.

@. 4lic- the =Cbutton.

Table , ! &alue abels

Value Label

2 20-23

3 24-27

4 28-31

5 32 or over

igure ! &alue abels Dialog "o# (Age)

DATA%*T2After defining the variables, users can enter data for each case. If variables are defined as havinga


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Descri/ti4e StatisticsAfter data has been entered, users may begin analyzing the data by using descriptive statistics.escriptive statistics are the most commonly used statistics for summarizing data frequency ormeasures of central tendency !mean,median,and mode).

,esearch Euestion 1

What ;ind o" co2&uter do &eo&le &re"er to own

%56%*C2A*A2SIS'e can use#re$uency analysisto ans(er the first research question. 5requency analysis is adescriptive statistical method that sho(s the number of occurrences of each response chosen bythe respondents. 'hen using frequency analysis, %A&' &tatistics can also calculate the mean,median, and mode to help users analyze the results and dra( conclusions. The follo(inge+ample (ill use a frequency analysis to ans(er Owned= from the list bo+ on the left.

7. 4lic- the transfer arro( button . The selected variable is moved to the Variable's(listbo+.

8. &elect theDisplay #re$uency tableschec- bo+ if necessary.

igure ! re8uenc. Anal.sis fro' Anal.9e :enu

igure 1; ! re8uencies Dialog "o#

2. 4lic- the StatisticsGbutton. TheFrequencies: Statisticsdialog bo+ opens !see ).. &elect the)ean,)edian,and)odechec- bo+es in the Central Tenencysection; select

theStd& deviationchec- bo+ in theDispersi!nsection.

ASW Statisti*s 1+ (SSS 1+,# art 1 3


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igure 11 ! re8uencies< Statistics Dialog "o#

B. 4lic- the !ontinuebutton. This returns you to theFrequenciesdialog bo+.1:. 4lic- the =Cbutton. An =ut&ut Viewer(indo( opens and displays the statistics and

frequency table !see5igure 1). The columns of the table

igure 1$ ! re8uencies =ut/ut

The measures of central tendency !mean, median, and mode) can be used to summarize varioustypes of data. 9ode can be used for nominal data, such as computer type, computer color,ethnicity, etc. 9ean or median can be usedfor intervalratio data, such as test scores, age, etc.The mean is also useful for data (ith a s-e(ed distribution.

Answer to ,esearch Euestion 1

What ;ind o" co2&uter do &eo&le &re"er to own



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Ans(er I:* or !o2&atible6+planation 3oo- at question D 2 in theSample Survey."otice that option D 0 is

What color do &eo&le &re"er "or their co2&uter

C=SSTA"S4rosstabs are used to e+amine the relationship bet(een t(o variables. To ans(er the secondresearch question, users (ill need to analyze t(o variables sing crosstabs (ill sho( the intersection bet(een these t(ovariables and reveal the computer type and color preferred by mostpeople.

To perform a crosstabs analysis1. InData View, clic- the Anal$7emenu, point to Descri&ti'e Statistics, and select

!rosstabsG !see 5igure 10). The Cr!sstabsdialog bo+ opens.

. &elect the variable

ProcessingSummary= and the

igure 1, ! Crosstab Anal.sis fro' Anal.9e :enu

igure 1- ! Crosstabs Dialog "o#



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igure 10 ! Crosstabs =ut/ut

Answer to ,esearch Euestion +

What color do &eo&le &re"er "or their co2&uter

Ans(er I:* or !o2&atible in beige color6+planation As sho(n in the

preferred computer type from the ro( variable !

Data :ani/ulationata files are not al(ays ideally organized in a form to meet specific needs. 5or e+ample, usersmay (ish to select a specific sub*ect or split the data file into separate groups for analysis.

S%%CTCAS%SIf you have t(o or more sub*ect groups in your data and you (ant to analyze each sub*ect in

isolation, you can use the select casesoption. 5or e+ample, the data (e are currently analyzinghas both male and female participants. Eo(ever, if you (ish to analyze only female cases, thenyou select


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igure 1 ! Select Cases fro' Data :enu

igure 17 ! Select Cases Dialog "o#

5rom the cross tabulation in the #utput $ie(er (indo( in belo(, loo- at the column for themost preferredcolor and the ro( for the computer types. &ince (e selected only female cases,(hat is the computer color most preferred by (omen/ Ten (omen chose


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Is co2&uter color &re"erence di""erent between genders

SPITTI*+AI%To ans(er the third research question,(e need to split the file. Hou can analyze one particulargroup of sub*ects using the select casesoption. Eo(ever, if you (ish to compare the response orperformance differences by groups (ithin one variable, it is best to use the split #ilesoption.

To split a file for analysis1. Turn off the select casesoption.. 4lic- the Datamenu and select Select !asesG.The Select Casesdialog bo+ opens.0. &elect theAll casesoption.@. 4lic- the =Cbutton. "otice that the male cases that (ere e+cluded are no( all included

in the data file.7. &elect the Datamenu and select S&lit FileG.!see5igure 1B). The Split Filedialog bo+

opens !see 5igure :).

igure 1 ! S/lit ile fro' Data :enu

igure $; ! S/lit ile Dialog "o#

8. &elect the variable


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igure $1 ! S/lit ile =ut/ut Data

Answer to ,esearch Euestion

Is co2&uter color &re"erence di""erent between genders

Ans(er es6+planation There is a computer color preference difference based on gender. 5rom thecrosstabulation output, females prefer


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"#T6 >nder the%atc& t!section of theFin an $eplacedialog bo+ !see 5igure ), !ontainsmeans %A&' &tatistics (ill find each instance of the (ordphrasenumber appearing in a cell,(hether or not it is the only information enclosed. The"ntire celloption (ill find the(ordphrasenumber that matches the entire cell as a (hole. &electing the +egins wit/and"ndswit/options (ill search the character indicated by the user.

e/orting#nce the statistical analysis is complete, the final step is to create a report. In the report, you may

include %A&' &tatistics output !e.g., graphs and tables) for supporting your analysis. >sing theC!pyandPastefunctions, the tablesgraphs generated in %A&' &tatistics can be copied from the=ut&utViewer(indo( and pasted into a *icroso"t Worddocument (ithout having to createne( tables or graphs.

To create a report using 9icrosoft 'ord1. In the =ut&utViewer(indo(, rightclic- a table. A bo+ appears around the table and a

red arro( to the left of the table !(hich means it is selected).. &elect !o&$ from the shortcut menu.0. #pen *icroso"t Word.@. ightclic- in the Worddocument and select Pastefrom the shortcut menu. The table is

copied into the Worddocument.



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A//endi#

SA:P%S6&%2

esearch 5uestions

1. 'hat -ind of computer do people prefer to o(n/

. 'hat color do people prefer for their computer/

0. Is computer color preference different bet(een genders/

Sur4e. 5uestions

1. 'hat is your name/ GGGGGGGGGGGGGGGGGGGGGGGGGGGGG

. 'hat is your gender/ GGGGGGGGGGGGGGGGGGGGGGGGGGGGG

0. 'hat is your ?.%.A./ GGGGGGGGGGGGGGGGGGGGGGGGGGGGG

@. 'hat is your age/

1. 1B or younger . :0 0. @2 @. 01 7. 0 or over

7. Eo( much do you ma-e in a month/

1. 3ess than K1::: . K1:::LK1@BB 0. K17::LK1BBB @. K:::LK@BB 7. #ver K7::

8. 'hat is your class standing/

1. 5reshman . &ophomore 0. Munior @. &enior 7. ?raduate

2. 'hat -ind of computer do you o(n/

1. Toshiba . Apple 0. IF9 or 4ompatible @. #ther 7. "one

. 'hat -ind of computer have you used/

1. IF9 or 4ompatible . Apple 0. Toshiba @. #ther 7. "one

B. 'hat color do you li-e to have for your computer/

1. Feige . Flac- 0. ?ray @. 'hite 7. #ther

For additional SSS !el"# $isit !tt"%&&'''.outu)e.*om&m*sula .
http://www.youtube.com/mycsulahttp://www.youtube.com/mycsula


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Introduction Part $PASWstands for Predicti'e Anal$tics So"tware. This program can be used to analyze datacollected from surveys, tests, observations, etc. It can perform a variety of data analyses andpresentation functions, including statistical analysis and graphical presentation of data. Amongits features are modules for statistical data analysis. These include 1) descriptive statistics, such

as frequencies, central tendency, plots, charts, and lists; and ) sophisticated inferential andmultivariate statistical procedures, such as analysis of variance !A"#$A), factor analysis, clusteranalysis, and categorical data analysis. %A&' &tatistics is particularly (ellsuited for surveyresearch, though by no means is it limited to *ust this topic of e+ploration.

This handout !Test of &ignificance) introduces 1) several data entry and data manipulationtechniques that help you save time, ) basic s-ills to perform tests of significance, such ascorrelations and t tests, and 0) an introduction to multiple response sets. The stepbystepinstructions (ill help you understand ho( to interpret the output of your tests from data suppliedby your research question!s). 5ollo( the steps carefully to get appropriate results. %lease notethat a slightly different process might yield une+pected and complicated results. This is a

continuation of the PASW Statistics Descri&ti'e Statisticshandout.


http(((.calstatela.eduitstrainingdatafilespas(12p.e+e. Instructions on ho( to do(nload and e+tract the data files are available at


*ull >./othesisThe null &yp!t&esis'(0)represents a theory that has been presented, either because it is believedto be true or because it is to be used as a basis for an argument. It is a statement that has not beenproven. It is also important to realize that the null hypothesis is the statement of no difference.5or e+ample, in a clinical trial of a ne( drug, the null hypothesis might be that the ne( drug isno better, on average, than the current drug !in other (ords, the ne( drug e+hibits the samebehavior as the old drug). The null hypothesis !E0) and the alternative hypothesis !E1) can bestated as

00 There is nodifference bet(een the t(o drugs.

01: There is a signifcant dierence between the two drugs.

&pecial consideration is given to the null hypothesis. This is due to the fact that the nullhypothesis relates to the statement being tested, (hereas the alternative hypothesis relates to thestatement to be accepted if and (hen the null is re*ected.

The final conclusion, once the test has been carried out, is al(ays given in terms of the nullhypothesis. The result is either Ne*ect E0in favor of E1N or No not re*ect E0N; the conclusion isnever Ne*ect E1N or NAccept E1.N

ASW Statisti*s 1+ (SSS 1+,# art - 12
http://www.calstatela.edu/its/docs/pdf/pasw17p1.pdfhttp://www.calstatela.edu/its/docs/pdf/pasw17p1.pdfhttp://www.calstatela.edu/its/training/datafiles/pasw17p2.exehttp://www.calstatela.edu/its/docs/download.phphttp://www.calstatela.edu/its/docs/pdf/pasw17p1.pdfhttp://www.calstatela.edu/its/training/datafiles/pasw17p2.exehttp://www.calstatela.edu/its/docs/download.php


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If the conclusion is No not re*ect E0,N this does not necessarily mean that the null hypothesis is

true. It only suggests that there is no sufficient evidence against E0in favor of E1. e*ecting thenull hypothesis then suggests that the alternative hypothesis may be true.

"#T6 The null hypothesis essentially states that the given cases or items under consideration arestatistically the same or e+hibit the same behavior (ithout any significant difference. The alternatehypothesis states that the given cases e+hibit different behavior or that they have a statistically significantdifference.

Statistical Tests&tatistics is a set of mathematical techniques used to summarize research data and determine(hether the data supports a proposed hypothesis. %A&' &tatistics includes tools that can be usedto analyze variables and determine the strength and nature of the relationship bet(een t(ovariables and (hether the means !averages) of t(o data sets !samples) are statistically the sameor different.

Tests of SignificanceThe follo(ing e+amples are sample research questions that can be ans(ered using %A&'

&tatistics analytical methods.

C=%ATI=*SA c!rrelati!nis a statistical device that measures strength or degree of a supposed linearassociation bet(een t(o or more variables. #ne of the more common measures used is thePears!n c!rrelati!n, (hich estimates a relationship bet(een t(o interval variables.

,esearch Euestion 1

Is there a relationshi& between acade2ic &er"or2ance and Internet access

00 There is no difference bet(een academic performance and Internet access.

01: There is a signifcant dierence between academic perormance andInternet access.

To run a correlation analysis1. 3ocate and open the


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Figure + :i'ariate !orrelations Dialog :o-

Figure +4 :i'ariate !orrelations =ut&ut Table

The Answer to ,esearch Euestion 1

Is there a relationshi& between acade2ic &er"or2ance and Internet access

Ans(er es6+planation As sho(n in 5igure @above, the correlation inde+ for the relationship bet(een


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,esearch Euestion +

Is there an instructional e""ect ta;ing &lace in the co2&uter class

00:There is noinuence o using the Internet on academic achievement orthis class.01There is an inuence o using the Internet on academic achievement orthis class.

The hypothesis is that Internet familiarity cannot influence the academic achievement in thecomputer class. The variables that reflect academic achievement are


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According to the &ig. of :.::1 !(hich is less than :.:7), the hypothesis is re*ected. Therefore, itcan be inferred that there (as instructional effect ta-ing place in the computer class.

I*D%P%*D%*T!SA:P%ST T%STAn"nepenentSa-ples T Testis used to determine the li-elihood that t(o independent datasamples came from populations that have identical means. If this (ere true, then the differencebet(een the means should be equal to zero. The null hypothesis in this case (ould be that the

t(o means are equal.

T(o variables are required in the data set. #ne variable is the measured parameter. 6+amplesinclude (eight, height, or frequency. The second variable divides the data set into t(o groups.3ight and ar- are the groups (hose means (ill be compared.

,esearch Euestion

Is there a di""erence in the a'erage nu2ber o" seedlings grown in the light

and those grown in the dar;

In this e+ample, : %etri dishes each contained 1: celery seeds. Ten of the dishes (ere -ept inthe dar- for one (ee-; the other 1: (ere placed under a gro( light for the same amount of time.At the end of the (ee-, the number of seeds that sprouted (as counted in each dish.

00:$ariance !light) P variance !dar-).01$ariance !light) Q variance !dar-).

00:There is nodifference bet(een seedlings under the light and in the dar- !(light=

(dar!).

01There is sig. dierence between seedlings under the light and in the dar! (

(light Q (dar!.

"#T6 The first set of hypotheses is testing the variance, (hile the proceeding set is testing for the mean.The variances have to be equal before (e can determine if the means are equal.

"#T6 Variance The arithmetic mean of the squared deviations from the mean, (hich is essentially usedto see ho( far the single samples are from the mean. 'e need to ma-e sure the variances are equal before(e can determine if the means are equal. If the variances are equal, users (ill be able to move to the TTest. If the variances are not equal, users (ill have to do more testing.

To run the Independent&amples T Test1. 3ocate and open the


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B. 4lic- the =Cbutton. The =ut&ut Viewer (indo( opens (ith several tables, including anIndependent&amples Test table !see 5igure B).

ASW Statisti*s 1+ (SSS 1+,# art - -


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Figure + Inde&endentSa2&les T Test Dialog :o-Figure +% De"ine


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Figure 3 De"ine Variable Sets "ro2 Anal$7e

*enu

Figure 1 De"ine *ulti&le ,es&onse Sets Dialog :o-

0. &elect the


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Figure + *ulti&le ,es&onse Fre)uencies Dialog :o-


In a sur'e$ o" airline &assengers which airline was selected as ha'ing been

"lown 2ost o"ten in the &re'ious si- 2onths

Figure Airline Fre)uenc$ Anal$sis =ut&ut

Ans(er ?nited6+planation As seen in the =ut&utViewer(indo(, there (ere 1 people surveyed and @@ totalresponses generated. #f the @@ total responses, >nited (as selected most often (ith 1 responses!representing 2.0R L the largest portion of the total responses).

:6TIP%%SP=*S%C=SSTA"S

'ithout the use of a multiple response set, each airline (ould have to be analyzed against thevariable that the passengers used to identify themselves as being afraid of flying. This (ouldrequire the use of a crosstab analysis. Eo(ever, the overall results (ould not allo( for easycomparison bet(een each of the airlines. The best (ay to ans(er the question (ould be toinclude the multiple response set into a crosstab analysis.

,esearch Euestion 5

In a sur'e$ o" airline &assengers which airline was selected 2ost o"ten b$

those &assengers who identi"ied the2sel'es as a"raid to "l$

ASW Statisti*s 1+ (SSS 1+,# art - -


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To incorporate a multiple response set into a crosstab analysis1. 4lic- the Anal$7emenu, point to *ulti&le,es&onse, and select !rosstabsG. The

%ultiple $esp!nse Cr!sstabsdialog bo+ opens !see 5igure 0@).

Figure 4 *ulti&le ,es&onse !rosstabs Dialog :o-

. &elect the


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Figure *ulti&le ,es&onse !rosstabsJ =&tions Dialog :o-


In a sur'e$ o" airline &assengers which airline was selected 2ost o"ten b$

those &assengers who identi"ied the2sel'es as a"raid to "l$

Figure *ulti&le ,es&onse !rosstabs =ut&ut

Ans(er ?SAir6+planation #f the 1 people surveyed, ten identified themselves as being afraid to fly. 'ithinthat group of survey respondents, >&Air (as the airline selected most often !seven times).

Data :ani/ulation%A&' &tatistics also provides tools to ma-e data manipulation a simple tas-.

C=P2I*+A*DPASTI*+&AIA"%P=P%TI%S4opying and pasting is very useful (hen the same properties need to be given to differentvariables.

To copy and paste variable properties1. 4lic- the Filemenu, point to /ew, and selectData.. 4lic- the VariableViewtab at the lo(er left corner of theData #ditor(indo( !see

5igure 0).

Figure % Variable View Tab

ASW Statisti*s 1+ (SSS 1+,# art - -2


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0. Type acti'eBin the first cell under theNamecolumn and press the #nterB-ey.@. 4lic- in the first cell under theDecimalscolumn and decrease the entry to


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Figure 4+ Selecting Target VariablesFigure 4 Data View Showing /ew Variables

Figure 44 Variable View Showing /ew Variables

I*S%TI*+&AIA"%SA*DCAS%SFy using Insert Variableand Insert !ases, variables and cases can be added into any locationof the data file in a simple, straightfor(ard manner. Assume that one (ants to insert a ne(variable named


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D%%TI*+&AIA"%SA*DCAS%S$ariables and cases can be deleted by using the Deletecommand.

To delete a variable or case1. InData View, clic- the variable heading or the case number to highlight (hat (ill be

deleted.. 4lic- the #ditmenu and select !lear. The variable or case is deleted.

:erging Data ilesThe merging data files function is useful for users (ho store each of their topics in separate filesand eventually need or (ant to combine them together. This allo(s users to import data from onefile into another as long as both sets of data !from each file) contain a common identifier for eachof the cases that the user (ishes to combine.

An identifier has no meaning other than to distinguish each case from one another, and toidentify the correlating cases from the additional data files. This identifier can be a unique value,

number, or letter combination to be applied to each case.

"#T6 The variables do not have to be the same across data files.

C%ATI*+T>%DATAI%=:%+I*+&cenario A psychological focus group on campus needs to create a file for a longitudinal studyfor ten students on campus. 6ach file (ill have the same students, but four different focal pointsof study pertaining to each question. #ver the five year span of the study, the ten students (ill beas-ed t(elve questions each year !one a month), and the same questions (ill be as-ed each year.At the end of the year, the three files (ill be combined in an annual questionnaire file to beproperly analyzed.

The merging data files function can be used to satisfy this requirement.

In/utting the Data in &ariable &iew5iles must be created first before being merged.

To create a data file for merging1. 4lic- the Filemenu, point to /ew, and select Data.. #nce the ne( file has been created, select the Variable Viewtab.0. 5or the first variable, name it IDB to be your identifier variable, and press the #nterB

-ey.@. 4hange the Type attribute by clic-ing the ellipses button and selecting theStringoption

from the Variable Typedialog bo+.7. 4hange the (idth to 13Band clic- the =Cbutton.8. 4lic- in the second variable cell, type Kanuar$B, and press the #nterB-ey.2. 4hange the Typeattribute toString.. In theLabelattribute, type What &et would $ou li;e to ownB !see 5igure @7).B. epeat steps 8 through to enter the data in Table @.

For additional SSS !el"# $isit !tt"%&&'''.outu)e.*om&m*sula.
http://www.youtube.com/mycsulahttp://www.youtube.com/mycsula


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Figure 45 De"ine Variables in Variable View

Table 4 Variables "or !ase Stud$

*onth Attribute T$&e 8ength 8abel Attribute

5ebruary &tring 1: 'hat is your favorite shape/

9arch &tring 1 It is 10:pm, (hat are you eating/

April &tring 1 'hat is your preferred beverage/

1:. #nce this information has been defined in Variable View, s(itch by clic-ing theDataViewtab to enter the corresponding case information.

11. 6nter Al"redBin case 1 of the*Dvariable, :ethelBin case of the*Dvariable, do(n toKessieSin case 1: of the*Dvariable. 6nter the corresponding information according toTable 7. &ee 5igure @8for the results.

Table 5 In&ut !ase In"or2ation

!ase ID Kanuar$ Februar$ *arch A&ril

1 Alfred og &tar %izza 'ater

Fethel 4at &quare 5ruit &oda %op

0 4hris 4at Triangle $eggies ?rape Muice

@ ante og ectangle &and(ich #range Muice

7 6rica Tiger #val 4hips Aloe 'ater

8 5ernando Tarantula 4ircle 4alzon Feer

2 ?renadine og #ctagon &alad 'hite 'ine

Earold Fees %olygon &oup "a-ed Muices

B Isadora Turtle hombus %anda6+press $ Muice

1: Messie Eamster #val 6gg &alad 3emonade

ASW Statisti*s 1+ (SSS 1+,# art -


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Figure 4 In&ut !ase In"or2ation

1. &ave the file by clic-ing the Filemenu and selecting Sa'e. The Sa+e Data Asdialog bo+opens.

10. &elect the Des;to&as the destination and type *erge 1Bin the File namete+t bo+.1@. 4lic- the Sa'ebutton.

17. 4lose the Output Viewer (indo(.

:%+I*+T>%DATAI%STo merge data files, all files must have a common variable. The common variable in this case isI.

To merge data files !5irst, ma-e sure the files have the same Is.)%& #pen the files


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8& TheA Variables t!%er/e 1.sa+dialog bo+ opens. &elect theAn e6ternal PAS3Statistics data #ileoption and clic- the :rowseGbutton !see 5igure @).

Figure 4% Add Variables to *erge 1.sa' Dialog :o-

.& 3ocate and select the


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Figure 53 Sorting Warning Dialog :o-

%7& 4lic- the =Cbutton to close the (arning message. The finished product should loo- li-e5igure 71.

Figure 51 *erged 1 and + Files

%%& epeat steps 01: for the


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A//endi#

56%STI=**AI%This survey is designed to investigate relationships bet(een Internet access and academicsuccess. It consists of three parts questions related to the bac-ground information of therespondent, questions about Internet use patterns, and several openended questions. %leaseselect appropriate ans(ers that best describe your activities on the Internet as truthfully aspossible. The results of this study (ill be used anonymously for the %A&' &tatistics %art Testof &ignificance (or-shop.

"ac?ground Infor'ation

1. Age GGGGGGGGGGGGGGGGGGGGGGGGGGGGG

. 9a*or GGGGGGGGGGGGGGGGGGGGGGGGGGGG

0. ?.%.A. GGGGGGGGGGGGGGGGGGGGGGGGGGG

@. 9onthly Income GGGGGGGGGGGGGGGGGGG

Internet Access

7. o you have a computer at home/

1. Hes . "o

8. 'here do you surf on the Internet/ !Hou can circle more than one option for this question.)

1. At school . At home 0. At (or- @. #ther GGGGGGGGGGGG

2. Eo( long do you stay online per day/

1. 3ess than 0: minutes . 1 hours 0. 9ore than t(o hours

Euestions % through 1( are designed to in'estigate the "re)uenc$ and t$&es o" acti'ities on

the Internet. These )uestions ha'e a 4 &oint 8i;ertscale ranging "ro2 strongl$ disagree to

strongl$ agree. Please circle the o&tion that best describes $our acti'ities on the Internet.

SDJ Strongl$ Disagree

DJ DisagreeAJ Agree

SAJ Strongl$ Agree

SD D A SA

. I am a very active Internet surfer. 1 0 @

B. I surf the Internet to loo- for articles for researchpapers. 1 0 @

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SD D A SA

1:. I surf the Internet to read current ne(s. 1 0 @

11. I use the Internet only to email my friends, family, and professors. 1 0 @

1. I surf the Internet to chec- movie schedules. 1 0 @

10. I surf the Internet to loo- for personal information !e.g., yello( pages). 1 0 @

1@. I surf the Internet to loo- for *ob openings 1 0 @

17. I use the Internet to play games. 1 0 @

18. I use the Internet to do(nload forms and files !e.g., income ta+ forms). 1 0 @

12. I surf the Internet to improve my computer s-ills. 1 0 @

1. I surf the Internet to purchase boo-s. 1 0 @

1B. I surf the Internet to purchase other merchandise !e.g., video tapes, clothes, computers). 1 0 @

Euestion +3 is an o&enended )uestion.

:. Are there any other Internet activities that are not included in this survey/ If so, pleasedescribe them belo(.

GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG




ASW Statisti*s 1+ (SSS 1+,# art +


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Introduction Part ,PASWstands for Predicti'e Anal$tics So"tware. This program can be used to analyze datacollected from surveys, tests, observations, etc. It can perform a variety of data analyses andpresentation functions, including statistical analysis and graphical presentation of data. Among

its features are modules for statistical data analysis. These include 1) descriptive statistics, suchas frequencies, central tendency, plots, charts, and lists; and ) sophisticated inferential andmultivariate statistical procedures, such as analysis of variance !A"#$A), factor analysis, clusteranalysis, and categorical data analysis. %A&' &tatistics is particularly (ellsuited for surveyresearch, though by no means is it limited to *ust this topic of e+ploration.

This handout !egression Analysis) provides basic instructions on ho( to ans(er researchquestions and test hypotheses through the use of linear regression !a technique (hich e+aminesthe relationship bet(een a dependent variable and a set of independent variables). The value ofthe dependent variable !e.g., salespersonJs total annual sales) can be predicted based on itsrelationship to the independent variables used in the analysis !e.g., age, education, and years of

e+perience). The t(o research questions proposed for this (or-shop are as follo(s1. Eo( much (ill each salesperson ma-e this year/. 'ho (ill qualify for a K1,::: bonus/


http(((.calstatela.eduitstrainingdatafilespas(12p0.e+e. Instructions on ho( to do(nload and e+tract the data files are available at


Si'/le egressionSi-ple re/ressi!nestimates ho( the value of one epenent +ariable!H) can be predicted basedon the value of one inepenent +ariable!). The linear equation for simple regression is asfollo(s

H a9 L b

&imple regression can ans(er the follo(ing research question

,esearch Euestion 10ow 2uch will each sales&erson 2a;e this $ear

SCATT%P=TAscatter pl!tdisplays the nature of the relationship bet(een t(o variables. It is recommended torun a scatter plot before performing a regression analysis to determine if there is a linearrelati!ns&ipbet(een the variables. If there is no linearrelationship !i.e., points on a graph arenot clustered in a straight line), there is no need to run a simple regression.

http://www.calstatela.edu/its/training/datafiles/pasw17p3.exehttp://www.calstatela.edu/its/docs/download.phphttp://www.calstatela.edu/its/training/datafiles/pasw17p3.exehttp://www.calstatela.edu/its/docs/download.php


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To run a scatter plot1. &tart PASW Statistics 1.

. 4lic- the =&enbutton on the Data #ditortoolbar. The Open Data dialog bo+ opens.0. 3ocate and open the


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2. 4lic- the first transfer arro( button to move the variable to the B A6isbo+.. &elect the variable


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0. &elect the variable


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Figure 5( !oe""icients =ut&ut

The slope and the yintercept as seen in5igure 7Bshould be substituted in the follo(ing linearequation to predict this yearJs sales P a9 L b. In this case, the values of a, b, -, and $(ill beas follo(s

a P [email protected] P @@:.B29P Hears of e+perience !values of independent variable)

P 3ast year sales !values of dependent variable)

P%DICTI*+T>IS2%A@SSA%SWIT>SI:P%%+%SSI=*:=D%To predict this yearJs sales for each salesman, the values of a andb should be substituted in thefollo(ing linear equation

H a9 L b

3ast year sales P !a U yeare+pe) V bThis year sales P [email protected] U yeare+p) V @@:.B2aP [email protected] @@:.B2

9P Hears of e+perience Wyeare+pSP This year sales

"#T6 The ne( independent variable,


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Figure 3 !o2&ute Variable Dialog :o-

0. In theNumeric "6pressionbo+, enter the follo(ing equation by typing or selectingfrom the dialog bo+ -eypad

1(54.5% M $eare-&+ L 443.(%B

"#T6 It is recommended to select the variable


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Figure + Variable View Tab

. 3ocate the variable


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The slopes and the yintercept as seen in 5igure 82should be substituted in the follo(ing linearequation to predict this yearJs sales N H a9L b L c

In this case, the values of a, b, -, and $(ill be as follo(saP [email protected] P 8:B.0B1cP !71:.0)9P Hears of e+perience !independent variable)

P Hears of education !independent variable)NP This year sales !dependent variable)

As indicated in the output table, the coefficient for


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8. 4lic- the =Cbutton. The results (ill be displayed in the multiplecolumn inData View!see 6rror eference source not found).

Figure ( *ulti&le ,egression ,esults

"#T6 The predictions of sales for each salesperson using t(o independent variables are listed under thene( variable named


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Figure 3 !o2&ute Variable Dialog :o-

8. 4lic- the I"Gbutton. The C!-pute Variable: "# Casesdialog bo+ opens !see 5igure 21).2. &elect the*nclude i# case satis#ies conditionoption.. 6nter the follo(ing e+pression by typing or selecting from the dialog bo+ -eypad

thissale 2ulti&le H +333B

Figure 1 !o2&ute VariableJ I" !ases Dialog :o-

"#T6 It is recommended that you select the variables and the XP sign directly from the variablelist bo+ and -eypad provided in the dialog bo+ to prevent mista-es.

ASW Statisti*s 1+ (SSS 1+,# art /2


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B. 4lic- the !ontinuebutton, and then clic- the =Cbutton.

"#T6 &alespersons D@B nder%!els, deselect theLinearchec- bo+ and select the !ubicchec- bo+.2. 4lic- the=Cbutton.

ASW Statisti*s 1+ (SSS 1+,# art /3


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Figure !ur'e #sti2ation Dialog :o-

Anal.9ing the esultsThis cubic model has an of BB.782R !see 5igure 2@). The 5ratio indicates a highlysignificant fit. The best fitting cubic polynomial is given by the follo( equation

!'here Hiis (eight and iis age);HiP :.:7 L :.:12 iV :.:1: iL :.::1 i0V ei

9ultiple regression can be used to fit polynomials of higher order. If is the dependent variable,use the Trans"or2and !o2&uteoptions of the Data #ditor!as discussed earlier in this lesson)to create ne( variables P U, 0 P U, @ P U0, etc., then use these ne( variables!, , 0, @, etc.) as a set of independent variables for a multiple regression analysis.



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Figure 4 Pol$no2ial ,egression Su22ar$ ,esults

Chart %ditinguring the final stage of research, enhancing the appearance of charts and figures can be veryhelpful for readers to understand (hat may seem to be confusing statistics. This (ill save thetime and effort to copy and paste an ob*ect from one program to another and to modify itsfeatures. The follo(ing steps e+plain some useful methods to enhance the appearance of a chart.

ADDI*+AI*%T=T>%SCATT%P=TAdding a straight line to fit the scattered pattern of a data chart can help emphasize the linearrelationship among the data.

To add a line to the scatter plot1. 4lic- the DotG.. &elect the Si2&le Scatteroption, and then clic- the De"inebutton.0. Transfer the


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Figure 5 !hart #ditor Window

Figure !hart *ar;ers

Figure Adding a Fit 8ine to the Scatter Plot

:A*IP6ATI*+T>%SCA%S=*! A*D2!A%SThe3a4isand 5a4iscan be ad*usted to enhance the overall appearance and readability of thechart. $arious elements of the a+es can be manipulated, such asscale, tic6san/ris, nu-ber#!r-at, and a4islabel.

To manipulate the scales on the a+is1. If necessary, open the


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Figure % 9a-is Pro&erties Dialog :o-J Scale

Tab

Figure ( 9a-is Pro&erties Dialog :o-J 8abels

Q Tic;s Tab

1:. 4lic- the Show


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Figure %1 a-is Pro&erties Dialog :o-J Scale

Tab

1@. 4hange the value in theLower margin '(bo+ to 3.17. 4lic- the A&&l$button, and then clic- the !losebutton.

Figure %+ :e"ore *ani&ulating the 9a-is Figure % A"ter *ani&ulating the 9a-is

ADDI*+ATIT%T=T>%C>ATAdding a title to the chart is a simple process that enhances the chartJs appearance.

To add a title to a chart1. In the !hart#ditor(indo(, clic- in a blan- area outside the first chart to select the

(hole chart, then move the mouse pointer to one of the selection handles until it becomesa t(oheaded arro(.

. rag the mouse pointer to reduce the chart size.0. 4lic- the Insert a te-t bo-button on the Standardtoolbar. The te+t bo+ appears

above the chart and thePr!pertiesdialog bo+ opens.@. Type


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8. &elect the Te4t Styletab in thePr!pertiesdialog bo+, select a color for the title te+t, clic-the A&&l$button, and then clic- the !losebutton.

2. 4lic- the :oldbutton on the Standardtoolbar, and change the Font Si7eto %C>ATAll elements on the chart can be colored differently to add emphasis or distinguish bet(eenelements.

To add colors to a chart1. In the !hart#ditor(indo(, select the chart element to change or add color to, such as

one of the plots !see 6rror eference source not found).. 4lic- the Show Pro&erties Windowbutton on the Standardtoolbar. ThePr!perties

dialog bo+ opens !see 6rror eference source not found).0. &elect the%ar6ertab, and then select a color from the color palette.@. To change the mar-er type, clic- the Typearro( in the%ar6ersection and select a

symbol from the menu !see 6rror eference source not found).7. $ie( the changes in thePre+ie9section.8. 4lic- the A&&l$button, and then clic- the !losebutton.



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Figure %5 Adding !olor to the !hart

Figure % Pro&erties Dialog :o-

II*+A"ACB+=6*DC==The bac-ground color can also be filled to ma-e the chart stand out.

To fill in a bac-ground color1. 4lic- inside a blan- area of the chart to select the entire chart area !see 6rror eference

source not found).. 4lic- the Show Pro&erties Windowbutton on the Standardtoolbar. ThePr!perties

dialog bo+ opens.

0. &elect the Fills(atch .

@. 4lic- the Patternarro( and select a bac-ground pattern.7. 4lic- the A&&l$button, and then clic- the !losebutton.

Figure % Filling a :ac;ground !olor



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ASW Statisti*s 1+ (SSS 1+,# art +


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Introduction Part -PASW stands forPredicti'e Anal$tics So"tware.This program can be used to analyze datacollected from surveys, tests, observations, etc. It can perform a variety of data analyses andpresentation functions, including statistical analysis and graphical presentation of data. Amongits features are modules for statistical data analysis. These include 1) descriptive statistics, suchas frequencies, central tendency, plots, charts, and lists; and ) sophisticated inferential andmultivariate statistical procedures, such as analysis of variance !A"#$A), factor analysis, cluster

analysis, and categorical data analysis. %A&' &tatistics is particularly (ellsuited for surveyresearch, though by no means is it limited to *ust this topic of e+ploration.

This handout !4hi&quare and A"#$A) introduces basic s-ills for performing hypothesis testsutilizing 4hi&quare test for ?oodnessof5it and generalized pooled t tests, such as A"#$A.The stepbystep instructions (ill guide the user in performing I!S56A%T%ST=+==D*%SS!=!ITThis procedure can be used to perform a hypothesis test about the distribution of a qualitative!categorical) variable or a discrete quantitative variable having only finite possible values. Itanalyzes (hether the observed frequency distribution of a categorical or nominal variable isconsistent (ith the e+pected frequency distribution.

With i#ed %#/ected &alues

Research Question # 1Can the hospital schedule discharge support staf evenly throughout

the week?

A large hospital schedules discharge support staff assuming that patients leave the hospital at afairly constant rate throughout the (ee-. Eo(ever, because of increasing complaints of staffshortages, the hospital administration (ants to determine (hether the number of dischargesvaries by the day of the (ee-.

For additional !andouts# $isit !tt"%&&'''.outu)e.*om&m*sula.
http://www.calstatela.edu/its/training/datafiles/pasw17p4.exehttp://www.calstatela.edu/its/docs/download.phphttp://www.youtube.com/mycsula.http://www.calstatela.edu/its/training/datafiles/pasw17p4.exehttp://www.calstatela.edu/its/docs/download.phphttp://www.youtube.com/mycsula.


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00J%atients leave the hospital at a constant rate !there is no difference bet(een the dischargerates for each day of the (ee-).

To perform the analysis1. &tart PASW Statistics 1.. 4lic- the =&enbutton on the Data #ditortoolbar. The Open Datadialog bo+ opens.0. "avigate to the data files folder, select the


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Figure %( !hiS)uare Test Dialog :o-

. &elect the


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,e&orting the anal$sis resultsJ03Je*ected in favor of 01.01J%atients do not leave thehospital at a constant rate.

#-&lanationJ5igure B1indicates that the calculated Z statistic, for si+ degrees of freedom, isB.0B. Additionally, it indicates that the significance value !:.:::) is less than the usualthreshold value of :.:7. This suggests that the null hypothesis, 03!patients leave the hospital at aconstant rate), can be re*ected in favor of the alternate hypothesis, 01!patients leave the hospital

at different rates during the (ee-).

With i#ed %#/ected &alues and within a Contiguous Subset of &aluesFy default, the 4hi&quare test procedure builds frequencies and calculates an e+pected valuebased on all valid values of the test variable in the data file. Eo(ever, it may be desirable torestrict the range of the test to a contiguous subset of the available values, such as (ee-days only!9onday through 5riday).

,esearch Euestion +

The hospital requests a ollow!up analysis" can staf e scheduledassu$ing that patients discharged on weekdays only %&onday

through 'riday( leave at a constant daily rate?

00J%atients discharged on (ee-days only !9onday through 5riday) leave at a constant daily

rate.

To run the analysis1. 4lic- the Anal$7emenu, point to /on&ara2etricTests, and select !hiS)uareG. The

C&iSquare Testdialog bo+ opens.. &elect the 4se speci#ied range option !see5igure B).0. 6nter +Bin theLowerbo+ and Bin the 4pperbo+.@. 4lic- the =Cbutton. The =ut&ut Viewer(indo( opens !see 5igure Band5igure B0).

"otice that the test range is restricted to 9onday through 5riday.

Figure (+ !hiS)uare RSubset@ Fre)uencies =ut&ut Table

Figure ( Test Statistics =ut&ut Table

"#T6 The e+pected values are equal to the sum of the observed values divided by the number ofro(s, (hile the observed values are the actual numbers of patients discharged.

,e&orting the anal$sis resultsJ00" "o not re#ect. %atients discharged on (ee-days only !9onday through 5riday) leave at aconstant daily rate.

ASW Statisti*s 1+ (SSS 1+,# art / 1


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#-&lanationJ5igure Bindicates that on average, about B patients (ere discharged from thehospital each (ee-day. The rate for 9ondays (as belo( average and the rate for 5ridays (asgreater than average. 5igure B0indicates that the calculated value of the 4hi&quare statistic (as7. at four degrees of freedom. Fecause the significance level !:.10) is greater than there*ection threshold of :.:7, 00!patients (ere discharged at a constant rate on (ee-days) could

not be re*ected.

>sing the 4hi&quare test procedure, it (as determined that the rate at (hich patients (eredischarged from the hospital (as not constant over the course of an average (ee-. This (asprimarily due to a greater number of discharges on 5ridays and fe(er discharges on &undays.'hen the range of the test (as restricted to (ee-days, the discharge rates appeared to be moreuniform. &taff shortages could be corrected by adopting separate (ee-day and (ee-end staffschedules.

With Custo'i9ed %#/ected &alues

,esearch Euestion

Does "irstclass 2ailing &ro'ide )uic;er res&onse ti2e than bul; 2ail

A manufacturer tries firstclass postage for direct mailings, hoping for faster responses than (ithbul- mail. #rder ta-ers record ho( many (ee-s each order ta-es after mailing.

03J5irstclass and bul- mailings do not result in different customer response times.

Fefore the 4hi&quare test is run, the cases must be (eighted. Fecause this e+ample comparest(o different methods, one method must be selected to provide the e+pected values for the testand the other (ill provide the observed values.

To (eight the cases

1. #pen the


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Figure (4 First!lass>:ul; *ail Wee; o" ,es&onse

Figure (5 Wee; o" ,es&onse Test Statistics

,e&orting the anal$sis resultsJ03Jo not re*ect. There (as no statistical difference bet(een customer response times usingfirstclass mailing and customer response times using bul- mailing.

#-&lanationJThe manufacturer hoped that firstclass mail (ould result in quic-er customerresponse. As indicated in 5igure B@,the first t(o (ee-s indicated different response times of fourand seven percentage points, respectively. The question (as (hether the overall differences

bet(een the t(o distributions (ere statistically significant.

The 4hi&quare statistic (as calculated to be 1.@B at eleven degrees of freedom !see 5igureB7). The significance value !p) associated (ith the data (as :.0@7, (hich (as greater than thethreshold value of :.:7. Eence, 00(as not re*ected because there (as no significant difference

bet(een firstclass and bul- mailings. The firstclass mail promotion did not result in responsetimes that (ere statistically different from standard bul- mail. Therefore, bul- postage (as moreeconomical for direct mailings.

=ne!Wa. Anal.sis of &ariance

One9ay analysis !# +ariance!#ne'ay A"#$A) procedures produce an analysis for aquantitative dependent variable affected by a single factor !independent variable). Analysis ofvariance is used to test the hypothesis that several means are equal. This technique is ane+tension of the t(osample t test. It can be thought of as a generalization of the pooled t test.Instead of t(o populations !as in the case of a t test), there are more than t(o populations ortreatments.

,esearch Euestion 4

Which o" the allo$s tested would be a&&ro&riate "or creating an underwater sensor arra$

ASW Statisti*s 1+ (SSS 1+,# art /


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To create an under(ater sensor array, four different alloys are tested for corrosion resistance.5ive plates of the same size of each alloy are placed under(ater for 8: days. After 8: days, thenumber of corrosion pits on each plate is measured.

03JThe four alloys e+hibit the same -ind of behavior and are not different from one another.

To run #ne'ay A"#$A1. #pen the


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Figure (% =neWa$ A/=VAJ =&tions Dialog :o-

8. &elect theDescriptive,Homogeneity o# variance test, and)eans plotchec- bo+es.2. 4lic- the !ontinuebutton.. 4lic- the =Cbutton. The =ut&ut Viewer(indo( opens.

Figure (( A/=VA Descri&ti'e =ut&ut

igure 1;; ! =ut/ut for Test of >o'ogeneit. of &ariances

igure 1;1 ! A*=&A =ut/ut

,e&orting the anal$sis resultsJ03Je*ect in favor of 01.01" The four alloys do not e+hibit the same -ind of behavior. They are statistically different fromone another.

#-&lanationJ5igure BBlists the means, standard deviations, and individual sample sizes of eachalloy. 5igure 1::provides the degrees of freedom and the significance level of the population;


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0. &elect theLSDchec- bo+, clic- the !ontinuebutton, and then clic- the =Cbutton. The=ut&ut Viewer(indo( opens.

"#T6 3& stands forList Square Di##erence, (hich compares the means one by one.

Figure 13 =neWa$ A/=VAJ Post 0oc *ulti&le !o2&arisons Dialog :o-

Figure 134 *ulti&le !o2&arisons =ut&ut

Figure 135 *eans Plot

,e&orting the anal$sis resultsJ

ASW Statisti*s 1+ (SSS 1+,# art / +


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03" &e#ect in avor o 01.01J't least one o the means is dierent.

#-&lanationJ Figure 134sho(s the results of comparing pairs of means bet(een different alloysets. 6ach ro( indicates the difference bet(een the t(o corresponding treatments. Alloys


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Figure 13 TwoWa$ A/=VA Data File

. InData View, clic- the Anal$7emenu, point to


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Figure 13( ?ni'ariateJ =&tions Dialog :o-

Figure 113 A/=VA Descri&ti'e =ut&ut Table

Figure 111 =ut&ut Table "or Tests o" :etweenSubects #""ects

ASW Statisti*s 1+ (SSS 1+,# art / +0


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,e&orting the anal$sis resultsJ03Je*ect in favor of 01for


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"#T6 If the 6+cel file contains multiple (or-sheets, select the desired (or-sheet by clic-ingthe 3or0s/eetarro(. Additionally, if only a specific range of cells in the (or-sheet is to beimported, the range must be specified in the -angebo+.

Figure 114 #-cel File

Figure 115 #-cel File I2&orted into PASW Statistics

The reverse situation may also arise, (here data in a %A&' &tatistics file must be analyzed using6+cel. This can be accomplished by e+porting the contents of the Data #ditorinto an 6+celspreadsheet.

To e+port %A&' &tatistics data into an 6+cel spreadsheet1. In the Data #ditor, clic- the Filemenu and select Sa'e AsG. The Sa+e Data Asdialog

bo+ opens !see 5igure 118).

. 4lic- theSave as typearro( and select the


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Figure 11 Sa'e Data As Dialog :o- Figure 11 Sa'e Data AsJ Variables Dialog :o-

0. &elect a destination drive by clic-ing theLoo0 inarro(.@. 6nter a name for the 6+cel file in the File namebo+.7. 4lic- the Sa'ebutton.

"#T6 >pon completion, the =ut&ut Viewer(indo( (ill open (ith a report summarizing thedetails and results of the e+port operation !see 5igure 11).

Figure 11% PASW Statistics #-&ort =ut&ut ,e&ort

To e+port %A&'&tatistics #utput charts into a %o(er%oint slide1. In the =ut&ut Viewer(indo(, clic- on the designated table. A bo+ appears around the

table and a red arro( to the left of it.. 4lic- the Filemenu and select #-&ortG. The24p!rt Outputdialog bo+ opens !see

5igure 11B).0. 4lic- the Typearro( and select the


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Figure 11( #-&ort =ut&ut Dialog :o-

Figure 1+3 Sa'e File Dialog :o-

6sing Scri/ting for edundant Statistical Anal.ses6very statistical analysis used by%A&' &tatistics is e+ecuted through a special programminglanguage. The specific code used for each analysis can be captured, stored as a script file, andedited if necessary. A series of scripts in a script file can be run either individually or all at thesame time. &cripting automates a series of statistical analyses that are performed on a data filethat changes data, but al(ays contains the same variables. &cripts are captured and edited in the%A&' &tatistics S$nta- #ditor(indo(.

ASW Statisti*s 1+ (SSS 1+,# art / +/


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The follo(ing e+ample illustrates the usefulness of capturing, storing, and running scripts. Thedata for the e+ample is ta-en from a classroom setting for a class that lasts one (ee-. At the endof each (ee-, data is compiled for each student. The variables in the set include the sub*ectname, gender, pretest scores, posttest scores, grade point average, computer o(nership, andmethod of administering e+aminations for that individual. 6ach (ee-, a report is generated thatans(ers a series of questions about the class from the previous (ee-. The questions ans(eredand the statistical analyses used are the same every (ee-, as described in Table 2.

Table Scri&ted Euestions and Statistical Techni)ues

Euestion Statistical Techni)ueRs@ to Answer Euestion

oes the data set include equal numbers ofeach gender and each test method/

&plit the file

4rosstabs

Is there a difference bet(een the male andfemale pretest scores/

&elect all cases

Independent&amples T Test

Is there a difference bet(een the male andfemale posttest scores/

Independent&amples T Test

Is there a difference bet(een the overall

pretest and posttest scores/

%aired&amples T Test

o gender, computer o(nership, and testmethod affect test scores/

Three'ay A"#$A

o gender, computer o(nership, and testmethod affect test scores differentlydepending on gender/

&plit the file

T(o'ay A"#$A

Is there a linear relationship bet(een thepretest and posttest scores for each gender/

&catter plot graph (ith file split

4an pretest scores predict posttest scores foreach gender/

&imple regression (ith file split

Is there an overall linear relationship bet(eenpretest and posttest scores/

&elect all cases &catter plot graph

4an pretest scores predict posttest scores/ &imple regression

To construct a script file that (ill automatically run the analyses1. #pen the

11).0. 4lic- the Vie9ertab, select theDisplay commands in t/e logchec- bo+, clic- the A&&l$

button, and then clic- the =Cbutton.

"#T6 The script file is built by performing each statistical analysis in the desired order. Allanalyses must be performed manually one time (hile the file is being built. In the currente+ample, the file (ill first be split, and then a crosstab table (ill be constructed.

@. 4lic- the Datamenu and select S&lit FileG. The Split Filedialog bo+ opens.7. &elect the !ompare groupsoption and transfer the


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2. In the PASW Statistics Data #ditor(indo(, clic- the Anal$7emenu, point toDescri&ti'e Statistics, and select !rosstabs. The Cr!sstabsdialog bo+ opens.

Figure 1+1 =&tions Dialog :o- igure 1$$ ! PASW Statistics S.nta# %ditor Window

. 9ove the


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To run an e+isting script file1. In the Data #ditor(indo(, clic- the Filemenu, point to =&en, and select S$nta-G

!see 5igure 17). The Open Synta4dialog bo+ opens.. 3ocate and open the

spss file for binding

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