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Advanced Quantitative Methods

- William L. Holzemer, RN, Ph.D., FAAN
- Professor, School of Nursing
- University of California, San Francisco
- bill.holzemer_at_nursing.ucsf.edu

Objectives

- Develop your definition of nursing science
- Use the Outcomes Model to think about your

area(s) of interest - Review quantitative methods
- Think about how we build knowledge to improve

health and nursing practice.

Assignments

- PhD Students -individual assignments
- MS Students group assignment
- Mini-literature review
- Outcomes Model
- Substruction
- Synthesis Tables
- Summary

Nursing Nursing Science?

- Definition of Nursing
- American Nurses Association
- Nursing is the assessment , diagnoses, and

treatment of human responses

Definition of Nursing

- Japan Nurses Association
- Nursing is defined as to assist the
- individual and the group, sick or well, to
- maintain, promote and restore health.

Definition of NursingInternational Council of

Nurses

- Nursing encompasses autonomous and collaborative

care of individuals of all ages, families, groups

and communities, sick or well and in all

settings. Nursing includes the promotion of

health, prevention of illness, and the care of

ill, disabled and dying people. Advocacy,

promotion of a safe environment, research,

participation in shaping health policy and in

patient and health systems management, and

education are also key nursing roles.

Common ElementsDefinitions of Nursing

- Person (individual, family, community)
- Health (Wellness Illness)
- Environment
- Nursing (care, interventions, treatments)

Nursing Science

- The body of knowledge that supports
- evidence-based practice

Nursing Science Uses Various Research

Methodologies

- Qualitative
- Understanding
- Interview/observation
- Discovering frameworks
- Textual (words)
- Theory generating
- Quality of informant more important than sample

size - Rigor
- Subjective
- Intuitive
- Embedded knowledge

- Quantitative
- Prediction
- Survey/questionnaires
- Existing frameworks
- Numerical
- Theory testing (RCTs)
- Sample size core issue in reliability of data
- Rigor
- Objective
- Public

Types of Research Methods (all have rules of

evidence!)

- Quantitative
- Non-Experimental or Descriptive
- Experimental or Randomized Controlled Trials
- Ethnography
- Content Analysis
- Models of analysis Parametric vs.

non-parametric

- Qualitative
- Grounded theory
- Ethnography
- Critical feminist theory
- Phenomenology

Models of analysis fidelity to text or words of

interviewees

Outcomes Model for Health Care Research(Holzemer,

1994)

Outcomes Model

- Heuristic
- Systems model (inputs are outputs, outputs become

inputs) - Relates to Donabedians work on quality of care

(Structure, Process, and Outcome Standards)

Outcomes Model Nursing Process

Outcomes Model for Health Care Research

Outcomes Model Your assignment(Think about a

project or program of research)

Where Should We Find Evidence-Based Practice

Guidelines?

- Clinical practice guidelines
- Nursing Standards/ Procedural Manuals
- Great demand, low level of delivery (Great

demand, growing level of delivery) - Knowledge base from research literature

Types of Evidence How do we know what we know?

- Clinical expertise
- Intuition
- Stories
- Preferences, values, beliefs, rights
- Descriptive/quasi-experimental studies
- Randomized clinical (controlled) trials (RCTs) -

the gold standard

Summary Introduction to Research

- Think about nursing research nursing science
- Outcomes Model designed to put boundaries around

your area of study and expertise (very difficult

challenge in nursing!) - Variable identification
- Understanding rigor correct methods for any

type of research design - Enhance enjoyment in reading research articles
- Understand the challenge of the words so easily

used, evidence-based practice.

Some Challenges

- Think about developing your definition of nursing

science. - Use the Outcomes Model to help you think about

your program of research. - Enhance your understanding of rigor in all types

of research designs. - Increase your enjoyment of reading research

articles. - Understand the complexities of evidence-based

practice.

When thinking about your research problem

- Is it significant?
- Are you really interested in it?
- Is it novel?
- Is it an important area?
- High cost, high risk?
- Can it be studied?
- Is it relevant to clinical practice?

Where do ideas come from?

- Literature reviews
- Newspaper stories
- Being a research assistant
- Mentors/teachers
- Fellow students
- Patients
- Clinical experience
- Experts in the field
- Build your area of expertise from multiple

sources.

Uses of Substruction

- Critique a published study
- Plan a new study

Substruction

- A strategy to help you understand the theory and

methods (operational system) in a research study - Applies to empirical, quantitative research

studies - There is no word, Substruction, in the

dictionary. It has an inductive meaning,

constructing and a deductive meaning,

deconstructing - Hueristic

Substruction

Substruction Building Blocks or Statements of

Relationships

Statements of Relationships

Substruction Research Design Perspective

Focus of Study (RCT?)

Substruction Theoretical System, an example

Pain Intervention Study

Post Surgical Patient Severity of illness age

gender

Pain Management Intervention Patient

communication Standing PRN orders Non

pharmacological tx

Pain Control Length of stay Patient Satisfaction

Substruction Operational System

- Pain Intensity
- Instrument
- VAS 10 cm scale
- (low to high pain)

- Functional Status
- Instrument1-5 Likert scale, 1low 5high

function - Scale continuous or discrete?

Scale continuous or discrete?

Scaling

- Discrete non-parametric (Chi square)
- Nominal gender
- Ordinal low, medium, high income
- Continuous parametric (t or F tests)
- Interval Likert scale, 1-5 functionality
- Ratio money, age, blood pressure

Issues

- What is the conceptual basis of the study?
- What are the major concepts and their

relationships? - Are the proposed relationships among the

constructs and concepts logical and defensible? - How are the concepts measured? valid? reliable?
- What is the level of scaling and does it relate

to the appropriate statistical or data analytical

plan? - Is there logical consistency between the

theoretical system and the operational system?

Is there a relationship between touch and pain

control, accounting for initial amount of

post-operative pain? rx,y.z

Literature Review

- We review the literature in order to understand

the theoretical and operational systems relevant

to our area of interest. - What is known about the constructs and concepts

in our area of interest? - What theories are proposed that link our

variables of interest?

Literature Review

- What is known?
- What is not known?
- Resources
- The Cochran Library
- Library Data Bases
- PubMed
- CINYL

Literature ReviewHow to combine, synthesis, and

demonstrate direction?

Literature Review

Table 1. Outline of study variables related to

your topic

Table 2. Threats to validity of research studies

related to topic

Table 3. Instruments

Table 4. Power analysis for literature review on

topic.

Literature Synthesis

- Synthesis - what we know and do not know
- Strengths rigor, types of design, instruments?
- Weaknesses lack of rigor, no RCTs, poorly

developed instruments - Future needs what is the next step?

Research Designs

Research Design Qualitative

- Ethnography
- Phenomenology
- Hermeneutics
- Grounded Theory
- Historical
- Case Study
- Narrative

Rigor in Qualitative Research

- Dependability
- Credibility
- Transferability
- Confirmability

Types of Quantitative Research Designs

- We will focus on RIGOR
- Experimental
- Non-experimental

X,Y, Z notation

- Z covariate
- Severity of illness
- X independent variable (interventions)
- Self-care symptom management
- Y dependent variable (outcome)
- Quality of life

Types of Quantitative Research Designs

- Descriptive X? Y? Z?
- What is X, Y, and Z?
- Correlational rxy.z
- Is there a relationship between X and Y?
- Causal ?X ? ?Y?
- Does a change in X cause a change in Y?

Rigor in Quantitative Research

- Theoretical Grounding Axioms postulates

substruction-validity of hypothesized

relationships - Design validity (internal external) of research

design Instrument validity and reliability - Statistical assumptions met (scaling, normal

curve, linear relationship, etc.) - (Note Polit Beck reliability, validity,

generalizability, objectivity)

- Literature Review Study Aims
- Study Aims Study Question
- Study Question Study Hypothesis

Aim, Question, and Hypothesis

- Study Aim To explore if it is possible to reduce

patient falls for elderly in nursing homes. - Study Question Does putting a sitter in a

patient room reduce the incidence of falls? - Study Hypothesis
- Null H0 There is no difference between

patients who have a sitter and those who do not

in the incidence of falls.

Experimental Designs

Definition Experimental Design

- There is an intervention that is controlled or

delivered - There is an experimental and control group
- There is random assignment to groups

Classic Experimental Design

- O1exp X O2exp
- ?
- R
- ?
- O1con O2con
- (pretest) (posttest)
- Oobservation
- 1 pretest or time one 2 posttest or time

two - X intervention
- R random assignment to groups

Classic Experimental Design

- O1exp X O2exp
- ?
- R
- ?
- O1con O2con
- (pretest) (posttest)
- The RCT is the Gold Standard for
- Evidence-Based Practice

Randomization

- Random assignment to groups (internal validity

issue) equals Z variables in both groups - Random selection from population to sample

(external validity issue) equals Z variables in

the sample that are true for the population

Goal

- Statement of Causal Relationship

Conditions Required to Make a Causal Statement

X causes Y

- X precedes Y
- X and Y are correlated
- Everything else controlled or eliminated. No Z

variables impacting outcome. - We never prove something, we gather evidence that

supports our claim.

Controlling Z variables

- Minimize threats to internal validity
- Limit sample (e.g. under 35 years only) to

control variation - Statistical manipulation (ANCOVA)
- Random assignment to groups

Dimensions of Research Designs Groups Time

- O1exp X O2exp
- ?
- Groups (n2 experimental control)
- ?
- O1con O2con
- --------------------------

--------------------- - ? Time (n2) ?
- (repeated measures)

Dimensions of Research Designs Groups Time

- Groups between factors
- Time within factors

Types of Designs

- O - descriptive, one time
- O1 O2 O3 - descriptive, cohort, repeated

measures) - O1 X O2 (not an experimental design!) -

pre-post-test

Types of Designs

- O1 X O2
- O1 O2
- RCT randomized controlled trial

Types of Designs

- O1 O2 O3 X O4 O5 O6
- O1 O2 O3 O4 O5 O6
- O1 X O2 Xno O3 X O4 Xno O5
- (repeated measures vs. time series designs)

Types of Design

- O1 X1 O2
- R O1 X2 O2
- O1 O2
- of groups? ___
- points in time? ___

Types of Designs

- Post-test only design
- X O2
- O2
- What is the biggest threat to this post-test only

design?

Types of Research Design

- Experimental (true)
- Quasi-Experimental (quasi)
- No random assignment to groups

Design Validity

- Statistical conclusion validity
- Construct validity of Cause Effect (X Y)
- Internal validity
- External

Design Validity

- Statistical Conclusion Validity rxy?
- Type I error (alpha 0.05)
- Type II error (Beta) Power 1-Beta, inadequate

power, i.e. low sample size - Reliability of measures
- Can you trust the statistical findings?

Design Validity

- Construct Validity of Putative Cause Effect

(?X ? ?Y?) - Theoretical basis linking constructs and concepts

(substruction) - Outcomes sensitive to nursing care
- Link intervention with outcome theoretically
- Is there any theoretical rationale for why X and

Y should be related?

Design Validity

- Internal Validity
- Threat of history (intervening event)
- Threat of maturation (developmental change)
- Threat of testing (instrument causes an effect)
- Threat of instrumentation (reliability of

measure) - Threat of mortality (subject drop out)
- Threat of selection bias (poor selection of

subjects) - Are any Z variables causing the observed changes

in Y?

Design Validity

- External Validity
- Threat of low generalizability to people, places,

time - Can we generalize to others?

Building Knowledge

- Goal is to have confidence in our descriptive,

correlational, and causal data. - Rigor means to follow the required techniques and

strategies for increasing our trust and

confidence in the research findings.

Sampling

- Sample selection, not assignment

Terms

- Population
- Sample
- Element

- - All possible subjects
- -A subset of subjects
- - One subject

What do we sample?

- People (e.g. subjects)
- Places (e.g. hospitals, units, cities)
- Time (e.g. season, am vs. pm shift )

Sampling What do we do?

- Random Assignment
- -is designed to equalize the Z variables in the

experimental and control groups

- Random Selection
- -is designed to equalize the z variables that

exist in the population to be equally distributed

in a sample

Types of Probability Sampling

- Probability
- Simple random sampling using a random table of

numbers - Stratified random sampling divide or stratify by

gender and sample within group - Systematic random sampling take every 10th name
- Cluster sampling select units (clusters) in

order to access patients or nurses

Types of Non-probability sampling

- Convenience first patients to walk in the door
- Purposive patients living with an illness
- Quota equal numbers of men women
- (volunteers)
- (convenience)

Types of Samples

- Homogeneous subjects are similar, all females,

all between the ages of 21-35 - Heterogeneous subjects are diverse, wide age

range, all types of cancer patients

Sampling Error

How to control sampling error?

- Use random selection of subjects
- Use random assignment of subjects to groups
- Estimate required sample size using power

analysis to ensure adequate power - Overestimate required sample size to account for

sample mortality (drop out)

Sample Size and Sampling Error

Sample Size Calculations

- Type of design
- Accessibility of participants
- Statistical tests planned
- Review of the literature
- Cost (time and money)

Strategies for Estimating Sample Size

- Ratio of subjects to variables in correlational

analysis. 31 up to 301 subjects to variables.

30 item questionnaire requires 90 to 900

subjects. - Chi square cant work if less than 5 subjects

per cell

Power Analysis

- Power - commonly set at 0.80
- Alpha - commonly set at 0.05 or 0.01
- Effect Size - based upon pilot studies or

literature review small, medium, large - Sample Size - subjects required to ensure

adequate power - Power is a function of alpha, effect size, and

sample size.

Power Analysis Programs

- SPSS Pakcage
- nQuery Adviser Release 4.0 (most recent?)
- http//www.statsolusa.com

Power

- Power is the ability to detect a difference

between mean scores, or the magnitude of a

correlation. - If you do not have enough power in a study, it

does not matter how big the effect size, i.e. how

successful your intervention, you can not

statistically detect the effect. - Many studies are under powered.

Effect Size

- Effect size can be thought of as how big a

difference the intervention made. - Statistical significance and clinical

significance are often not the same thing

Effect Size

- Small (correlations around 0.20)
- Requires larger sample size
- Medium (correlations around 0.40)
- Requires medium sample size
- Large (correlations around 0.60)
- Requires smaller sample size

Effect Size

- Meanexp Meancon
- Effect Size

- SD e c

Eta Squared (?2)

- In ANOVA, it is the proportion of dependent

variable (Y) explained. - Estimate of Effect Size
- Similar to R2 in multiple regression analysis.

alpha

- alpha relates to hypothesis testing and how often

you are willing to make a mistake in drawing a

conclusion - alpha is equivalent to Type 1 error or saying

that the intervention worked, when in fact the

effect size observed, is just due to chance - alpha of 0.01 is more conservative than 0.05 and

therefore, harder to detect differences

Hypothesis Testing Is it true or false?

- Null hypothesis H0
- Mean (experimental) Mean (control)
- Alternative hypothesis H1
- Mean (experimental) / Mean (control)

Hypothesis Testing and Power

Quiz

- If sample size goes up, what happens to power?
- If alpha goes from .05 to .l01, what happens to

required sample size? - If power falls from .80 to .60, what type of

error is most likely to occur? - If effect size is estimated based upon the

literature as large, what effect does this have

on the required sample size?

Sample Loss in RCT

Measurement

- If it exists, it can be measured
- R. Cronbach

What we measure

- Knowledge, Attitudes, Behaviors (KAB)
- Physiological variables
- Symptoms
- Skills
- Costs

Classical Measurement Theory

Type of Measures

- Standardized evidence as follows
- Systematically developed
- Evidence for instrument validity
- Evidence for instrument reliability
- Evidence for instrument utility time, scoring,

costs, sensitive to change over time - Non-standardized

Types of Measurement Error

- Systematic - can work to minimize systematic

error due to poor instructions, poor reliability

of measures, etc. - Random - can do nothing about this, always

present, we never measure anything perfectly,

there is always some error.

Validity

- Question Does the instrument measure what it is

supposed to measure? - Theory-related validity
- Face validity
- Content validity
- Construct validity
- Criterion-related validity
- Concurrent validity
- Predictive validity

Theory-related Validity

- Face validity
- participant believability
- Content validity (observable)
- Blue print
- Skills list
- Construct validity (unobservable)
- Group differences
- Changes of times
- Correlations/factor analysis

Criterion-related Validity

- Concurrent
- Measure two variables and correlate them to

demonstrate that measure 1 is measuring the same

thing as measure 2 same point in time. - Predictive
- Measure two variables, one now and one in the

future, correlate them to demonstrate that

measure 1 is predictive of measure 2, something

in the future.

Reminder

- Design Validity
- Does the research design allow the investigator

to answer their hypothesis? - (Threats of internal and external validity)

- Instrument Validity
- Does the instrument measure what it is supposed

to measure?

Instrument Reliability

- Question can you trust the data?
- Stability change over time
- Consistency within item agreement
- Rater reliability rater agreement

Instrument Reliability

- Test-retest reliability (stability)
- Pearson product moment correlations
- Cronbachs alpha (consistency) one point in

time, measures inter-item correlations, or

agreements. - Rater reliability (correct for change agreement)
- Inter-rater reliability Cohens kappa
- Intra-rater reliability Scotts pi

Cronbachs alpha

alpha

SD

Cronbach alpha Reliability Estimates

- gt 0.90
- Excellent reliability, required for

decision-making at the individual level. - 0.80
- Good reliability, required for decision-making at

the group level. - 0.70
- Adequate reliability, close to unacceptable as

too much error in the data. Why?

Internal Consistency Cronbachs alphaPerson A

Internally consistentPerson B Internally

inconsistent

Error in Reliability Estimates

- Error 1 (Reliability Estimate)2
- If alpha 0.90, 1-(0.90)2
- 1-0.89 .11 error
- If alpha 0.70, 1 (0.70)2
- 1-.49 .51 error
- If alpha 0.70, it is the 5050 point
- of error vs. true value

Reliability Values

- Range 0 to 1
- No negative signs like correlations
- Cohens kappa and Scotts pi are always lower,

i.e. 0.50, 0.60

Utility Things you would like to know about an

instrument.

- Time to complete (subject fatigue)?
- Is it obtrusive to participants?
- Number of items (power analysis)?
- Cultural, gender, ethnic appropriateness?
- Instructions for scoring?
- Normative data available?

Reporting on Instruments

- Concept(s) being measured
- Length of instrument or number of items
- Response format (Likert scale, etc.)
- Evidence of validity
- Evidence of reliability
- Evidence of utility

Quiz

- Can a scale be valid and not reliable?
- Can a scale be reliable and not valid?

Scale Development

- Generation items from focus groups/interviews
- Scaling decisions capture variation
- Face validity - check with experts and

participants - Standardize scale (evidence for validity,

reliability, utility) - Estimate correlates of concept
- Explore sensitivity to change over time

Translation

- Forward translation (A to B)
- Backward translation (B to A)
- Conceptual equivalency across cultures
- Using of slang, idioms, etc.

Data Analysis

Data Analysis Why?

- Capture variability (variance) how the scores

vary across persons - Parsimony data reduction technique, how to

describe many data points in simple numbers - Discover meaning and relationships
- Explore potential biases in data (sampling)
- Test hypotheses

Where to begin

- After data is collected, we begin a long process

of data entry cleaning - Data entry requires a code book be developed for

the statistical program you plan to use, such as

SPSS. - Data codebooks allow you to give your variables

names, values, and labels.

Data Entry Cleaning

- Data entry is a BIG source of error in data
- Double data entry is one strategy
- Cleaning data looking for values outside the

ranges, e.g. age of 154 is probably a typo. - We examine frequencies, high score, low scores,

outliers, etc.

Coding Variables

- Capture data in its most continuous form

possible. - Age 35 years - get the actual value
- vs.
- Check one _lt25
- _ 25-35
- _ 36-45
- _ gt45

Dichotomous Variables

- Do not do this
- 1 Male
- 2 Female
- Do this!
- 1 male
- 0 female
- Why? Add function

Dummy Coding

- Ethnicity
- 1 Black 2 White 3 Hispanic
- N-1 or 3-1 2 variables
- Black 1 Black 0 White and Hispanic
- White 1 White 0 Black and Hispanic

Missing Data

- SPSS assigns a dot . to missing data
- SPSS often gives you a choice of pairwise or

listwise deletion for missing values. - Mean Substitution give the variable the average

score for the group, e.g. age, adds no variation

to the data set.

Missing Data

- Pairwise just a particular correlation is

removed, best choice to conserve power - Listwise removes variables, required in repeated

measures designs.

Measures

- Central Tendency
- Relationships
- Effects

Measures of Central Tendency

- Mean arithmetic average score
- Standard deviation (SD) how the scores cluster

around the mean - Range high and low score.
- (Example M 36.4 years
- SD 4.2
- Range 22-45)

Formulas

Mean

Measures of Central Tendency

- Mean arithmetic average
- Median score which divides the distribution in

half (50 above and 50 below) - Mode the most frequently occurring value
- When does the meanmedianmode?

Normal Curve very robust!

Normal Curves

Normal Curve(MeanMedianMode)

Non-Normal Curves

Scaling

- Discrete
- (qualitative)
- Nominal
- Ordinal
- Continuous (quantitative)
- Interval
- ratio

- Non-parametric
- (no assumptions required Chi square)
- Parametric
- (assumes the normal curve, e.g. t and F tests)

Degrees of Freedom

- Statistical correction so one does not over

estimate

Degrees of Freedom for ball 1?

Degrees of Freedom for ball 2?

Degrees of Freedom for ball 3?

Degrees of Freedom

- Sample size (n-1)
- Number of groups (k-1)
- Number of points in time (l-1)

Relationships or Associations

Measures of Association Correlations

- Range -1 to 1
- Dimensions
- Strength (0-1)
- Direction ( or -)
- Definition a change in X results in a

predictable change in Y shared variation or

variance.

Correlations

- Sample specific (each sample is a subset of the

population) - Unstable
- Dependent upon sample size
- Everything is statistically significant with a

very large sample size may not be clinically

significant. - Expresses relation not a causal statement

Types of Correlations

- Pearson product moment r
- continuous by continuous variable
- Phi correlation
- discrete by discrete variable (Chi square)
- Rho rank order correlation
- discrete ranks by ranks
- Point-biserial
- discrete by continuous variable
- Eta Squared

Estimate the value of the correlation

Variance

Shared variance r2

Shared variance r2

Types of Data Analyses

- Descriptive X? Y? Z?
- Measures of central tendency
- Correlational rx,y?
- Is there a relationship between X and Y?
- Measures of relationships (correlations)
- Causal ?X ? ?Y?
- Does a change in X cause a change in Y?
- Testing group differences (t or F tests)

Testing Effects of Interventions

Testing Group Differences

- t tests
- F tests (Analysis of Variance or ANOVA)
- (t tests are F tests with two groups)

Types of tests of group differences

- Between groups
- (unpaired)
- Within groups
- (paired or repeated measures if two groups it is

also test-retest) - requires identified subjects

Classic Experimental Design

- O1exp X O2exp
- ?
- R
- ?
- O1con O2con
- (pretest) (posttest)
- Group Between Factor
- Time Within Factor

Tests of Significance

Testing Group Differences

- Between Variance
- F (or t)
- Within Variance

Examining Variance

Examining Variance No difference between the

means

Examining Variance Big difference between means

Examining Variance Three groups

Types of Designs

- O1 O2 O3
- change within group over time, repeated measures

design

Types of Designs

- O1e X O2e
- O1c O2c
- change within group from O1e to O2e
- change between groups O2e and O2c

How to analyze this design?

- O1e O2e O3e X O4e O5e O6e
- O1c O2c O3c O4c O5c O6c
- Two group repeated measures analysis of variance.

- One between factor (group) and one within factor

(time) with six levels.

Post-test only design

- X O2e
- O2c
- Unpaired t test
- Null hypothesis
- H0 O2e O2c
- Alternative directional hypothesis
- H1 O2e gt O2c

- Standard Deviation
- how scores vary around a mean
- Standard Error of the Mean
- how mean scores vary around a population mean

Standard Error of the Mean Average of sample SDs

Conceptual

- MeanE MeanC
- t
- standard error of the mean

Assumptions of ANOVA

- Normal distribution
- Independence of measures
- Continuous scaling
- Linear relationship between variables

3 X 2 ANOVA

- O1exp X1 O2exp
- ?
- R O1exp X2 O2exp
- ?
- O1con O2con
- One between factor group (3 levels)
- One within factor time (2 levels)

Omnibus F Test

- O1exp X1 O2exp
- ?
- R O1exp X2 O2exp
- ?
- O1con O2con
- F test group Is there a difference among the

three groups? - F test time Is there a difference between time

1 and 2? - If yes to either question, where is the

difference? - Interaction Group by Time

Post-hoc comparisons

- O1exp1 X1 O2exp1
- ?
- R O1exp2 X2 O2exp2
- ?
- O1con O2con
- Types Scheffé, Tukey control for degrees of

freedom in different ways compares all possible

two way comparisons - H0 O2exp1 O2exp2 O2con If you reject

Null, or F test is significant, then you can look

for two-way differences. - (O2exp1 O2exp2?) or (O2exp2 O2con?) or

(O2exp1 O2con?)

Tests of Significance

Galloping alpha

- Danger in conducting multiple t tests or doing

item-level analysis on surveys - alpha probability of rejecting the Null

hypothesis - alpha 0.05 divided by number of tests,

distributes alpha over tests - If conducting 10 t tests, alpha at 0.005 per test

(0.05/100.005)

ANOVA

- ANOVA analysis of variance
- ANCOVA analysis of co-variance, includes Z

variable(s) - MANOVA multivariate analysis of variance (more

than one dependent variable) - MANCOVA multivariate analysis of co-variance,

includes Z variable(s).

Multiple Regression Analysis

- Correlational technique
- Unstable values
- Sample specific
- Reliability of measures very important
- Requires large sample size
- Easy to get significance with large sample size

Multiple Regression Analysis

- Attempts to make causal statements of

relationship - Y X1X2X3
- Y dependent variable (health status)
- X1-3 predictors or independent variables
- Health Status Age Gender Smoking

Multiple Regression Questions

- What is the contribution of age, gender, and

smoking to health status? - How much of the variation in health status is

accounted for by variation in age, gender, and

smoking?

Multiple Regression Analysis

- Creates a correlation matrix.
- Selects the most highly correlated independent

variable with the dependent variable first. - Extract the variance in Y accounted for by that X

variable. - Repeats the process (iterative) until no more of

the variance in Y is statistically explained by

the addition of another X variable.

Health Status Age Gender Smoking

Multiple Regression Shared Variance

Smoking 40

Age 25

Gender 4

Multiple Regression

- Correlation results in a r
- Multiple regressions results in an r2
- R squared is the total amount of the variance in

Y that is explained by the predictors, removing

the overlap among the predictors.

Multiple Regression

- Types
- Step-wise based upon highest correlation, that

variable is entered first (computer makes the

decision), theory building - Hierarchical choose the order of entry, forced

entry, theory testing

Multiple Regression

- Allows one to cluster variables into Blocks.
- Block 1 Demographic variables
- (age, gender, SES)
- Block 2 Psychological Well-Being
- (depression, social support)
- Block 3 Severity of Illness
- (CD4 count, AIDS dx, viral load, OIs)
- Block 4 Treatment or control
- 1 treatment and 0 control

Regression Analysis

- Multiple regression one Y, multiple Xs.
- Logistic regression Y is dichotomous, popular

in epidemiology, Ydisease or no disease odds -

risk ratio (not explained variance) - Canonical variate analysis multiple Y and

multiple X variables Y1Y2Y3X1X2X3 - -linking physiological variables with
- psychosocial variables.

Multivariate Regression Models

- Path Analysis and now Structural Equation

Modeling - Software program AMOS
- Measurement model is combined with predictive

model - Keep in the picture the multicolinearity of

variables (they are correlated!) - Allows for moderating variables (direct and

indirect effects.

Multiple Dependent Independent Path Analysis

Modeling

Structural Equation Modeling

Factor Analysis

- Exploration of instrument construct validity
- Correlational technique
- Requires only one administration of an instrument
- Data reduction technique
- A statistical procedure that requires artistic

skills

Conceptual Types of Factor Analysis

- Exploratory see what is in the data set
- Confirmatory see if you can replicate the

reported structure.

Factor Analysis

- Principal Components
- (principal factor
- or
- principal axes)

Correlation Matrix of Scale Items Which items

are related?

Factor Analysis

- An iterative process
- Factor extraction

Factor Analysis

Definitions

- Communality Square item loadings on each factor

and sum over each ITEM - Eigenvalue Square items loading down for each

factor and sum over each FACTOR - Labeling Factors figments of the authors

imagination. Items 1 2 Factor I Items 3

4 Factor II.

Factor Rotation

- Factors are mathematically rotated depending
- upon the perspective of the author.
- Orthogonal right angels, low inter-factor

correlations, creates more independence of

factors, good for multiple regression analysis,

may not reflect well the actual data. (varimax) - Oblique different types, lets factors

correlate with each other to the degree they

actually do correlate, some like this and believe

it better reflects that actual data, harder to

use in multiple regression because of the

multicolinearity. (oblimax)

Summary Data Analysis

- Measures of Central Tendency
- Measures of Relationships
- Testing Group Differences
- Correlational
- Multiple regression as a predictive (causal)

technique. - Factor analysis as a scale development, construct

validity technique

Ethical Guidelines for Nursing Research

- Vulnerability a power relationship between

health care provider and patient, family, or

client. - Vulnerable participants in research require more

protection from harm.

Ethical Principles that Guide Research

- Beneficence doing good
- Non-malfeasances doing no harm
- Fidelity creating trust
- Justice being fair
- Veracity telling the truth
- Confidentiality protecting or safeguarding

participants identifying information

Ethical Principles that Guide Research

- Confidential
- names kept guarded
- vs.
- Anonymous
- no identifiers

- Best Wishes