Midwife Licensure Exam Research & Evidence-Based Practice — Nursing Research Process & Evidence-Based PracticeRevision Notes

Exam Tips

• If a question describes a nurse 'systematically searching and applying published guidelines to change a care protocol,' the correct answer is EBP — not research.
• If a question describes a hospital 'tracking infection rates to reduce catheter-associated UTI in the ICU,' that is QI.
• If a question describes a nurse 'conducting a study to test a new wound care intervention,' that is nursing research.

Key Points

• Nursing research is a SYSTEMATIC INQUIRY — it follows a defined, orderly process, not a casual investigation.
• Purposes of nursing research: IDENTIFY, DESCRIBE, EXPLAIN, PREDICT, and CONTROL phenomena relevant to nursing practice.
• Research improves care quality, supports clinical decision-making, informs health policy, and advances professional autonomy.
• Under RA 9173 (Philippine Nursing Act of 2002), research competence is a core expected competency of every registered nurse.
• The Board of Nursing (BON) under the Professional Regulation Commission (PRC) mandates that nurses be both producers and consumers of research.
• Research GENERATES new knowledge; Evidence-Based Practice (EBP) APPLIES existing best evidence; Quality Improvement (QI) improves a specific local process — these three are DISTINCT.
• Staff nurses in clinical settings are primarily EVIDENCE CONSUMERS — reading literature, questioning unsupported routines, and participating in data collection.

Definitions

Section Title

Nature and Purpose of Nursing Research

Common Mistakes

• Confusing EBP with nursing research — research CREATES new knowledge; EBP USES existing evidence.
• Thinking QI is the same as research — QI improves local processes and does not aim to generalize findings.
• Forgetting that RA 9173 establishes research competence as a nurse's legal professional obligation.

Exam Tips

• Memory trick for IV and DV: IV → I (Influence) → it INFLUENCES the outcome; DV → D (Depends) → it DEPENDS on the IV.
• In an NLE question like 'A nurse tests whether music therapy (IV) reduces anxiety scores (DV) in post-op patients' — music therapy = IV; anxiety scores = DV.
• The literature review comes BEFORE formulating the framework and hypothesis — always in that order in the quantitative sequence.

Key Points

• The research process in QUANTITATIVE studies follows an orderly, linear sequence of 10 steps.
• Step 1 — IDENTIFY AND STATE THE PROBLEM: Define the gap in knowledge and its clinical significance.
• Step 2 — REVIEW THE LITERATURE: Synthesize what is already known; locate the gap that justifies the study.
• Step 3 — FORMULATE THE THEORETICAL/CONCEPTUAL FRAMEWORK: The lens or guiding theory of the study (e.g., Orem's Self-Care Deficit Theory as framework for a study on patient teaching).
• Step 4 — STATE RESEARCH QUESTIONS, OBJECTIVES, OR HYPOTHESES.
• Step 5 — SELECT THE RESEARCH DESIGN AND METHODOLOGY.
• Step 6 — IDENTIFY POPULATION AND SAMPLE; select a 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 and recommend application.
• VARIABLES: Independent Variable (IV) = the presumed cause or intervention (the one manipulated); Dependent Variable (DV) = the outcome or effect (the one measured).
• NULL HYPOTHESIS (H0): States NO relationship or difference — this is what statistical testing TRIES TO REJECT.
• ALTERNATIVE/RESEARCH HYPOTHESIS (H1): States an EXPECTED relationship or difference.
• QUALITATIVE research follows a less linear, more iterative process — the researcher may move back and forth between steps.

Definitions

Section Title

The Research Process — 10 Sequential Steps

Common Mistakes

• Reversing the IV and DV — always ask: 'What is being DONE (IV)?' and 'What is being MEASURED as a result (DV)?'
• Confusing the null hypothesis with the research hypothesis — H0 always says 'no difference/relationship'; H1 says there IS a difference/relationship.
• Skipping the literature review step — this step is critical because it justifies the need for the study.
• Thinking qualitative research also follows the same rigid linear sequence as quantitative — qualitative is more flexible and iterative.

Exam Tips

• Memory trick: TRUE EXPERIMENT = MCR (Manipulation + Control + Randomization). If any one is missing, it is NOT a true experiment.
• If the NLE question says 'randomly assigned to treatment or control group' → TRUE EXPERIMENT / RCT.
• If the question says 'explores the experience of patients with terminal cancer' → PHENOMENOLOGY.
• Cross-sectional = SNAPSHOT in time; Cohort = follows FORWARD; Case-control = looks BACKWARD.

Key Points

• Two broad approaches: QUANTITATIVE (numbers, hypothesis testing, deductive, seeks generalizability) and QUALITATIVE (narrative/text, meaning exploration, inductive, seeks understanding).
• QUANTITATIVE designs: Experimental, Quasi-experimental, Non-experimental/Observational.
• TRUE EXPERIMENT — the GOLD STANDARD for establishing cause and effect — requires ALL THREE hallmarks: MANIPULATION of IV, CONTROL GROUP, and RANDOMIZATION.
• The RANDOMIZED CONTROLLED TRIAL (RCT) is the strongest single experimental design.
• QUASI-EXPERIMENTAL: Has manipulation but LACKS randomization and/or a control group. Weaker causal inference but often more feasible in clinical settings.
• NON-EXPERIMENTAL/OBSERVATIONAL designs: Descriptive, Correlational, Cohort (prospective), Case-control (retrospective), Cross-sectional.
• DESCRIPTIVE design: Describes characteristics of a phenomenon — no manipulation, no relationship testing.
• CORRELATIONAL design: Examines relationships between variables WITHOUT manipulation. IMPORTANT: correlation ≠ causation.
• COHORT (prospective): Follows groups FORWARD in time to observe outcomes. Example: following a group of nurses exposed to shift work to see who develops hypertension.
• CASE-CONTROL (retrospective): Compares those WITH and WITHOUT an outcome, looking BACKWARD for exposure. Example: comparing patients who developed DVT with those who did not, to identify risk factors.
• CROSS-SECTIONAL: Data collected at ONE POINT IN TIME — like a snapshot. Example: a survey of nurses' stress levels in one hospital conducted in a single week.
• QUALITATIVE traditions: PHENOMENOLOGY (lived experience), GROUNDED THEORY (theory generation from data), ETHNOGRAPHY (culture and behavior), CASE STUDY (in-depth analysis of a single case or small group).

Definitions

Section Title

Research Approaches and Designs

Common Mistakes

• Saying a study is experimental just because it has an intervention — you must check for ALL THREE elements: manipulation, control group, AND randomization.
• Thinking correlation proves causation — correlational studies show relationships only, not cause and effect.
• Mixing up cohort (goes FORWARD in time) with case-control (looks BACKWARD in time).
• Confusing phenomenology with grounded theory — phenomenology explores lived experience; grounded theory generates new theory.

Exam Tips

• Key word clues: 'every 5th patient' = SYSTEMATIC; 'randomly selected from male and female groups separately' = STRATIFIED; 'whoever is available in the ward' = CONVENIENCE; 'participants recruit their friends' = SNOWBALL.
• All PROBABILITY methods have RANDOM as part of the process — simple random, stratified RANDOM, cluster with RANDOM selection.
• For NLE: probability sampling → supports generalizability; non-probability → does not.

Key Points

• POPULATION: The entire group of interest. TARGET POPULATION = the group to which findings will be generalized. ACCESSIBLE POPULATION = the portion of the target population actually available to the researcher.
• SAMPLE: The subset of the population that is actually studied.
• SAMPLING: The process of selecting the sample from the population.
• Two major categories: PROBABILITY (random) sampling and NON-PROBABILITY sampling.
• PROBABILITY SAMPLING supports generalizability because every member has a known, non-zero chance of being selected.
• SIMPLE RANDOM SAMPLING: Every member has an EQUAL, INDEPENDENT chance of selection (e.g., drawing names from a hat, random number table).
• SYSTEMATIC SAMPLING: Every kth member from a list is selected. Example: selecting every 10th patient from an admission logbook.
• STRATIFIED RANDOM SAMPLING: Population divided into STRATA (subgroups), then randomly sampled within each stratum. Ensures representation of all subgroups.
• CLUSTER (MULTISTAGE) SAMPLING: Random selection of GROUPS/CLUSTERS, then sampling within clusters. Used when a complete population list is unavailable. Example: randomly selecting hospitals, then randomly selecting nurses within those hospitals.
• NON-PROBABILITY SAMPLING: Not everyone has a known chance of selection — WEAKER generalizability, common in qualitative research.
• CONVENIENCE SAMPLING: Readily available subjects. Easiest to do, most common, but most prone to bias.
• QUOTA SAMPLING: Like convenience but with preset numbers (quotas) per subgroup.
• PURPOSIVE/JUDGMENTAL SAMPLING: Participants are hand-picked because they have specific characteristics needed for the study.
• SNOWBALL/NETWORK SAMPLING: Participants REFER other participants. Useful for hard-to-reach or hidden populations (e.g., IV drug users, undocumented workers).
• A LARGER, WELL-CHOSEN sample reduces SAMPLING ERROR and increases statistical POWER (ability to detect real effects).
• SAMPLING ERROR: The difference between sample statistics and true population parameters — reduced by increasing sample size and using probability sampling.

Definitions

Section Title

Population and Sampling

Common Mistakes

• Thinking systematic sampling is non-probability — it IS a probability method because the starting point is randomly selected.
• Confusing stratified sampling with quota sampling — stratified uses RANDOM selection within strata; quota uses CONVENIENCE within subgroups.
• Assuming a large sample always means better research — sample size matters, but so does HOW the sample is selected.
• Forgetting that purposive sampling is appropriate and rigorous in qualitative research even though it is non-probability.

Formulas

Exam Tips

• Classic NLE analogy: Archery target — arrows in the SAME SPOT but off-center = RELIABLE but NOT VALID. Arrows scattered all over = NEITHER reliable nor valid. Arrows clustered at the bullseye = BOTH reliable and valid.
• Cronbach's alpha ≥ 0.70 = acceptable reliability — memorize this threshold.
• If the question says 'a panel of experts reviewed all items to ensure complete coverage of the concept' → CONTENT VALIDITY.

Key Points

• Common data collection methods: QUESTIONNAIRES, structured/unstructured INTERVIEWS, OBSERVATION, BIOPHYSIOLOGIC MEASURES (e.g., BP, lab values), and existing RECORDS/DOCUMENTS.
• Two critical properties of any measurement instrument: VALIDITY and RELIABILITY.
• VALIDITY: The instrument measures WHAT IT IS INTENDED TO MEASURE.
• Types of validity: CONTENT validity (does it cover all aspects of the concept?), CONSTRUCT validity (does it measure the theoretical construct?), CRITERION validity (does it correlate with a gold-standard measure? — includes concurrent and predictive).
• RELIABILITY: The instrument yields CONSISTENT, REPRODUCIBLE results across time, raters, or items.
• Types of reliability: TEST-RETEST reliability (consistent results over time), INTERNAL CONSISTENCY (items measure the same thing, measured by CRONBACH'S ALPHA), INTERRATER RELIABILITY (consistency between observers/raters).
• CRONBACH'S ALPHA coefficient: Ranges from 0 to 1. A value of 0.70 or higher is generally considered acceptable for research purposes.
• CRITICAL RULE: An instrument CAN BE RELIABLE WITHOUT BEING VALID — but it CANNOT BE VALID WITHOUT BEING RELIABLE.
• Think of it this way: A scale that always reads 5 kg too heavy is RELIABLE (consistent) but NOT VALID (not accurate). A scale that sometimes reads accurately and sometimes does not is NEITHER reliable nor valid.
• For data quality: always aim for BOTH high validity AND high reliability.

Definitions

Section Title

Data Collection and Instrument Quality

Common Mistakes

• Saying 'reliable means valid' — this is WRONG. A reliable instrument is consistent but may consistently measure the WRONG thing.
• Forgetting that validity REQUIRES reliability — you cannot have a valid instrument that gives inconsistent results.
• Confusing content validity with face validity — face validity is a superficial check (looks like it measures the concept); content validity is a more rigorous expert evaluation.

Exam Tips

• For NLE: 'What should the nurse do FIRST before a participant joins a research study?' → Obtain INFORMED CONSENT.
• When a question asks about protecting a 10-year-old participant: Answer must include BOTH parental consent AND the child's assent.
• PHREB = Philippine Health Research Ethics Board — the national body overseeing research ethics in the Philippines under DOST.
• Anonymity > Confidentiality in terms of privacy protection strength.

Key Points

• Foundational ethical principles from the BELMONT REPORT: RESPECT FOR PERSONS (autonomy), BENEFICENCE, and JUSTICE.
• Nursing research ethics adds: NONMALEFICENCE (do no harm) and FIDELITY (faithfulness to agreements).
• INFORMED CONSENT: Voluntary, informed agreement to participate. Must include: PURPOSE, PROCEDURES, RISKS, BENEFITS, CONFIDENTIALITY measures, and the RIGHT TO WITHDRAW at any time WITHOUT PENALTY.
• CONFIDENTIALITY: The researcher knows who gave the data but protects the identity. ANONYMITY: Even the researcher cannot link data to individuals (stronger protection).
• In the Philippines, data protection aligns with the DATA PRIVACY ACT OF 2012 (RA 10173) — distinct from RA 9173 (Nursing Act). Know both RA numbers!
• VULNERABLE GROUPS requiring added protections: CHILDREN, pregnant women, prisoners, cognitively impaired individuals, and the critically ill.
• For MINORS: Requires PARENTAL CONSENT plus the minor's own ASSENT (agreement). Both are needed.
• ETHICS REVIEW: All studies involving human participants must be reviewed by an INSTITUTIONAL REVIEW BOARD (IRB) or ETHICS REVIEW COMMITTEE (ERC).
• In the Philippines, national oversight follows the NATIONAL ETHICAL GUIDELINES FOR HEALTH AND HEALTH-RELATED RESEARCH issued by the PHILIPPINE HEALTH RESEARCH ETHICS BOARD (PHREB) under the Department of Science and Technology (DOST).
• RISK-BENEFIT RATIO: Benefits to participants and society must OUTWEIGH risks — a core principle for IRB approval.
• RIGHT TO SELF-DETERMINATION: Participants decide freely whether to join or leave a study.
• RIGHT TO FULL DISCLOSURE: All information needed to make an informed decision must be shared BEFORE consent is obtained.
• DECEPTION in research (withholding some information): Only acceptable when justified scientifically, involves minimal risk, and is followed by DEBRIEFING.

Definitions

Section Title

Ethics in Nursing Research

Common Mistakes

• Confusing RA 10173 (Data Privacy Act) with RA 9173 (Philippine Nursing Act) — both are relevant to research but serve different functions.
• Thinking a signed consent form is sufficient — informed consent is a PROCESS of education and voluntary decision, not merely getting a signature.
• Forgetting that ASSENT is needed from minors IN ADDITION to parental consent — one alone is not enough.
• Assuming QI projects do not need ethical review — while IRB requirements for QI are less stringent, ethical principles still apply.

Formulas

Exam Tips

• Memory trick for Type I vs. Type II: '1 is first — you made a mistake by rejecting too early (false positive). 2 is second — you missed it (false negative).'
• For NLE: 'Which statistical test compares pain scores between three nursing units?' → ANOVA (three groups).
• For two groups: t-test. For three or more groups: ANOVA. For categorical variables (like sex vs. disease presence): Chi-square. For relationship between two continuous variables: Correlation (r).
• r = ±0.00–0.25 = WEAK; ±0.26–0.50 = MODERATE; ±0.51–0.75 = MODERATE-STRONG; ±0.76–1.00 = STRONG (general guidelines).
• In a SKEWED distribution: mean ≠ median ≠ mode → use MEDIAN as the best measure of central tendency.

Key Points

• Nurses must INTERPRET statistics, not necessarily compute complex formulas. Focus on understanding what the numbers mean.
• LEVELS OF MEASUREMENT (from lowest to highest): NOMINAL, ORDINAL, INTERVAL, RATIO.
• NOMINAL: Categories with NO order (e.g., sex: male/female; blood type: A, B, AB, O; religion).
• ORDINAL: ORDERED categories but intervals between them are NOT equal (e.g., pain scale 0-10, Likert scales, staging of cancer).
• INTERVAL: Ordered with EQUAL INTERVALS but NO TRUE ZERO (e.g., temperature in °C or °F — 0°C does not mean 'no temperature').
• RATIO: Equal intervals WITH A TRUE ZERO (e.g., weight, height, pulse rate, blood pressure, age — 0 kg means 'no weight').
• DESCRIPTIVE STATISTICS: Summarize and describe data — measures of central tendency and variability.
• MEAN: The arithmetic average. BEST for interval/ratio data in a NORMAL distribution. SENSITIVE TO OUTLIERS.
• MEDIAN: The MIDDLE value when data are ordered. BEST for SKEWED data or ORDINAL data. Not affected by outliers.
• MODE: The MOST FREQUENTLY OCCURRING value. The ONLY measure appropriate for NOMINAL data.
• STANDARD DEVIATION (SD): Measures how SPREAD OUT data are around the mean. Larger SD = more variability.
• NORMAL (BELL-SHAPED) DISTRIBUTION: Mean = Median = Mode. The 68-95-99.7 RULE: ~68% of data within ±1 SD, ~95% within ±2 SD, ~99.7% within ±3 SD.
• INFERENTIAL STATISTICS: Allow generalization from sample to population and hypothesis testing.
• P-VALUE: The probability that a result occurred BY CHANCE. CONVENTIONAL CUTOFF: p < 0.05 = STATISTICALLY SIGNIFICANT → REJECT the null hypothesis.
• Statistical significance ≠ Clinical significance. A finding can be statistically significant but NOT clinically meaningful.
• TYPE I ERROR (ALPHA, α): Rejecting a TRUE null hypothesis — a FALSE POSITIVE. 'We concluded there was a difference, but there was not.'
• TYPE II ERROR (BETA, β): FAILING to reject a FALSE null hypothesis — a FALSE NEGATIVE. 'We concluded there was no difference, but there actually was.'
• COMMON STATISTICAL TESTS: t-test (compare means of TWO groups), ANOVA (compare means of THREE OR MORE groups), Chi-square (association between CATEGORICAL/NOMINAL variables), Correlation coefficient r (relationship between TWO continuous variables).
• CORRELATION COEFFICIENT (r): Ranges from -1 to +1. SIGN = direction (positive: both variables increase together; negative: one increases as other decreases). MAGNITUDE = strength (closer to ±1 = stronger; 0 = no linear relationship).

Definitions

Section Title

Basic Statistics Interpretation

Common Mistakes

• Confusing Type I and Type II errors — use the mnemonic: Type I = 'False Alarm' (false positive); Type II = 'Miss' (false negative).
• Thinking p < 0.05 means the study is CLINICALLY important — statistical significance and clinical significance are DIFFERENT concepts.
• Using the mean with severely skewed data or ordinal data — use the MEDIAN instead.
• Thinking r = 0 means no relationship — it means no LINEAR relationship; other types of relationships may still exist.
• Forgetting that mode is the ONLY measure of central tendency usable for NOMINAL data.

Exam Tips

• Memory trick for 5 A's: 'Ask Anong Problema At Ayusin' → ASK, ACQUIRE, APPRAISE, APPLY, ASSESS.
• For NLE: 'What is the FIRST step when implementing EBP?' → ASK a focused clinical question (using PICO).
• Strongest evidence = Systematic review/meta-analysis of RCTs. Weakest = Expert opinion.
• When NLE gives a scenario and asks 'what level of evidence is this?' — RCT = Level 2; case-control study = Level 4; expert opinion = Level 7.
• EBP question: 'A nurse uses PubMed to search for the latest evidence on pain management' → this is the ACQUIRE step.

Key Points

• EBP = BEST AVAILABLE EVIDENCE + CLINICAL EXPERTISE + PATIENT VALUES/PREFERENCES (three equal pillars).
• The PICO(T) framework frames a focused clinical question: P = Population/Patient, I = Intervention, C = Comparison, O = Outcome, T = Time (optional).
• Example PICO: 'In adult post-operative patients (P), does patient-controlled analgesia (I) compared to nurse-administered opioids (C) result in lower pain scores (O) within the first 24 hours post-op (T)?'
• THE 5 A's OF EBP (in order): ASK → ACQUIRE → APPRAISE → APPLY → ASSESS/EVALUATE.
• ASK: Formulate a focused clinical question using PICO(T).
• ACQUIRE: Search databases (PubMed, CINAHL, Cochrane Library) for best available evidence.
• APPRAISE: Critically evaluate evidence for VALIDITY, IMPORTANCE, and APPLICABILITY to your patient.
• APPLY: Integrate the evidence with clinical judgment and patient preferences into a care decision.
• ASSESS/EVALUATE: Evaluate the OUTCOME after applying the evidence; adjust practice as needed.
• LEVELS OF EVIDENCE HIERARCHY (strongest to weakest):
• Level 1: SYSTEMATIC REVIEWS and META-ANALYSES of RCTs (strongest).
• Level 2: Individual well-designed RCTs.
• Level 3: Controlled trials WITHOUT randomization / quasi-experimental studies.
• Level 4: COHORT and CASE-CONTROL studies.
• Level 5: Systematic reviews of descriptive/qualitative studies.
• Level 6: Single descriptive or qualitative studies.
• Level 7: EXPERT OPINION (weakest).
• META-ANALYSIS: Statistically combines results of MULTIPLE studies on the same topic to produce a pooled estimate of effect — STRONGEST form of evidence.
• SYSTEMATIC REVIEW: A rigorous, structured review and synthesis of all available literature on a specific question — may or may not include meta-analysis.

Definitions

Section Title

Evidence-Based Practice (EBP) — The 5 A's and Levels of Evidence

Common Mistakes

• Placing expert opinion at the top of the evidence hierarchy — it is at the BOTTOM (Level 7).
• Forgetting the 'Assess/Evaluate' step of EBP — the 5 A's are a cycle, not just a one-time process.
• Thinking EBP means ONLY following research — it also integrates CLINICAL EXPERTISE and PATIENT PREFERENCES.
• Confusing systematic review with literature review — a literature review is broader and less rigorous; a systematic review uses explicit, reproducible methods.