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 thestudy 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 fornursing 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


Objectives, Questions, and Hypotheses

ResearchPurpose

ObjectivesQuestions

Hypotheses

Detailed plan for data

collection and

analysis


Objectives, Questions, and 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”


Formulating Objectives or Aims 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


Formulating Objectives or Aims 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.”


Formulating Objectives or Aims 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 witha 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 ofhypotheses: 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 Hypotheses (Cont’d)

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, andhospitalizations 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


Variables and Causal 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.


Variables and Causal 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 by5% will have an effect on their weight. (The hypothesis doesn’t state whether theweight will increase or decrease.)


Nondirectional Versus Directional Hypotheses (Cont’d)

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 isthe opposite of the research hypothesis

Example: “There is no relationship between thetiming of swamp drainage and the prevalenceof malarial illnesses”


Null Versus Research Hypotheses (Cont’d)

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 thatcan 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, orexperimental variable) is an intervention or stimulusmanipulated or varied by the researcher to cause aneffect 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 ordependent 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


Operationalizing Variables or Concepts 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


Operationalizing Variables or Concepts 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.


Operationalizing Variables or Concepts 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.



Conceptual and operational definitions (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



Conceptual and operational definitions (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 atthe onset of the study may well be supplanted by better conceptual definitions, at the study’s conclusion

Chapter 008

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