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~Drowning in ~Drowning in Data~Data~

SPSS Data AnalysisSPSS Data Analysis

3/26/123/26/12Sumiko Takayanagi, Ph.D.

Sr. StatisticianUCLA School of Nursing

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Today’s PresentationToday’s Presentation SPSS Environment SPSS Environment

Review of SPSS BasicsReview of SPSS Basics

Inferential Statistics in SPSS Inferential Statistics in SPSS Independent t-testIndependent t-test Two-Way Analysis of VarianceTwo-Way Analysis of Variance Multiple RegressionMultiple Regression

ConclusionConclusion

ReferencesReferences

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Features of SPSSFeatures of SPSS Originally developed for the people in Originally developed for the people in

Social Science Areas, therefore, no heavy Social Science Areas, therefore, no heavy programming background requiredprogramming background required

Designed as User Friendly and has Pull Designed as User Friendly and has Pull Down Menus to Execute Statistical Down Menus to Execute Statistical CommandsCommands

Ability to do Data Management & Ability to do Data Management & ManipulationsManipulations

Ability to Store Programs & Produce Ability to Store Programs & Produce Reports/GraphsReports/Graphs

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SPSS Program FlowSPSS Program Flow

Data Modification/

Transformation

Pull-DownMenu

SPSSDataFile

OutsideData

Source

RawData

Data Analysis

Importing

Direct E

ntry

SyntaxMenu

OR

(Data Steps) (Analysis Steps)

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Data View Window - Data Entry Site - Data Entry Site(Columns=Variables, Rows=Cases)(Columns=Variables, Rows=Cases)

Title bar

Tool bar

Data View window

Information barPull-down Menu bar

Active cell Action bar

VariableNames

Help Menu

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Variable View WindowData Definition SiteData Definition Site

64 CharactersMax, No spaceBetween Beg letter, @, #, or $

Variable Description

Length

Numeric,String, &Others

Click here to see this view

Value Code

Description

# of Decimals

Missing value

Description

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1. OK - results/action will be executed

OK PasteVS.

buttons

Before we Before we see see

Examples…Examples… <Output File>

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1.Hit Paste to obtain Syntax Window

2. Run Syntaxto obtain the results in theOutput Window

<Syntax File>

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Example - School Data Example - School Data Raw DataRaw Data

Subject 1Subject 1 Subject #Subject # (1)(1) FemaleFemale (1)(1) IntensiveIntensive (1)(1) Reading (90)Reading (90) Math Math (67)(67)

Subject 2Subject 2 Subject # Subject # (2)(2) FemaleFemale (1)(1) ModerateModerate (2)(2) Reading Reading (72)(72) Math Math (46)(46)

Subject 3Subject 3 Subject # Subject # (3)(3) MaleMale (0)(0) BasicBasic (3)(3) ReadingReading (41)(41) MathMath (73)(73)

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School DataSchool DataVariable ViewVariable View

Variable View Activated

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School DataSchool DataCompleted Dataset – Data Completed Dataset – Data

ViewView

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School DataSchool DataCompleted Dataset – Completed Dataset –

Variable ViewVariable View

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Importing Excel Data file Importing Excel Data file to SPSSto SPSS

2. Go to File Menu

3. Click “Read Text Data”

4. Click Files of type to Excel & choose Excel file

5. Hit Open

6. Check Worksheet #, Variable on the 1st row, & Hit OK

1. Open the SPSS Data file

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School DataSchool DataCompleted Dataset – Data Completed Dataset – Data

ViewView

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Click to Obtain Click to Obtain Data File InformationData File Information

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Variable InformationVariable Information

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Value Code InformationValue Code Information

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Basic Statistical Basic Statistical Methods Methods

Independent t-testIndependent t-test Two-Way ANOVATwo-Way ANOVA Multiple Multiple

Regression Regression

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AssumptiAssumptionsons

1. 1. NormalityNormality

2. Variance 2. Variance EqualityEquality

3. 3. IndependenIndependencece

# of # of VariablesVariables

CharacteristiCharacteristicscs

School DataSchool Data

N=100N=100

Dependent = Dependent = 11

ContinuousContinuous Math ScoreMath Score

Range of 0-Range of 0-100100

Independent Independent = 1= 1

CategoricalCategorical

2-levels2-levelsGenderGender

Independent t-testIndependent t-test– Is there a significant difference – Is there a significant difference

between 2 groups?between 2 groups?

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How to calculate t-How to calculate t-value?value?

Mean Mean DifferenceDifference

Group Group VariabilityVariability

t-value=

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t-testt-test

MediumVariability

HighVariability

LowVariability

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Independent t-testIndependent t-test

1. Go to Analyze.

2. Choose Compare Means.

3. Choose IndependentSamples t Test.

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t-testt-test

1. Choose Dependent& Independent Variables.

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Variance Equality Test t - statistics

t = Z1 – Z2 = 63.20 – 54.10 = 9.093 = 3.295 SD1

2 + SD22 (13.914)2 +(13.064)2 2.760

N1 N2

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t = Mean Diff Std. Error Diff

Dependent Variable

Descriptives & Analysis

Independent Variable

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Conclusion & Chart

There is a There is a significansignificant t difference difference in math in math ability ability between between males and males and females. females. Males Males performeperformed better d better than than females.females.

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AssumptiAssumptionsons

1. 1. NormalityNormality

2. Variance 2. Variance EqualityEquality

3. 3. IndependenIndependencece

# of # of VariablesVariables

CharacteristiCharacteristicscs

School DataSchool Data

N=100N=100

Dependent = Dependent = 11

ContinuousContinuous Math ScoreMath Score

0-1000-100

Independent Independent >1>1

Categorical-Categorical-

2 or more 2 or more levelslevels

GenderGender

Program Program TypeType

Factorial ANOVAFactorial ANOVA– Is there any main or the – Is there any main or the

interaction effects?interaction effects?

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2 x 3 Factorial ANOVA2 x 3 Factorial ANOVADesign DiagramDesign Diagram

GenderGender

ProgramProgram

MaleMale FemaleFemale

MildMild 56, 86, 70, 56, 86, 70, 69, …..69, …..

55, 72, 67, 55, 72, 67, 48, …..48, …..

ModerateModerate 86, 59, 67, 86, 59, 67, 80, …..80, …..

63, 78, 55, 63, 78, 55, 46, …..46, …..

IntensiveIntensive 89, 92, 86, 89, 92, 86, 71, ….. 71, …..

72, 76, 54, 72, 76, 54, 56, …..56, …..

Math Test Scores

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2-Way Factorial ANOVA2-Way Factorial ANOVA

1.Go to General Linear Model & choose Univariate.

2. Choose One Dependent & Two Independent Variables.

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Factorial ANOVA Factorial ANOVA (2x3)(2x3)

1. Freq of IV and Raw Means

2. Equality of Variance Test

Descriptives

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Factorial ANOVAFactorial ANOVA

Main Effects &Interaction

Main Analysis

Results:Results: Main effect – Sig. difference in Main effect – Sig. difference in gendergender and in and in

program typeprogram type.. Interaction – Sig. interaction between gender Interaction – Sig. interaction between gender

and program type. and program type.

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Factorial ANOVAFactorial ANOVA

Scheffe & LSD Methods

MultipleComparison

Sig. Differentlevel

Which levels are actuallyDifferent ??

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Factorial ANOVAFactorial ANOVA

Significant Effects Significant Effects Males performed Males performed

better than females.better than females. Students in the Students in the

Intensive program Intensive program performed better performed better than in the Mild than in the Mild program.program.

Males in the Males in the Intensive program Intensive program performed better performed better than in other than in other programs, but no programs, but no performance performance difference in females. difference in females.

Conclusion

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AssumptioAssumptionsns

1. 1. NormalityNormality

2. 2. Variance Variance EqualityEquality

3. 3. IndependeIndependencence

4. Linear4. Linear

RelationshRelationshipip

# of # of VariablesVariables

CharacteristiCharacteristicscs

Health Survey DataN=100N=100

Dependent Dependent =1=1

ContinuousContinuous LDL ValueLDL Value

0-2000-200

Independent Independent > 1> 1

Continuous Continuous or or Dichotomous Dichotomous (0 or 1) (0 or 1) VariablesVariables

HT, WT, BMI, HT, WT, BMI, & &

ExerciseExercise

Multiple RegressionMultiple Regression – Which IVs can predict the DV and to estimate – Which IVs can predict the DV and to estimate

the effects of these variables on DV?the effects of these variables on DV?

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Multiple Regression Multiple Regression DiagramDiagram

LDL

HT

WT

BMI

Exercise

DV

IV

All 4 IVs are predicting LDL

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Health Survey Data of N=100

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Multiple RegressionMultiple Regression

1.Choose Regression

2. Choose Linear Regression

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2. Choose Statistics you need.

3. Choose Residual Plots.

1. Choose DV, IV, & Method.

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Descriptives& Correlation

Tables

CorrelationCoefficients & correspondingp-values.

DescriptiveStats.

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Main Analysis

R=r between pred and observ value of the DV

B=Reg Coefficient

Global test to see if any coefficient is different from “0”

R2=how much of the variability in the outcome is accounted

for by the predictors (regression sum of squared/total sum of squares)

Adj. R Sq=Adj for the # of Parameters in the model

Beta=Stdized. Reg Coefficient.Something is Wrongif Beta >1!!

t & Sig=IV predictability

Tolerance &VIF

Partial/PartCorrelations

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Residual Normality Linearity and Equal Variance & residual independence

Residual Analysis

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IVs explain about IVs explain about 40% of the 40% of the variability of LDL variability of LDL level.level.

The significant The significant predictors of LDL predictors of LDL were BMI and Hrs of were BMI and Hrs of Exercise.Exercise.

The collinearity The collinearity statistics didn’t statistics didn’t show exceptionally show exceptionally large large multicollinearity multicollinearity among predictors. among predictors.

Assumptions of Assumptions of residual normality residual normality and equal variance and equal variance were met.were met.

Conclusion Multiple Multiple

RegressionRegression

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Key ConceptsKey Concepts

Statistical Models depend on the Statistical Models depend on the theory and data. Choose your model theory and data. Choose your model wisely to see if it can answer your wisely to see if it can answer your research questions.research questions.

Check Assumptions. Model Check Assumptions. Model conclusions may not be valid unless conclusions may not be valid unless the assumptions were met. If not, the assumptions were met. If not, use appropriate corrections, do data use appropriate corrections, do data transformations, or even use other transformations, or even use other statistical methods.statistical methods.

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ConclusionsConclusions

Statistical judgments come Statistical judgments come into our daily lives. Statistics into our daily lives. Statistics are more than mathematical are more than mathematical calculations or scientific calculations or scientific research, but they are the research, but they are the way of logical thinking…way of logical thinking…

Thank youThank you