Midwife Licensure Exam Research & Evidence-Based Practice — Nursing Research Process & Evidence-Based PracticeCheat Sheet
One-page cheat sheet for Midwife Licensure Exam Research & Evidence-Based Practice — Nursing Research Process & Evidence-Based Practice. Every formula, definition, and key fact you need for this chapter, condensed to a single printable page. Designed for the final review session before the Midwife Licensure Exam 2026.
Exam context
For the Midwife Licensure Examination, Professional Regulation Commission (PRC) — Board of Midwifery tests Research & Evidence-Based Practice under a "Core" label, with Nursing Research Process & Evidence-Based Practice in the 1st slot across 1 chapters. Midwife Licensure Exam candidates must clear the 75% weighted average cut on the 2026 paper, which draws about a meaningful share of Research & Evidence-Based Practice questions. Date to watch: April and November 2026 (expected).
Nursing Research Process & Evidence-Based Practice - Cheat Sheet
Your last-minute revision companion for the Nursing Research chapter. Master the 10-step research process, research designs, sampling methods, ethics, statistics interpretation, and EBP framework in 30 minutes. This sheet covers every high-yield concept tested in the PRC NLE and Board of Nursing competencies.
Sections
Section Title
The 10-Step Nursing Research Process
Important Facts
- Step 1: Identify and state the problem — define the gap and its significance.
- Step 2: Review the literature — synthesize existing knowledge, locate the gap.
- Step 3: Formulate the theoretical/conceptual framework — the lens guiding the study.
- Step 4: State research questions, objectives, or hypotheses.
- Step 5: Select the research design and methodology.
- Step 6: Identify population and sample; select sampling method.
- Step 7: Collect data using valid, reliable instruments.
- Step 8: Analyze data using appropriate statistics.
- Step 9: Interpret findings and draw conclusions.
- Step 10: Communicate/disseminate findings and recommend application.
Key Definitions
Term
Nursing Research
Example
A study examining the effect of early mobilization on hospital-acquired pneumonia rates in post-operative patients.
Definition
Systematic inquiry designed to develop, refine, and expand nursing knowledge to improve patient outcomes and inform clinical practice.
Term
Research Gap
Example
No evidence exists on the optimal timing of patient education post-discharge for medication adherence in a specific Filipino population.
Definition
The difference between what is known and what needs to be known; the justification for conducting research.
Diagrams To Know
- Linear sequence of the 10-step research process (flowchart)
- Relationship between problem statement, literature review, and research questions
Section Title
Variables and Hypotheses
Important Facts
- IV is what you GIVE or MANIPULATE; DV is what you MEASURE.
- H₀ is tested statistically; if p < 0.05, you REJECT H₀ and support H₁.
- A directional hypothesis predicts the direction of the relationship (e.g., 'will increase').
- A non-directional hypothesis predicts a relationship but not the direction (e.g., 'will differ').
- Only quantitative research typically uses hypotheses; qualitative research uses research questions.
Key Definitions
Term
Independent Variable (IV)
Example
Type of dressing (wound care intervention) in a study on wound healing rates.
Definition
The presumed cause or intervention that is manipulated by the researcher.
Term
Dependent Variable (DV)
Example
Wound healing time (measured in days) as the outcome of different dressing types.
Definition
The presumed effect or outcome that is measured as a result of the IV.
Term
Null Hypothesis (H₀)
Example
H₀: There is no difference in infection rates between sterile and non-sterile dressing techniques.
Definition
States that NO relationship or difference exists between variables; this is what statistical testing attempts to reject.
Term
Alternative/Research Hypothesis (H₁)
Example
H₁: Sterile dressing technique results in lower infection rates than non-sterile technique.
Definition
States that a relationship or difference DOES exist; reflects the researcher's predicted outcome.
Diagrams To Know
- IV → DV causal pathway diagram
- Hypothesis testing logic (reject vs. fail to reject H₀)
Section Title
Quantitative vs. Qualitative Research
Important Facts
- Quantitative = numerical, objective, deductive (specific to general); hypothesis-testing.
- Qualitative = narrative, subjective, inductive (general to specific); exploratory.
- Quantitative seeks generalizability to larger populations.
- Qualitative seeks deep understanding of a specific phenomenon or group.
- Mixed-methods research combines both approaches for comprehensive insight.
Key Definitions
Term
Quantitative Research
Example
A study measuring mean blood pressure reduction after a 12-week exercise program across 500 participants.
Definition
Measures variables numerically; tests hypotheses; seeks objective, generalizable results using deductive logic.
Term
Qualitative Research
Example
In-depth interviews with patients to understand their lived experience of chronic pain management.
Definition
Explores meaning, experience, and process through narrative data; uses inductive logic to develop understanding.
Term
Phenomenology (Qualitative Tradition)
Example
Understanding the experience of Filipino nurses working in intensive care units.
Definition
Explores the lived experience of a phenomenon; seeks to describe subjective meaning.
Term
Grounded Theory (Qualitative Tradition)
Example
Developing a theory of how nurses cope with moral distress in resource-limited settings.
Definition
Develops a theory from data; the theory emerges from analyzing the data inductively.
Term
Ethnography (Qualitative Tradition)
Example
Observing traditional healing practices in a rural Philippine community and their integration with Western medicine.
Definition
Studies a culture or subculture; describes beliefs, practices, and social interactions within a group.
Diagrams To Know
- Continuum of quantitative vs. qualitative research
- Qualitative research traditions (phenomenology, grounded theory, ethnography, case study)
Section Title
Quantitative Research Designs
Important Facts
- TRUE EXPERIMENT = Manipulation + Control Group + Randomization (all three required).
- RCT is the GOLD STANDARD for establishing causation and sits at the TOP of evidence hierarchy.
- Quasi-experimental allows manipulation but LACKS randomization or control; more practical but weaker causal inference.
- Correlational research does NOT equal causation; r ≠ cause-and-effect.
- Cohort (prospective) follows people forward; case-control (retrospective) looks backward.
- Descriptive research is often the FIRST step; identifies what exists before testing why.
- Cross-sectional is a 'snapshot' at one time; cannot prove causation.
Key Definitions
Term
Experimental (True Experiment)
Example
Randomizing patients to receive either new wound care protocol (intervention) or standard care (control), with random assignment.
Definition
Has THREE hallmarks: manipulation of IV, control group, and randomization; STRONGEST design for establishing cause-and-effect.
Term
Randomized Controlled Trial (RCT)
Example
RCT testing effectiveness of a new patient education video on medication adherence in hypertensive patients.
Definition
The gold standard experimental design; random assignment to intervention or control group; sits at the TOP of evidence hierarchy.
Term
Quasi-Experimental
Example
Implementing a new fall-prevention protocol on one unit (intervention) and comparing outcomes to a unit without the protocol (no randomization).
Definition
Has manipulation of IV but LACKS randomization and/or control group; weaker causal inference than true experiment but often more feasible clinically.
Term
Descriptive (Non-Experimental)
Example
Survey describing the prevalence of catheter-associated urinary tract infections (CAUTIs) in Philippine hospitals.
Definition
Describes characteristics, prevalence, or frequency of a phenomenon; NO manipulation; answers 'What is happening?'
Term
Correlational (Non-Experimental)
Example
Examining the correlation between shift length and medication errors; correlation does NOT prove causation.
Definition
Examines relationships between variables WITHOUT manipulation; determines strength and direction of association.
Term
Cohort Study (Prospective)
Example
Following 1000 nurses exposed to bloodborne pathogens for 5 years to determine infection rates.
Definition
Follows groups FORWARD over time; starts with exposure, then observes outcomes; weaker than RCT but stronger than case-control.
Term
Case-Control Study (Retrospective)
Example
Comparing nurses who developed back injuries to nurses without, examining past ergonomic practices.
Definition
Compares people WITH an outcome to those WITHOUT, looking BACKWARD for exposure history.
Term
Cross-Sectional Study
Example
A survey measuring stress levels and job satisfaction among nurses on a single day.
Definition
Data collected at ONE point in time; provides a 'snapshot'; cannot establish temporal order (cause before effect).
Diagrams To Know
- Hierarchy of quantitative research designs (from strongest to weakest for causation)
- Experimental vs. quasi-experimental vs. non-experimental characteristics
Section Title
Population and Sampling
Important Facts
- PROBABILITY SAMPLING (random) supports generalizability; non-probability does NOT.
- Simple Random: every member has equal, independent chance (use random number tables, computer).
- Systematic: every kth member from a list (e.g., every 10th name); easy to execute.
- Stratified Random: divide population into strata, then randomly sample within each; ensures representation.
- Cluster (Multistage): random selection of groups/clusters, THEN sampling within; useful for geographically dispersed populations.
- Convenience: readily available subjects; WEAKEST, but common in qualitative and pilot studies.
- Quota: convenience with preset numbers per subgroup; still non-probability.
- Purposive/Judgmental: hand-picked for a specific characteristic; common in qualitative research.
- Snowball/Network: participants refer others; useful for hard-to-reach populations (e.g., undocumented migrants).
- LARGER SAMPLES reduce sampling error and increase power to detect real effects.
- Sample size should be determined BEFORE the study starts (often via power analysis).
Key Definitions
Term
Population
Example
All registered nurses working in public hospitals in the Philippines.
Definition
The entire group of interest from which the sample is drawn.
Term
Target Population
Example
Registered nurses in Metro Manila public hospitals.
Definition
The group to which the researcher intends to generalize findings.
Term
Accessible Population
Example
Registered nurses working in three specific public hospitals in Metro Manila (the hospitals that gave permission).
Definition
The portion of the target population that is actually available for study.
Term
Sample
Example
200 nurses randomly selected from the three hospitals.
Definition
The subset of the population that is actually studied.
Term
Sampling
Example
Using random number generation to select 200 nurses from a list of 1500.
Definition
The process of selecting a subset (sample) from a population.
Term
Sampling Error
Example
If the population mean is 100 but the sample mean is 98, the sampling error is 2.
Definition
The difference between sample statistics and population parameters; ALWAYS present in sampling. Larger samples reduce sampling error.
Term
Probability (Random) Sampling
Example
Simple random, systematic, stratified, cluster sampling methods.
Definition
Every member has a known, non-zero chance of selection; SUPPORTS generalizability.
Term
Non-Probability Sampling
Example
Convenience, quota, purposive, snowball sampling methods.
Definition
Selection is NOT random; some members have no chance or unknown chance of selection; DOES NOT support generalizability.
Diagrams To Know
- Probability vs. non-probability sampling methods comparison
- Population → Accessible Population → Sample relationship
Common Values
Value
≥0.70
Symbol
α
Quantity
Cronbach's Alpha (minimum acceptable)
Value
≥0.80
Symbol
α
Quantity
Cronbach's Alpha (good)
Section Title
Data Collection Methods & Instrument Quality
Important Facts
- VALIDITY = Does it measure what it should? RELIABILITY = Does it measure consistently?
- An instrument can be RELIABLE without being VALID (consistently wrong).
- An instrument CANNOT be VALID without being RELIABLE (you cannot be accurate if you are inconsistent).
- AIM FOR BOTH: valid AND reliable instruments.
- Common data collection methods: questionnaires, interviews (structured/unstructured), observation, biophysiologic measures, existing records.
- Questionnaires: quick, economical, but low response rates; self-report bias.
- Interviews: rich data, flexible; time-consuming; interviewer bias possible.
- Observation: see actual behavior; observer bias; Hawthorne effect (behavior changes when observed).
- Biophysiologic measures: objective (e.g., BP, heart rate, blood glucose); equipment-dependent.
- Existing records: convenient, economical; incomplete, not designed for research purposes.
Key Definitions
Term
Validity
Example
A pain scale that truly measures pain (not anxiety or depression).
Definition
The instrument measures what it INTENDS to measure; it is accurate.
Term
Content Validity
Example
A medication administration knowledge test that includes questions on calculations, drug interactions, and patient safety.
Definition
The instrument adequately samples the construct being measured; covers the full domain.
Term
Construct Validity
Example
A stress scale that measures the psychological construct of stress (not just physical symptoms).
Definition
The instrument measures the theoretical construct it claims to measure.
Term
Criterion Validity
Example
A depression screening tool validated against psychiatric diagnosis.
Definition
The instrument's scores correlate with an external criterion (gold standard).
Term
Reliability
Example
A thermometer that gives the same reading when the patient's temperature is measured twice in succession.
Definition
The instrument yields CONSISTENT, reproducible results; it is dependable.
Term
Test-Retest Reliability
Example
A stress scale administered on Monday and again on Friday to the same person should yield similar scores.
Definition
Consistency over TIME; the same instrument given to the same person at two time points yields similar scores.
Term
Internal Consistency Reliability
Example
All items on a depression scale should measure depression (not anxiety or pain).
Definition
Consistency WITHIN the instrument; all items measure the same construct. Measured by Cronbach's alpha.
Term
Interrater Reliability
Example
Two nurses assessing a patient's pain using the same pain scale should assign similar scores.
Definition
Consistency BETWEEN raters; different observers using the same instrument agree on their observations.
Term
Cronbach's Alpha
Example
A scale with Cronbach's alpha of 0.82 has good internal consistency.
Definition
A coefficient measuring internal consistency; ranges 0–1. Values ≥0.70 are acceptable; ≥0.80 is good.
Diagrams To Know
- Validity and reliability relationship (Venn diagram or 2x2 matrix)
- Types of validity and reliability at a glance
Section Title
Research Ethics (CRITICAL for NLE)
Important Facts
- INFORMED CONSENT is the cornerstone of research ethics; must be VOLUNTARY and INFORMED.
- Consent forms must be written in language participants understand (plain language, native language if appropriate).
- Minors need PARENTAL CONSENT + the minor's ASSENT.
- Pregnant women, prisoners, institutionalized persons are VULNERABLE; require additional protections.
- CONFIDENTIALITY ≠ ANONYMITY: confidentiality means researcher knows but keeps secret; anonymity means researcher doesn't know.
- Research must have MORE BENEFIT than RISK (beneficence > harm).
- Participants have the RIGHT TO WITHDRAW at ANY TIME without penalty.
- Data must be SECURE (locked, encrypted, limited access).
- In the PHILIPPINES, research oversight follows PHREB (DOST) guidelines and institutional ethics committees.
- RA 10173 (Data Privacy Act) mandates protection of personal information.
- Vulnerable groups get ENHANCED protection: extra review, closer monitoring, additional safeguards.
- Children cannot provide informed consent; parents/guardians consent, children assent (if age-appropriate).
Key Definitions
Term
Informed Consent
Example
A patient reading and signing a consent form before participating in a medication study, with questions answered.
Definition
Voluntary, informed agreement to participate; participant understands purpose, procedures, risks, benefits, and right to withdraw anytime without penalty.
Term
Assent (Pediatric Research)
Example
A 10-year-old child agreeing to participate in a study after having it explained in simple language.
Definition
The child's agreement to participate (not legal consent, which comes from parents).
Term
Confidentiality
Example
Data is linked to a subject ID code, and the key linking names to codes is kept secure.
Definition
Researcher knows the participant's identity but keeps data private; protects the identity.
Term
Anonymity
Example
A survey with no identifying information; data is returned without names.
Definition
Researcher does NOT know the participant's identity; data cannot be traced to any individual.
Term
Belmont Report Principles
Example
Nursing adds NONMALEFICENCE (do no harm) and FIDELITY (loyalty, honesty with participants).
Definition
Foundational ethical principles: RESPECT FOR PERSONS (autonomy), BENEFICENCE (maximize benefit), JUSTICE (fair distribution of benefits and burdens).
Term
Institutional Review Board (IRB)
Example
In the Philippines, studies follow PHREB (Philippine Health Research Ethics Board) or institutional ethics committees.
Definition
A committee that reviews and approves research involving human subjects; ensures ethical compliance.
Term
Vulnerable Populations
Example
Children, pregnant women, prisoners, cognitively impaired, critically ill patients, low-income individuals.
Definition
Groups with limited ability to protect themselves; require EXTRA safeguards.
Term
Minimal Risk
Example
A questionnaire survey; observational study.
Definition
Research in which the probability and magnitude of harm is not greater than in everyday life.
Term
Data Privacy Act of 2012 (RA 10173)
Example
Securing participant data, limiting access, obtaining consent for data use.
Definition
Philippine law protecting personal data; researchers must comply when handling participant information.
Diagrams To Know
- Belmont principles and how they guide research ethics
- Vulnerable populations and their additional protections
- Informed consent process flowchart
Formulas
Formula
Mean = Σ(x) / n
Meaning
Σ(x) = sum of all values; n = number of values; gives the average.
Watch Out
Mean is SENSITIVE to outliers; one extreme value can pull the average up or down.
When To Use
When data is normally distributed (no extreme outliers); best for interval/ratio data.
Formula
Median = middle value
Meaning
The value that divides data in half; 50th percentile.
Watch Out
Median ignores values; changes in high or low values don't affect it (good when outliers exist).
When To Use
When data is skewed (has outliers); best for ordinal data or non-normal distributions.
Formula
Mode = most frequent value
Meaning
The value that appears most often in the dataset.
Watch Out
May not exist (all values occur equally) or may be multiple modes; doesn't reflect actual data well in non-categorical data.
When To Use
The ONLY measure of central tendency for NOMINAL (category) data.
Formula
Standard Deviation (SD) = √[Σ(x - mean)² / n]
Meaning
Measures how spread out data is from the mean; larger SD = more variability.
Watch Out
SD is in the SAME UNITS as the original data; don't confuse with variance (SD²).
When To Use
To describe variability and identify outliers; used in calculating confidence intervals.
Formula
Correlation coefficient (r) ranges from -1 to +1
Meaning
r = -1 (perfect negative), r = 0 (no linear relationship), r = +1 (perfect positive); sign shows direction, magnitude shows strength.
Watch Out
CORRELATION ≠ CAUSATION; even a strong correlation does NOT prove cause-and-effect.
When To Use
When examining the LINEAR relationship between two continuous variables.
Common Values
Value
p < 0.05
Symbol
p
Quantity
Statistical significance cutoff
Value
r = 0
Symbol
r
Quantity
Correlation: no relationship
Value
r = +0.70 to +1.00
Symbol
r
Quantity
Correlation: strong positive
Value
r = +0.30 to +0.69
Symbol
r
Quantity
Correlation: weak positive
Value
r = -0.70 to -1.00
Symbol
r
Quantity
Correlation: strong negative
Value
α = 0.05
Symbol
α
Quantity
Type I error (alpha) default
Value
1 - β ≥ 0.80 (80%)
Symbol
Power
Quantity
Minimum acceptable power
Section Title
Basic Statistics Interpretation
Important Facts
- LEVELS OF MEASUREMENT determine which statistics to use: nominal (mode), ordinal (mode/median), interval (mode/median/mean), ratio (all).
- NORMAL DISTRIBUTION: 68% fall within ±1 SD, 95% within ±2 SD, 99.7% within ±3 SD.
- Mean is BEST for normal data; median is BEST for skewed data or ordinal data.
- Mode is the ONLY option for nominal data.
- p < 0.05 = statistically significant; p ≥ 0.05 = not statistically significant.
- Statistically SIGNIFICANT ≠ clinically SIGNIFICANT; small differences can be statistically significant in large samples.
- TYPE I ERROR (false positive) = concluding effect exists when it doesn't (α = 0.05 by convention).
- TYPE II ERROR (false negative) = concluding effect doesn't exist when it does (β; power = 1 - β).
- CORRELATION ranges -1 to +1; r = 0 = no linear relationship; positive = move together; negative = move opposite.
- t-test compares MEANS of TWO groups; ANOVA compares THREE or MORE groups.
- Chi-square tests ASSOCIATIONS between categorical (nominal/ordinal) variables.
- Confidence Interval (CI): range of values likely to contain the true population parameter; e.g., 95% CI = 95% confident the true value is within this range.
Key Definitions
Term
Nominal Data
Example
Sex (male/female), blood type (A, B, AB, O), religion.
Definition
Categories with NO order (mutually exclusive); only MEASURE: mode.
Term
Ordinal Data
Example
Pain scale (0–10), educational level (elementary, high school, college), Likert scales (strongly agree to disagree).
Definition
Ordered categories but UNEQUAL intervals between levels; MEASURES: mode, median (not mean).
Term
Interval Data
Example
Temperature in Celsius (0°C ≠ no temperature), IQ scores.
Definition
Ordered with EQUAL intervals between values but NO true zero; MEASURES: mode, median, mean.
Term
Ratio Data
Example
Weight (0 kg = no weight), pulse, blood pressure, height, blood glucose.
Definition
Ordered, equal intervals, WITH a TRUE ZERO; MEASURES: all (mode, median, mean); STRONGEST level.
Term
Normal (Bell-Curve) Distribution
Example
Height, IQ, blood pressure in healthy populations often approximate normal distribution.
Definition
Symmetrical, bell-shaped curve; mean = median = mode; 68–95–99.7% rule applies.
Term
Skewed Distribution
Example
Income (right-skewed, tail to the right); age at death (left-skewed, tail to the left).
Definition
Asymmetrical; mean is pulled toward the tail.
Term
p-value
Example
p = 0.03 means there is a 3% probability the result occurred by chance.
Definition
Probability that a result occurred by CHANCE alone; answers 'If the null hypothesis is true, how likely is this result?'
Term
Statistical Significance
Example
If p = 0.03, the result IS statistically significant (reject H₀).
Definition
Result is UNLIKELY due to chance; conventionally p < 0.05 is considered statistically significant.
Term
Type I Error (Alpha, α)
Example
Concluding a new drug is effective when it actually isn't.
Definition
REJECTING a TRUE null hypothesis; a FALSE POSITIVE; concluding there IS a difference when there isn't.
Term
Type II Error (Beta, β)
Example
Concluding a new drug doesn't work when it actually does.
Definition
FAILING TO REJECT a FALSE null hypothesis; a FALSE NEGATIVE; concluding there is NO difference when there is.
Term
Statistical Power (1 - β)
Example
Power = 0.80 means 80% chance of detecting a real effect if it exists; aim for ≥0.80.
Definition
The probability of correctly REJECTING a false null hypothesis; ability to detect a REAL effect.
Diagrams To Know
- Normal distribution with 68–95–99.7% rule
- Type I vs. Type II error matrix
- Correlation strength interpretation (r values)
Section Title
Evidence-Based Practice (EBP) Framework
Important Facts
- EBP = Best Evidence + Clinical Expertise + Patient Values (all THREE components).
- ASK: Frame a focused PICO(T) question; avoid broad 'yes/no' questions.
- ACQUIRE: Search databases (PubMed, CINAHL, Cochrane); use keywords derived from PICO.
- APPRAISE: Judge study quality (validity, reliability, applicability, relevance); use critical appraisal tools.
- APPLY: Adapt evidence to your setting and patient; consider barriers, resources, patient preferences.
- ASSESS: Evaluate outcomes; did implementing this evidence improve results? Adjust as needed.
- EVIDENCE HIERARCHY (strongest to weakest): Systematic reviews/meta-analyses of RCTs > individual RCTs > quasi-experimental > cohort/case-control > qualitative/descriptive > expert opinion.
- RCT sits #2 in hierarchy because it is a single study; systematic reviews of RCTs are strongest because they synthesize all RCTs.
- QI uses local data for local improvement; research produces knowledge meant to be shared and generalizable.
- Clinical expertise + research evidence = better outcomes than either alone.
- Patient values/preferences are NOT optional; patient-centered care requires honoring them.
Key Definitions
Term
Evidence-Based Practice (EBP)
Example
Deciding to use a new wound care dressing based on current research, your clinical experience, and the patient's preference for frequency of dressing changes.
Definition
Integration of BEST AVAILABLE RESEARCH EVIDENCE + CLINICAL EXPERTISE + PATIENT VALUES/PREFERENCES to guide care decisions.
Term
PICO(T) Framework
Example
In adult ICU patients (P), does early mobilization (I) versus standard care (C) reduce hospital-acquired pneumonia (O) within 30 days (T)?
Definition
Structure for formulating a focused clinical question: Population, Intervention, Comparison, Outcome, (Time).
Term
The 5 A's of EBP
Example
1) ASK a clinical question; 2) ACQUIRE evidence; 3) APPRAISE quality; 4) APPLY to practice; 5) ASSESS outcomes.
Definition
Ask, Acquire, Appraise, Apply, Assess — the steps for implementing EBP.
Term
Systematic Review
Example
Cochrane review of interventions for catheter-associated urinary tract infection prevention.
Definition
Comprehensive, reproducible summary of all available evidence on a topic; sits at the TOP of evidence hierarchy.
Term
Meta-Analysis
Example
Pooling data from 10 RCTs on hand hygiene interventions to calculate a combined effect size.
Definition
Statistical combination of results from multiple studies to determine overall effect; often part of a systematic review.
Term
Clinical Guideline
Example
Philippine Health Ministry guidelines on infection prevention; ANA standards of care.
Definition
Evidence-based recommendations for clinical practice; developed by experts and organizations.
Term
Quality Improvement (QI) vs. Research
Example
QI: Implementing a checklist to reduce medication errors in your hospital unit. Research: Testing whether a new drug reduces errors in multiple centers.
Definition
QI: Uses DATA to improve a SPECIFIC LOCAL PROCESS; does NOT generate generalizable knowledge. Research: GENERATES NEW KNOWLEDGE.
Diagrams To Know
- The 5 A's of EBP cycle (circular/iterative process)
- Evidence hierarchy pyramid (strongest at top)
- EBP integration: evidence + expertise + patient values (Venn diagram)
Section Title
Common Quantitative Tests (Quick Reference)
Important Facts
- Parametric tests (t-test, ANOVA, Pearson r) assume NORMAL DISTRIBUTION; use for interval/ratio data.
- Non-parametric tests (Mann-Whitney, Wilcoxon, Spearman rho) do NOT assume normal distribution; use for ordinal/skewed data.
- t-test is for COMPARING TWO groups; ANOVA is for THREE or MORE groups.
- Paired t-test is for SAME group at two times (before/after); independent t-test is for DIFFERENT groups.
- Chi-square is ONLY for categorical data (nominal/ordinal); asks if association between two categories exists.
- Correlation (r) describes relationship strength/direction but DOES NOT prove causation.
- Spearman's rho is the non-parametric version of Pearson's r; used for ordinal data or non-normal continuous data.
Key Definitions
Term
t-test (independent samples)
Example
Comparing mean weight loss in a diet group versus a control group.
Definition
Compares MEANS of TWO INDEPENDENT groups (e.g., treatment vs. control).
Term
t-test (paired/dependent)
Example
Comparing blood pressure before and after a medication in the same patients.
Definition
Compares MEANS of the SAME group at TWO TIME POINTS (before and after).
Term
ANOVA (Analysis of Variance)
Example
Comparing test scores across three different teaching methods.
Definition
Compares MEANS of THREE or MORE groups.
Term
Chi-Square Test
Example
Testing if there is an association between gender (male/female) and smoking status (yes/no).
Definition
Tests ASSOCIATION between TWO CATEGORICAL (nominal/ordinal) variables; compares observed vs. expected frequencies.
Term
Pearson Correlation (r)
Example
Correlation between age and blood pressure in a sample of hypertensive patients.
Definition
Measures LINEAR relationship between TWO CONTINUOUS variables; r ranges -1 to +1.
Term
Mann-Whitney U Test
Example
Comparing pain ratings (ordinal scale) between two groups.
Definition
Non-parametric test comparing TWO INDEPENDENT groups when data is NOT normally distributed or ordinal.
Term
Wilcoxon Signed-Rank Test
Example
Comparing stress scores before and after an intervention in the same patients (ordinal data).
Definition
Non-parametric test comparing the SAME group at TWO TIME POINTS when data is NOT normally distributed.
Diagrams To Know
- Decision tree: which statistical test to use (based on data type and group number)
Section Title
Philippine Nursing Context & RA 9173
Important Facts
- RA 9173 mandates nursing research competence as part of professional practice.
- Philippine nurses must be able to READ, UNDERSTAND, and APPLY research to practice.
- RA 10173 (Data Privacy Act) applies to all research with human subjects in the Philippines.
- Informed consent should be in language the participant understands (Filipino/Tagalog if appropriate).
- Research in Philippine healthcare settings requires institutional approval and ethical oversight.
- Filipino nurses working abroad must still adhere to RA 9173 standards and apply EBP.
Key Definitions
Term
RA 9173 (Nursing Act of 2002)
Example
Registered nurses are expected to stay current with evidence and question practices that lack scientific basis.
Definition
Philippine law regulating nursing practice; mandates competencies including ability to be a research consumer and apply EBP.
Term
Board of Nursing (BON) Competencies
Example
Nurses must demonstrate ability to interpret research findings and integrate evidence into patient care decisions.
Definition
Expected nursing competencies set by the PRC; includes research literacy and EBP application.
Term
Data Privacy Act 2012 (RA 10173)
Example
Securing informed consent in Filipino language; protecting participant identity; limiting data access.
Definition
Philippine data protection law; mandatory compliance when conducting research with human participants.
Term
PHREB (Philippine Health Research Ethics Board)
Example
Studies in Philippine institutions must follow PHREB guidelines; some institutions have their own ethics committees aligned with PHREB.
Definition
National body under DOST providing ethical oversight and guidelines for health research in the Philippines.
Diagrams To Know
- Philippine research ethics framework (PHREB, institutional committees, RA 10173)
Must Remember
- A TRUE EXPERIMENT has ALL THREE: Manipulation, Control Group, AND Randomization. RCT is the gold standard (strongest for causation and #2 in evidence hierarchy after systematic reviews).
- INDEPENDENT VARIABLE (IV) = cause/intervention (what you GIVE/MANIPULATE). DEPENDENT VARIABLE (DV) = effect/outcome (what you MEASURE). NULL HYPOTHESIS states NO difference; statistical testing tries to REJECT it.
- PROBABILITY SAMPLING (random: simple, systematic, stratified, cluster) SUPPORTS GENERALIZABILITY. Non-probability (convenience, quota, purposive, snowball) does NOT. Larger samples reduce sampling error.
- VALIDITY = measures what it SHOULD (accurate). RELIABILITY = measures CONSISTENTLY (dependable). An instrument can be reliable without being valid (consistently wrong), but CANNOT be valid without being reliable.
- INFORMED CONSENT is the cornerstone of ethics: VOLUNTARY, INFORMED agreement. Minors need PARENTAL CONSENT + ASSENT. Vulnerable groups (children, pregnant women, prisoners, cognitively impaired, critically ill) need EXTRA protections. Data must be kept CONFIDENTIAL; RA 10173 (Data Privacy Act) applies in the Philippines.
- p < 0.05 = STATISTICALLY SIGNIFICANT (reject H₀). TYPE I ERROR = false positive (rejecting true H₀; α = 0.05 by convention). TYPE II ERROR = false negative (failing to reject false H₀; power = 1 - β; aim for ≥0.80).
- NORMAL DISTRIBUTION: 68% within ±1 SD, 95% within ±2 SD, 99.7% within ±3 SD. MEAN (sensitive to outliers, best for normal data) vs. MEDIAN (best for skewed/ordinal) vs. MODE (ONLY for nominal). Correlation coefficient r ranges -1 to +1: sign = direction, magnitude = strength. CORRELATION ≠ CAUSATION.
- EBP = BEST AVAILABLE RESEARCH EVIDENCE + CLINICAL EXPERTISE + PATIENT VALUES/PREFERENCES. Use PICO(T) to frame the question. The 5 A's: ASK → ACQUIRE → APPRAISE → APPLY → ASSESS. EVIDENCE HIERARCHY (strongest to weakest): Systematic reviews/meta-analyses of RCTs > individual RCTs > quasi-experimental > cohort/case-control > qualitative/descriptive > expert opinion.
- QUALITY IMPROVEMENT (QI) uses data to improve a LOCAL PROCESS (not generalizable). RESEARCH GENERATES NEW KNOWLEDGE meant to be shared. QUALITATIVE RESEARCH (phenomenology, grounded theory, ethnography, case study) explores MEANING and EXPERIENCE through narrative data (inductive).
- In the PHILIPPINES: RA 9173 (Nursing Act) mandates research competence. RA 10173 (Data Privacy Act) governs data protection. PHREB (Philippine Health Research Ethics Board, DOST) provides national ethical oversight. Institutions have ethics review committees. Board of Nursing (BON) expects nurses to be research consumers and apply EBP.
Last Minute Tips
- EXPERIMENTAL = Manipulation + Control + Randomization (ALL THREE required). If any ONE is missing, it's quasi-experimental or non-experimental. The RCT is #2 in evidence hierarchy (after systematic reviews); remember that systematic reviews SIT ABOVE single RCTs because they synthesize multiple studies.
- When you see 'CORRELATION,' immediately think 'NOT causation.' Even r = 0.95 does not prove cause-and-effect. Only experimental and quasi-experimental designs (with manipulation) can suggest causation.
- For STATISTICS: If the question asks 'which measure of central tendency?'—ask yourself: Is it normal distribution (mean), skewed (median), or nominal/categories (mode ONLY)? For hypothesis testing: p < 0.05 reject H₀; p ≥ 0.05 fail to reject H₀. Don't confuse 'statistically significant' with 'clinically significant.'
- ETHICS QUICK CHECK: Informed consent? Confidentiality/anonymity? Benefit > risk? Vulnerable group protection (minors = parental consent + assent)? IRB/ethics review? If any are missing, it's an ethics violation. RA 10173 is mandatory in the Philippines.
- EBP INTEGRATION: Research GENERATES knowledge; EBP APPLIES knowledge. Remember: BEST EVIDENCE alone isn't EBP—you need clinical judgment AND patient preferences. Use PICO(T) to frame clinical questions. When appraising evidence, systematic reviews of RCTs are your gold standard; expert opinion is weakest. Always assess outcomes after applying evidence.
Comparison Tables
Rows
Values
- YES (required)
- YES
- NO
Property
Manipulation of IV
Values
- YES (required)
- OFTEN NOT or weak
- NO
Property
Control Group
Values
- YES (required)
- NO (lacks this)
- NO
Property
Randomization
Values
- STRONGEST
- MODERATE
- WEAK
Property
Strength for Causation
Values
- OFTEN NOT FEASIBLE (ethical/practical barriers)
- MORE FEASIBLE
- MOST FEASIBLE
Property
Feasibility in Clinical Setting
Values
- RCT of new wound care vs. standard care (random assignment)
- Comparing fall rates on unit with new protocol (no randomization) vs. unit without
- Survey describing CAUTI prevalence in hospitals
Property
Example
Columns
- Feature
- Experimental (True Experiment)
- Quasi-Experimental
- Non-Experimental
Table Title
Experimental vs. Quasi-Experimental vs. Non-Experimental Designs
Rows
Values
- Random; every member has known chance
- NOT random; selection bias possible
Property
Selection Process
Values
- YES (strong)
- NO (weak)
Property
Supports Generalizability
Values
- Can be calculated and controlled
- Cannot be estimated
Property
Sampling Error
Values
- Simple random, systematic, stratified, cluster
- Convenience, quota, purposive, snowball
Property
Common Types
Values
- Often more time-consuming and expensive
- Quick and inexpensive
Property
Cost/Time
Values
- Quantitative research when generalizability is goal
- Qualitative research, exploratory studies, hard-to-reach populations
Property
Best Use
Columns
- Feature
- Probability Sampling
- Non-Probability Sampling
Table Title
Probability (Random) vs. Non-Probability Sampling
Rows
Values
- Measures what it INTENDS to measure (accuracy)
- Yields CONSISTENT, reproducible results (dependability)
Property
Definition
Values
- Are you measuring the RIGHT thing?
- Are you measuring it the SAME way each time?
Property
Question
Values
- NO (cannot be valid without being reliable)
- YES (can be reliable but not valid — consistently wrong)
Property
Can Exist Without the Other
Values
- Content, construct, criterion validity
- Test-retest, internal consistency, interrater reliability
Property
Types
Values
- Expert judgment, correlation with gold standard
- Cronbach's alpha (α ≥0.70), correlation coefficients
Property
Measurement
Columns
- Aspect
- Validity
- Reliability
Table Title
Validity vs. Reliability
Rows
Values
- Lived experience; meaning of a phenomenon
- What is the experience of...?
- Lived experience of nurses working in ICU; what does it feel like?
Property
Phenomenology
Values
- Theory development; process; how behavior is explained
- How do people cope with/experience...?
- How do nurses manage moral distress? (theory emerges)
Property
Grounded Theory
Values
- Culture; beliefs, values, social interactions of a group
- What are the cultural norms/values in...?
- Healing practices in a rural Philippine community
Property
Ethnography
Values
- In-depth exploration of a single case/small group
- What is unique about this case?
- One patient's journey through recovery; one unit's improvement process
Property
Case Study
Columns
- Tradition
- Focus
- Guiding Question
- Example
Table Title
Qualitative Research Traditions at a Glance
Rows
Values
- Sum of all values ÷ number of values (average)
- Interval/ratio data; NORMAL distribution
- Uses all data; mathematically stable
- VERY SENSITIVE to outliers; can be misleading if extreme values exist
Property
Mean
Values
- Middle value; 50th percentile
- Ordinal data; SKEWED distributions; any outliers
- NOT affected by outliers; best for non-normal data
- Ignores extreme values; less precise than mean in normal data
Property
Median
Values
- Most frequently occurring value
- ONLY FOR NOMINAL (category) data
- ONLY measure for categories; simple to understand
- May not exist; may have multiple modes; doesn't fully represent data
Property
Mode
Columns
- Measure
- Definition
- Best For (Data Type)
- Advantage
- Disadvantage
Table Title
Measures of Central Tendency: When to Use
Rows
Values
- Very strong evidence against H₀
- REJECT H₀
- Result is HIGHLY unlikely due to chance; result is very statistically significant
Property
p < 0.001
Values
- Strong evidence against H₀
- REJECT H₀
- Result is statistically SIGNIFICANT; likely a real effect, not chance
Property
p = 0.01 to 0.05
Values
- Borderline; conventional cutoff
- REJECT H₀ (at p < 0.05 convention)
- At the threshold of significance
Property
p = 0.05
Values
- Weak evidence against H₀
- FAIL TO REJECT H₀
- Result is NOT statistically significant; may be due to chance; no strong evidence of effect
Property
p > 0.05
Columns
- p-Value
- Interpretation
- Decision
- Clinical Meaning
Table Title
p-Value Interpretation & Statistical Significance
Rows
Values
- Rejecting a TRUE null hypothesis
- FALSE POSITIVE; conclude effect exists when it DOESN'T
- Study says new drug is effective, but it actually isn't
- Implement ineffective intervention; waste resources; potential harm
Property
Type I Error (α)
Values
- Failing to reject a FALSE null hypothesis
- FALSE NEGATIVE; conclude NO effect when one EXISTS
- Study says drug is ineffective, but it actually IS effective
- Reject beneficial treatment; miss opportunity to help patients
Property
Type II Error (β)
Columns
- Error Type
- Definition
- What Happened
- Example
- Consequence
Table Title
Type I vs. Type II Error
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