Nursing Research

Sources of Data

  • Primary Sources (Preferred) – data originate from the research itself; are factual and not subject to interpretation by others.
  • Secondary Sources – data are interpreted or analyzed by another person (not the original researcher); these are “secondhand” accounts.

Ethical Issues in Nursing Research

  • Institutional Review Boards (IRBs)
    • Ensure the rights, safety, and welfare of human research subjects in their institution, hospital, or clinic.
    • Authority to approve or reject research proposals submitted to their institution/hospital (per FDA guidelines).
    • If an IRB member has a conflict of interest, they must recuse themselves from deliberation and abstain from voting.
  • Committee Members
    • IRB members are affiliated with the institution.
    • Physicians, clinicians, or retail pharmacists who are not affiliated are generally not included unless hired as consultants.
    • Prefer experienced staff members, not recent graduates.
    • Size of the IRB and number of members depend on the type of institution.
  • Vulnerable Populations
    • Almost all biomedical/behavioral research in the United States requires informed consent.
    • Special protections and consent requirements for vulnerable populations:
    • Infants and children younger than 18
    • Pregnant patients
    • Fetuses
    • Prisoners
    • Refugees, ethnic minorities
    • Persons with mental or physical disabilities, visual or hearing impairment
    • Persons who are economically disadvantaged

Belmont Report

  • A foundational document outlining ethical principles for research with human subjects.
  • Issued in 1979 by the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research.

Tuskegee Syphilis Experiment

  • Infamous study of 600 African American sharecroppers (1932–1972) in Alabama.
  • Men were tested for syphilis; those with positive results were never informed or treated.
  • This led to laws protecting human subjects’ rights and mandating informed consent.

Informed Consent of Human Subjects

  • Subjects must be informed that they have the right to withdraw at any time without adverse consequences.
  • Additional requirements for minors and vulnerable subjects:
    • Describe the study.
    • Inform what they are expected to do (e.g., questionnaires, labs).
    • Describe risks or discomfort present now and in the future (if applicable).
    • Describe benefits now and in the future (if applicable).
    • Discuss alternatives to the study.
    • Allow enough time for questions.
    • Discuss compensation or rewards for participation.
    • Discuss confidentiality and data security to protect identity.
    • Provide number and/or email address of a contact for concerns or problems with the study.
  • Minors
    • Anyone younger than 18 years.
    • Emancipated Minor Criteria: legal court document declaring emancipation; active duty in the U.S. military; legally binding marriage (or divorce from a legally binding marriage).

Consent Versus Assent

  • Consent may be given only by individuals aged 18 years or older.
  • A minor (not emancipated) can give assent from age 7 to 17 and cannot legally give consent.
  • The child should be assured they can withdraw after discussing with parents.
  • The parent or legal guardian must consent to the minor’s participation.
  • The researcher needs parental permission to speak with the minor to obtain assent (the child signs a separate assent form).

Research-Related Terms

  • Statistical Significance
    • α: Significance level or p-value; usually set as p < 0.05 or p < 0.01.
    • A significance level of p < 0.05 means a 5% probability results are due to chance.
    • A significance level of p < 0.01 means a 1% probability results are due to chance; therefore, an ext{α} = p < 0.01 is considered better than p < 0.05.
  • Control group: Subjects in an experiment who do not receive treatment.
  • N: Total size of the population.
  • n: Number of subjects in the subpopulation.
  • Significance level: Also known as the “α” or the “p-value.” The p-value is usually set at p < 0.05 or p < 0.01.
  • Independent variable: Variable that is being manipulated to influence the dependent variable; the researcher has control over it in experimental studies.
  • Dependent variable: The outcome or response resulting from the manipulation of the independent variable.
  • Hypothesis: An idea (or supposition) that can be tested and refuted.
    • Null hypothesis (H0): The opposite of the hypothesis being studied.
    • Example: If the hypothesis is “Corn plants grow faster when exposed to sunlight,” the null is “Corn plants will not grow faster when exposed to sunlight.” If the data meet the set pp-value threshold, the results are significant and the null hypothesis can be rejected; if not, there is no demonstrated relationship, and results may be due to chance.

Normal Curve and Measures of Distribution

  • Normal curve: A bell-shaped curve.
  • Measures of distribution:
    • Mean: The average; extmean=extsumofscoresn=extsumn.ext{mean} = \frac{ ext{sum of scores}}{n} = \frac{\frac{}{ } ext{sum}}{n}. For example, with values 5, 5, 5, 10, 10, the mean is extmean=5+5+5+10+105=7.ext{mean} = \frac{5+5+5+10+10}{5} = 7. (sums shown for clarity)
    • Median: The middle value when data are ordered.
    • Example: 1, 3, 4, 5, 7, 10, 14 → median is 5.
    • Mode: The most frequently occurring value.
    • Example: 3, 5, 7, 7, 7, 8, 9, 10, 10 → mode is 7.
    • Range: Difference between the largest and smallest values.
    • Example: 2, 3, 5, 7, 10, 15 → range is 15 − 2 = 13.

Research Designs

  • Prospective: Studies done in the present or future; data are obtained now and measured in the future. Longitudinal studies are a type of prospective study.
  • Retrospective: Studies done on events that have already occurred (e.g., chart reviews, recall of events); also called ex post facto.
  • Longitudinal: Long-term studies that follow the same group (cohort) over many years to observe and measure variables; observational (no manipulation).
    • Example: Framingham Heart Study – tracked the same subjects (N = 5{,}029) to study cerebrovascular disease risk factors.
  • Cohort: Groups sharing a common characteristic (e.g., gender, age, job, ethnicity); useful for studying causative or risk factors.
    • Example: Nurses’ Health Study – longitudinal cohort studying oral contraceptives and lifestyle factors.
  • Cross-sectional: Compares differences and similarities between two or more groups at one point in time.
  • Case Study: In-depth investigation of a single person, group, or phenomenon.
  • Descriptive: Observational studies; researchers observe and collect information without manipulating the environment.
  • Correlational: Evaluates relationships between at least two variables; mechanisms of association rather than causation.
    • Positive correlation: Variables move in the same direction.
    • Negative correlation: One variable increases while the other decreases.
    • No correlation: Variables are not related.
  • Experimental: Random sampling and random assignment; at least one control group and one or more treatment groups; manipulation; causality can be inferred if A + B leads to C.
  • Quasi-Experimental: Similar to experimental design but without randomization; subjects recruited by convenience sampling.

Deductive Versus Inductive Reasoning

  • Deductive Reasoning (top-down logic): Start with a theory and derive specific hypotheses; used in quantitative studies.
  • Inductive Reasoning (bottom-up logic): Start with specific observations to develop generalizations and theory; used in qualitative studies.

Qualitative Versus Quantitative Studies

  • Qualitative Studies
    • Data: Words, narratives, subjective opinions.
    • Number of subjects: Usually few.
    • Subject recruitment: Small, not randomized.
    • Data gathering: In-depth interviews, focus groups, observations; audio/video recorded and transcribed.
    • Logic: Inductive; specific data generalized to broader themes.
    • Design: Flexible and may evolve with the situation or subjects.
    • Statistical Testing: Interprets themes and patterns; uses limited statistics (e.g., hi^2).
    • Notes: Researcher is a participant and observer to varying degrees.
  • Quantitative Studies
    • Data: Numerical and measurable.
    • Number of subjects: Large; may involve databases.
    • Subject recruitment: Randomization possible in experimental designs.
    • Data gathering: Questionnaires, instruments, measurements, surveys.
    • Logic: Deductive.
    • Design: Systematic and predefined before research begins.
    • Statistical Testing: extPearsoncorrelation,extpairedtext−test,extsimple/multipleregression,extANOVA,ext{Pearson correlation}, ext{ paired } t ext{-test}, ext{ simple/multiple regression}, ext{ ANOVA}, etc.
    • Notes: Researcher aims to be objective; biases and funding sources should be disclosed.

Research Process

  • Phase I — Conception: Formulate research question/problem; review literature; develop hypotheses.
  • Phase II — Design and Planning: Select design; identify population/sample; determine protocols, resources, and ethical considerations; prepare proposal; submit to IRB for approval.
  • Phase III — Implementation: Recruit participants (obtain consent); implement design; collect data.
  • Phase IV — Analysis: Organize, analyze, and interpret data.
  • Phase V — Dissemination: Prepare final report; publish and disseminate findings (e.g., journal articles, poster presentations, lectures).

Human Genetic Symbols

  • Exam questions may include genetic symbols (Table 29.2):
    • Healthy male: empty square ☐
    • Diseased/affected male: filled square ■
    • Healthy female: empty circle ○
    • Diseased/affected female: filled circle ●
    • Death: diagonal slash across symbol
  • Legend:
    • Healthy male - empty square
    • Diseased male - filled square
    • Healthy female - empty circle
    • Diseased female - filled circle
    • Death - diagonal slash across symbol