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Chapter
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6
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Quantitative Research Designs:
Experimental, Quasi-Experimental,
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and Descriptive
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Chapter Outline
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▶ Introduction ▶ Descriptive Quantitative Designs
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▶ Experimental Study Designs ▶ Additional Types of Designs
▶ Quasi-Experimental Designs ▶ Researcher Interview: Intervention Research,
Dr. Leslie Cunningham-Sabo, PhD, RDN
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learning OutCOmes
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▶ Discuss five considerations when planning a ▶ Discuss the advantages and disadvantages of
research design. various quasi-experimental designs.
▶ Explain the three essential components of ▶ Compare and contrast the descriptive cross-
experimental designs, and compare and sectional, repeated cross-sectional, comparative,
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contrast the following experimental designs: and descriptive correlational designs.
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randomized controlled trials, crossover, factorial, ▶ Discuss the advantages and disadvantages of
and Solomon four group designs. various descriptive designs.
▶ Discuss the advantages and disadvantages of ▶ Read a research study and identify the design
various experimental designs. used and analyze study results.
▶ Compare and contrast the nonequivalent
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control group and interrupted time series Distinguish between secondary data analysis
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designs. and secondary research.
intrOduCtiOn
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Designing a research study requires making a number of decisions on the steps you
will take to answer your research question(s). Like an architect, you need to prepare a
blueprint for your project. If you have ever met with an architect before, you know that
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156 Chapter 6: Quantitative Research Designs: Experimental, Quasi-Experimental, and Descriptive
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the process usually starts with a lot of questions. Research design is no different. The
following questions address a number of key design features that must be considered.
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1. What is the research question? Will there be an intervention? Testing the effects of an
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intervention is the hallmark of experimental and quasi-experimental research.
If there is an intervention with human participants, the researcher will assign
participants to be exposed to the independent variable, such as a modified diet or
nutrient supplement, or be part of the control group. Experimental and quasi-
experimental designs are used to test a hypothesis.
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2. Instead of an intervention, will researchers observe study participants and take measure-
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ments? For example, researchers might observe a group over a longer period
of time to see if exposure to certain factors (such as a diet high in fruits and
vegetables) affects their risk of disease. This type of design is called a cohort study
design. It is commonly used in the field of epidemiology, a discipline within
public health that looks at the rates of health-related states (such as disease) in dif-
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ferent groups of people and why they occur, and then looks at how this informa-
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tion can be used to control health problems. Study designs used in epidemiology
are discussed in Chapter 7.
3. What are the variables? What comparisons are going to be made between or within
groups? Comparisons are needed to examine relationships between the indepen-
dent and the dependent variable.
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4. When and how often will data be collected or measurements taken? Many experimental
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studies measure the dependent variable at least before and after the intervention.
Weight loss studies, for example, often take measurements for a year or more
to see whether participants kept the weight off. Data may be collected at just
one point in time, such as in a cross-sectional study, or more frequently. In a
longitudinal study, participants are observed and measurements are taken over
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a long period of time. Longitudinal studies either go forward in time (prospec-
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tive) or backward in time (retrospective).
5. What will the setting be for the study? The setting could be a hospital, community
center, or other location. Some studies use multiple sites.
6. In an intervention study with at least two groups, will the participants be randomly assigned
to a group? True experimental research involves random assignment to groups
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so participants each have an equal chance of receiving any of the treatments
(including no treatment) under study. Quasi-experimental research does not
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have randomization of participants to groups.
7. In a human intervention study, will participants, researchers, and staff be blinded from
knowing to which group a participant was assigned? Blinding helps to prevent or
minimize sources of bias, such as expectation bias. Expectation bias is when
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researchers’ expectations of what they believe the study results should be get in
the way of accurately taking measurements and reporting results.
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8. What controls will be put in place to reduce the influence of extraneous variables? Extra-
neous variables are factors outside of the variables being studied that might
influence the outcome of a study and cause incorrect conclusions. A good quan-
titative design identifies and rules out as many of these competing explanations
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as possible.
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A good research design helps you answer the research question while effectively reducing
threats to design validity.
Quantitative research designs are often used to look at causal relationships, but they
can also be used to look at associations or relationship between variables. Quantitative
research studies can be placed into one of five categories, although some categories do vary
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Experimental Study Designs 157
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a bit from book to book. First are experimental designs with an intervention, control
group, and randomization of participants into groups. Next are quasi-experimental
designs with an intervention but no randomization. Descriptive designs do not have
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an intervention or treatment and are considered nonexperimental. They usually aim to
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provide information about relevant variables but do not test hypotheses. Good descrip-
tive studies provoke the “why” questions of analytic (cause-and-effect) research. Two
additional categories are epidemiologic and predictive correlational designs.
When you read about designs in this chapter, examples of studies are given to illus-
trate the design. The examples include some discussion of the results of statistical tests,
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as well as sample tables from the studies. In a quantitative study, statistics are often used
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to answer one of these questions:
1. Is there a difference among the groups?
Example: “LA Sprouts: A Garden-Based Nutrition Intervention Pilot Pro-
gram Influences Motivation and Preferences for Fruits and Vegetables in Latino
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Youth” ( Journal of the Academy of Nutrition and Dietetics , 2012)
2. Is there an association or relationship among the variables?
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Example: “Preventable Incidence and Mortality of Carcinoma Associated With
Lifestyle Factors Among White Adults in the United States” ( JAMA Oncology ,
2016)
You can often tell from the title of an article whether the study is looking at differences
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among groups or an association among variables.
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Experimental and quasi-experimental designs have an intervention, so they involve
questions about differences—often the difference between an outcome measured in the
experimental and control groups. Correlational studies look at associations. Table 6.1
shows examples of statistics that may be used to answer these two questions.
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TIP
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When you read a study, first read the abstract to determine whether there is an intervention. If so,
the study is either experimental or quasi-experimental. If not, the study will fit into one of the other
categories. If you see the word “association” in the title, the study is likely to be a descriptive, epidemiological,
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or predictive correlation design.
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experimental study designs
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To be considered an experimental design, the following must be present.
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1. An intervention or treatment . The researcher manipulates the independent variable
by, for example, requiring the intervention group to eat a diet that has been
modified, take a supplement containing a nutrient or phytochemical, or take part
in an educational program.
2. Control for extraneous variables . Various control techniques, such as randomiza-
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tion and having a control group, are used. Having a control group allows the
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researcher to compare and evaluate the performance of the experimental group
on the outcome (dependent) variable.
3. Randomization . The researcher randomly assigns each participant to a group so
that each person has an equal chance of being in either group. This removes the
problem of selection bias so that comparable, balanced groups of similar size are
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158 Chapter 6: Quantitative Research Designs: Experimental, Quasi-Experimental, and Descriptive
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Table 6.1 Statistics That Look at Differences and Statistics That Look at Associations
Statistics That Look at Differences
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Measurement
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Test level of dependent
Name statistic Purpose Number of groups variable
Independent samples t To test the difference between 2 Interval/ratio
t-test the means of two independent
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groups.
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Paired samples t-test t To test the difference between 2 Interval/ratio
(or dependent t-test) the means from two paired
groups (such as before-and-
after observations on the same
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One-way analysis of F To test the difference among More than two Interval/ratio
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variance (ANOVA) means of more than two groups
independent groups for one
independent variable (with more
than one level).
Two-way analysis of F To test the difference among More than two Interval/ratio
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variance (ANOVA) means for two independent groups
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variables, of which each can have
multiple levels.
Repeated measures F To test the difference among One group Interval/ratio
ANOVA (one-way three or more means in the
within-subjects) same group over time. (Extended
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design of dependent samples
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t-test).
Chi-square χ2 To analyze nominal and ordinal Two or more groups Nominal/Ordinal
data to find differences between
groups.
Statistics That Look at Associations
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Measurement
Test level of dependent
Name statistic Purpose variable
Pearson product- r To measure the strength and direction of the relationship Interval/ratio
moment correlation between two variables.
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Spearman rank- ρ To measure the strength and direction of the relationship Ordinal, interval, or
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order correlation between two variables. (Nonparametric version of ratio
Pearson product-moment correlation)
Linear regression To predict the value of a dependent variable and Interval/ratio
measure the size of the effect of the independent
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variable on a dependent variable while controlling for
covariates.
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Logistic regression Same as linear regression; used when dependent value Binary/dichotomous
is binary.
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