Chapter 008

1Copyright © 2013, 2009, 2005, 2001, 1997 by Saunders, an imprint of Elsevier Inc.
Chapter 8
Objectives, Questions, and
Hypotheses and Study Variables

Framework, Purpose, and
Problem
 A framework: an abstract, logical structure of
meaning that guides the development of the
study and enables the reader to link the findings to
the body of knowledge in nursing
 The research purpose: a clear, concise statement of
the specific goal or aim of the study that is generated
based on the research problem
 A research problem: an area of concern where there
is a gap in the knowledge base needed for
nursing practice

Data Collection Plan
 Administer Surveys 1A and 2A to both
experimental subjects and control group, at
baseline
 Administer Surveys 1B and 2B to all subjects,
six weeks after completion of interventional
phase for experimental group

Data Analysis Plan
 Using ANOVA and MANOVA, compare
scores on Survey 1 (A and B) and Survey 2
(A and B), across and within groups

How Did the Researcher Get
Here From There?
 Objectives, questions, and hypotheses bridge
the gap between general intentions of
research and concrete plans for how to
conduct real research

Hypotheses
Research
Purpose
Objectives
Questions
Hypotheses
Detailed
plan for
data
collection
and
analysis

Hypotheses (Cont’d)
 Objectives, questions, and hypotheses
 Are on a more specific plane than is the research
purpose
 Identify:
• Actual (measurable) variables to be studied
• Way variables are related
• (Often) the population in which the researcher will study
that relationship

Formulating Research Objectives
or Aims
 Research objectives or aims are clear,
concise, declarative statements expressed in
the present tense, intended to provide
specific focus to the conduct of a study

Research Objective
 Focuses on one or two variables (or
concepts)
 Indicates whether the variables are to be
identified or described
 May identify relationships or associations
among variables
 May determine differences between groups or
compare groups on selected variables
 May predict a dependent variable based on
selected independent variables

Formulating Objectives or Aims
in Quantitative Studies
 Gap statement:
“But little is known about stress
symptomatology in high-power executives,
nor the strategies they consequently employ,
nor responses to those strategies”

in Quantitative Studies (Cont’d)
 Sample quantitative aims could be:
 To identify symptoms of stress in corporation
executives
 To identify symptom-management strategies
executives employ
 To determine which of these are the most
successful, over time

in Qualitative Studies
 Gap statement:
“The process of elective separation from an
employer of long standing has been
inadequately researched; a better
understanding is needed, so that employment
counselors can be attuned to the natural
history of emotions, regrets, hopes, and
empowerments that may result from this
action.”

in Qualitative Studies (Cont’d)
 Sample qualitative aims could be:
 To clarify the process of separation from a long-
term employer (LTE)
 Identify emotions connected with separation from
an LTE
 Explain the stages of separation from an LTE

Formulating Research Questions
 A research question is a concise,
interrogative statement that is worded in the
present tense and includes one or more
variables (or concepts)
 The word interrogative means question-
asking
 The research question frequently ends with
a question mark

Quantitative Research Question
Examples
 What symptoms of stress do high-power
executives report?
 How do high-power executives manage their
symptoms of stress?
 Do their management strategies work,
relative to stress symptomatology?

Qualitative Research Question
Examples
 What is the process of voluntary separation
from a long-term employer?
 What is the employee’s experience, in terms
of emotions, perceptions, and feelings?

Formulating Hypotheses
 A hypothesis is the formal statement of the
expected relationships between two or more
variables in a selected population
 It is found in some quantitative, but no
qualitative, research

Purpose of Hypotheses
 Specify the variables that will be measured
 Suggest the type of research that will be
chosen
 Identify the population that will be examined
 Predict the study outcome
 Are again addressed, one by one, by the
researcher when reporting the study findings

Types of Hypotheses
 Four categories used to describe types of
hypotheses:
 Causality
 Complexity
 Directionality
 Statistical wording

Types of Hypotheses (Cont’d)
 Causality: a hypothesis that attributes a
cause is causal; one that attributes only
relationship is associative
 Complexity: a hypothesis describing the
relationship between two variables is simple;
one describing the relationship among three
or more variables is complex

 Direction: a hypothesis that predicts the
direction of change in a variable, after
interaction with another variable, is
directional; if no direction of change is stated,
the hypothesis is nondirectional

 Statistical wording: a hypothesis stated in the
format required by statistical testing is a null
hypothesis; a hypothesis stated in a more
informal way is a research hypothesis

Associative Versus Causal
Hypotheses
 Associative
 As one variable changes, the other changes, too
 No cause is identified
 A common type of hypothesis for correlational
research and other non-interventional research.
Example: the incidence of cases of influenza,
and month of the year

Associative Versus Causal
 Causal
 The researcher changes the presence or amount of one
variable, and as a consequence, the other variable changes
in value
 A causal hypothesis specifies the direction of the change in
the dependent variable; consequently, a causal hypothesis
is directional
 A common type of hypothesis for interventional research
(experimental, quasi-experimental)
Example: increased funding for free flu shots, and
hospitalizations for flu

Variables and Causal
Relationships
 Causal relationships identify a cause-and-
effect interaction between two or more
variables, which are referred to as
independent and dependent variables

Relationships (Cont’d)
 Independent variable (intervention, treatment,
or experimental variable) is manipulated or
varied by the researcher to cause an effect
on the dependent variable
 Memory jog: the independent variable is the
only one the researcher manipulates,
because researchers are very independent
people.

Relationships (Cont’d)
 Dependent variable (outcome or response
variable) is measured to examine the effect
created by the independent variable
 Memory jog: the value of the dependent
variable DEPENDS on the value of the
independent variable.

Simple Versus Complex
Hypotheses
 A simple hypothesis predicts the relationship
between two variables.
 A complex hypothesis predicts the
relationship (associative or causal) among
three or more variables

Nondirectional Versus Directional
Hypotheses
 A nondirectional hypothesis states that a
relationship exists but does not predict the
nature of the relationship
Example: Decreasing dogs’ protein intake by
5% will have an effect on their weight. (The
hypothesis doesn’t state whether the
weight will increase or decrease.)

Nondirectional Versus Directional
 A directional hypothesis states the nature or
direction of the relationship between two or
more variables
Example: Decreasing dogs’ protein intake by
5% will result in weight loss
 A causal hypothesis predicts the effect of an
independent variable on a dependent
variable, specifying the direction of the
relationship (Thus, all causal hypotheses are
directional.)

Null Versus Research
Hypotheses
 The null hypothesis (H0), is referred to as a statistical
hypothesis; it is used for statistical testing and
interpretation of these results; states that there is no
relationship between the variables
 If the null hypothesis is not stated, the reader of the
research article should be able to state it: it is
the opposite of the research hypothesis
Example: “There is no relationship between the
timing of swamp drainage and the prevalence
of malarial illnesses”

Null Versus Research
 A research hypothesis is the alternative
hypothesis (H1 or Ha) to the null hypothesis—
its opposite
 States that there is a relationship between
two or more variables
Example: “If swamp drainage is undertaken
before afternoon temperatures crest to more
than 10 degrees Celsius, malaria prevalence
will decrease.”

Testing Hypotheses
 Hypotheses, generated from theory ideas,
are tested by quantitative research, not
qualitative
 A testable hypothesis contains variables that
can be measured or manipulated in practice
variables in a testable hypothesis must be
operationally defined)
 The hypothesis tested is the null hypothesis

Describing the Results
 Results are described with the use of certain
terminology:
 If a hypothesis is supported by the research findings, the
hypothesis is never proven. Instead, “there is evidence to
support the hypothesis” is the proper language
 If a null hypothesis is NOT supported by the research
findings, the hypothesis is rejected. This means that there is
evidence to support the opposite of the null hypothesis (the
research hypothesis)
 If a null hypothesis is supported by the research findings, the
hypothesis is accepted

Testing Versus Generating
Theory
 Quantitative research tests a theory
 Qualitative research generates a theory

Testing Versus Generating
Theory (Cont’d)
 Qualitative researchers construct narratives;
from narratives, connections emerge
 If the method is grounded theory, a theory might
be constructed; that theory could be tested, or not
 If it is tested, quantitative research is used to test it
If the theory is supported, it might be added to, by
using more qualitative research
 If the theory is not supported, the theorist
generates more qualitative research to build new
theoretical ideas

Identifying And Defining Study
Variables
 Variables are qualities, properties, or
characteristics of persons, things, or
situations that change or vary in a study
 More precisely, a variable is something that
can have more than one value (present,
absent, half, full, blue-green- brown, etc.)
 May be referred to as concepts rather than
variables in some qualitative research

Types of Variables
 Independent variable
 Dependent variable
 Research variable
 Extraneous variable
 Demographic variable
 Moderator variable
 Mediator variable

Independent And Dependent
Variables
 Causal relationships identify a cause-and-effect
interaction between two or more variables, which are
referred to as independent and dependent variables
 The independent variable (intervention, treatment, or
experimental variable) is an intervention or stimulus
manipulated or varied by the researcher to cause an
effect on the dependent variable
 A dependent variable is the outcome, response, or
behavior that the researcher wants to predict or
explain and is measured in the study. The dependent
variable changes, in response to the independent
variable

Research Variables or Concepts
 Found in non-interventional research
 The qualities, properties, or characteristics identified
in the research purpose and objectives or questions
that are measured in a study
 Used when the intent of the study is to measure
variables as they exist in a natural setting without the
implementation of a treatment [note: when there is no
intervention or treatment, there are no independent or
dependent variables]

Extraneous Variables
 Exist in all studies but are of primary concern
in quantitative studies
 Classified as:
 Recognized or unrecognized
 Controlled or uncontrolled
 Can affect the outcomes
 Confuse interpretation of results

Extraneous Variables (Cont’d)
 Controlled for in three ways:
 During design: using a huge sample and random
assignment to group
 During design: excluding subjects who possess
the variable
 During statistical analysis: demonstrating that the
variable did not affect the results

Confounding Variables
 Confounding variables are either:
 Not recognized until the study is in process
 Recognized before the study is initiated, but are
unable to be controlled

Demographic Variables
 Attributes of the subjects that are used to
describe the sample
 Age, gender, ethnicity, nationality, highest
educational degree held, yearly income,
occupation
 Measured at the beginning of the study
 Unrelated to independent variable or
dependent variable
 Expressed collectively as “sample
characteristics”

Example of Sample
Characteristics
“The twenty-one study participants had a mean age of
34 years, with a range from 22 through 57; fourteen
were female, and seven male; all were African-
American, except for two Caucasian and three Asian-
American; nineteen were citizens of the United States,
and two were Canadian citizens; twelve of the sample
had completed high school, five were college
graduates, and four held master’s degrees; the mean
yearly income was $62,714 yearly; ten were primary
schoolteachers, four were secondary school teachers,
six were college or university teachers, and one was a
high school principal.”

Moderator and Mediator
Variables
 Moderator variable
 Occurs with the independent variable
 Strengthens or weakens the effect of the
independent variable
 Mediator variable
 Brings about the effects of the intervention after
the intervention has occurred

Operationalizing Variables or Concepts
Quantitative Research
 Variables are qualities, properties, or
characteristics of persons, things, or
situations that change or vary in a study
 Simpler definition: a variable is a concept that
can be measured
 If a concept can be measured, it is a variable
 If a concept cannot be measured, it is
doomed to remain a concept

Quantitative Research (Cont’d)
 The operational definition states how a
concept will be measured
 If a concept can be operationally defined, it
can be measured
 If a concept cannot be operationally defined,
it is conceptually defined
 A conceptual definition gives the meaning of
the concept but not a method of
measurement

Operational Definition
 An operational definition is derived from a set
of procedures or progressive acts that a
researcher performs either to manipulate an
independent variable or to measure the
existence or degree of existence of the
dependent variable or research variable
 In other words, the operational definition sets
the rule for how a variable will be used in a
research study

Quantitative Research Example
 General background statement: unwise life
choices are more plentiful in persons who fail
out of college than they are in persons who
do not fail out of college
 Main concepts: unwise life choices, failing out
of college
 Conceptual and operational definitions:
 Failing out of college is conceptually defined as
being notified by a college or university that one
may not continue enrollment there due to
academic underachievement.

Quantitative Research Example (Cont’d)
 Conceptual and operational definitions
(Cont’d):
 Failing out of college is operationally defined as
the research subject’s statement (during the initial
intake interview) that (s)he has, in the past, failed
out of college. It is quantified, for the purposes of
this study, as 0 for not failing out, and as 1 or
more for the stated number of times the subject
has failed out of college.

(Cont’d):
 Unwise life choices are conceptually defined as
subject decisions that have narrowed the subject’s
purview of attractive possibilities, resulting in
decreased opportunities for employment, travel,
remuneration, reputation, avocation, or self-
esteem

(Cont’d):
 Unwise life choices are operationally defined as
the research subject’s identification (during the
initial intake interview) that a certain life choice
was unwise, along with an explanation of why.
Unwise life choices so identified will be quantified
as 0 for none, and as 1 or more for the stated
number of choices that the subject identifies as
unwise, corroborated and clarified by the research
assistant completing the interview

Operationalizing and Conceptual
Definitions Qualitative Studies
 Since there will be no measurement
(counting, averaging, et cetera) in a
qualitative study, except for the sample
characteristics, operationally defining
variables may not be very meaningful.
 Nonetheless, many thesis committees require
both conceptual and operational definitions,
even for qualitative research

Operationalizing and Conceptual
Definitions Qualitative Studies (Cont’d)
 A qualitative study often involves describing and
defining life experiences of the participants
 The resultant narrative may provide a clearer
conceptual understanding than existed prior to the
study
 The initial conceptual definitions decided upon at
the onset of the study may well be supplanted by
better conceptual definitions, at the study’s
conclusion

Chapter 008

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Chapter 008

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