Wolford College Research Final

Front 1

Sampling: Purpose

Back 1

• Gain information from a small group so that findings can be generalized to the larger population
• Sample must represent the larger population
• Must have a clear rationale for sampling techniques to ensure correct selection of subjects

Front 2

• Population-

Back 2

– entire set of subjects, objects, events or elements being studied
– Possess specific attributes

Front 3

• Sample

Back 3

– Small subset of the population
– Must be carefully selected so that it represents population

Front 4

• Target population

Back 4

– Population used for a study
– The entire set of elements about which the researcher would like to make generalizations

Front 5

• Accessible population

Back 5

– The portion of the target population that is readily available to the researcher and represents the target population as closely as possible.

Front 6

Sampling

Back 6

• Process of selecting individuals for a study in such a way that individuals represent the larger group from which they were selected.
• Allows researcher to draw inferences & make generalizations about the population without examining every element in the population

Front 7

• Probability sampling

Back 7

– Every subject, object or element in population has an equal chance or probability of being chosen

Front 8

• Nonprobability sampling

Back 8

– Not selected randomly
• Probability of inclusion and degree to which the sample represents the population are unknown

Front 9

Probability sampling: Simple random
Example

Back 9

– Example: sample included patients aged 20- 80 years having a CABG for the first time. Sample of 40 patients who experienced SVT & 40 patients who did not experience SVT was randomly selected from a list obtained from the medical records department using the Classification of Disease code- the list was not in any systematic chronologic or alphabetic order.

Front 10

Probability sampling: Simple random

Back 10

• Every subject has an equal & independent chance of being chosen
• Use of Table of Random Numbers
– Can be generated by a computer
– Assign all potential subjects a number
– Close eyes & point to a number on the Table
– This tells which subject has been selected.
• Disadvantages of Simple Random Sampling
– Time consuming
– May not be possible to obtain complete list of target population

Front 11

Probability Sampling: Stratified
Random Sampling

Back 11

– Selecting a sample to identify sub-groups of the population that are represented in the
sample.
– Reduces possibility that the sample might be unrepresentative of the population
– Achieves a greater degree of representativeness with each subgroup (strata)

Front 12

Probability Sampling: Cluster

Back 12

• Successive random sampling of units
• First unit to be sampled is a large grouping or cluster
• Then draw from a smaller cluster
• Example- a sample of nursing students

Front 13

Probability Sampling: Systematic

Back 13

Subjects or elements are selected from a list by taking every kth individual
• Is not strictly probability sampling because not all members have an equal chance of being selected
• But is considered random especially if list is randomly ordered

Front 14

Probability Sampling: Overall Comments

Back 14

• Every subject has equal chance of being chosen
• Allows researcher to estimate the magnitude of the sampling error
– Difference between the population values and sample values
• Is expensive, time consuming & inconvenient

Front 15

Nonprobability

Back 15

• Chance plays no role in determining the sample
• Limits the ability to generalize

Front 16

Nonprobability: Convenience

Back 16

• Collection of data from subjects or objects readily available or easily accessible to the researcher
• Subjects not selected from a larger population
• Data are collected from whoever is available & meets study criteria

Front 17

Nonprobability: Convenience

Back 17

• Advantage: easy, saves time & amp; money
• Disadvantages: sampling bias, the sample used may not represent the population
• Type of Convenience called “snowball”
– First subjects in study are asked to refer researcher to other people who meet the eligibility criteria
– so the sample grows like a “snowball”

Front 18

Nonprobability: Quota

Back 18

• Researcher identifies strata of the population & amp; determines the proportions of the elements needed from the various segments of the population
• All segments are represented in the sample in the proportions in which they occur in the population

Front 19

Nonprobability: Purposive

Back 19

• Also called judgmental sampling
• Based on belief that are searcher’s knowledge about the population can be used to hand pick the cases to be included in the sample
• Uses:to develop an instrument (pretest)
– When researcher wants experts in the field
• Disadvantages: no external objective method for assessing typicalness of subjects

Front 20

Nonprobability Sampling

Back 20

• Acceptable for pilot, exploratory or in- depth qualitative research
• Presents problems for quantitative research
– Not every element of population has a chance of being selected for the sample
– Some segment of the population will be underrepresented

Front 21

Sample Size (Quantitative Research)

Back 21

• Standard rule: use the largest sample possible
• Two critical questions for determining adequacy of sample
– How representative is the sample relative to the target population?
– To whom does the researcher wish to generalize the results of the study?

Front 22

Sample Size (Quantitative Research)
Factors

Back 22

– Type of sampling procedure used
– Type of sample estimation formula being used
– Degree of precision required
– How many attributes under investigation
– Relative frequency of occurrence of phenomenon of interest in population
– Projected cost of utilizing a particular strategy

Front 23

Sample Size: Power Analysis

Back 23

• A statistical method to calculate the exact number of subjects needed
• Based on effect size: concerned with the strength of the relationship(s) among research variables or the impact made by the independent variable.

Front 24

Sample Size: Homogeneity

Back 24

• The degree to which objects or subjects are similar

Front 25

Sample Size: Effect size

Back 25

• Effect size is concerned with the strength of the relationships among research variables

Front 26

Sample Size: Attrition

Back 26

• Loss of subjects over time
• Increases if time lag between data collection points is great, population is mobile, high-risk

Front 27

Sampling Questions
• A(n) _______ is a subset of the units that comprise the population.

Back 27

Sample

Front 28

Sampling Questions
• The main criterion for evaluating a sample is its __________________.

Back 28

Representative

Front 29

Sampling Questions
• A sample would be considered ______ if it systematically overrepresented or underrepresented a segment of the population.

Back 29

Biased

Front 30

Sampling Questions
• If a population is completely ___________ with respect to key attributes, then any sample is as good as any other.

Back 30

Homogenous

Front 31

Sampling Questions
• Quota samples are essentially convenience samples from selected ______ of the population.

Back 31

Strata

Front 32

Sampling Questions
• The most basic type of probability sampling is referred to as _______________

Back 32

Simple Random

Front 33

Sampling Questions
• When disproportionate sampling is used, an adjustment procedure known as ____________ is normally used to estimate population values.

Back 33

Weighting

Front 34

Sampling Questions
• In systematic samples, the distance between selected elements is referred to as the ____________.

Back 34

Sampling interval

Front 35

Sampling Questions
• Differences between population values and sample values are referred to as ________________.

Back 35

Sampling Error

Front 36

Sampling Questions
• As the size of the sample __________ the probability of drawing a deviant sample diminishes.

Back 36

Increases

Front 37

Sampling Questions
• If a researcher wanted to draw a systematic sample of 100 from a population of 3000, the sampling interval would be ___.

Back 37

30

Front 38

Measurement

Back 38

• Consists of rules for assignment number of objects to represent quantities of attributes OR
• Process of assigning numbers to variables
• Because “whatever exists in some amount can be measured”

Front 39

Measurement: Rules (1)

Back 39

• A process must be used to assign numbers
• Rules for measuring variables for research studies must be invented
– Under what conditions
– What method (survey, observation etc)
– Numeric values to be used

Front 40

Measurement: Rules (2)

Back 40

• Example: survey for parent’s opinions of sex education in school
• Strongly disagree • Agree • Slightly agree • Undecided
• Slightly disagree • Disagree • Strongly disagree • Would want to quantify the responses–how?

Front 41

Measurement: Advantages

Back 41

• Removes the guess work from gathering information
• Objectivity: can be independently verified by others
• Produces precise information
• Language of communication

Front 42

Errors of Measurement

Back 42

• If instrument is not accurate then the measures it produces contains a certain degree of error
• Obtained score = true score + error

Front 43

Errors of Measurement Disadvantage

Back 43

• Errors of measurement are problematic because they represent an unknown quantity and they are variable

Front 44

Errors of Measurement
Factors contributing to errors of measurement:

Back 44

– Situational contaminants- scores can be affected by
the conditions under which they are produced
– Transitory personal factors-scores of an individual
may be influenced by temporary personal states
– Response-set biases- number of relatively enduring characteristics of the respondents that can interfere with accurate measures of the target attribute.

Front 45

Errors of Measurement
• Administration variations

Back 45

alterations in the methods of collecting data from one subject to the next

Front 46

Errors of Measurement
Instrument clarity

Back 46

if directions for obtaining measures are vague or poorly understood, then scores may be affected

Front 47

Errors of Measurement
Item sampling

Back 47

errors are sometimes introduced as a result of the sampling of items used to measure an attribute

Front 48

Errors of Measurement
Instrument format

Back 48

technical characteristics of an instrument can influence the obtained measurement
– Open ended questions may produce different information than closed–ended questions

Front 49

Instruments used in Measurement

Back 49

• Devices used to record data obtained from the subject
• Interviews, direct observations, mechanical equipment etc

Front 50

Reliability

Back 50

• The degree of consistency with which an instrument measures the attribute it is supposed to be measuring
• Reliability = stability, consistency, dependability of a measuring tool
• The less variation an instrument produces – the higher its reliability

Front 51

Reliability: Types
Stability

Back 51

– Stability- extent to which same results are obtained on repeated administrations of the instrument
• Test-retest procedure- administration of the same test to a sample of individuals on two occasions then compare the scores obtained
• Compute a reliability coefficient

Front 52

Reliability coefficient

Back 52

– Looks for a relationship between two phenomena
Positive Correlations 1.0
Negative Correlation -1.0

Front 53

Reliability Advantages

Back 53

– Test-retest method is easy
– Can be used with self- report, observational & physiologic
measures
– Used with traits that are relatively enduring such as personality, abilities or physical attributes such as height

Front 54

Reliability Disadvantages

Back 54

– Many traits do change over time- regardless of the measure eg (behaviors, attitudes etc)
– Second administration may be influenced by memory of first administration
– Subjects may change as a result of taking the test the first time
– Responses could be haphazard if subject is bored or impatient with instrument

Front 55

Reliability: Internal Consistency

Back 55

• Addresses the correlation of various items within the instrument – also called homogeneity
• All parts of the scale are measuring the same characteristic
• Most widely used method for reliability
• Requires only one test administration

Front 56

Reliability: Internal Consistency
• Split-half technique

Back 56

– The items composing a test are split into two groups and scored independently
– Score on the two half-tests are used to compute a correlation coefficient

Front 57

Reliability: Internal Consistency
• Split-half technique Advantages

Back 57

– Easy to use
– Eliminates most of disadvantages of test- retest

Front 58

Reliability: Internal Consistency
• Split-half technique Disadvantages

Back 58

– Reliability estimates can be obtained by using different splits (odd/even; first/second half)

Front 59

Reliability: Internal Consistency
• Chronbach’s alpha or
Kuder-Richardson 20 (KR 20)

Back 59

• Chronbach’s alpha or Kuder-Richardson 20 (KR 20)
• Produce a reliability coefficient that can be interpreted as the other correlation coefficients
• Yields values of 0 to 1.0
• Better than split-half because it computes all possible splits to estimate homogeneity

Front 60

Reliability: Internal Consistency
• Equivalence

Back 60

– Comparing two versions of same instrument or two observers (inter-rater reliability) measuring the same event
– Goal is to determine the consistency or equivalence of the instruments in yielding measurements of the same traits in the same people

Front 61

Reliability: Internal Consistency
• Interrater reliability

Back 61

• is estimated by having two or more trained observers watching some event simultaneously & independently recording the relevant variables according to a predetermined plan or coding system
•Results are used to compute an index of equivalence or agreement
•Can use a correlation coefficient to compare results

Front 62

Reliability: Internal Consistency
• Interpretation of Reliability Coefficients

Back 62

– Reliability is the proportion of true variability to the total obtained variability
r= VT Vo

Front 63

Validity

Back 63

• Refers to the degree to which an instrument measures what it is supposed to be measuring.
• Accuracy of the measure
• If an instrument is designed to measure hopelessness-how can researcher be sure that it is measuring hopelessness? What if the instrument is really measuring depression?

Front 64

Validity: Methods to Determine
• Face validity

Back 64

– Whether the instrument looks as though it is measuring the appropriate critical variable (or construct).
– Not the strongest way to determine validity

Front 65

Validity: Methods to Determine
• Content validity

Back 65

– How well the instrument represents the characteristic to be assessed
– Used for affective measures (feeling, emotions, psychological traits) & cognitive measures (knowledge)
– Based on judgment of experts

Front 66

Validity: Content Examples

Back 66

• To develop an instrument which is to measure affective characteristics, a researcher would
– Have knowledge of the subject
– Do a thorough investigation of the literature
– Conduct some type of investigational study

Front 67

Validity: Content Examples
• Cognitive scale

Back 67

– Must ask the question: “how representative are the questions on this test of the universe of all questions that might be asked on this topic?”
– If a researcher were interested in developing a scale that tested people’s knowledge of the seven danger signals of cancer, the instrument would have to include the 7 danger signals identified by the ACS
– CAUTION

Front 68

Validity: Content
Disadvantages, Methods

Back 68

• Based on judgment
• Not completely objective methods of ensuring the adequate content coverage of an instrument
• May use a panel of experts in the content area to evaluate & document the content validity
– Has a least 3 members
– Ask experts if items are relevant and appropriate
– Whether individual items measure all dimensions of
the construct
• May use a Content Validity Index

Front 69

Validity: Criterion-Related

Back 69

• Emphasis is on establishing the relationship between the instrument and some other criterion
• Whatever attribute is being measured, the instrument is said to be valid if its scores correlate highly with some other criterion
• Pick a criterion to compare an instrument to

Front 70

Validity: Criterion-Related
Example

Back 70

• instrument to measure birth control use among sexually active teenage girls- criterion to compare it to may be subsequent premarital pregnancy
• Measure professionalism among nurses (attribute to be measured) to number of articles published (criterion)
• Measure effectiveness of nursing care to supervisory ratings of nurse
• Criterion must be clear cut & objective

Front 71

Validity: Criterion-Related
Predictive validity

Back 71

• Criterion related validity is a relationship but also is a predictive relationship
• Predictive validity
– Degree to which an instrument can accurately forecast the future

Front 72

Validity: Concurrent

Back 72

– Judgment is to the degree to which an instrument can accurately identify a difference in the present
– Use 2 instruments to measure the same concept

Front 73

Validity: Construct
Types of questions

Back 73

• What is the instrument really measuring?
• If instrument measures pain- how does researcher know scale is not measuring anxiety?

Front 74

Validity: Construct
Methods and Example

Back 74

known groups technique
– Groups that are expected to differ
– For example:
• Fear of labor : Use primipara & multipara women
• Limitations in functional ability: use one group of people with emphysema and one group with no emphysema

Front 75

Validity: Construct
• Factor analysis

Back 75

– Method for identifying clusters of related variables
– Each cluster is called a factor
• Represents a group of items that identify the same characteristic
– Procedure is used to identify and group together different measures of some underlying attribute
– Is a complex equation

Front 76

Validity

Back 76

• Validity is never proven, verified
• It is supported by evidence
• An instrument may be useful in one situation but not another
– An anxiety scale may be useful in a group of presurgical patients but not in a group of nursing students getting ready to take an exam
• Each new use requires new supporting data

Front 77

Reliability & Validity

Back 77

• Are not totally independent qualities
• A measuring device that is valid must be reliable
• An instrument can be reliable without being valid

Front 78

Reliability & Validity
Low reliability/low validity

Back 78

Draw

Front 79

Reliability & Validity
High reliability/ low validity

Back 79

Draw

Front 80

Reliability & Validity
High reliability, high validity

Back 80

Draw

Front 81

Levels of Measurement
Variables Types:Continuous

Back 81

-Values can be represented on a continuum
-infinite number of values between two points
-Example: weight

Front 82

Levels of Measurement
Variable Types:Discrete

Back 82

-has a finite number of values between any two points
-representing discrete quantities
-Example: number of children, 1, 2, 3

Front 83

Levels of Measurement
Variables: Discrete
Categorical

Back 83

-have only a few discrete values
-Ex: marital status, blood type
-Dichotomous- have only two values
-married/not married, alive/dead, pregnant/not pregnant

Front 84

Types of Measurement

Back 84

-4 levels
- Type of measurement will determine the statistics that can be used
- Each level is classified in relation to certain characteristics

Front 85

Types of Measurement
Nominal

Back 85

- First level of measurement
- Variables that are discrete & noncontinuous
- Categories: gender, marital status,
- Can be dichotomous: has two categories
- is the most primitive method of classifying information
- Is the demographic data
- Only simple statistical tests can be used

Front 86

Types of Measurement
Nominal Examples

Back 86

-Gender
-Marital Status
-Blood Type
-Types of meds

Front 87

Types of Measurement
Nominal

Back 87

- Classifications must be mutually exclusive
- Collectively exhaustive
- Numbers used in nominal measurement cannot be treated mathematically
- cannot calculate the “average” gender
- calculate in percentages

Front 88

Types of Measurement
Ordinal Level

Back 88

- Second level
-Variables are assessed incrementally
- pain
- ability to perform ADLs
- Goes beyond categorization
- Variables are ordered according to some criterion

Front 89

Types of Measurement
Ordinal Level Differences

Back 89

- Difference between nominal & ordinal measurement
- there is an ordering (relative standing)
-a value assigned show the relationship to the other subjects
- does not reveal anything about how MUCH greater one level of an attribute is than another
- cannot say one person has twice the functional capacity of another

Front 90

Types of Measurement
Ordinal Level Restrictions

Back 90

- Types of statistical tests and mathematics are restricted

Front 91

Ordinal Level

Back 91

Pain Slight Moderate Intense
Anxiety experienced Frequently Occasionally Rarely
Bonding Absent Minimal Moderate Strong

Front 92

Interval Level

Back 92

- Third level
- scale that is quantitative in nature
- there is both rank-ordering of objects on an attribute & distance between numeric values
- Increments on the scale can be measured & are equidistant
are continuous variables

Front 93

Interval Level
- Examples

Back 93

-SAT tests: score of 550 is higher than 500
-which is higher than 450
-all are equidistant apart

Front 94

Interval Level

Back 94

- Gives more information than first two categories
- Major disadvantage: cannot give absolute magnitude of the attribute
- no real or rational zero

Front 95

Interval Level
Example

Back 95

- Thermometer: is interval level
- no “0” in the Fahrenheit scale
- 600 is not twice as hot as 300

Front 96

Interval Level

Back 96

- Does expand statistical tests that can be used
- can add subtract data- so there can be an average

Front 97

Ratio Level

Back 97

- 4th & highest level of data
- Characterized by variables that are assessed incrementally with equal distances between increments
- A scale that has an absolute (meaningful) zero

Front 98

Ratio Level

Back 98

- All arithmetic operations can be used
- All statistical procedures can be used
- Is ideal for researchers but not attainable for many variables
- Example: weight, height

Front 99

Converting Data to Lower Level
of Measurement

Back 99

- Data can always be converted to a lower level but not higher
- loss of accuracy & information
- Example: how could you change age from ratio to ordinal level?
- Use categories: 10-15 15-20;20-25etc
- What is lost by doing this?

Front 100

Levels of Measurement
Examples

Back 100

- What are adolescents’ views and practices relating to tattooing?
- Data collected on:
- if the subject had a tattoo
- grades in school
- purpose of tattooing (scale)
- age at first tattoo

Front 101

Give the highest possible level of measurement that a researcher could obtain for each of the following:

Back 101

-Attitudes towards the mentally handicapped
-Birth order
-Length of labor
-White blood cell count
-Blood type
-Tidal volume
-Unit assignment for nursing staff -Motivation for achievement -Amount of sputum
- Pulse Rate

Front 102

Descriptive Statistics:
Frequency Distributions

Back 102

-A systematic arrangement of numeric values from lowest to highest
-With a count of the number of times each value was obtained

Front 103

Descriptive Statistics:
Frequency Distributions
2 Parts

Back 103

Two parts:
-Observed values or measurement
-Frequency or count of the observations falling into each class
-Classes of observation must be mutually exclusive & collectively exhaustive

Front 104

Descriptive Statistics:
Frequency Distributions

Back 104

Table

Front 105

Descriptive Statistics:
Frequency Distributions

Back 105

- Can take raw data & organize into a Histogram

Front 106

Descriptive Statistics:
Frequency Distributions

Back 106

- Histogram of scores

Front 107

Descriptive Statistics:
Shape of Distributions

Back 107

-Set of numbers can have an infinite number of shapes
- Symmetrical: two halves that are mirror images of each other

Front 108

Descriptive Statistics:
Frequency Distributions

Back 108

Bar Graph

Front 109

Descriptive Statistics:
Frequency Distributions

Back 109

- Asymmetrical distribution
- Usually described as skewed
- One tail is longer than the other
- When longer tail is pointing toward right
-the distribution is positively skewed

Front 110

Descriptive Statistics:
Frequency Distributions
Positive Skew

Back 110

Positive Skew

Front 111

Descriptive Statistics:
Frequency Distributions

Back 111

Negative Skew

Front 112

Descriptive Statistics:
Frequency Distributions

Back 112

- Frequent shape is called normal distribution or bell-shaped curve
- Is unimodal
- Symmetrical
- Not too peaked

Front 113

Descriptive Statistics:
Frequency Distributions
Bell Curve

Back 113

Bell Curve

Front 114

Descriptive Statistics: Central Tendency

Back 114

- Yields more important information -Are indexes of typicalness
- more representative if values come from center
- “average” is term used to designate central tendency

Front 115

Descriptive Statistics:
Central Tendency
Mode

Back 115

- numeric value in a distribution that occurs most frequently
- 50 51 51 5253535353545556

Front 116

Descriptive Statistics:
Central Tendency
What is mode?

Back 116

-Is quick and easy
-Rather unstable
- tend to fluctuate widely within a population
-Can be used to give the most typical subject.... Such as....”the typical subjects was an unmarried white female living in an urban area with no prior history of STDs”

Front 117

Descriptive Statistics:
Central Tendency
Median

Back 117

-Point at which & below which 50% of cases fall
-2 2 33 334 5 6 789 (12scores)

Front 118

Descriptive Statistics:
Central Tendency
Mean

Back 118

-Point on the score that is equal to the sum of the scores divided by the number of scores
-The mean is affected by the value of every score
-Most widely used measure of central tendency (interval & ratio level data)

Front 119

Descriptive Statistics: Variability

Back 119

- How spread out of dispersed the data are
-Two distributions of scores can be totally different but means can be identical

Front 120

Descriptive Statistics: Variability
Range

Back 120

-Highest score minus the lowest score in a distribution
-Easy to compute
-Highly unstable index- only based on 2 scores
-Ignores the variations in scores between the two extremes
-Used as a gross descriptive index

Front 121

Descriptive Statistics: Variability
Semi-quartile Range

Back 121

- A point below which any percentage of the scores fall
- Calculated on that basis of quartiles within a distribution
-Half the range of scores within which the middle 50% of scores lie
-Upper quartile Q3= point below which 75% of the cases fall
Q1= point below which 25% of the scores lie

Front 122

Descriptive Statistics: Variability
Standard Deviation

Back 122

- Most widely used measure of variability
- SD summarizes the average amount of deviation of values from the mean
- Based on deviation scores:
- Calculate mean
-Subtract mean from each individual score
- Squaring each deviation score & then adding them together
-Divide by number of cases
-Take square root

Front 123

Descriptive Statistics: Variability: SD Calculation

Back 123

Image

Front 124

Descriptive Statistics: Variability: SD

Back 124

- SD is an index of the variability of scores in a data set
- Tells us how much the scores deviate from that mean
- So scores deviate 1.76 from mean
- That is: scores are more clustered around mean

Front 125

Descriptive Statistics: Variability: SD

Back 125

- Can be used to describe an important characteristic of a distribution
- Used to interpret the score of performance of an individual in relation to other in the sample
- Is a stable estimate of a population parameter (ratio or interval data)

Front 126

Descriptive Statistics: Variability: SD

Back 126

Image

Front 127

Assumptions

Back 127

• Beliefs that are held to be true but have not necessarily been proven
• OR
• A principle that is accepted as being true based on logic or reason, without proof

Front 128

Assumptions
• Type 1

Back 128

1) universal: beliefs assumed to be true by a large percentage of society
- all humans need love

Front 129

Assumptions
• Type 2

Back 129

2) Derived from a theory of previous
research
-stress causes disease

Front 130

Assumptions
• Type 3

Back 130

3) specific to a certain research study
- evidence of a fit between what the researcher believes can happen & the data produced... assumptions that prediction is possible, facts can be verified, testing of theoretical relationships
-In other studies (qualitative: assume patient is an active participant in some social environment )

Front 131

Assumptions

Back 131

• Assumptions ARE NOT THE research question or hypothesis

Front 132

Limitations

Back 132

• Def. Uncontrolled variables that may affect study results and limit the generalizability of the findings
•Limitations mentioned could include:
– Sample deficiencies
– Design flaws
– Weaknesses in data collection

Front 133

Correlation

Back 133

• Most common method of describing a relationship between two measures
• Correlational research examines relationships among variables interest without any intervention on the part of the investigator
• Variables are interval or ratio

Front 134

Correlation
Pearson’s r

Back 134

• Calculated when data are interval/ratio
• -1to+1

Front 135

Inferential Statistics

Back 135

• To estimate the probability that statistic found in the sample accurately reflects the population parameter
• To test hypotheses about a population

Front 136

Probability

Back 136

• Defined: likelihood that something will occur
• Researchers analyze data to determine the likelihood that differences in study groups are the result of chance as opposed to the manipulation of variables

Front 137

Probability

Back 137

• Errors can always occur in a study
• Always a chance that the differences occurring in the study due to chance rather than the treatment
• SO.....Research results cannot claim to prove anything but that there is a low probability that the results are due to chance

Front 138

Probability

Back 138

Always a sampling error
– Discrepancy between the characteristics of the sample & the population
• Researcher must decide if sample being used is good estimate of population parameters

Front 139

Probability Levels

Back 139

• Calculating statistics provide probability that result is caused by sampling fluctuations
• Usually set no higher than .05
• p<.05 means there is less than 5% chance that results are due to chance

Front 140

Null Hypothesis

Back 140

• Statement that there is no actual relationship between variables & that any such observed relationship is only a function of chance or sampling fluctuations
• Statistical hypothesis testing is a process of rejection

Front 141

Type I & Type II Errors

Back 141

• Errors in decisions about rejecting or accepting the Null

Front 142

Type I

Back 142

• Rejecting the null when it is true
• EG if a researcher concludes that experimental treatment was more effective than the control in alleviating anxiety, when the study outcomes were actually a result of sample differences in anxiety scores
• The lower the p level, the less likely for a Type I error

Front 143

Type II

Back 143

• Not rejecting null when there was a significant difference between groups
• Researcher concludes that differences in group anxiety levels were result of chance – when the experimental treatment did have an effect on anxiety

Front 144

Type II-Significance

Back 144

• Level of significance set at .05 because if there is more than 1 chance in 20 that the outcome of interest has occurred by chance rather than manipulation, the results are not considered to be of value

Front 145

Type II: ways to decrease chance of Type II errors

Back 145

• Larger sample size
• Decrease sources of wide variation (control)
• Increase level of significance

Front 146

Scenario

Back 146

Test for genetic defect- if defect exists & is diagnosed early- it can be successfully treated
•If not diagnosed, & treated, child will become severely debilitated.
•If child is diagnosed as having defect & treated, no damage occurs
•Null hypothesis: The test for the genetic defect will reveal no difference between control & experimental groups.
•Type I error: diagnosing defect when it does not exist- child not harmed because treatment won’t hurt him
•Type II error: declaring child normal when child is not so child is severely damaged.

Front 147

Type I & Type II errors

Back 147

• Would like to eliminate them
• Can’t without using entire population

Front 148

Statistical Significance

Back 148

Image

Front 149

Statistical vs Clinical Significance

Back 149

• Statistical significance- the differences observed are probably true differences & not result of chance fluctuations in sampling
• Clinical significance- findings must have meaning for patient care
– Nurses must determine if results have meaning to patient care & practice

Front 150

Two tailed tests

Back 150

– Most often used
– Both ends (or tails) of sampling distribution are used to determine the range of improbable values

Front 151

Two tailed tests

Back 151

There is a difference between males & females in their approval of physical conflict
Images

Front 152

One Tailed

Back 152

Females are less approving of physical conflict than males
Image

Front 153

Hypothesis Testing Procedure

Back 153

• Determine the test statistic to be used
• Establish the level of significance

Front 154

Parametric/Nonparametric

Back 154

• Two classes of statistical tests
• Each have own characteristics

Front 155

Parametric Tests

Back 155

• Involve the estimation of at least one parameter
• Require measurement(s) on at least the interval scale
• Assume the variables are normally distributed in the population
• Powerful tests and offer flexibility
• Preferred for data analysis

Front 156

T-test (Student’s T)

Back 156

• Test for differences between groups
• Use mean scores between groups and test for differences
• A t-value is generated
• Probable values are computed
• p level is determined
• Null rejected/accepted
• Used for independent groups

Front 157

Paired T-test

Back 157

• Two measures from the same subjects over time

Front 158

Analysis of Variance (ANOVA)- one-way

Back 158

• Tests for differences between means for 3 or more groups
• One independent variable
• ANOVA yields an F-ratio statistic

Front 159

Multifactor ANOVA

Back 159

• Two or more independent variables on a dependent variable

Front 160

Non parametric tests

Back 160

• Tests for nominal or ordinal level data
• Shape of distribution is not a concern
• Sample size can be small

Front 161

Chi-square X2

Back 161

• Used with categories of data
• Hypotheses concerning the proportions of cases that fall into the various categories
• Chi-square is computed from contingency tables
• Differences are calculated based on what occurs from a study compared to what the expected frequencies are
• Must have at least 5 in each group

Front 162

Chi-square X2

Back 162

Image

Front 163

Power

Back 163

• Ability of a study to identify relationship or detect real differences among variables

Front 164

Power Analysis

Back 164

• Method for reducing risk of Type II errors (Not rejecting null when there was a significant difference between groups)
• Estimating occurrence of Type II error

Front 165

Power Analysis

Back 165

• Used to estimate the sample size needed to obtain a significant result and allow the researcher to conclude that the research hypothesis is supported

Front 166

Power Analysis
Four elements

Back 166

– Significance level or alpha
– Sample size
– Effect size
– power

Front 167

Power Analysis
Alpha

Back 167

• probability of making a type I error
• Usually set at .05

Front 168

Power Analysis
• Beta

Back 168

– Probability of making a type II error
– Should be no more than 4 times the value of alpha
– .20 (based on alpha of .05)

Front 169

Power Analysis
Power

Back 169

–1-beta =1-.2=.8
– Conventional standard accepted for power is .80

Front 170

Power Analysis
• Effect size

Back 170

– Strength of the relationship among study variables
– Measures how false the null hypothesis is
– that is how strong the effect of the independent variable is on the dependent variable
– When relationships are strong- large samples are not needed

Front 171

Power Analysis
• Effect size determined from

Back 171

– ROL
– Researchers’ own pilot data
– Estimates based on clinical experience

Front 172

Power Analysis

Back 172

• With all four of these pieces of information, a sample size can be determined using the Power Analysis formula

Front 173

Qualitative Traditions: General Characteristics

Back 173

• “Multi-method focus that involves an interpretive, naturalistic approach t its subject matter
• A holistic approach to questions that recognize that human realities are complex
• Research question is very broad
•Individual’s perspective is very important

Front 174

Qualitative Traditions: General Characteristics

Back 174

• Concerned with in‐depth description of people or events
• Data collected through interviews or observations
• Uses an inductive approach
• An emergent design

Front 175

Qualitative Traditions: General Characteristics

Back 175

• Flexible‐ capable of adjusting to what is being learned during the course of data collection
• Merges together various methodologies
• Holistic‐ strives for understanding of the whole

Front 176

Qualitative Traditions: General Characteristics

Back 176

• Focused on understanding a phenomenon or social setting‐ not on making predictions about the setting or phenomenon

Front 177

Qualitative Traditions: General Characteristics

Back 177

• Requires that the researcher become intensely involved for lengthy periods of time
• Requires that the researcher becomes the
research instrument
• Ongoing analysis of the data in order to formulate subsequent strategies &amp; to determine when field work is done

Front 178

Qualitative Traditions: General Characteristics

Back 178

• Forces researcher to define their role &amp; identify their own biases

Front 179

Designs & Planning

Back 179

• Plans for data collection setting
– Gaining entry
– Obtaining consent
– Identifying the site’s “major players”
– Determining maximum amount of time available for the study
– Equipment needs: tape recorder, lap‐top etc • Training of assistants

Front 180

Designs &amp; Planning

Back 180

• Data collection format
– Plans for “unforeseen” events
– Setting for interviews
• Researcher &amp; team must analyze their own biases &amp; ideology

Front 181

Phases

Back 181

• Orientation &amp;overview
– Must decide what they don’t know and how to handle the phenomenon
• Focused exploration
– Focused scrutiny and in‐depth exploration of those aspects of the phenomenon that are judged to be salient
•Confirmation &amp;amp; closure
– Try to establish that their findings are trustworthy

Front 182

Design Features

Back 182

• No IV or DV identified before data collection begins
– May look back to find antecedent factors leading up to the occurrence of that phenomenon
• Flexible
• No manipulation o rcontrol
• Group comparisons not planned but may occur
• Can be cross‐sectional or longitudinal
• Setting‐real‐world

Front 183

Qualitative Research –Types: Ethnographic

Back 183

– Social scientific descriptor of a people &amp; the cultural basis of their identify
– Study of cultures/behaviors
– Only focus is to describe.... Is a theoretical
• data about cultural groups
• Is oldest qualitative approach
– Cultural behavior‐ what members of the culture do
– Cultural artifacts: what members of the culture make &amp;amp; use
– Cultural Speech‐ what people say

Front 184

Qualitative Research –Types: Critical Social Theory

Back 184

– Also called feminism
– Goal is to raise consciousness, politicize or activism
– Research focus is on oppressed groups

Front 185

Qualitative Research –Types: Content Analysis

Back 185

– Used to describe the basic content of data
• Speeches
• Books
• Interviews
• Media (films, photography)
– Counts the number of times something occurs‐ eg the number of times the same words are used

Front 186

Qualitative Research –Types: Content Analysis

Back 186

• Data are coded to identify recurrent themes
• Used to describe attitudes, expectations &amp; perceptions
• Can be used alone or in conjunction with other methods

Front 187

Qualitative Research Types
Narrative Analysis

Back 187

• Method applied to stories of meaningful account of events over time
• Structure of a story includes what form it takes such as the history of a disease or an account of personal experiences &amp; how events are related to each other

Front 188

Qualitative Research –Types: Grounded theory

Back 188

• Data are collected &amp; analyzed
• Study of social processes &amp; structures
• Uses open‐ended interviewing, sensitization to
concepts
• Main focus: theory is developed that is grounded in the data

Front 189

Qualitative Research –Types: Historical

Back 189

– Identification, location &amp; evaluation &amp; synthesis of data from past
– Purpose is to answer questions concerning causes, effects or trends relating to past events that may
shed light on present behaviors or practices.

Front 190

Qualitative Research –Types:
Case studies

Back 190

– In‐depth investigations of a single entry or a small series of entities
• May be an individual but also can be families, groups, etc.
• Focuses on why the individual thinks, behaves or develops in a certain way rather than on what the status, actions or thoughts are.

Front 191

Qualitative Research ‐Analysis

Back 191

• Perform a content analysis
•Analyzes the narrative data to determine themes or patterns
• Very tedious‐no systematic rules for analyzing &amp; presenting qualitative data
• Must interpret data‐can be subjective so must develop a method to classify &amp; index materials
• Coding of data‐once categories have been established
‐ then data is re‐reviewed for coding

Front 192

Evaluation of Qualitative Research

Back 192

• Must be rigorous
• Credibility: the “truth” of the findings from the subjects
that is: “is this what you were saying?”
• Transferability‐ study’s ability to preserve meanings, interpretations &amp; inferences when applied to another similar context

Front 193

Evaluation of Qualitative Research
•Confirmability

Back 193

obtaining direct &amp; repeated affirmation of what the research has heard, seen or experienced

Front 194

When is Qualitative Research a Good Choice?

Back 194

• When a topic has not been studied
• When quantitative approach does not or would not give a full picture

Front 195

Qualitative Research
Examples

Back 195

-Grounded Theory- what is the social process underlying a nurses' experience with implementing developmental care in a neonatal ICU?
-What are the critical health-related, social &amp; economic issues of the Afgan refugee community?

Front 196

Application to Practice

Back 196

-How do the results of this study help me care for patients?
-How does this study help me understand my relationship with my patients &amp; their families"