Quantitative Analysis

June 25th, 2014

By Dr. James Lani

Statistics Solutions

Data Cleaning and Preparation

• Select the correct analysis

(RQ and level of measurement• Clean your data• Describe variables• Conduct the

analyses/assess assumptions• Present the findings• Summarize the findings

Putting the Pieces Together

Describe

Variables

Clean Data

Present &

Summarize

Findings

Conduct Analyses/

Assess Assumpti

ons

Quantitative Results StrategyGarbage In, Garbage Out

Assess data for outliers (±3.29);

Multiple imputation for missing data;

Create composite score (with reverse coding if necessary);

Conduct Cronbach’s alpha (α);

Assess for normality

Descriptive StatisticsMeans & Standard Deviations, Frequency & Percentages

Variable n %

Location

Urban 72 48.0

Rural 78 52.0

Ethnicity

White 36 24.0

Hispanic 13 28.7

Other 71 47.3

Table 1Frequencies and Percentages for Nominal Variables

Chi-SquareGoodness of Fit & Test of Independence

Chi-square analysis answers what research questions?

Assumptions of analysis:• Each cell has count of

1;• 80% of cells have an

expected value of 5.

Conducting analysis;

Presenting findings;

Write up in narrative;

Tables and figures.Republican Democrat Green Independent Libertarian

X X X X X

Repub. Democrat Green Indep. Libert.

Male X X X X X

Female X X X X X

Pearson Correlation

Examines the relationship between two or more scales level variables

Assumptions of the analysis:

• Linearity• Homoscedacit

y• Normality

AssumptionsLinear

Non-Linear

Homoscedasticity Met

Heteroscedasticity

Normal

Non-normal

Independent Samples t-testLet’s look at differences in IQ by Gender

Examines mean differences on a scale level dependent variable by a dichotomous nominal level independent variable.

Assumptions of analysis:

• Homogeneity of variance

• Normality

Males Females

Part 1=1 Part 4=2

Part 2=2 Part 5=3

Part 3=3 Part 6=4

X=2 X=3

Males Females

Part 1=1.9 Part 4=2.9

Part 2=2.0 Part 5=3.0

Part 3=2.1 Part 6=3.1

X=2 X=3

One-Way ANOVALet’s look at differences on Scores by Political Affiliation

Examines mean differences on a scale level dependent variable by a dichotomous nominal level independent variable.

Assumptions of analysis:

• Homogeneity of variance

• Normality

Males Females Independent

Part 1=1.9 Part 4=2.9 Part 7=3.9

Part 2=2.0 Part 5=3.0 Part 8=6.0

Part 3=2.1 Part 6=3.1 Part 9=8.1

X=2 X=3 X=6

Dependent Samples t-testLet’s look at differences between science scores Pretest vs. Posttest

Examines the mean difference between two paired scale level variables.

Assumptions of analysis:• Normality

Science Pretest Science Posttest

Part 1=1 Part 1=2

Part 2=2 Part 2=3

Part 3=3 Part 3=4

X=2 X=3

Science Pretest Science Posttest

Part 1=1.9 Part 1=2.9

Part 2=2.0 Part 2=3.0

Part 3=2.1 Part 3=3.1

X=2 X=3

Repeated-Measures ANOVALet’s look at differences among test scores Pretest vs. Posttest vs. Follow Up

Examines mean differences among two or more scale level variables

Assumptions of analyses:

• Sphericity• Homogeneity of

variance

Science Pretest

Science Posttest

Science Follow-Up

Part 1=1.9 Part 1=2.9 Part 1=

Part 2=2.0 Part 2=3.0 Part 2=

Part 3=2.1 Part 3=3.1 Part 3=

X=2 X=3 X=

Linear RegressionDoes IQ predict Creativity?

Examines if one or more scale, ordinal, or nominal level independent variables predict a scale level dependent variable.

Assumptions of analysis:• Normality,

Multicollineality, Homoscedastcity

IV DV

Regressions:Multiple, Logistic, Ordinal, Multinomial

It’s all about the level of measurement of the DV

Mediation AnalysisDoes Education mediate the relationship between IQ and Creativity?

Examines if one scale level mediator variable explains the relationship between a scale level independent variable and a scale level dependent variable

Assumptions of analysis:• Assumptions of regression

3 Regression Equations

IV M; must be significantIV DV; must be significantM, IV M, IV DV; IV is no longer significant

Education (M)

IQ (IV) Creativity (DV)

Moderation AnalysisDoes Age moderate the relationship between IQ and Creativity?

Examines if one scale level moderator variable strengthens or weakens the relationship between a scale level independent variable and a scale level dependent variable

Assumptions of analysis:• Assumptions of regression

Regression with 2 blocks

Step 1: IQ and Age enteredStep 2: Interaction term entered

Moderation is supported if interaction is significant.

Age (Mod)

IQ (IV)

IQ x Age Interaction

Creativity

Note. To avoid multicollinearity, center IV/Mod (subtract mean), then create the interaction term.

Moderation Analysis (Continued)

Does Age moderate the relationship between IQ and Creativity?Variable namesName of independent variable: IVMeaning of moderator value “0” Men Intercept/Constant: 3Meaning of moderator value “1” Women

Unstandardised Regression CoefficientsIndependent variable: 0.6Moderator: 0.4Interaction: -0.8

Means/SD’s of variablesMean of independent variable:0SD of independent variable: 1

www.jeremydawson.co.uk/slopes.html

Statistics Solutions ProQuantitative Analysis Made Easy!

Statistics Solutions Pro is the most user-friendly, intuitive, statistical analysis interpreter on the planet. This tool is essential for all students and researchers who need to use statistical analyses to draw appropriate conclusions from data. We hope you find Statistics Solutions Pro as beneficial and fun to use as we do. Enjoy!Only $299 for

students

Visitwww. StatisticsSolutionsPro.com

for a free trial

I’m All Yours:Questions and Answers

Join us August 30th in Atlanta for our

Dissertation Workshop

https://www.statisticssolutions.com/dissertation-workshop/