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BSN: 3rd Quarter > Research EXAM #3 > Flashcards

Research EXAM #3 Flashcards

1
Q

Type 1 vs. Type 2 error

A

Type I — false (+) if investigator rejects null hypothesis that is actually true in the population
Type II — false (-) if investigator fails to reject a null hypothesis that is actually false in the population

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2
Q

Statistical tests to improve validity

A

Determine probability of errors:​

Type I​

Type II​

Calculate and report Effect Size​

Ensure data meets the basic assumptions of the statistical tests​

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3
Q

Empirical literature

A

this type of literature is based on experiences or observations and displays
how theories apply to individual behavior or observation. E.g. if a medication is going to reduce
BP, then you hypothesize that because the medication reduces BP in will lower the risks for
heart attacks. This notion is based on observation rather than theory

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4
Q

Seminal literature

A

foundational and classic literature (think o fit as original, first creation of
the concept)

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5
Q

Construct variable

A

in simple terms it refers to an explanatory variable that cannot be directly
observed. We may be able to observe its effect but not a directly observe it. A good example is
gravity, we know a lot about what gravity does and its effect but we cannot directly see or
observe gravity. So gravity maybe considered a construct variable. Anxiety is another construct
variable. We can observe the outcomes of anxiety such as tension, stress, anger etc.

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6
Q

Correlational (relationship) design

A

demonstrates a relationship between the variables it may
be a positive relationship or negative relationship

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7
Q

Common Types of Statistical Tests (Inferential tests)

A
  1. Independent t-tests: tests for differences in the means between 2 groups
  2. Paired t-tests: tests for differences between paired measurements e.g. pretest before
    the intervention and post-test after the intervention
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8
Q

Analysis of variance (ANOVA)

A

F-test - is a statistical test used in experimental or quasi-
experimental research (quantitative). Test is applicable when measuring the means between 2
or more study groups. Only measures one dependent variable. Example in a diabetic study one
group receives oral medication, one group is diet controlled, one group is placebo.

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9
Q

Multivariate analysis of variance (MANOVA)

A

a statistical test used when measuring 2 or
more dependent variables (outcomes) for the research
– e.g does music therapy and relaxation techniques affect anxiety and panic attacks?​

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10
Q

ANOVA vs. MANOVA are

A

statistical tests

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11
Q

What is the purpose of a chi-square test?

A

helps detect the relationship between 2 variables but does not demonstrate
the depth or direction of the relationship between the variables

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12
Q

Correlational coefficient (r)

A

measures correlation (relationship) between 2 variables.

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13
Q

External validity

A

Generalize the findings from a research study to other populations, places, situations
— It is SO good that nothing needs to change —> generalization = QUANTitative

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14
Q

Why is validity important in search?

A

It helps to measure what it is supposed to measure

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15
Q

Internal validity

A

Ensures the intervention worked and the outcome was not based on other causes
—The confidence experimental treatment/condition has made a difference and rival explanations were systemically ruled out through study design and control
— IV caused the outcome (DV)

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16
Q

Generalization is synoymous with ____.
Transferability is synonymous with ____.

A

QUANTitative = generalization
QUALitative = transferability

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17
Q

Instrumentation

A

The instrument or data collection process has changed
— e.g. operative error: post-partum tool and how explained vs. someone who is not well-informed, may not use the tool/instrument to the best of its abilities

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18
Q

Describe some biases that can be interjected into a research study

A

— Personal
— Selection
— Subject

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19
Q

Factors that can interfere with internal validity

A

– Historical threats
– Maturation
– Testing
– Instrumentation
– Consent effect
– Treatment effect
– Hawthorne effect
– Multiple-treatment effect
– Subject selection
– Attrition

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20
Q

Describe a historical threat

A

subjects behave in a certain manner because of their exposure to events outside the experiment
– e.g. the occurrence of an actual earthquake during a field study of the effects of training in earthquake preparedness

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21
Q

Bias in research

A

Sampling error:​

A number that demonstrates the difference in the results between the sample and the population it was drawn from​

Treatment effects:​

A threat to internal validity because the subjects may perform differently​

Measurement error:​

Difference between actual attribute (true score) and the amount of attribute that was represented (observed score)​

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22
Q

Maturation related to internal validity

A

Too much time has passed with long study
Changes in the research subjects not due to the intervention, because time has passed

— e.g. deterioration of physical characteristics: vision, hearing, taste, memory
— e.g. pain relief w/ cancer patient disease is maturing (stage I —> stage III)

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23
Q

Testing related to internal validty

A

Familiarity of the research subject w/ testing, especially retesting occurs
— e.g. pre-test then take post-test; knowledge became enhanced

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24
Q

Consent effect

A

Threat to internal validity occurs. Because the subject who consents to student may differ from those who do not in way that affects outcome of the study
— e.g. change your mind to study after finding out you’re going to be recorded therefore change their mindset/thoughts

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25
Q

Treatment effect

A

Subject may perform differently b/c they know they are being in a study therefore act the way that looks good
— e.g. masks their behavior b/c if in an anger study, you will hide that anger

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26
Q

How to control extraneous variables

A

– Eliminate the threat
– Control the threat
– Account for the threat or write-up

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27
Q

Hawthorne effect

A

Seen as person of power and subject takes on subordinate role; research subjects change their behavior in a study because they are aware of being under observation
— e.g. You are researching the smoking rates among bank employees as part of a smoking cessation program. You collect your data by watching the employees during their work breaks. If employees are aware that you are observing them, this can affect your study’s results

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28
Q

Multiple-treatment effect

A

An inability to isolate the effects of treatments because multiple treatments are being used at the same timeA

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29
Q

Subject selection/Subject effects (bias)

A

A threat to internal validity due to introduction of bias through selection or composition of comparing groups assignment of research subjects to groups in a bias manner, not random
- e.g. Health studies that recruit participants directly from clinics miss all the cases who don’t attend those clinics or seek care during the study.

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30
Q

Attrition

A

Loss of research subjects during the study (possibly due to how long the study is); drop out of study
— e.g. children from troubled families increase the likelihood to drop out/move away in the middle of school year
— e.g. yoga education vs. paper education over 8 weeks —> only 1 showed up/completed the paper education study vs. everyone that completed the yoga education

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31
Q

What factors can affect external validity?

A

– Population
– Ecological
– Time & historical effects
– Novelty
– Experimenter effect

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32
Q

Threats to external validity

A

— Selection effects
— Time
— History
— Novelty effects
— Experimenter effects

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33
Q

Threats to trustworthiness in QUALitative research

A

— Hawthorne effect
— Selection effects
— Historical effects
— Researcher bias

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34
Q

Researcher bias

A

The primary investigator (PI) poses their thoughts into the study

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35
Q

Population validity

A

Can the findings be generalized from the sample to a larger group?
The capacity to confidently generalize the results of a study from one group of subjects to another population group
— e.g. can the findings of patients from one medical center be generalized to patients in an entire health care system?

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36
Q

Ecological validity

A

external validity
Findings can be generalized and applied to other settings
Can findings be generalized from one set of environmental conditions to another?
— e.g. Can the findings be generalized from a medical-surgical unit to a long-term unit.

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37
Q

Time & historical effects r/t external validity

A

Threats to external validity

38
Q

Novelty effect

A

threat to external validity

results based on the newness of the treatment rather than actual procedure - hard to decipher what caused the outcome
Occurs when subject reacts to something b/c its novel/new, rather than actual treatment or intervention itself
— e.g.

39
Q

Experimenter effect

A

threat to external validity
Results may reflect personal biases of the researcher
Interaction w/ researcher conducting the3 study or applying intervention
— e.g.

40
Q

These improve validity of qualitative research

A

– Verbatim accounts
– Triangulation
– Bracketing
– Audit trials
– Member checking

41
Q

Verbatim accounts

A

“Word-for-word” statement of a response

NOTE: Improves validity of QUALitative research

42
Q

Triangulation

A

A means of enhancing credibility by cross-checking information and conclusions using multiple data sources using multiple methods or researching to ensure credibility
— The study, phenomenon using multiple theories/perspectives to help with data

NOTE: improve validity of QUALitative research

43
Q

Bracketing

A

A researcher separates own experiences from what is being observed in the study, to reduce bias

A method of limiting the effects of researcher bias and setting them aside by demonstrating awareness of potential suppression of research

NOTE: improve validity of QUALitative research

44
Q

Audit trials

A

establish findings are based on participant’s responses not researcher

45
Q

Member checking

A

data is sent back to participants to check for accuracy
Having participants review and comment on accuracy of transcripts, interpretation or conclusions
— e.g.

46
Q

What is the purpose of descriptive research?

A

Actively describes population, situation, or phenomenon
— the what, when, where, why, how can be an answer

47
Q

Name some descriptive type of research studies

A

— Case reports
— Case
— Cross-section
— Sectional
— Ecological study

48
Q

Longitudinal study

A

Studies conducted by following subjects over a period of time w/ data collection occurring at prescribed intervals

49
Q

Cross-sectional study

A

Study conducted by looking at single phenomenon across multiple populations at a single point in time w/ no follow-up design
– e.g. a medical study examining the prevalence of cancer amongst a defined population. The researcher can evaluate people of different ages, ethnicities, geographical locations, and social backgrounds.

50
Q

Case study

A

Exploration of single unit of study such as person/family group or community

51
Q

Single-subject

A

Uses a single case/subject in which baseline data are collected, an intervention is applied, and responses are tracked over time

52
Q

Epidemiology

A

Branch of medicine dealing w/ incidence, distribution, possible control of diseases + other factors r/t health

53
Q

Pilot study

A

“Feasibility” = small-scale preliminary study
— e.g. “live & learn”/assess planned study’s cost, time, efficiency, accuracy on smaller scale; see if there are any problems
— make sure instrument is gathering data that you need to

54
Q

Name the different types of methodology used in research

A

1) Simple
2) Systematic
3) Stratified
4) Cluster

55
Q

Difference b/w experimental design and quasi-experimental design

A
56
Q

Experimentation

A

Use scientific methods to establish the cause and effect relationships among a group of variables that make up a study
— e.g. clinical trial where participants placed in control and treatment groups —> determine the degree which interventions in the treatment group is effective or not

57
Q

Quasi-experimental

A

Attempts to establish cause-effect relationships among groups of variables that make up a study
— QUANTitative research method
— # data collection an statistical analysis

e.g. You hypothesize a new after-school program will lead to higher grades
— choose 2 similar groups of children who attend different schools
—> implements new program
—> not implementation

58
Q

Level of measurements - what are they and examples, descriptive designs the different types and what they mean, central tendency what is it examples.

A
59
Q

internal and external validity, how do you maintain this in research, what does it mean?

A
60
Q

longitudinal, predictive, single-study, correlation, survey – what are they?

A

– Predictive:
– Single-study:
– Correlation:
– Survey:

61
Q

ways qualitative researchers maintain accuracy in in their research?

A
62
Q

generalizability, transferability, experimental, quasi-experimental, gold standard.

A
63
Q

Examples of descriptive research question and designs

A

– Descriptive: What is nurses’ knowledge about best practices r/t to oral care?
– Survey: How are nurses involved in decision making about patient care, the work environment, and organizational practices?
– Cross-sectional: What are the differences in job satisfaction among nurses at different stages of their careers?
– Longitudinal: What is the effect of urinary incontinence on the quality of life of long-term-care residents over time?
– Case study: What are the appropriate assessments and interventions for a patient experiencing paraplegia after heart surgery?
– Single-subject study: What were the responses of an individual w/ type 2 DM to culturally appropriate counseling from a nurse?
– Correlation: What is the relationship b/w patient satisfaction and the timeliness and effectiveness of pain relief in a fast-track emergency unit?
– Predictive: Can feeding performance in neonates be predicted by indicators of feeding readiness?

64
Q

Central tendencies

A

CT - single number used to summarize the data​

Mean (average)​

Mode (number occurring the most in the data set)​

Median (middle number in the data set)​

​

65
Q

Derived variable is not the primary variable

A

Not IV or DV that you study
— e.g. can derive BMI from height and weight

66
Q

Value of descriptive data

A

Descriptive data are summarized and analyzed
Provide useful information about the research participants
Offers a quick glance
Central tendency - measures how variables are alike
Variability - measures how variables are different
Helps the reader understand the information

— It summarizes and analyzes

67
Q

1st step of analyzing data in QUANTitative research, how is the information gathered

A

From surverys

68
Q

Examples of descriptive data

A

Derived from the data set:
Age of research participants (data set of34 males, 35 females)
Years of work experience
Scores on job satisfaction

69
Q

Nominal data

A

also known as category
— e.g. colors, male or female, black or white, BSN students vs. PA students

70
Q

Ordinal data

A

Categorical data that are ranked: Pain scale has numbers from 1 to 10. On the scale 5 > 3, 10 > 8. We can’t tell that the difference between the two sets even though they are on the same scale

71
Q

Categorical Data examples vs. Continuous data

A

Categorical:
— Nominal = BSN vs. PA students
— Ordinal = pain scale
Continuous:
— Interval = temperature (NO TRUE ZERO)
— Ratio = height, length, age (USES TRUE ZERO AND GOES UP)

72
Q

4 levels of measurement

A

Ratio
Interval
Ordinal
Nominal

73
Q

Levels of measurement: categorical vs. continuous

A

Nominal
Categorical data: leaders/nonleaders, males/females, ADN students/BSN students
Ordinal
Categorical data that are ranked: Pain scale has numbers from 1 to 10. On the scale 5 > 3, 10 > 8. We can’t tell that the difference between the two sets even though they are on the same scale
Interval
Data ranked with equal intervals e.g. temperature (don’t have a true zero, you can measure temperature below zero)
Ratio
Interval data that have a true zero e.g. height, length, age

74
Q

EXAM IMPORTANT INFORMATION

A

— Scatter plot
— Measurement of height is considered = ratio
— What is the 1st step in analyzing quantitative research data? Summarize descriptive data
— Standard normal distribution: mean = 0 and std. deviation = 1
— Measures of central tendencies = mean, median, mode
— A measure that is consistent, biased, and not accurate = systematic
— Median in their data
— Know mean, median, mode, range
— Suppressive variable/confounded/extraneous
— Ratio, ordinal, nominal

75
Q

Scatterplot’s purpose

A

Indicates the nature of the relationship between 2 variables - strength and direction

76
Q

Purpose of box and whiskers plot

A

Shows distribution over time
Box contains 25th to 75th percentiles of the distribution

77
Q

Difference b/w positive z-score vs negative z-score

A

– z-score depicts the number of standard deviations above or below the mean an observation falls
–Positive z-score: Above the mean
–Negative z-score: Below the mean

78
Q

Standard scores

A

Describe the relative position of an observation within a distribution

79
Q

A priori

A

The researcher has to lay out EVERYTHING in front before conducting the study (everything they’re doing, methodology, design, sample sizes, location of sample sizes, laid out) —> IRB and come back —> CANNOT change b/c jeopardize subject (even if they thought of something better that can impact the study)

80
Q

Lack of disclosure

A

They don’t tell because it’s going to change the behavior; allowed b/c the details they leave out will not be details that cause harm/death to participants
— e.g. observe if you’re friendly with each other (in a psych facility and outdoors

81
Q

Common statistical errors

A

*Using inappropriate statistic for level of measurement
*Incorrectly entered data
*Data presented without context
*Lack of disclosure
*Overinterpretation of results
*Inconsistent or misleading presentation
— must have enough details to judge the findings

82
Q

Evaluating descriptive data

A

*Enough details are provided to judge
*Presentation is understandable and clear
*Complete statistics are provided for each variable
*Graphical representations are consistent
*Statistics are not misleading, misinterpreted, or confusing

83
Q

How do we use descriptive data?

A

*Check trends in patient status
*Review quality performance
*Analyze distribution of disease in populations
*Develop evidence about clinical outcomes
*Use as basis for designing an intervention study
*Judge patient conditions and make improvements

84
Q

Descriptive data is important when related to

A

Age and gender

85
Q

Prevalence vs. incidence

A

Prevalence: # of existing cases
— e.g. # of diseases in a population
Incidence: # of NEW cases

86
Q

Retrospective studies

A

A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the disease or condition (controls).
– e.g. a group of 100 people with AIDS might be asked about their lifestyle choices and medical history in order to study the origins of the disease.

87
Q

Prospective studies

A
88
Q

Methodology refers to which method(s) in a study?

A

— Quantitative method
— Qualitative
— Mixed method

89
Q
A

Does not use randomized groups

90
Q

Range

A

The highest and lowest values in the data set

91
Q

Researcher attempts to show the spread of the data from the mean. Which of the following refers to the spread?

A

Variance

BSN: 3rd Quarter (13 decks)

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