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Designing Quantitative Studies


Designing Quantitative Studies Dr. Belal Hijji, RN, PhD December 2 & 9, 2010 – PowerPoint PPT presentation

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Title: Designing Quantitative Studies

Designing Quantitative Studies
  • Dr. Belal Hijji, RN, PhD
  • December 2 9, 2010

Learning Outcomes
  • Identify aspects of quantitative design
  • Have an overview of the quantitative research

Read Polit Beck chapter 8
Aspects of Quantitative Research Design
  • When doing a research study, important decisions
    need to be made about the studys design. These
    decisions will affect the overall believability
    of the findings.
  • Intervention Nurse researchers may want to test
    the effects of a specific intervention (an
    innovative programme to promote breast
  • Comparisons In most studies, researchers develop
    comparisons to provide a context for interpreting
    results. The most common types of comparisons
  • Comparisons between two groups or more For
    example, if we want to study the emotional
    consequences of having an abortion. To do this,
    the researcher may compare the emotional status
    of women who had abortion with that of women with
    an unintended pregnancy who delivered a baby.
    Without this comparison, it would be difficult to
    know whether the womens emotional status was of
    concern without comparing it with that of others

  • Comparison of one groups status at two or more
    points in time For example, we might want to
    assess patients levels of stress before and
    after introducing a new procedure to reduce
    preoperative stress.
  • Comparison of one groups status under different
    circumstances. An example is to compare patients
    heart rates during two different types of
  • Controls for extraneous variables Variables that
    confound the relationship between the independent
    and dependent variables and that need to be
    controlled in the research design. For example,
    In a drug trial, patients might take other
    medicines that could affect the medical condition
    under study. Such patients must either refrain
    from taking these medicines or be excluded from
    the study.

  • Timing of data collection In most studies, data
    are collected from participants at a single point
    in time. For example, nurses may be asked once to
    fill in a questionnaire about their knowledge and
    practice of pressure ulcer care. Some designs
    call for multiple contacts with participants,
    usually to determine how things changed over
    time. The researcher, therefore, must decide on
    the number of data collection points to address
    the research question properly. For example, we
    may want to test the effect of an educational
    intervention on nurses knowledge and practice of
    blood transfusion at 1 week and 1 month after

  • Research sites and settings Sites are the
    overall locations for the research, and settings
    are the more specific places where data
    collection will occur.
  • Communication with participants In designing a
    study, the researcher must decide how much
    information to provide to study participants. For
    example, if we want to conduct an observational
    study on nurses hand decontamination practices,
    full disclosure to participants before obtaining
    their consent is ethically correct, but this can
    undermine the value of the research when they are
    exposed to the elements of the observation

Overview of Research Design Types
  • Between-subjects and within-subject designs As
    far as we know, quantitative studies involve
    making comparisons between separate groups of
    people. For example, the hypothesis that
    tamoxifen reduces the rate of breast cancer in
    high-risk women could be tested by comparing
    women who received the drug and those who did
    not. Both groups of women are different.
    Sometimes, we want to make comparisons for the
    same study participants. For example, a
    researcher may want to study patients heart rate
    before and after a nursing intervention. This
    example calls for a within-subjects design.

  • The time dimension While most studies involve
    data collection at a point in time, Sometimes, it
    is appropriate to collect data at multiple
    points. First, when studying time-related
    processes such as healing, learning, and physical
    growth. Second, when determining time sequences
    of phenomena. If it is hypothesise that
    infertility results in depression, then it would
    be important to determine that depression did not
    precede infertility. Third, developing
    comparisons over time to determine whether
    changes have occurred, such as when a study is
    concerned with documenting trends in the smoking
    behaviour over 10-year period.
  • Studies are often categorised in terms of how
    they deal with time. The major distinction is
    between cross-sectional and longitudinal designs.
    Both are described next.

Cross-Sectional Designs
  • In these designs, data collection occurs at a
    single point in time when it is appropriate for a
    study to describe the status of phenomena or for
    describing relationships between phenomena. For
    example, we may want to determine whether
    psychological symptoms in menopausal women are
    correlated contemporaneously at the same time
    with physiologic symptoms.

Longitudinal Designs
  • In these designs, data collection occurs at
    multiple points in time over an extended periods.
    There are several types of longitudinal designs.
  • Trend studies are investigations in which
    different samples from a population are studied
    over time with respect to some phenomenon. Trend
    studies permit researchers to examine patterns
    and rates of change over time and to predict
    future developments. For example, we may study
    trends in alcohol consumption in a country over
    15-year period to see whether heavy drinking had
    fallen or remained unchanged.
  • Cohort studies are a particular type of a trend
    study in which specific subpopulations are
    examined over time. The samples are usually drawn
    from specific age-related subgroups, for example
    men born between 1960 and 1965 may be studied
    over time with respect to health care

  • In panel studies, the same people are used to
    supply data at two or more points in time to
    determine which individuals who changed and those
    who did not and then examine the characteristics
    of both groups. For example, we may explore over
    time the antecedent characteristics of smokers
    who were later able to quit.

Experimental designs
  • Basic experimental designs
  • Simple posttest-only design An example is a
    study that tests the effect of gentle massage on
    the pain level of nursing home residents. Another
    example is to study the effect of an educational
    intervention on urinary incontinence on the
    subsequent help-seeking behaviour of older
  • Pretest posttest design Suppose we want to
    find out whether convective airflow blankest are
    more effective than conductive water-flow blanket
    in cooling critically ill patients with fever. We
    measure the dependent variable twice before and
    after the intervention. Based on the results, we
    can say whether one blanket type is more
    effective than the other in reducing fever.

  • Solomon Four-Group Design When a pretest-
    posttest design is used, the posttest measure of
    the dependent variable may be affected by the
    treatment as well as pretesting. To avoid this
    problem, Solomon four-group is called for, which
    involves to experimental and two control groups.

Data Collection Before After Group
X X Experimental- with pretest
X Experimental- without pretest
X X Control- with pretest
X Control- without pretest
  • Factorial Design In this design, the researcher
    concurrently manipulates two or more variables.
    For example, if we are interested in comparing
    two therapeutic strategies for premature infants
    tactile tangible stimulation versus auditory
    stimulation. Additionally, we want to know if the
    daily amount of stimulation (15, 30, or 45
    minutes) affects infants progress. The dependent
    variables are measures of infant development
    (weight gain and cardiac responsiveness)

Example of Factorial Design
Tactile A2 Auditory A1
A2 B1 A1 B1 15 Min. B1
A2 B2 A1 B2 30 Min. B2 Daily exposure
A2 B3 A1 B3 45 Min. B3