Research EXAM #2 Flashcards
QUANtitative research vs. QUALitative research
Quantitative research: A traditional approach to research in which variables are identified and measured in a reliable and valid way.
— To use measurement to determine the effectiveness of interventions.
— Involves measuring objective characteristics or responses of subjects, and is reported using numbers.
*e.g. — *
Qualitative research: A naturalistic approach to research in which the focus is on understanding the meaning of an experience from the individual’s perspective.
— To use
*e.g. — *
Chapter 5 Objectives
Discuss the rationale for conducting a thorough search of the literature.
Discuss tools that measure the impact of studies.
Review the concept of “open access” and describe how it is making research information more accessible.
Describe the types of literature used to support a research study, including studies that constitute the “evidence pyramid.”
Understand the steps of a well-thought-out search strategy to find evidence-based information.
Compare a literature search for research to a literature search for a practice guideline.
Critically appraise the literature review section of a research article.
Reflect on the ways that research literature can be used as evidence for nursing practice.
Discuss the rationale for conducting a thorough search of the literature.
— Adds credence to the importance of the topic
— Provides studies that can be replicated
— This is a source of time savings for researchers!
— Locates instruments that have already been tested
— Existing instruments save time (and often money)
— No need to develop and test an instrument, which often takes months or years
Chapter 6 Objectives
Establish the link between the research question and the study design.
Evaluate the characteristics that are the basis for design decisions.
Differentiate the kinds of questions that require quantitative, qualitative, and mixed method designs.
Identify the types of variables that reflect the concepts in a research question.
Review designs that describe populations, test relationships, or examine causality.
Relate the type of design to the strength of evidence it can support
What are the basis for design selection?
Purpose to be achieved
Ethical limitations
Measurements needed
Researcher skills and resources
Time frame
Amount of control needed
Ultimate audience
From research question —> design
If you see these words..
— Cause = experimental design
— Change
— Measure an effect
— Test a relationship
— Predict an outcome
— Quantify an occurrence
— Describe a phenomenon
— Develop theory
..it will lead to this design:
— Experimental design
— Correlation
— Regression study
— Prevalence/Incidence
— Qualitative
—Qualitative or quantitative
What is a phenomenon related to research question
—> Examining a meaning of an experience or phenomena (e.g. How did the victims of a bomb threat feel in school? “What was your first rxn of a bomb threat?”)
There are 3 phases of design
1) Identify assumptions about the knowledge to be gained from the study
2) Select a design that serves the purpose of the study
3) Develop detailed plans for implementation of the study
Type of Variables
— Descriptive
— Independent (IV)
— Dependent (DV)
— Extraneous = confounding variables = anything other than the IV and DV
Chapter 7 Objectives
Define a population and discuss the rationale for sampling.
Contrast probability sampling with nonprobability sampling.
Discuss sampling options and select an appropriate strategy.
Describe methods for estimating necessary sample size.
Discuss methods for avoiding selection bias.
Appraise how the sampling method affects research as evidence.
Exploratory vs confirmatory designs
Exploratory —> Research to explore and describe a phenomenon of interest and generate new knowledge.
— often qualitative or mixed method studies; classified as descriptive even if they happen to describe relationships and associations. Explore and describe a given phenomenon.
Methods used = survey, mixed-methods (initial)
E.g. = reasons RNs choose clinical specialty may determine the specific characteristics that the RN was looking for (such as certifications required and work hrs), but also examine the value-based reasons a particular selection was made
— Confirmatory —> Research in which a relationship between variables has been posed and the study is designed to examine this hypothesis.
Common descriptive designs
Survey design — targets instruments or procedures that ask > 1 question that may/may not be answered
Cross-sectional study — can evaluate people of different ages, ethnicity, geographical locations, social backgrounds
Longitudinal study — repeated observations of the ame variables over short/long periods of time
Case study — in-depth, detailed examination of a particular case w/in a real-world context
Single subject design — the subject serves as his/her own control, rather than using another individual/group
Phenomenology — uses research to understand phenomenon’s universal nature by exploring views of those who have experienced it
Ethnography — observing cultures, customs
Research that examines relationships
— Correlation research: quantifies strength and direction of a relationship
— Predictive research: search for variables that can explain or predict an outcome
Chapter 8 Objectives
Discuss the link between the research question and the measurement strategy.
Describe the types of reliability and validity and explain how they are assessed.
Evaluate sources of measurement error and plan strategies to minimize their effects.
Compare the advantages and disadvantages of data collection methods.
Discuss the importance of having clearly prescribed data management procedures.
Determine how the measurement strategy supports application of the data to evidence-based practice.
Deductive vs. Inductive reasoning
Deductive: process of reasoning from the general to the specific
Inductive: process of reasoning from specific observations to broader generalizations
Inductive reasoning
An observation
Exploration of “The Gap”
General focus of interest
Development of the problem statement
Development of the purpose statement
Refinement of the research question(s)
3 Categories for Gray Matter in Research
1) Patient sensitive
2) Staff member sensitive
3) Organizationally sensitive
Numbers are a key element of measurement in nursing because they are?
— Objective
— Standard
— Consistent
— Precise
— Statistically testable
— An accurate representation of attributes
Prevalence vs. Incidence design
— Prevalence differs from incidence in that prevalence includes all cases, both new and preexisting, in the population at the specified time
— Incidence is limited to new cases only
Regression study
— Regression analysis is a way of mathematically sorting out which of those variables does indeed have an impact
The #s used in research measurements to represent the underlying characteristics are what?
Examples of patient-sensitive concepts regarding nursing
Anxiety
Skin integrity
Functional independence
Blood glucose
Blood pressure
Quality of life
Examples of nursing-sensitive concepts
Burnout
Lower back injuries
Medication errors
Pain management
Patient falls
Restraint prevalence
Organizationally sensitive concepts
Cost
Length of stay
Readmisión
Resource utilization
Satisfaction with nursing care
Satisfaction with nursing care
Confirmatory studies
Correlation research
Dependent variable
Descriptive studies
Descriptive variables + example
— Characteristics that describe the sample and provide a composite picture of the subjects of the study; they are not manipulated or controlled by the researcher.
Describe the sample/some characteristic of the phenomenon under study.
— May represent demographic data about the subjects (e.g. age, gender, ethnicity) or measurable characteristics (e.g. BP, weight, hematocrit)
E.g. What is the perception of the effectiveness of complementary medicine among intensive care unit nurses?
Descriptive variable = Perception of effectiveness
Exploratory studies
Research to explore and describe a phenomenon of interest and generate new knowledge.
— often qualitative or mixed method studies; classified as descriptive even if they happen to describe relationships and associations. Explore and describe a given phenomenon.
Methods used = survey, mixed-methods (initial)
Extraneous variables
= confounding variables
— Rival explanations for an outcome: threats to internal/external validity
— Ways to deal with: eliminate, control them, own up to them
Independent variable
Predictive research
Research design
What is a variable?
What is a sample?
Selection of objects/observations taken from a population of interest
(E.g. All apples at an orchard at a given time; wish to know how big all apples at the orchard are, but cannot measure all of them, so we take some from the population)
5 Methods of Sampling
1) Simple random = ideal; equally likely which is unbiased and representative; difficult when dealing with people
PRO —
CON —
2) Convenience = MOST COMMON + NONPROBABLITY METHOD; ask people nearby, ask the next 20 objects off a product line; done doing survey
3) Systematic
4) Cluster
3) Stratified
Convenience sampling
Ecological validity
Effect size
Exclusion criteria
External validity
QUANTITATIVE
Inclusion criteria vs. Exclusion criteria
Inclusion criteria:
— Objective attributes that are necessary
— Clinical
— Demographic
— Geographic
_________________
Exclusion criteria:
— Attributes that may affect the outcome
— Comorbid conditions
— Behavioral (high potential for attrition)
How to determine sample size: QUANTitative vs. QUALitative
Qualitative samples:
— Redundancy and Saturation
— When is the point at which no new information is being generated?
NOTE: When you hit redundancy you can stop interviewing people because you’ve hit data saturation
___________
Qualitative samples:
— Power
— How large a sample is needed to detect a difference in the outcome variable?
(E.g. Hypothesis: Smoking will increase lung cancer (directional hypothesis b/c placing a key word)
Independence r/t research
Population vs. Population validity
Factors affecting Power in quantitative samples
Biggest effect: sample size
Level of accuracy required
Number of variables to be studied
Variability in the population
Magnitude of effect
Independence of the data
Concerns w/ Small Samples
— Was the sample representative?
— Did sample size contribute to Type II error?
Types of Probability Samples
Simple random:
— Application of a table of random numbers
— Requires access to a list of population members
Systematic random:
— Random selection of starting point and every nth subject after
— Useful for prospective studies
Stratified random
Randomly drawn from stratifications based on some characteristic of interest
(E.g. patients w/ emphysema, not choosing all of them b/c smoking can lead to emphysema asking are they all smokers that caused the emphysema or was it due to the 2ndary or they got emphysema w/ no smoking relation)
Cluster random
Random selection of entire groups
Measure every subject in the group
Purposeful selection
Sample
Sampling error vs. Sampling frame
Selection bias
Snowball sampling (referral sampling, respondent-driven sampling)
Unit of analysis
Type 1 vs. Type 2 errors
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
Primary vs. Secondary data
Primary — original data derived from your research endeavors
Secondary — data derived from your primary data
4 Types of Hypothesis
1) Null hypothesis: one to be tested; want to test if that is correct; null is true until rejected (innocent until proven guilty)
(E.g. “The mean data scientist salary in the US is $113k”)
— Accept if: Null is close enough to the true mean
— Reject if: Null is too far from the true mean
2) Alternative hypothesis: everything else that’s tested
*(e.g. “ The mean data scientist salary is NOT $113k”)
— The change that challenges the status quo
3) Directional: A one-sided statement of the research question that is interested in only one direction of change.
4) Non-directional: A two-sided statement of the research question that is interested in change in any direction.
Methodology vs. Descriptive designs + examples
Methodology
Different types of quantitative + qualitative research design
QUANTITATIVE = external validity, effectiveness
— Descriptive
— Survey
— Correlational
— Quasi-experimental
— Experiment
__________________________________________
QUALITATIVE = saturated data, transferability,
— Ethnography = customs, culture
— Narrative = interviews ,documents over a period of time (chronology based)
— Phenomenology = used to study an event/activity as it happens, from various angles
— Ground theory = construction of hypothesis and theories through collecting and analyzing data
Literature review
— Scholarly works from peer-reviewed sources (articles, publications, gray literature, dissertations and original academic studies)
— Trustworthy = .edu, .gov, .org
Seminal work
commonly quoted as foundational
— Works that are groundbreaking or historically important
— Research that has stood the test of time
Empirical literature
— Reports tests of relationships and actual outcomes
— Tests arguments for effectiveness
— Research studies focused on the question posed
— Need to report literature that supports as well as literature that does not
Primary vs. Secondary sources + examples
Primary:
— Provide a first-hand account of an event or time period and are considered to be authoritative.
— They represent original thinking, reports on discoveries or events, or they can share new information.
Secondary:
— Secondary sources involve analysis, synthesis, interpretation, or evaluation of primary sources.
— They often attempt to describe or explain primary sources.
Distinguish the different types of designs in research
Systematic reviews
— To deliver a meticulous summary of al the available primary research in response to a research question
— Identify, evaluate, summarize
E.g. secondary research
Generalizability + examples
— Allows us to form coherent interpretations in any situation and to act purposefully and effectively in daily life
— Qualitative studies and generalizations (degree which the findings can be generalized from the study sample to the entire population)
Transferability + examples
— Gives us the opportunity to sort through given methods and conclusions to decide what to apply to our circumstances
“When you read your text compare the terminology to your notes and PowerPoints until they make sense to you“
What is measurement in nursing research?
Determination of the quantity of a characteristic that is present; it involves assigning of numbers or some other classification.
4 common classifications of QUANTITATIVE research in nursing
1) Descriptive = seeks to describe the current status of an ID’d variable; does NOT usually begin with a hypothesis
2) Correlational = determine the extent of a relationship b/w 2 or more variables using statistical data
3) quasi-experimental designs = attempts to establish cause-effect relationships among variables
4) Experimental = uses scientific method to establish the cause-effect relationships among a group of variables that make up a study
How to create a qualitative research question
1) ID broad research question
2) Select broad research tradition (match b/w the research ? And the characteristics of the tradition)
3) Determine criteria for selection of participants who can best inform the question
4) Locate a source of informants and invite their participation through informed consent
5) Design general data collection procedures
Systemic error
Error having a nonzero mean, so that its effect is not reduced when observations are averaged
Blinded review
A type of review in which the peer reviewer is unaware of the author’s identity, so personal influence is avoided.
Replication in a study
Repeating a specific study in detail on a different sample. When a study has been replicated several times and similar results are found, the evidence can be used with more confidence.
Characteristics of surveys + questionnaires
— Most common data collection method
— Systematic tool used to collect data
— Quantitative/Qualitative/or both
— Variety of options for delivery
— Open ended or closed
Differences in the 2 types of measurement error
1) Random error
— Human factors
— Bias
— Confusion
— Environmental variations
2) Systematic error
— A measure is consistently biased
Characteristics of writing questions
— Must be clear
— Succinct
— Unambiguous
— Needs to be interpreted the same way by respondents
— Avoids leading questions
— Avoids emotionally laden/loaded questions (e.g. have you finally stopped cheating on exams?)
The #s used in research measurements to represent the underlying characteristics are what?
— Clearly linked to the research question
— Appropriate to represent the variable of interest
— Consistently accurate
Primary data
— Data are collected directly from subjects
— Physiological measures
Secondary data
— Use of data originally collected for another purpose
— Efficient and convenient to use
— Reduces the time needed for a study
— Researcher cannot control the conditions under which the data were collected
— Critical: does the secondary data answer the research question?
Definition of unambiguous
Does not have any double meanings; avoid any leading questions where you WANT them to hear/say
What are 2 kinds of definitions of attribute variables? Provide an example for each.
1) Conceptual definition: describes concept that is the foundation of the variable by using other concepts
— Purpose: they help ensure the researcher is measuring the right things
E.g. using depression = include the presence of sadness, lack of pleasure, and changes in eating/sleeping habits
2) Operational definition: operations that must be performed to accurately represent the concepts
— Purpose: ensure the researcher is measuring attributes reliably
E.g. using a scale to record weight loss/gain in kg’s or using a log to record hours of sleep/night
How do we minimize measurement error?
— Clear, objective measurement procedures
— Reduce variability due to anything except the subject response
— Calibration of instrumentation
— Use of existing, tested instruments
— Ensure instrument reliability
Difference in reliability vs validity. Give example
Reliability: Finding the data consistent repeatedly; the instrument consistently measures a given trait with precision
Validity: tools collecting what it was designed to collect (e.g. collecting data on anxiety; is the tool collecting data on anxiety or excitement? — Is the tool collecting the data it was designed to obtain/achieve?
Instrument reliability
Internal reliability: Stability within the instrument
Item-total correlation: Stability among individuals
Inter-rater reliability: Stability between raters
Test-retest: Stability over time
Types of validity
Internal = credibility of everything free from ANY errors that could’ve posed an issue AND insuring that your IVariable caused the outcome and NOTHING else
External = your study is SO accurate and SO good, these findings can be applied to a similar population —> QUANTITATIVE RESEARCH STUDY
— Content: the content of the instrument reflects the attribute
— Construct: instrument represents the conceptual issues
— Criterion-related validity: concurrent, predictive, discriminate
— Responsiveness
Sensitivity vs. specificity
Sensitivity: measure that indicates an instrument has the capacity to detect disease if it is present
Specificity: measures that indicates an instrument has the capacity to differentiate when a disease is not present; all rival explanations are ruled out
NOTE: both consist of being a diagnostic measure
Data collection methods
Traditional methods
Online data collection
Technology-based data collection
Interviews
Focus groups
Observation
4 Steps in data-driven decision-making
1) Formulate a hypothesis
2) Find the right test
3) Execute the test
4) Make a decision
The strongest evidence is
Well-designed clinical trials
Randomized experimental designs
Multiple studies reporting replicable findings
Meta-analysis or meta-synthesis
Examples of Population + Sample
Population: Critical Care Nurses
Sample: Members of the ACCN
Population: Adults with coronary artery bypass grafts
Sample: Attendees at cardiac rehabilitation
