Diagramming Research Questions: A Multivariate Revelation! Conference Documents...Multivariate analysis of variance (interactive structure two or several times) Multivariate analysis

1

Diagramming Research Questions:

A Multivariate Revelation!

Cathy Carlson, PhD,

APRN, FNP-BC, RN-BC

Associate Professor

Northern Illinois University

Conflict of Interest Disclosure

• No Conflicts of Interest for ALL listedcontributors.

• Cathy Carlson, PhD, APRN, FNP-BC, [email protected]

A conflict of interest is a particular financial or non-financial circumstance that might compromise, or appear to compromise, professional judgment. Anything that fits this should be included. Examples are owning stock in a company whose product is being evaluated, being a consultant or employee of a company whose product is being evaluated, etc.

• Taken in part from “On Being a Scientist: Responsible Conduct in Research”. National Academies Press. 1995.

Objectives1. To understand how a research problem can be diagrammed

creating a concept map leading to a choice of a technique ofmultivariate analysis

2. To review fundamental research classification of researchvariables and their relationships

3. To understand the basic format of a research problem and thenotation system used to represent its construction into a concept map.

4. Review of notation system and examples related topain management research

5. To practice mapping several independent research problems from articles in Pain Management Nursingand predict the chosen technique of multivariate analysis

2

Story Time!

Levels of Measurement

• Nominal• Attributes are only named

• Ordinal• Attributes can be ordered

• Interval• Distance is meaningful

• Ratio• Absolute zero

Considered

Quantitative Data

The Notation System

• Stands for a latent variable,which is a non-measuredcharacteristic such as‘quality’ or ‘anxiety’

OR

• PAIN

Circle

3

The Notation System Cont……

• Stands for a manifest variable, which is a directly measured characteristic

• Indicates a quantitativemeasurement level (intervalor ratio-scale)

• For lower measurement levels, the rectangle issubdivided

Square or

Rectangle


• Stands for a dummy variable with 2categories such as gender withmale/female

• A dummy variable with twocategories is a variable which iscoded as 0 and 1 where 1 gender is 0and the other gender is 1

• In doing so, a dichotomous variableis artificially lifted to a higher level of measurement

0

1

Male

Female


• A variable with 3 categories

OR

• More with one less columnthan rows

0 0

1 0

0 1

Mild

Moderate

Severe

4


• Stands for a statisticalrelationship between 2variables that is not causalin nature

• Stands, just like a ,for a statistical relationshipthat is not conceived of interms of cause and effect

X Y

Two Way Arrow

Line


• Stands for a dependent relationship in terms of causeand effect

One-Way Arrow


• Stands for effect of interaction,which means the combinedeffect of 2 independent variables on the dependent variable

Interaction

5

To Decide Which Statistical Analysis to Use:

•We follow a series ofquestions/subclassifications of data.

1. Is This a Dependent orNondependent Technique?

• Is there an implied cause and effect relationship?

• The dependent variable must be specified

• The techniques are asymmetrical

Dependent or Nondependent?

Dependent Techniques• Multiple regression

• Partial correlation

• Analysis of variance

• Analysis of covariance

• Discriminant analysis

Nondependent Techniques• Factor analysis

• Cluster analysis

6

2. How Many Dependent Variables AreThere?

One Dependent Variable• Multiple regression




Several Dependent Variables• Multivariate multiple

regression

• Multivariate analysis of variance

• Multivariate analysis of covariance

• Multiple discriminant analysis

3. What Is the Level of Measurement ofthe Dependent Variable?

Quantitative• Multiple regression




Nominal• Discriminant analysis

4. What is the Level of Measurementfor the Independent Variables?

• Most of the techniques used most frequently requirequantitative measures

• Analysis of variance allows for nominal-level measures

• If there is a combination of levels of measurement, analysisof variance becomes analysis of covariance

7

Level of Measurement of Independent Variables

Nominal• Analysis of

Variance

Quantitative• Multiple regression



Mixed

• Analysis of

Covariance

5. Is the Structure Additive orInteractive?

• Linear models, by design, treat the effects of theindependent variables as additive

• In contrast, analysis of variance allows for the examinationof interactions between independent variables

Additive or Interactive Structure

Additive Structure• Multiple regression



• Factor and cluster analysis

• Multidimensional scaling

Interactive Structure• Analysis of variance


• Multivariate analysis of variance

• Multivariate analysis of covariance

8

6. Are the independent variablesorthogonal?

• Geometric interpretation• Perpendicular = no relationship

• Consequences of nonorthogonality = multicollinearity• Means the independent variables are highly correlated

• Most techniques assume orthogonality• Techniques exist that force othogonality

7. Is the starting point a Problem-Variable or a Problem-Relation?

• Most dependent techniques fall in the problem-variablecategory

• Problem-relation is looking at the influence of theindependent variable on the dependent variable

• Questionable causality = spurious

Starting Point

Problem-Variable• Multiple regression




Problem-Relation• Partial correlation analysis

• Other advanced versions

9

8. Are there distinct hierarchal steps?

One Hierarchical Step• Multiple Regression

Several Hierarchical Steps• Path analysis

• Partial correlation analysis

• Others

9. Are the Variables Latent orManifest?

• Latent variables• Not directly measured

• Theoretical Constructs

• Represented by manifest (measured) variables in groups of 2 or more

• The relationship between manifest and latent variablesis nondependent

• Techniques with latent variables• Factor analysis

• Canonical correlation analysis

• Multidimensional scaling

Multivariate Analysis

• Many statistical techniques focus on just one or twovariables

• Multivariate analysis (MVA) techniques allow more than twovariables to be analysed at once

• Multiple regression is not typically included under this heading, but can be thought of as a multivariate analysis

10

Analysis of Variance (ANOVA)

Let’s Look at an Example

• Are there differences in pain intensity 24 hours after a THA based on gender, race, and educational level?

Pain Intensity

Male

Female

Educational Level

Gender

Race

11

What if We Added Weight to the Independent Variables?

Pain Intensity

Male

Female

Weight

Race

Multiple Regression Analysis

Let’s Look at an Example

• Do age, anxiety, and # of comorbidities improve the ability to predict pain intensity 48 hours after a THA?

12

Multiple Regression Analysis

Pain

IntensityAnxiety

# of

Comorbidities

Age

Factor Analysis

?

Let’s Look at an Example Previous

Practices

Felt Needs/

Problems

Innovativeness

Norms of the

Social System

Prior

Conditions

13

Articles from Pain

Management Nursing

Process

• Read the sections given to you in your handout.

• Quickly go through the 9 questions discussed previously

• Sketch your diagram

• Compare your sketch to your handout to identify thestatistical method used

• Then we will compare notes and discuss

Article #1

14

Article #2

Article #3

Thank you for your time and attention!

Handouts for Concurrent Session 4A

Diagramming Research Questions: A Multivariate Revelation!

Cathy Carlson, PhD, APRN, FNP-BC, RN-BC Associate Professor

Northern Illinois University

Variable Symbols

Stands for a latent variable, which is a non-measured characteristic

such as ‘quality’ or ‘anxiety.’

Stands for a manifest variable, which is a directly measured

characteristic. A square or rectangle indicates a variable of quantitative

measurement level (interval or ratio-scale). For lower measurement

levels, the rectangle is subdivided as explained below.

Stands for a dummy variable with 2 categories such as gender with

male/female. A dummy variable with two categories is a variable

which is coded as 0 and 1 where 1 gender is 0 and the other gender is

1. It does not matter which category (male/female) is assigned the 0 or

the 1 score for it is merely the intention to ‘calculate’ with the

numerical values 0 and 1. In doing so, a dichotomous variable is

artificially lifted to a higher level of measurement.

A variable with 3 categories

Stands for a statistical relationship between 2 variables that is not

causal in nature.

Stands, just like a , for a statistical relationship that is not

conceived of in terms of cause and effect.

Stands for a dependent relationship in terms of cause and effect.

Stands for effect of interaction, which means the combined effect of 2

independent variables on the dependent variable.

Multiple regression analysis (convergent causal structure)

Analysis of variance (ANOVA) (the interactive structure)

?

Analysis of covariance (ANCOVA) (interactive structure)

Partial correlation analysis (spurious or indirect causality)

Discriminant analysis (discriminant structure)

Canonical correlation analysis (the canonical structure)

.

.

.

?

.

.

.

?

Factor and cluster analysis (latent structure)

Multidimensional scaling (latent structure of similarities)

Multivariate multiple regression (convergent causal structure

two or several times)

Multivariate analysis of variance (interactive structure two or

several times)

Multivariate analysis of covariance (interactive structure two or several

times)

Multiple discriminant analysis (discrimination structure with more

than two population groups)