Module 1: Levels of Measurement and Data Displays Flashcards
Evidence-based practice (EBP)
A problem-solving approach to clinical decision making within a healthcare organization that integrates the best available scientific evidence with the best available experiential (patient and practitioner) evidence
Depends on solid statistical evidence; and considers internal and external influences on nursing practice
Steps:
- Develop an answerable clinical question
- Search for relevant research-based statistics
- Appraise and synthesize the evidence
- Integrate the evidence with other factors
- Assess effectiveness of the change
Types of EBP studies
EBP studies:
1. Descriptive studies: describe the characteristics of the population or phenomenon studied (i.e. What symptoms emerge during CA treatment?); Use descriptive statistics
- Explanatory studies: conducted to have a better understanding of the existing problem, but will not provide conclusive results (i.e. Among persons with lung CA, are women more likely to report pain than men?); Use inferential statistics
- Prediction and control studies (RCT): trial participants are randomly allocated either to the group receiving the treatment under investigation or to a control group receiving standard treatment (or a placebo) (i.e. Will mindful meditation reduce pain to a greater degree than distraction?); Use inferential statistics
Nursing research
Types of nursing research:
- BASIC research: knowledge production
- APPLIED research: knowledge implementation (problem-solving)
Nursing research develops knowledge to: (1) build the scientific foundation for clinical practice, (2) prevent disease and disability, (3) manage and eliminate symptoms caused by illness, and (4) enhance end-of-life and palliative care.
Two paradigms:
1. POSITIVIST paradigm: Reality exists and there is ONLY 1 truth; the real world is driven by natural causes that can be quantified and analyzed (i.e. Answer research questions and test hypotheses)
- NATURALIST paradigm: Reality is MULTIPLE, subjective, and constructed by individuals within their context
Hypothesis
A prediction statement that specifies the EXPECTED relationship between variables
3 types of hypothesis:
1. NULL: There is NO significant difference between specified population samples (i.e. There is no association between sleep and pain)
- NON-DIRECTIONAL: Expects an association, but does not predict the direction of the difference or relationship (i.e. There is an association between sleep and pain)
- DIRECTIONAL: Tells us what we believe that association is; provides direction (i.e. Persons with poor sleep will have greater subsequent day pain)
Hypothesis testing errors
Types of errors:
1. Type 1: Reject a true null hypothesis; the probability of making this error is α (it is also called false positive)
- Type II: Fail to reject a false null hypothesis; the probability of making this error is β, which depends mainly on sample size and variance (it is also called false negative)
**Correctly..
Failing to reject a true null = 1-α
Rejecting a false null = 1-β (also known as “power of test”, or the probability of avoiding a type II error)
Variable
A characteristic that VARIES from person to person OR within a person over time (i.e. Hair color, blood type, BP, Ht, Wt)
Two categories of variables (“ICED”):
- INDEPENDENT variable: also known as the CAUSAL or predictor variable
- DEPENDENT variable: also known as the EFFECT or outcome variable
**Constant: a characteristic that does NOT vary
Measurement
The assignment of numbers to represent the amount of an attribute present in an object or person, using specific metric rules (i.e. Temperature, BP, Ht, and Wt have rules for measuring)
Advantages:
- Removes guesswork
- Provides precise information
- Less vague than words
Scales for measurement
Functions:
1. Provides the unit of measurement — Level of measurement
- Provides the range and type of possible values — Infinite vs. finite values; and measurement units can be either continuous or discrete
Level of measurement
Researchers strive to use the HIGHEST level of measurement possible (ratio), especially to gather more information on the dependent variable, and use more powerful statistical tests
Determines the type of data analysis one is able to perform
There are 4 levels of measurement in statistics: Nominal (lowest), ordinal, interval, and ratio (highest)
NOMINAL (or Categorical)
Criteria:
1. Exclusive (only belonging to one group/category) AND exhaustive (meaning we have captured the absolute number of categories)
- Uses numbers to categorize attributes; each category is assigned a number (that does NOT have quantitative importance) for the purpose of analysis
- Discrete variables
i. e. Blood type: A+ = 1, A- = 2, B+ = 3; Sex: Male = 1, Female = 2; Race, religion
ORDINAL
Criteria:
1. Exclusive, exhaustive, AND rank ordered
- Ranked based on their relative standing on attribute
- Discrete variable
- UNEQUAL intervals between rankings — does NOT tell how much greater one level is than another
i. e. Educational attainment, assistance with ADLs, pt satisfaction with care
INTERVAL
Criteria:
1. Exclusive, exhaustive, rank ordered, AND numerically EQUAL intervals
- Does NOT have a meaningful/true zero; only defend position on the scale
- Continuous or discrete variables
i. e. Temperature (℃ and ℉) can have a zero value, but can also have a +/- value; IQ, GRE, SAT scores
RATIO
Criteria:
1. Exclusive, exhaustive, rank ordered, numerically equal intervals, AND a meaningful zero (point at which the variable is absent)
- Provides information about the absolute magnitude of the attribute
- Continuous or discrete variables
i. e. Weight: someone who weighs 200 lbs. is twice as heavy as someone who weighs 100 lbs.; In the Kelvin scale, zero represents a total lack of thermal energy; Urine output, bleeding, burn surface area, BP, AR, RR
Statistics in research
Used to:
- Describe and summarize data
- Make predictions about future events
- Make generalizations about population occurrences based on sample observations
- Identify associations/relationships or differences between sets of observations
Types of statistics:
1. DESCRIPTIVE statistics: used to describe or characterize SAMPLE characteristics by summarizing them
- INFERENTIAL statistics: a set of statistical techniques that provide predictions about POPULATION characteristics based on information obtained from a sample taken from that population
Univariate descriptive statistics
Statistic test that involves ONE dependent variable
Provides information about the:
1. Frequency distribution — Percentages & percentiles, count
- Central tendency (where the masses huddle) — Mean, median, mode
- Dispersion/variability (data spread) — Minimum/maximum, range, standard deviation, variance, IQR
