quantitative analysis: conducting, interpreting, & writing
DESCRIPTION
In this webinar Dr. Lani discusses key points in successfully completing your quantitative analysis. You will learn how to conduct common statistical analyses, how to examine assumptions, how to easily generate APA 6th edition tables and figures, how to use Statistics Solutions Pro, how to identify and interpret the appropriate statistics, and how to present and summarize your findings.TRANSCRIPT
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
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