Chapter 1 A Primer of the Scientific Method and Relevant Components.docx

Chapter 1 A Primer of the Scientific Method and Relevant
Components
The primary objective of this book is to help researchers
understand and select appropriate designs for their
investigations within the field, lab, or virtual environment.
Lacking a proper conceptualization of a research design makes
it difficult to apply an appropriate design based on the research
question(s) or stated hypotheses. Implementing a flawed or
inappropriate design will unequivocally lead to spurious,
meaningless, or invalid results. Again, the concept of validity
cannot be emphasized enough when conducting research.
Validity maintains many facets (e.g., statistical validity or
validity pertaining to psychometric properties of
instrumentation), operates on a continuum, and deserves equal
attention at each level of the research process. Aspects of
validity are discussed later in this chapter. Nonetheless, the
research question, hypothesis, objective, or aim is the primary
step for the selection of a research design.
The purpose of a research design is to provide a conceptual
framework that will allow the researcher to answer specific
research questions while using sound principles of scientific
inquiry. The concept behind research designs is intuitively
straightforward, but applying these designs in real-
life situations can be complex. More specifically, researchers
face the challenge of (a) manipulating (or exploring) the social
systems of interest, (b) using measurement tools (or data
collection techniques) that maintain adequate levels of validity
and reliability, and (c) controlling the interrelationship between
multiple variables or indicating emerging themes that can lead
to error in the form of confounding effects in the results.
Therefore, utilizing and following the tenets of a sound research
design is one of the most fundamental aspects of the scientific
method. Put simply, the research design is the structure of
investigation, conceived so as to obtain the “answer” to
research questions or hypotheses.The Scientific Method

All researchers who attempt to formulate conclusions from a
particular path of inquiry use aspects of the scientific method.
The presentation of the scientific method and how it is
interpreted can vary from field to field and method (qualitative)
to method (quantitative), but the general premise is not altered.
Although there are many ways or avenues to “knowing,” such as
sources from authorities or basic common sense, the sound
application of the scientific method allows researchers to reveal
valid findings based on a series of systematic steps. Within the
social sciences, the general steps include the following: (a) state
the problem, (b) formulate the hypothesis, (c) design the
experiment, (d) make observations, (e) interpret data, (f) draw
conclusions, and (g) accept or reject the hypothesis. All
research in quantitative methods, from experimental to
nonexperimental, should employ the steps of the scientific
method in an attempt to produce reliable and valid results.
The scientific method can be likened to an association of
techniques rather than an exact formula; therefore, we expand
the steps as a means to be more specific and relevant for
research in education and the social sciences. As seen in Figure
1.1, these steps include the following: (a) identify a research
problem, (b) establish the theoretical framework, (c) indicate
the purpose and research questions (or hypotheses), (d) develop
the methodology, (e) collect the data, (f) analyze and interpret
the data, and (g) report the results. This book targets the critical
component of the scientific method, referred to in Figure
1.1 as Design the Study, which is the point in the process when
the appropriate research design is selected. We do not focus on
prior aspects of the scientific method or any steps that come
after the Design the Study step, including procedures for
conducting literature reviews, developing research questions, or
discussions on the nature of knowledge, epistemology,
ontology, and worldviews. Specifically, this book focuses on
the conceptualization, selection, and application of common
research designs in the field of education and the social and
behavioral sciences.

Again, although the general premise is the same, the scientific
method is known to slightly vary from each field of inquiry (and
type of method). The technique presented here may not exactly
follow the logic required for research using qualitative methods;
however, the conceptualization of research designs remains the
same. We refer the reader to Jaccard and Jacoby (2010) for a
review on the various scientific approaches associated with
qualitative methods, such as emergent- and discovery-oriented
frameworks.
Figure 1.1 The Scientific Method
Validity and Research Designs
The overarching goal of research is to reach valid outcomes
based upon the appropriate application of the scientific method.
In reference to
Independent and Dependent Variables
In simple terms, the independent variable (IV) is the variable
that is manipulated (i.e., controlled) by the researcher as a
means to test its impact on the dependent variable, otherwise
known as the treatment effect. In the classical experimental
study, the IV is the treatment, program, or intervention. For
example, in a psychology-based study, the IV can be a
cognitive-behavioral intervention; the intervention is
manipulated by the researcher, who controls the frequency and
intensity of the therapy on the subject. In a pharmaceutical
study, the IV would typically be a treatment pill, and in
agriculture the treatment often is fertilizer. In regard to
experimental research, the IVs are always manipulated
(controlled) based on the appropriate theoretical tenets that
posit the association between the IV and the dependent variable.
Statistical software packages (e.g., SPSS) refer to the IV
differently. For instance, the IV for the analysis of variance
(ANOVA) in SPSS is the “breakdown” variable and is called
a factor. The IV is represented as levels in the analysis (i.e., the
treatment group is Level 1, and the control group is Level 2).
For nonexperimental research that uses regression analysis, the

IV is referred to as the predictor variable. In research that
applies control in the form of statistical procedures to variables
that were not or cannot be manipulated, the IVs are sometimes
referred to as quasi- or alternate independent variables. These
variables are typically demographic variables, such as gender,
ethnicity, or socioeconomic status. As a reminder, in
nonexperimental research the IV (or predictor) is not
manipulated whether it is a categorical variable such as hair
color or a continuous variable such as intelligence. The only
form of control that is exhibited on these types of variables is
that of statistical procedures. Manipulation and elimination do
not apply (see types of control later in the chapter).
The dependent variable (DV) is simply the outcome variable,
and its variability is a function of IV and its impact on it (i.e.,
treatment effect). For example, what is the impact of the
cognitive-behavioral intervention on psychological well-being?
In this research question, the DV is psychological well-being. In
regard to nonexperimental research, the IVs are not
manipulated, and the IVs are referred to as predictors and the
DVs are criterion variables. During the development of research
questions, it is critical to first define the DV conceptually, then
define it operationally.
A conceptual definition is a critical element to the research
process and involves scientifically defining the construct so it
can be systematically measured. The conceptual definition is
considered to be the (scientific) textbook definition. The
construct must then be operationally defined to model the
conceptual definition.
An operational definition is the actual method, tool, or
technique that indicates how the construct will be measured
(see Figure 1.2).
Consider the following example research question: What is the
relationship between Emotional Intelligence and
conventional Academic Performance?
Figure 1.2 Conceptual and Operational Definitions

Internal Validity
Internal validity is the extent to which the outcome was based
on the independent variable (i.e., the treatment), as opposed to
extraneous or unaccounted-for variables. Specifically, internal
validity has to do with causal inferences—hence, the reason
why it does not apply to nonexperimental research. The goal of
nonexperimental research is to describe phenomena or to
explain or predict the relationship between variables, not to
infer causation (although there are circumstances when cause
and effect can be inferred from nonexperimental research, and
this is discussed later in this book). The identification of any
explanation that could be responsible for an outcome (effect)
outside of the independent variable (cause) is considered to be a
threat. The most common threats to internal validity seen in
education and the social and behavioral sciences are detailed
in Table 1.1. It should be noted that many texts do not
indentify sequencing effects in the common lists of threats;
however, it is placed here, as it is a primary threat in repeated-
measures approaches.
Construct Validity
Construct validity refers to the extent a generalization can be
made from the operationalization (i.e., the scientific
measurement) of the theoretical construct back to the
conceptual basis responsible for the change in the outcome.
Again, although the list of threats to construct validity seen
in Table 1.3 are defined to imply issues regarding cause-effect
relations, the premise of construct validity should apply to all
types of research. Some authors categorize some of these threats
as social threats to internal validity, and some authors simply
categorize some of the threats listed in Table 1.3 as threats to
internal validity. The categorization of these threats can be
debated, but the premise of the threats to validity cannot be

argued (i.e., a violation of construct validity affects the overall
validity of the study in the same way as a violation of internal
validity).
Statistical Conclusion Validity
Statistical conclusion validity is the extent to which the
statistical covariation (relationship) between the treatment and
the outcome is accurate. Specifically, the statistical inferences
regarding statistical conclusion validity has to do with the
ability with which one can detect the relationship between the
treatment and outcome, as well as determine the strength of the
relationship between the two. As seen in Table 1.4, the most
notable threats to statistical conclusion validity are outlined.
Violating a threat to statistical conclusion validity typically will
result in the overestimation or underestimation of the
relationship between the treatment and outcome in experimental
research. A violation can also result in the overestimation or
underestimation of the explained or predicted relationships
between variables as seen in nonexperimental research.
Design Logic
The overarching objective of a research design is to provide a
framework from which specific research questions or
hypotheses can be answered while using the scientific method.
The concept of a research design and its structure is, at face
value, rather simplistic. However, complexities arise when
researchers apply research designs within social science
paradigms. These include, but are not limited to, logistical
issues, lack of control over certain variables, psychometric
issues, and theoretical frameworks that are not well developed.
In addition, with regard to statistical conclusion validity, a
researcher can apply sound principles of scientific inquiry while
applying an appropriate research design but may compromise
the findings with inappropriate data collection strategies, faulty
or “bad” data, or misdirected statistical analyses. Shadish and
colleagues (2002) emphasized the importance of structural

design features and that researchers should focus on the theory
of design logic as the most important feature in determining
valid outcomes (or testing causal propositions). The logic of
research designs is ultimately embedded within the scientific
method, and applying the principles of sound scientific inquiry
within this phase is of the utmost importance and the primary
focus of this guide.
Control
Control is an important element to securing the validity of
research designs within quantitative methods (i.e.,
experimental, quasi-experimental, and nonexperimental
research). However, within qualitative methods, behavior is
generally studied as it occurs naturally with no manipulation or
control. Control refers to the concept of holding variables
constant or systematically varying the conditions of variables
based on theoretical considerations as a means to minimize the
influence of unwanted variables (i.e., extraneous variables).
Control can be applied actively within quantitative methods
through (a) manipulation, (b) elimination, (c) inclusion, (d)
group or condition assignment, or (e) statistical
procedures.Manipulation.
Manipulation is applied by manipulating (i.e., controlling) the
independent variable(s). For example, a researcher can
manipulate a behavioral intervention by systematically applying
and removing the intervention or by controlling the frequency
and duration of the application (see section on independent
variables).Elimination.
Elimination is conducted when a researcher holds a variable or
converts it to a constant. If, for example, a researcher ensures
the temperature in a lab is set exactly to 76° Fahrenheit for both
conditions in a biofeedback study, then the variable of
temperature is eliminated as a factor because it is held as a
constant.Inclusion.
Inclusion refers to the addition of an extraneous variable into
the design to test its affect on the outcome (i.e., dependent

variable). For example, a researcher can include both males and
females into a factorial design to examine the independent
effects gender has on the outcome. Inclusion can also refer to
the addition of a control or comparison group within the
research design.Group assignment.
Group assignment is another major form of control (see more on
group and condition assignments later). For the between-
subjects approach, a researcher can exercise control through
random assignment, using a matching technique, or applying a
cutoff score as means to assign participants to conditions. For
the repeated-measures approach, control is exhibited when the
researcher employs the technique of counterbalancing to
variably expose each group or individual to all the levels of the
independent variable.Statistical procedures.
Statistical procedures are exhibited on variables, for example,
by systematically deleting, combining, or not including cases
and/or variables (i.e., removing outliers) within the analysis.
This is part of the data-screening process as well. As illustrated
in Table 1.5, all of the major forms of control can be applied in
the application of designs for experimental and quasi-
experimental research. The only form of control that can be
applied to nonexperimental research is statistical control.
Comparison and Control Groups
The group that does not receive the actual treatment, or
intervention, is typically designated as the control group.
Control groups fall under the group or condition
assignment aspect of control. Control groups are comparison
groups and are primarily used to address threats to internal
validity such as history, maturation, selection, and testing.
A comparison group refers to the group or groups that are not
part of the primary focus of the investigation but allow the
researcher to draw certain conclusions and strengthen aspects of
internal validity. There are several distinctions and variations of
the control group that should be clarified.

· Control group. The control group, also known as the no-
contact control, receives no treatment and no interaction.
· Attention control group. The attention control group, also
known as the attention-placebo, receives attention in the form
of a pseudo-intervention to control for reactivity to assessment
(i.e., the participant’s awareness of being studied may influence
the outcome).
· Nonrandomly assigned control group. The nonrandomly
assigned control is used when a no-treatment control group
cannot be created through random assignment.
· Wait-list control group. The wait-list control group is withheld
from the treatment for a certain period of time, then the
treatment is provided. The time in which the treatment is
provided is based on theoretical tenets and on the pretest and
posttest assessment of the original treatment group.
· Historical control group. Historical control is a control group
that is chosen from a group of participants who were observed
at some time in the past or for whom data are available through
archival records, sometimes referred to as cohort controls (i.e.,
a homogenous successive group) and useful in quasi-
experimental research.
Sampling Strategies
A major element to the logic of design extends to sampling
strategies. When developing quantitative, qualitative, and mixed
methods studies, it is important to identify the individuals (or
extant databases) from whom you plan to collect data. To start,
the unit of analysismust be indicated. The unit of analysis is the
level or distinction of an entity that will be the focus of the
study. Most commonly, in social science research, the unit of
analysis is at the individual or group level, but it can also be at
the programmatic level (e.g., institution or state level).
There are instances when researchers identify units nested
within an aggregated group (e.g., a portion of students within a
classroom) and refer to this as nested designs or models. It
should be noted that examining nested units is not a unique
design, but rather a form of a sampling strategy, and the

relevant aspects of statistical conclusion validity should be
accounted for (e.g., independence assumptions). After
identifying the unit, the next step is to identify
the population (assuming the individual or group is the unit of
analysis), which is the group of individuals who share similar
characteristics (e.g., all astronauts). Logistically, it is
impossible in most circumstances to collect data from an entire
population; therefore, as illustrated in Figure 1.4, a sample (or
subset) from the population is identified (e.g., astronauts who
have completed a minimum of four human space-flight missions
and work for NASA).
The goal often, but not always, is to eventually generalize the
finding to the entire population. There are two major types of
sampling strategies, probability and nonprobability sampling. In
experimental, quasi-experimental, and nonexperimental (survey
and observational) research, the focus should be on probability
sampling (identifying and selecting individuals who are
considered representative of the population). Many researchers
also suggest that some form of probability sampling for
observational (correlational) approaches (predictive designs)
must be employed—otherwise the statistical outcomes cannot be
generalizable. When it is not logistically possible to use
probability sampling, or as seen in qualitative methods not
necessary, some researchers use nonprobability sampling
techniques (i.e., the researcher selects participants on a specific
criterion and/or based on availability). The following list
includes the major types of probability and nonprobability
sampling techniques.
Probability Sampling Techniques
· Simple random sampling. Every individual within the
population has an equal chance of being selected.
· Cluster sampling. Also known as area sampling, this allows
the researcher to divide the population into clusters (based on
regions) and then randomly select from the clusters.
· Stratified sampling. The researcher divides the population into

homogeneous subgroups (e.g., based on age) and then randomly
selects participants from each subgroup.
· Systematic sampling. Once the size of the sample is identified,
the researcher selects every nth individual (e.g., every third
person on the list of participants is selected) until the desired
sample size is fulfilled.
· Multistage sampling. The researcher combines any of the
probability sampling techniques as a means to randomly select
individuals from the population.
Nonprobability Sampling Techniques
· Convenience sampling. Sometimes referred to
as haphazard or accidental sampling, the investigator selects
individuals because they are available and willing to participate.
· Purposive sampling. The researcher selects individuals to
participate based on a specific need or purpose (i.e., based on
the research objective, design, and target population); this is
most commonly used for qualitative methods (see Patton, 2002).
The most common form of purposeful sampling is criterion
sampling (i.e., seeking participants who meet a specific
criterion). Variations of purposive sampling include theory-
guided, snowball, expert, and heterogeneity
sampling. Theoretical sampling is a type of purposive sampling
used in grounded-theory approaches. We refer the reader to
Palinkas et al. (2014) for a review of recommendations on how
to combine various sampling strategies for the qualitative and
mixed methods.
The reader is referred to the following book for an in-depth
review of a topic related to sampling strategies for quantitative
and qualitative methods:
· Levy, P. S., & Lemeshow, S. (2009). Sampling of populations:
Methods and applications (4th ed.). New York, NY: John Wiley
& Sons.
Now that we covered a majority of the relevant aspects to
research design, which is the “Design the Study” phase of the
scientific method, we now present some steps that will help

researchers select the most appropriate design. In the later
chapters, we present a multitude of research designs used in
quantitative, qualitative, and mixed methods. Therefore, it is
important to review and understand the applications of these
designs while regularly returning to this chapter to review the
critical elements of design control and types of validity, for
example. Let’s now examine the role of the research
question.Research Questions
Simply put, the primary research question sets the foundation
and drives the decision of the application of the most
appropriate research design. However, there are several terms
related to research questions that should be distinguished. First,
in general, studies will include an overarching observation
deemed worthy of research. The “observation” is a general
statement regarding the area of interest and identifies the area
of need or concern.
Based on the initial observation, specific variables lead the
researchers to the appropriate review of the literature and a
theoretical framework is typically established. The purpose
statement is then used to clarify the focus of the study, and
finally, the primary research question ensues. Research studies
can also include hypotheses or research objectives. Many
qualitative studies include research aims as opposed to research
questions. In quantitative methods (this includes mixed
methods), the research question (hypotheses and objectives)
determines (a) the population (and sample) to be investigated,
(b) the context, (c) the variables to be operationalized, and (d)
the research design to be employed.
Types of Inquiry
There are several ways to form a testable research inquiry. For
qualitative methods, these can be posed as research questions,
aims, or objectives Part I Quantitative Methods for
Experimental and Quasi-Experimental Research
Part I includes four popular approaches to the quantitative
method (experimental and quasi-experimental only), followed

by some of the associated basic designs (accompanied by brief
descriptions of published studies that used the design). Visit the
companion website at study.sagepub.com/edmonds2e to access
valuable instructor and student resources. These resources
include PowerPoint slides, discussion questions, class activities,
SAGE journal articles, web resources, and online data sets.
Figure I.1 Quantitative Method Flowchart
Note: Quantitative methods for experimental and quasi-
experimental research are shown here, followed by the approach
and then the design.
Research in quantitative methods essentially refers to the
application of the systematic steps of the scientific method,
while using quantitative properties (i.e., numerical systems) to
research the relationships or effects of specific variables.
Measurement is the critical component of the quantitative
method. Measurement reveals and illustrates the relationship
between quantitatively derived variables. Variables within
quantitative methods must be, first, conceptually defined (i.e.,
the scientific definition), then operationalized (i.e., determine
the appropriate measurement tool based on the conceptual
definition). Research in quantitative methods is typically
referred to as a deductive process and iterative in nature. That
is, based on the findings, a theory is supported (or not),
expanded, or refined and further tested.
Researchers must employ the following steps when determining
the appropriate quantitative research design. First, a measurable
or testable research question (or hypothesis) must be
formulated. The question must maintain the following qualities:
(a) precision, (b) viability, and (c) relevance. The question must
be precise and well formulated. The more precise, the easier it
is to appropriately operationalize the variables of interest. The
question must be viable in that it is logistically feasible or
plausible to collect data on the variable(s) of interest. The
question must also be relevant so that the result of the findings
will maintain an appropriate level of practical and scientific

meaning. The second step includes choosing the appropriate
design based on the primary research question, the variables of
interest, and logistical considerations. The researcher must also
determine if randomization to conditions is possible or
plausible. In addition, decisions must be made about how and
where the data will be collected. The design will assist in
determining when the data will be collected. The unit of
analysis (i.e., individual, group, or program level), population,
sample, and sampling procedures should be identified in this
step. Third, the variables must be operationalized. And last, the
data are collected following the format of the framework
provided by the research design of choice.
Experimental Research
Experimental research (sometimes referred to as randomized
experiments) is considered to be the most powerful type of
research in determining causation among variables. Cook and
Campbell (1979) presented three conditions that must be met in
order to establish cause and effect:
1. Covariation (the change in the cause must be related to the
effect)
2. Temporal precedence (the cause must precede the effect)
3. No plausible alternative explanations (the cause must be the
only explanation for the effect)
The essential features of experimental research are the sound
application of the elements of control: (a) manipulation, (b)
elimination, (c) inclusion, (d) group or condition assignment, or
(e) statistical procedures. Random assignment (not to be
confused with random selection) of participants to conditions
(or random assignment of conditions to participants
[counterbalancing] as seen in repeated-measures approaches) is
a critical step, which allows for increased control (improved
internal validity) and limits the impact of the confounding
effects of variables that are not being studied.
The random assignment to each group (condition) theoretically
ensures that the groups are “probabilistically” equivalent
(controlling for selection bias), and any differences observed in

the pretests (if collected) are considered due to chance.
Therefore, if all threats to internal, external, construct, and
statistical conclusion validity were secured at “adequate” levels
(i.e., all plausible alternative explanations are accounted for),
the differences observed in the posttest measures can be
attributed fully to the experimental treatment (i.e., cause and
effect can be established). Conceptually, a causal effect is
defined as a comparison of outcomes derived from treatment
and control conditions on a common set of units (e.g., school,
person).
The strength of experimental research rests in the reduction of
threats to internal validity. Many threats are controlled for
through the application of random assignment of participants to
conditions. Random selection, on the other hand, is related to
sampling procedures and is a major factor in establishing
external validity (i.e., generalizability of results). Randomly
selecting a sample from a population would be conducted so
that the sample would better represent the population. However,
Lee and Rubin (2015) presented a statistical approach that
allows researchers to draw data from existing data sets from
experimental research and examine subgroups (post hoc
subgroup analysis). Nonetheless, random assignment is related
to design, and random selection is related to sampling
procedures. Shadish, Cook, and Campbell (2002) introduced the
term generalized causal inference. They posit that if a
researcher follows the appropriate tenets of experimental design
logic (e.g., includes the appropriate number of subjects, uses
random selection and random assignment) and controls for
threats of all types of validity (including test validity), then
valid causal inferences can be determined along with the ability
to generalize the causal link. This is truly Chapter 2 Between-
Subjects Approach
The between-subjects approach, also known as a multiple-
group approach, allows a researcher to compare the effects of
two or more groups on single or multiple dependent variables
(outcome variables). With a minimum of two groups, the

participants in each group will only be exposed to one condition
(one level of the independent variable), with no crossover
between conditions. An advantage of having multiple groups is
that it allows for the (a) random assignment to different
conditions (experimental research) and (b) comparison of
different treatments. If the design includes two or more
dependent variables, it can be referred to as a multivariate
approach, and when the design includes one dependent variable,
it is classified as univariate.Pretest and Posttest Designs
A common application to experimental and quasi-experimental
research is the pretest and posttest between-subjects approach,
also referred to as an analysis of covariance design (i.e., the
pretest measure is used as the covariate in the analyses because
the pretest should be highly correlated with the posttest). The 1-
factor pretest and posttest control group design is one of the
most common between-subjects approaches with
many variations (one factor representing one independent
variable and sometimes referred to as a single-factor
randomized-group design). This basic multiple-group design can
include a control group and is designed to have multiple
measures between and within groups. Although there is a
within-subject component, the emphasis is on the between-
subject variance. The advantage of including pretest measures
allows for the researcher to test for group equivalency (i.e.,
homogeneity between groups) and for providing a baseline
against which to compare the treatment effects, which is the
within-subject component of the design (i.e., the pretest is
designated as the covariate in order to assess the variance
[distance between each set of data points] between the pretest
and posttest measures).
There is no set rule that determines the number of observations
that should be made on the dependent variable. For example, in
a basic pretest and posttest control group design, an observation
is taken once prior to the treatment and once after the treatment.
However, based on theoretical considerations, the investigator
can take multiple posttest treatment measures by including a

time-series component. Depending on the research logistics,
groups can be randomly assigned or matched, then randomly
assigned to meet the criteria for experimental research, or
groups can be nonrandomly assigned to conditions (quasi-
experimental research). With quasi-experimental research, the
limitations of the study significantly increase as defined by the
threats to internal validity discussed earlier.k-Factor Designs
The between-subjects approach can include more than one
treatment (factor) or intervention (i.e., the independent
variable) and does not always have to include a control group.
We designate this design as the k-factor design, with or without
a control group. Shadish et al. (2002) refer to this design as an
alternative- or multiple-treatment design. We prefer the k-factor
design as a means to clearly distinguish exactly how many
factors are present in the design (i.e., the k represents the
number of factors [independent variables]). To clarify, the
treatments in a 3-factor model (k = 3), for example, would be
designated as XA, XB, and XC (each letter of the alphabet
representing a factor) within the design structure. The within-
subjects k-factor design is referred to as the crossover design
and is discussed in more detail later in this book under
repeated-measures approaches.
A between-subjects k-factor design should be used when a
researcher wants to examine the effectiveness of more than one
type of treatment and a true control is not feasible. Within
educational settings, a control group is sometimes not
accessible, or there are times when a university’s Institutional
Review Board considers the withholding of treatment from
specific populations as unethical. Furthermore, some
psychologists and educators believe that using another treatment
(intervention) as a comparison group will yield more
meaningful results, particularly when the types of interventions
being studied have a history of proven success; therefore, a k-
factor design is the obvious choice. We present a variety of
examples of 2-, 3-, and 4-factor pretest and posttest designs, as
well as posttest-only designs with and without control groups.

Most common threats to internal validity are related, but not
limited, to these designs:
· Experimental. Maturation, Testing, Attrition, History, and
Instrumentation
· Quasi-Experimental. Maturation, Testing, Instrumentation,
Attrition, History, and Selection Bias
We refer the reader to the following article and book for full
explanations regarding threats to validity, grouping, and
research designs:
· Shadish, W. R., & Cook, T. D. (2009). The renaissance of
field experimentation in evaluating interventions. Annual
Review of Psychology, 60, 607–629.
· Shadish, W. R., Cook, T. D., & Campbell, D. T.
(2002). Experimental and quasi-experimental designs for
generalized causal inference. Boston, MA: Houghton Mifflin.
Diagram 2.1 Pretest and Posttest Control Group Design
Note: In regard to design notations, a dashed line (- - -) would
separate Groups 1 and 2 in the design structure if the
participants were not randomly assigned to conditions, which
indicates quasi-experimental research.
Example for Diagram 2.1
Chao, P., Bryan, T., Burstein, K., & Ergul, C. (2006). Family-
centered intervention for young children at-risk for language
and behavior problems. Early Childhood Education Journal,
34(2), 147–153.
Chapter 3 Regression-Discontinuity Approach
The regression-discontinuity (RD) approach is often referred to
as an RD design. RD approaches maintain the same design
structure as any basic between-subjects pretest and posttest
design. The major differences for the RD approach are (a) the
method by which research participants are assigned to
conditions and (b) the statistical analyses used to test the
effects. Specifically, the researcher applies the RD approach as

a means of assigning participants to conditions within the
design structure by using a cutoff score (criterion) on a
predetermined quantitative measure (usually the dependent
variable, but not always). Theoretical and logistical
considerations are used to determine the cutoff criterion. The
cutoff criterion is considered an advantage over typical random
or nonrandom assignment approaches as a means to target
“needy” participants and assign them to the actual program or
treatment condition.
The most basic design used in RD approaches is the two-group
pretest–posttest control group design. However, most designs
designated as between-subject approaches can use an RD
approach as a method of assignment to conditions and
subsequent regression analysis. RD approaches can also be
applied using data from extant databases (e.g., Luytena, Tymms,
& Jones, 2009) as a means to infer causality without designing a
true randomized experiment (see also Lesik, 2006, 2008). As
seen in Figure 3.1, the cutoff criterion was 50 (based on a
composite rating of 38 to 62). Those who scored below 50 were
assigned to the control group, and those who scored above were
assigned to the treatment group. As the figure shows, once the
posttest scores were collected, a regression line was applied to
the model to analyze the pre–post score relationship (i.e., a
treatment effect is determined by assessing the degree of change
in the regression line in observed and predicted pre–post scores
for those who received treatment compared to those who did
not).
Some researchers argue that the RD approach does not
compromise internal validity to the extent the findings would
not be robust to any violations of assumptions (statistically
speaking). Typically, an RD approach requires much larger
samples as a means to achieve acceptable levels of power (see
statistical conclusion validity). We present two examples of
studies that employed RD approaches: one that implemented an
intervention, and one that used observational data. See Shadish,
Cook, and Campbell (2002) for an in-depth discussion of issues

related to internal validity for RD approaches, as well as
methods for classifying RD approaches as experimental
research, quasi-experimental research, and fuzzy regression
discontinuity (i.e., assigning participants to conditions in
violation of the designated cutoff score).
Figure 3.1 Sample of a Cutoff Score
· Experimental. History, Maturation, and Instrumentation
· Quasi-Experimental. History, Maturation, Instrumentation, and
Selection Bias
We refer the reader to the following articles and book chapter
for full explanations regarding RD approaches:
· Imbens, G. W., & Lemieux, T. (2008). Regression
discontinuity designs: A guide to practice. Journal of
Econometrics, 142, 615–635.
· Trochim, W. (2001). Regression-discontinuity design. In N. J.
Smelser, J. D. Wright, & P. B. Baltes (Eds.), International
encyclopedia of the social and behavioral sciences (Vol. 19, pp.
12940–12945). North-Holland, Amsterdam: Pergamon.
· Trochim, W., & Cappelleri, J. C. (1992). Cutoff assignment
strategies for enhancing randomized clinical trials. Controlled
Clinical Trials, 13, 190–212.
Diagram 3.1 Regression-Discontinuity Pretest–Posttest Control
Group Design1
Note: OA refers to the preassignment measure, and C refers to
the cutoff score.
Bryant, D. P., Bryant, B. R., Gersten, R., Scammacca, N., &
Chavez, M. M. (2008). Mathematic intervention for first- and
second-grade students with mathematics difficulties: The effects
of tier 2 intervention delivered at booster lessons. Remedial and
Special Education, 29(1), 20–31.

Chapter 3 Regression-Discontinuity Approach
The regression-discontinuity (RD) approach is often referred to
as an RD design. RD approaches maintain the same design
structure as any basic between-subjects pretest and posttest
design. The major differences for the RD approach are (a) the
method by which research participants are assigned to
conditions and (b) the statistical analyses used to test the
effects. Specifically, the researcher applies the RD approach as
a means of assigning participants to conditions within the
design structure by using a cutoff score (criterion) on a
predetermined quantitative measure (usually the dependent
variable, but not always). Theoretical and logistical
considerations are used to determine the cutoff criterion. The
cutoff criterion is considered an advantage over typical random
or nonrandom assignment approaches as a means to target
“needy” participants and assign them to the actual program or
treatment condition.
The most basic design used in RD approaches is the two-group
pretest–posttest control group design. However, most designs
designated as between-subject approaches can use an RD
approach as a method of assignment to conditions and
subsequent regression analysis. RD approaches can also be
applied using data from extant databases (e.g., Luytena, Tymms,
& Jones, 2009) as a means to infer causality without designing a
true randomized experiment (see also Lesik, 2006, 2008). As
seen in Figure 3.1, the cutoff criterion was 50 (based on a
composite rating of 38 to 62). Those who scored below 50 were
assigned to the control group, and those who scored above were
assigned to the treatment group. As the figure shows, once the
posttest scores were collected, a regression line was applied to
the model to analyze the pre–post score relationship (i.e., a
treatment effect is determined by assessing the degree of change
in the regression line in observed and predicted pre–post scores
for those who received treatment compared to those who did
not).

Some researchers argue that the RD approach does not
compromise internal validity to the extent the findings would
not be robust to any violations of assumptions (statistically
speaking). Typically, an RD approach requires much larger
samples as a means to achieve acceptable levels of power (see
statistical conclusion validity). We present two examples of
studies that employed RD approaches: one that implemented an
intervention, and one that used observational data. See Shadish,
Cook, and Campbell (2002) for an in-depth discussion of issues
related to internal validity for RD approaches, as well as
methods for classifying RD approaches as experimental
research, quasi-experimental research, and fuzzy regression
discontinuity (i.e., assigning participants to conditions in
violation of the designated cutoff score).
Figure 3.1 Sample of a Cutoff Score
· Experimental. History, Maturation, and Instrumentation
· Quasi-Experimental. History, Maturation, Instrumentation, and
Selection Bias
We refer the reader to the following articles and book chapter
for full explanations regarding RD approaches:
· Imbens, G. W., & Lemieux, T. (2008). Regression
discontinuity designs: A guide to practice. Journal of
Econometrics, 142, 615–635.
· Trochim, W. (2001). Regression-discontinuity design. In N. J.
Smelser, J. D. Wright, & P. B. Baltes (Eds.), International
encyclopedia of the social and behavioral sciences (Vol. 19, pp.
12940–12945). North-Holland, Amsterdam: Pergamon.
· Trochim, W., & Cappelleri, J. C. (1992). Cutoff assignment
strategies for enhancing randomized clinical trials. Controlled
Clinical Trials, 13, 190–212.
Diagram 3.1 Regression-Discontinuity Pretest–Posttest Control
Group Design1

Note: OA refers to the preassignment measure, and C refers to
the cutoff score.
Bryant, D. P., Bryant, B. R., Gersten, R., Scammacca, N., &
Chavez, M. M. (2008). Mathematic intervention for first- and
second-grade students with mathematics difficulties: The effects
of tier 2 intervention delivered at booster lessons. Remedial and
Special Education, 29(1), 20–31.
Chapter 4 Within-Subjects Approach
Major challenges when conducting research are often related to
(a) access to participants and (b) an inability to randomly assign
the participants to conditions. With these limitations in mind,
researchers often employ a within-subjects approach. Although
the pretest and posttest designs of between-subjects approaches
include a within-subject component, the objective is not
necessarily to test the within-subject variances as intended with
within-subject approaches. The within-subjects approach to
research assumes one group (or subject) serves in each of the
treatment conditions.
This approach is referred to as repeated measures because
participants are repeatedly measured across each condition. The
advantage to this approach is that it can be used with smaller
sample sizes with little or no error variance concerning
individual differences between conditions (i.e., the same
participants exist in each condition). Some disadvantages to this
approach are the threats to internal validity, which are primarily
maturation and history, and the biggest issue is sequencing
effects (i.e., order and carryover effects). More specifically,
performance in one treatment condition affects the performance
in a second treatment condition. If possible, it is recommended
to randomize the order of the treatments (also known
as counterbalancing) to control for sequencing effects.
The simplest within-subjects approach is the one-group with a
single pretest and posttest measure (quasi-experimental research
1-factor design), which is presented here. This design can be

extended to multiple pretest and posttest measures and is
designated as an interrupted time-series (ITS) design and is
sometimes called the “time-series” approach. For this guide, we
categorize the ITS design under the repeated-measures
approach. Traditionally, it was believed that ITS designs should
include upward of 100 observations (in regard to statistical
power), but many of these designs, when applied, often have
anywhere from 10 to 50 observations and are often designated
as short ITS designs.Repeated-Measures Approach
The repeated-measures approach is structured so the researcher
can collect numerous measures from the participants.
Specifically, designs that include repeated measures allow
researchers to gather multiple data points over time to study the
rate of change as a function of treatment or time. These types of
designs typically are more advanced, which require advanced
statistical analysis to summarize the data. Most single-case
approaches must use repeated-measures approaches. This
approach allows for the single unit of analysis to serve as its
own control to minimize treatment effects. Designs that employ
repeated-measures approaches are also useful in longitudinal
studies when examining trends or phenomena over a designated
period of time. There are several designs that use the repeated-
measures approach.
It is important to clarify that designs within the repeated-
measures approach are classified as experimental as long as
participants are randomly exposed to each condition (i.e.,
counterbalancing must occur because sequencing effects are the
biggest threat to internal validity within this approach).
However, there are repeated-measures approaches that are
considered nonexperimental research. The ITS design is an
example of nonexperimental research and is often referred to as
a longitudinal data structure because data is collected at varying
time points over days, months, or even years. The application of
this approach, as with all approaches, is considered along with
theoretical tenets and logistical considerations.
Repeated-measures approaches can also include a between-

subjects component as seen in the pretest and multiple-posttest
design and the switching-replications design (the emphasis is
usually on the between- and within-subject variances, which are
sometimes not referred to as repeated measures because
technically each group is not exposed to each condition). We
present one example of the pretest and multiple-posttest
design and two examples of a switching-replication design (one
experimental and one quasi-experimental). This design allows
the researcher to assess the effects of the treatment on the first
group while withholding the treatment to the second group. The
second group is designated as a wait-list control group. This
design includes only one treatment or factor. We also present a
similar design, the crossover design (also known as
a changeover design), which includes at a minimum two factors,
but it can include more (Ryan, 2007; Shadish, Cook, &
Campbell, 2002). Some researchers, as seen in the experimental
example presented later, refer to a switching-replications design
as a crossover design. To be clear, the switching-replications
design includes one treatment and a wait-list control group,
while the crossover design includes a minimum of two
treatments and no control.
Switching-Replications Design: A Primer
The switching-replications design is one of the most effective
experimental designs at controlling for threats to internal
validity. Perhaps more importantly, it eliminates the need to
deny any potentially beneficial intervention to participants due
to random assignment (to control group). The design is
straightforward: The treatment is replicated (repeated) with
each group, with one group receiving the treatment first. In
theory, external validity should also be improved through the
use of two independent administrations of the same
intervention. Treatment environment and condition always vary
somewhat over time (outside of a laboratory setup), thus having
the treatment replicated at a later time (with the potential of
many variations in the treatment application and environment

and history) with similar results would demonstrate
generalizability.
However, the standard design structure for a switching-
replication design should not be chosen if the research can use
random assignment to conditions because it is nearly impossible
to avoid violating the standard statistical assumptions
associated with repeated-measures analysis. Therefore, we
propose a variant called the wait-list continuation design when
random assignment is available for application. The design
includes both a within- and between-subjects component (i.e.,
mixed-subjects approach). A wait-list control group is
incorporated and doesn’t include the pretest for that condition.
In effect, each group serves as both treatment and control at
different points in time and allows for statistical analysis
without relying on statistical assumptions to fall into place
naturally (e.g., multivariate normality and sphericity). We
provide a mock statistical analysis of this design in Appendix E.
Chapter 5 Factorial Designs
An extension of the k-factor design is the factorial design. The
simplest factorial design includes, at a minimum, two factors
(i.e., independent variables), each with two levels (Kazdin,
2002; Vogt, 2005). Two factors each with two levels is
designated as a 2 × 2 factorial design. Factorial designs are
denoted by the form sk. The s represents the number of levels,
and k represents the number of factors (e.g., 2 × 2 is the same
as 22). Recall that a factor is another term for the independent
variable, or treatment, or intervention.
Many k-factor designs can be transformed into factorial designs
(based on theoretical and logistical considerations) by
partitioning the factors into at least two levels and by
subsequently changing the statistical analysis used to examine
the data. For example, a researcher is interested in looking at
the effects of a math intervention (1: factor) partitioned into
two levels (1–visual math, 2–auditory math) and how it differs
by gender (2: factor; 1–males, 2–females) on a math

competency exam (i.e., dependent variable). Unlike the k-factor
design, factorial designs allow for all combinations of the factor
levels to be tested on the outcome (i.e., male and female
differences for auditory-style teaching compared to male and
female differences for visual-style teaching). Thus, factorial
designs allow for the examination of both the interaction effect
(the influence of one independent variable on the other
independent variable) and the maineffects (the influence of each
independent variable on the outcome).
We must caution the social and behavioral science researcher
not to get overzealous with the application of more complex
factorial designs outside of the 2 x 2 design. A general
assumption related to the factorial design is that there is no
interaction between the factors, but this is typically impossible
when including humans as test subjects. The factorial design
was originally developed for agricultural and engineering
research where the variables are static (e.g., amount of fertilizer
or blade length) and does not suffer from the typical threats to
internal validity that occurs when human participants are the
test subjects (e.g., testing, sequencing effects).
The factorial design is not one design; rather, it is considered a
family of designs. For example, some research requires that the
number of levels for each factor is not the same. The simplest
version would be a 2 × 3 design (i.e., one independent variable
has two levels and the other has three). Factorial designs can
also include three factors (e.g., 2 × 2 × 2 represents three
independent variables, each with two levels). Factorial designs
can use within-subjects or between-subjects approaches, and
they can include pretest and posttest or posttest-only measures
(most contain only posttests). The within-subjects approach to
factorial designs is set up so there is one group, and each
participant serves in each of the treatment conditions. The
between-subjects approach allows the researcher to test multiple
groups across conditions without exposing each participant to
all treatment conditions. This approach requires larger sample
sizes, and random assignment is highly recommended to control

for differentiation and selection bias.
Another option to the factorial design is the mixed-subjects
approach. A mixed-factorial design includes both a within- and
between-subjects approach. For instance, a 2 × 3 mixed-
factorial design would be constructed so the first factor at two
levels is tested as within subjects, and the second factor at three
levels would be tested as between subjects. To determine the
number of groups (also referred to as cells), the number of
levels for each factor can be multiplied (e.g., 2 × 2 = 4 groups;
2 × 3 = 6 groups). The strength of this design is that it allows a
researcher to examine the individual and combined effects of
the variables. There are many types and variations of factorial
designs not illustrated in this reference guide.
We provide three examples of a 2-factor between-subjects
factorial design (a pretest and posttest design [2 × 2] and two
posttest-only designs [2 × 2 and 3 × 2]) and one example of a 2
× 2 within-subjects factorial design. We also provide two
examples of a between-subjects factorial design with three
factors (2 × 2 × 2 and 2 × 3 × 2) and one example of a mixed-
factorial design (2 × 2 × 2).
Factorial designs that include within-subjects components are
also affected by the threats to internal validity listed under the
repeated-measures approach (e.g., sequencing effects).
· Experimental. Maturation, Testing, Diffusion, and
Instrumentation
· Quasi-Experimental. Maturation, Testing, Instrumentation,
Diffusion, and Selection Bias
We refer the reader to the following article and book for full
explanations regarding factorial designs:
· Dasgupta, T., Pillai, N. S., & Rubin, D. B. (2014). Causal
inference from 2K factorial designs using potential
outcomes. Journal of the Statistical Society: Series B
(Statistical Methodology), 77(4), 727–753.
· Ryan, T. P. (2007). Modern experimental design. Hoboken,

NJ: Wiley.
Diagram 5.1 2 × 2 Factorial Pretest and Posttest Design
(Between-Subjects)
Stern, S. E., Mullennix, J. W., & Wilson, S. J. (2002). Effects
of perceived disability on persuasiveness of computer-
synthesized speech. Journal of Applied Psychology, 87(2), 411–
417.
Research Question (main and interaction effects): What are the
effects of perceived disabilities on the persuasiveness of
computer-synthesized and normal speech?
Procedures: Participants completed an attitude pretest and were
randomly assigned to watch an actor deliver a persuasive speech
under one of the following four conditions: (a) disabled using
normal speech, (b) nondisabled using normal speech, (c)
disabled using computer-synthesized speech, or (d) nondisabled
using computer-synthesized speech. Participants then completed
an attitude posttest survey. Additionally, the following
Chapter 6 Solomon N-Group Design
The Solomon four-group design (Solomon, 1949) was developed
specifically to combine the strengths of both types of between-
subjects approaches (pretest only and the pretest and posttest
design) as a means to minimize the weaknesses associated with
using only one type. As a result, most of the major threats to
internal validity (e.g., testing) and construct validity (e.g.,
pretest sensitization) are minimized. The inclusion of a control
(or comparison) group to a research design can strengthen the
internal validity and the overall validity of the findings.
However, as noted earlier, there are strengths and costs in using
between-subjects pretest and posttest control group designs
compared to that of between-subjects posttest-only control
group designs. The Solomon four-group design is an extension
of the factorial design and is considered one of the strongest
experimental designs, but its application in the social sciences

is uncommon. Many investigators believe that logistical
considerations (e.g., time, costs, number of participants,
statistical analysis) are too much to overcome when applying
this design. Although Solomon’s original work did not include a
sound statistical analysis for this design, researchers have
attempted to offer statistical solutions and recommendations for
power analysis (Sawilowsky, Kelley, Blair, & Markman, 1994;
Walton Braver & Braver, 1988).
Originally, the Solomon four-group design was developed to
include only four groups. Specifically, the four-group design
includes one treatment (or factor; k = 1), with Group 1
receiving the treatment with a pretest and posttest, Group 2
receiving the pretest and posttest with no treatment, Group 3
receiving the treatment and only a posttest, and finally Group 4
receiving only the posttest. This allows the researcher to assess
the main effects, as well as interaction effects between the
pretest and no-pretest conditions. However, it has been
proposed that the original design can include more than one
treatment, thus extending the design to six groups for 2-factor
models or eight groups for 3-factor models (i.e., Solomon N-
group design). These designs allow researchers to test the
effects of more than one type of treatment intervention against
one another. Therefore, the design can be referred to as
a Solomon four-, six-, or eight-group design. We present
examples of research that used a Solomon four-group design
(k = 1), one example of a six-group design (k = 2), and one
example of an eight-group design (k = 3).
· Experimental. This design controls for all threats to internal
validity except for Instrumentation.
· Quasi-Experimental. Instrumentation and Selection Bias
We refer the reader to the following article for full explanations
and recommended analyses regarding Solomon four-, six-, and
eight-group designs:
· Steyn, R. (2009). Re-designing the Solomon four-group: Can

we improve on this exemplary model? Design Principles and
Practices: An International Journal, 3(1), 1833–1874.
Diagram 6.1 Solomon Four-Group Design
Note: It is highly recommended that random assignment be used
when applying the Solomon N-group designs.
Probst, T. M. (2003). Exploring employee outcomes of
organizational restructuring. Group & Organization
Management, 28(3), 416–439.
Research Questions
· Main effect: Does job security, job satisfaction, commitment,
physical and mental health decline, and turnover intention
increase following the announcement and implementation of
organizational restructuring? Do individuals who are affected
by organizational restructuring report lower levels of job
security, less job satisfaction, more negative affective reactions,
greater intentions to quit, lower levels of physical and mental
health, higher levels of role ambiguity, and higher levels of
time pressure than individuals not affected by organizational
restructuring?
· Interaction effect: The authors of this study did not explore
interaction effects. A 2 × 2 factorial design would allow for the
examination of the interactions within a Solomon four-group
design. In this study, each independent variable has two levels
(treatment and no-treatment; pretest and no-pretest). See the
chart that follows for an example of a 2 × 2 factorial design for
this study.
Procedures: A stratified random sample of 500 employees from
five state agencies going through reorganization was selected.
The stratification was based on whether the employee was
affected by the reorganization. A total of 313 employees (63%
of the sample) participated in the study. The sample was divided
into two groups: those affected by the reorganization (n = 147)
and those unaffected by the reorganization (n = 166). In

addition, all participants were randomly assigned to either a
pretest (n = 126) or no pretest (n = 187) group. Data were
collected at two different time points: (a) immediately prior to
the workplace reorganization announcement and (b) 6 months
following the merger announcement. There were four different
groups of participants: (a) pretested and affected by the
reorganization, (b) pretested but unaffected by the
reorganization, (c) affected but not pretested, and (d) unaffected
and pretested. A survey assessing each of the variables (see
Research Questions) of interest was administered prior to the
merger announcement and 6 months into the reorganization.
Design: Experimental research using a between-subjects
approach and a Solomon four-group design
Recommended Parametric Analysis: 2-way factorial ANOVA or
maximum likelihood regression (appropriate descriptive
statistics and effect-size calculations should be included)
Chapter 7 Single-Case Approach
The single-case approach is often referred to as the single-
participant or single-subject design. In addition, some single-
case approaches use more than one participant (N = 1) and are
referred to as small-n designs, but the emphasis and unit of
analysis remain on the single subject as reporting guidelines are
regularly updated and produced (see Tate et al., in preparation).
We remain consistent with our terminology and refer to these as
single-case approaches and reserve the word design for the
specific type of design defined within the approach. A single-
case approach is used to demonstrate a form of experimental
control with one participant (in some instances more than one
participant). As seen in within-subject and between-subject
approaches, the major contingencies required to qualify as a
“true” experiment are randomization of conditions to
participants (i.e., counterbalancing) or random assignment of
participants to conditions. However, in single-case approaches,
the participant serves as his or her own control, as well as

serving in the treatment during which repeated measures are
taken. More specifically, each condition is held constant and the
independent variable is systematically withheld and
reintroduced at various intervals as a means to study the
outcome. The interval between the variable being withheld and
reintroduced is based on theoretical and logistical
considerations. A rule of thumb may be to consider equal
intervals; however, there may be conditions that require
washout periods, creating unequal intervals.
As a reminder, the treatment is also the same as a factor or
intervention, and it is the independent variable. Although there
are still debates concerning the number of experimental
replications required to determine causation, as well as issues
related to power, single-case approaches take a unique approach
to experimentation. The threats to internal validity associated
with the single-case approach are similar to those found in the
within-subjects approach (e.g., sequencing effects), primarily
because of the issues related to collecting repeated measures. In
most cases, this approach meets the critical characteristics of
experimental control (see Manolov, Solanas, Bulté, & Onghena,
2010, and Shadish, 2014a, for a review of robustness and power
of randomization tests in A-B-A-B designs).
There are many forms, variations, and names of research
designs for single-case approaches. We discuss four of the
major designs here, with the understanding that this is not a
comprehensive coverage of all the designs developed within this
approach. The primary goal of the single-case approach is to
measure the dependent variable and at the very minimum
measure it against the presence and absence of the independent
variable (treatment or intervention). Therefore, the design logic
of a single-case approach starts with the baseline, which is
designated as A, and then the treatment is designated as B.
See Table 7.1 for the explanation of design notations that are
unique to single-case approaches.
The most basic design within this approach is the A-B design

(i.e., the dependent variable is measured during the baseline and
then again during the treatment). Most single-case approach
designs represent some variation and extension of the A-B
design. It is important to note that, in order to qualify as an
experiment, a researcher would, at a minimum, need to employ
an A-B-A design (i.e., this is to establish that there is indeed a
functional relationship between the independent and dependent
variables). There are many other variations of this design
structure such as A-B-A-B, B-A-B, or A-B-C-A (C is used to
represent a second treatment or independent variable). Any
variation of the A-B design can be employed based solely on
theoretical and logistical considerations.
When a researcher wants to study more than one treatment at a
time, a multi-element design (also referred to
as multitreatment or alternating-treatment designs) can be
employed. This design requires rapid shifts between or within
treatments to establish experimental control, and it allows an
investigator to research two or more treatments (sometimes up
to five or six). The third type of design within this approach is
the multiple baseline design. While the A-B and multi-element
require a withdrawal or reversal of conditions, the multiple
baseline design requires no withdrawal or reversal (i.e., some
treatments have carryover effects, so withdrawal or reversal is
not theoretically appropriate). Specifically, two or more
baselines are established, and the intervention is introduced at
various points (usually across participants), but it is never
removed. Most multiple baseline designs include more than one
participant, but they may be used on a single participant
applying the multiple baselines across multiple behaviors (as
measured by the dependent variables). As previously noted,
many of the single-case approach applications include more
than one participant; however, each participant is analyzed
individually.
Finally, there is a changing criterion design. Similar to the
multiple baseline design, the changing criterion design allows
for a gradual systematic manipulation of a targeted outcome and

does not require a reversal or return to baseline phase as in the
A-B design. This design is best applied when the researcher is
interested in observing the stepwise increases of the targeted
behavior.
We included three examples of the A-B design. Specifically, an
A-B-A design, an A-B-A-B design, and an A-B-A-B-C-B-C
design are presented. We also introduce one example of a
changing criterion design and two multiple baseline designs (a
1-factor and a 2-factor design), which are forms of the basic A-
B design.
The reader is referred to Dixon et al. (2009) to learn how to
create graphs in Microsoft Excel for designs within the single-
case approach. We also refer the reader to Shadish (2014a) for a
review of a the most recent issues regarding the analysis of the
single-case approach such as modeling trend, modeling error
covariances, computing standardized effect size estimates and
assessing statistical power. In addition, we recommend the
following article and book for a comprehensive overview of the
single-case approach, specific forms of analysis for this
approach, and software designed to analyze data from the family
of A-B designs:
· Gast, D. L., & Ledford, J. R. (2014). Single case research
methodology: Applications in special education and behavioral
sciences (2nd ed.). London, England: Routledge.
Running Head: RENEWABLE ENERGY VERSES FOSSIL
FUELS 1
RENEWABLE ENERGY VERSES FOSSIL FUELS
4

Renewable Energy versus
Fossil Fuels
Student Name
Instructor Name
Course
Date
Summary 1
High-Titer Methane from Organosolv-Pretreated Spruce and
Birch
Fossil fuels are not new to the world. During the industrial
revolution, they were the primary source of fuel that was used
to propel locomotives. Fossil fuels include gas, crude oil, and
also coal. Since there have been many innovations that have
taken the place of the transportation and manufacturing
industries, the number of locomotives and machines has
increased in the long run. As it is stated, the energy industry is
estimated to be approximately 1.6 trillion dollars, and the key
players in the market still remain to be fossil fuels. According
to statistics given by the International Energy Agency, fossil
fuels are the leading producers of energy in the long run. They
account for approximately 80% of the entire world production
(Matsakas, Nitsos, Vörös, Rova, & Christakopoulos, 2017).
However, energy production can be segregated to distinct

sources, which include: hydroelectric power that contributes to
2.5% of the total energy production, nuclear energy that
accounts for 4.8% of the entire global production, combustible
biomass that accounts for 10.5% among other sources. Over the
past decades, there are multiple pieces of research that have
been made to consider for the existence of fossil fuels, but they
all conclude that the shortage of fossil fuels is imminent
(Matsakas, Nitsos, Vörös, Rova, & Christakopoulos, 2017).
As the demand for renewable sources of energy continues to
elevate, the adverse effects of fossil fuels, on the other hand,
continues to increase. This has resulted in the use of novel raw
materials in the long run. In the process, it has been discovered
that lignocellulose biomass is very efficient in the production of
biogas in addition to being used for anaerobic respiration.
However, this process employs both spruce and birch as raw
materials in the anaerobic respiration process (Matsakas, Nitsos,
Vörös, Rova, & Christakopoulos, 2017). The impacts of distinct
operational settings on organosol pre-treatment on the outcome
of methane were studied. When acid catalyst was included in
the anaerobic proves, the spruce output increased with no
impact on the birch used. The shorter treatment period was very
crucial, with both inputs in place. When the ethanol content
used was low, the methane yields in the anaerobic process
increased. However, for optimum activity of birch, ethanol
needed to be in large quantities.
Summary 2
An Overview of the Portuguese Energy Sector and Perspectives
for Power-to-Gas Implementation
Energy dependence is a parameter that is used to show the
extent to which a country relies on foreign aid in order to
finance its energy production. It is a measure through which a
country relies on imports in order to meet the energy demands
from different regions. The indicator is obtained through
subtracting the net exports of energy from the net imports of
energy whose result is divided by the sum of domestic energy
consumption summed to international maritime bankers

(Miguel, Mendes, & Madeira, 2018). According to statistics
taken in 2015, Portugal had the most considerable energy
dependence among the European countries. As compared to the
other countries in the same period, Portugal seems to use more
resources from imports to finance their energy demands. Most
of the European nations are not affected by the prevailing
energy dependence since they are in a position to satisfy
themselves domestically. Portugal is a renowned country as a
result of its fossil fuels that existed in the country from the
beginning of the Second World War. The country is rich in coal,
hydroelectric power, and other renewable sources of energy
(Miguel, Mendes, & Madeira, 2018). Among the European
countries, Portugal has the highest deposits of coal, natural gas,
oil, and also infinite pool of renewable resources like biomass,
geothermal, Photovoltaic, and hydroelectric power.
Here have also been endless perspectives of power to gas in the
country. According to the IEA, daily electricity output in
Portugal often exceeds the national demands, and the surpluses
obtained in the process are either exported or pumped into the
various hydrogen plants. However, the consumption of natural
gas is increasing on a daily basis, and the power to gas
technologies, especially the ability to methane techniques would
be helpful in the long run. This will offer the opportunity to
integrate both the gas and power grids (Miguel, Mendes, &
Madeira, 2018). Implementing the process will make it possible
for the country to temporarily store the energy in the form of
gas, which can either be used in the spot, injected into the
available natural gas grids, or stored for future is in the
reservoirs. Power to methane applications in Portugal can
benefit the advance wind power energy sources that can be
installed within five miles of proximity from the gas
infrastructure. This potential infrastructure of distribution and
storage of energy makes Portugal a better place to implement
the power of methane technologies (Miguel, Mendes, &
Madeira, 2018). Nevertheless, it has to be in close proximity to
the carbon (IV) oxide sources, and the supply of the gas needs

to be adequate.
Summary 3
Current and Future Trends of the Automotive Industry
The impact of the recession on automakers
The economy of most of the countries in the world has been
deteriorating in the long run. Although investors had a lot of
confidence in the recovery of the economy, the process often
takes longer than anticipated. The primary considerations of the
then future industry are to boost both productivities as well as
the competitiveness of the automobile industry. However, there
are internal forces that are affecting the industry in the long
run. Like in the United States, the declining value if the United
States dollar and the increased number of automobiles
industries are affecting the overall price of the raw materials
required for the production of automobiles (Minorikawa &
Suda, 1990). Another variable that is changing the domestic
automobile makers in the United States is the increased demand
for high tech equipment which has improved the production
growth as well as exports in the long run. The impact of the
automobile industry is more felt in the growing economies
where the prevailing economic conditions are not favorable.
These factors may include the threats associated with inflation,
pressure on the current exchange rates, elevating hazards of the
overhead asset bubbles, and low debt (Minorikawa & Suda,
1990).
The future trends in the automobile industry are inspired by the
continued invention and innovation in information and
communication technology. The first trend is automated driving.
There are already self-driven electric cars in the market today
from different manufacturers all over the globe. However, the
technology in this automatic vehicles is expected to change in
the future (Minorikawa & Suda, 1990). Not only will artificial
intelligence be incorporated in the cars, but also the setup of the
machines will also change. The other anticipated trend in the
future is electric mobility. This is the energy-saving process in
all classes of vehicles in the future. The connectivity of electric

power will be improved in the long run. Safety is also another
trend that is slowly taking place in the automobile industry.
Protection means that the number of accidents will be minimal
to none, and the volume of traffic jams will also reduce
(Minorikawa & Suda, 1990). Digitization of cars is another
trend that is anticipated in the near future. Scanning refers to
increased intelligence of vehicles, convenience as well as
comforts in the long run. Finally, information and entertainment
will be improved so that human-machine communication can be
classified. In short, there will exist a means of dialogue between
cars and people.
Summary 4
Strengths and weaknesses of renewable energy and fossil fuels
Strengths
Fossil fuels have been the primary source of energy for an
extended period of time since the industrial revolution began. In
addition to its ease of storage, it is also easily transported to the
area of consumption. It is easy to store and transport gasoline,
coal, fuel oil, and other means of transport. Fossil fuels can,
therefore, be excavated in one area, processed in a different
location, and finally consumed in another region (Al-Sarihi &
Cherni, 2018). On the other hand, renewable sources of energy
are easy to produce since the sources of energy are natural.
There are no expenses incurred to get sunlight, wind, or waves.
There are also unlimited opportunities in the production of
biomass and mass decomposition of the final products.
Weaknesses
Renewable sources of energy require a large surface area to
volume ratio in order to get the expected amount of energy.
Solar panels and windmills require enough space in a strategic
region. These sources of energy also share into the vagaries of
nature. In the event the weather is not favorable, then there will
be no energy consumed. This illustrates why there is a need for
storage, especially if the projects are being done in rural areas
(Al-Sarihi & Cherni, 2018). On the other hand, fossil fuels are
mainly composed of chemicals. Once they are ignited, they

release hydrocarbons that often mix with other gases to produce
the greenhouse effects. The second disadvantage of fossil fuels
is that they can cause environmental degradation after mining is
done. The oil spills and open sites are harmful to worldwide
communities. Since fossil fuels are distributed by companies,
they may end up suffering from interruptions and price
fluctuations as a result of uncontrollable factors like political
stability.
Name one thing that was most surprising to most of the group
members and why
The most interesting aspect that I found among the group
members is the fact that both renewable and fossil fuels are
being used complimentary. While a country may have a vast
pool of fossil fuels, it is also investing heavily in renewable
sources of energy. The shift in this situation can be attributed to
the change in technology over the long run. As a result of the
increased innovation in technology, there are many methods that
are being incorporated today to produce energy at a lower cost.
References
Matsakas, L., Nitsos, C., Vörös, D., Rova, U., &
Christakopoulos, P. (2017). High-Titer Methane from
Organosolv-Pretreated Spruce and Birch. Energies, 10(3), 263.

DOI: 10.3390/en10030263
Miguel, C., Mendes, A., & Madeira, L. (2018). An Overview of
the Portuguese Energy Sector and Perspectives for Power-to-
Gas Implementation. Energies, 11(12), 3259. DOI:
10.3390/en11123259
Morikawa, H., & Suda, S. (1990). Current Status and Future
Trends of Electronic Packaging in Automotive Applications.
SAE Technical Paper Series. DOI: 10.4271/901134
Al-Sari, A., & Cherni, J. A. (2018). Assessing the strengths and
weaknesses of renewable energy initiatives in Oman: an
analysis with strategic niche management. Energy
Transitions, 2(1-2), 15–29. DOI: 10.1007/s41825-018-0008-9
PAGE
2
Assignment 1: Development of a Research Scenario
Crossover Design
Develop a hypothetical research scenario that would necessitate
the use of a Crossover Design. The research will be considered
experimental.
Group
Pretest
Treatment
Midtest
Treatment
Posttest

1
O1
XA
O2
XB
O3
2
O1
XB
O2
XA
O3
Time ►
1. Identify the research scenario including the relevant two
independent variables and one dependent variable.
2. Develop the appropriate primary research question to be
associated with this design.
3. Discuss the sampling strategy and technique used to access
the appropriate sample.
4. Identify the assignment technique to be utilized.
5. In accordance with the assignment technique and comparison
group, discuss the various control techniques that will be
utilized with this specific design.
6. Discuss the major threats to validity associated with this
design and type of research (experimental). How will these
threats be addressed in accordance based on the discussion of
the control techniques in number 5?
7. Enter the relevant variables in the chart below.
8. Briefly discuss any limitations associated with this research
scenario and the specific design.

Assignment
Group
Pretest
Treatment
Midtest
Treatment
Posttest
R
1
R
2
Time ►
Element
Met
Partially Met
Not Met
Relevant IVs and DVs
(4 points)
The IVs and DVs are clearly described, appropriate, and
justified with a citation.
The IVs and DVs are described, appropriate, but not justified
with a citation.
The IVs and DVs are not clearly described, or seem
inappropriate, or are not justified with a citation.
Research Question

(4 points)
The research question is clearly described, appropriate, and
The research question is described and appropriate, but not
The research question is not clearly described, or not justified
with an appropriate citation.
Sampling Strategy
(2 points)
The sampling strategies are appropriate.
The sampling strategies are somewhat appropriate.
The sampling strategies are inappropriate.
Control and Validity (3 points)
Control and Validity is properly outlined and is included,
detailed, and appropriate.
Control and Validity is not complete or properly outlined.
Control and Validity is not included, or not appropriate.
APA Style
(2 points)
The paper is written in correct APA style.
There are minor APA style errors.
There are numerous APA style errors.
PAGE
2
Assignment 1: Development of a Research Scenario
Pre- and Posttest Control Group Design
Develop a hypothetical research scenario that would necessitate
the use of a Pre- and Posttest Control Group Design. The
research can be either experimental or quasi-experimental.
Group
Pretest
Treatment

Posttest
1
O1
X
O2
2
O1
-
O2
Time ►
1. Identify the research scenario including the relevant
independent and dependent variables.
2. Develop the appropriate primary research question to be
associated with this design.
3. Discuss the sampling strategy and technique used to access
the appropriate sample.
4. Indentify the assignment technique to be utilized. Discuss
whether it will be experimental or quasi-experimental research.
5. Identify what type of comparison group will be used opposite
of the treatment group. Why would this type of comparison
group used?
6. In accordance with the assignment technique and comparison
group, discuss the various control techniques that will be
utilized with this specific design.
7. Discuss the major threats to validity associated with this
design and type of research (experimental or quasi-
experimental). How will these threats be addressed in
accordance based on the discussion of the control techniques in
number 6?
8. Enter the relevant variables in the chart below.

9. Briefly discuss any limitations associated with this research
scenario and the specific design.
Assignment
Group
Pretest
Treatment
Posttest
-
Time ►
Element
Met
Partially Met
Not Met
Relevant IVs and DVs
(4 points)
The IVs and DVs are clearly described, appropriate, and
The IVs and DVs are described, appropriate, but not justified
with a citation.
The IVs and DVs are not clearly described, or seem
inappropriate, or are not justified with a citation.
Research Question
(4 points)
The research question is clearly described, appropriate, and

The research question is described and appropriate, but not
The research question is not clearly described, or not justified
with an appropriate citation.
Sampling Strategy
(2 points)
The sampling strategies are appropriate.
The sampling strategies are somewhat appropriate.
The sampling strategies are inappropriate.
Control and Validity (3 points)
Control and Validity is properly outlined and is included,
detailed, and appropriate.
Control and Validity is not complete or properly outlined.
Control and Validity is not included, or not appropriate.
APA Style
(2 points)
The paper is written in correct APA style.
There are minor APA style errors.
There are numerous APA style errors.

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