Thinking Like a Researcher Language of Research

Thinking Like a Researcher Language of Research
Chapter 3 Thinking Like a Researcher Language of Research McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

Language of Research Terms used in research Concepts Constructs
Conceptual schemes Models Operational definitions Terms used in research Several terms are used by researchers to converse about applied and theoretical business problems. A concept is a bundle of meanings or characteristics associated with certain concrete, unambiguous events, objects, conditions, or situations. The importance of conceptualization is discussed in the following slide. A construct is a definition specifically invented to represent an abstract phenomena for a given research project. Exhibit 3-1, a depiction of job redesign constructs, is provided in Slide 2-13. A conceptual scheme is the interrelationship between concepts and constructs. An operational definition defines a variable in terms of specific measurement and testing criteria. An example of an operational definition is provided in Slide 2-14. A variable is used as a synonym for the construct being studied. Slides 2-15 through 2-20 expand on different types of variables. A proposition is a statement about observable phenomena that may be judged as true or false. (Slide 2-21) A hypothesis is a proposition formulated for empirical testing. (Slides 2-22 through 2-25) A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. Slide 2-26 shows an example of a theory. A model is a representation of a system constructed to study some aspect of that system. Slide 2-27 shows an example of a model. Theory Variables Propositions/ Hypotheses

Language of Research A concept is a bundle of meanings or characteristics associated with certain concrete, unambiguous events, objects, conditions, or situations. Conceptualization – The mental process whereby fuzzy and imprecise notions (concepts) are made more specific and precise. A construct is a definition specifically invented to represent an abstract phenomena for a given research project. A conceptual scheme is the interrelationship between concepts and constructs.

Conceptualization Conceptualization – The process through which we specify what we mean when we use particular terms in research. We cannot meaningfully answer a question without a working agreement about the meaning of the outcome. Conceptualization processes a specific agreed-on meaning for a concept for the purposes of research.

Concepts to Variables to Indicators
Research normally begins at the theoretical level Concepts developed from theory, inductive method Should have general agreement on what a concept means - definition Conceptualization is the process of refining the definition of a concept to allow for operationalization

Next step is to operationalize the concept This step allows us to observe the real world Move from abstract concepts to variables that can be observed and measured Want achieve accuracy and precision Concepts are abstract; variables are concrete and specific Comprehensive definition provides the framework for operationalization

Real, Nominal, and Operational Definitions
Specification – The process through which concepts are made more specific. A nominal definition is one that is simply assigned to a term without any claim that the definition represents a “real” entity. An operational definition specifies precisely how a concept will be measured – that is, the operations we will perform. Creating Conceptual Order Conceptualization Nominal Definition Operational Definition Real World Measurement

Example: operationalize conservatism المحافظة – كون الشخص محافظ Def.: A political philosophy emphasizing traditional social values, classical liberal economic doctrine and opposition to radical change منطق فلسفي سياسي يؤكد على القيم الاجتماعية التقليدية, يؤمن بالنظام الاقتصادي الليبرالي الكلاسيكي, ويعارض التغييرات الجذرية

Dimensions Variables of the Concept Conservatism employed as
Abstract concept Variables employed as measures of the concept Conservatism Social Conservatism Support for Traditional Values (e.g. family,religious) Opposed to same sex marriage Opposed to abortion Economic Opposed to Economic Redistribution Belief in Free Market priority on lowering taxes opposed to progressive taxation support for free trade opposed to ‘big’ government Last row are questions that would be included in a survey questionnaire

Measures are simple direct items that provide data on a concept (e.g. income, age) Indicators, on the other hand, normally consist of several measures combined in some way to provide data on a more complex and indirect phenomenon (e.g. conservatism) Explaining and justifying the choices that were made in operationalizing concepts is crucial Replication and transparency in research make this necessary

Clear conceptualization
Language of Research Clear conceptualization of concepts Success of Research Shared understanding of concepts We must attempt to measure concepts in a clear manner that others can understand. If concepts are not clearly conceptualized and measured, we will receive confusing answers.

Job Redesign Constructs and Concepts
Exhibit 3-1 Exhibit 3-1 illustrates some of the concepts and constructs relevant to job redesign. The concepts at the bottom of the exhibit (format accuracy, manuscript errors, and keyboarding speed) are the most concrete and easily measured. Keyboarding speed is one just concept in the group that defines a construct that the human resource analyst calls Presentation Quality. It is not directly observable like keyboarding speed. It is a term used to communicate (a label) the combination of meanings presented by the three concepts. Concepts at the next level are vocabulary, syntax, and spelling. As they are related, the analyst groups them into a construct she calls language skill. Language skills is placed at a higher level of abstraction in the exhibit because two of the concepts that comprise it, vocabulary and syntax, are more difficult to observe and measure. The construct of job interest is not yet measured nor are its components specified. Researchers often refer to such constructs as hypothetical constructs because they are inferred only from the data—they are presumed to exist but no measure tests whether such constructs actually exist. If research shows the concepts and constructs in this example to be interrelated, and if the connections can be supported, then the analyst has the beginning of a conceptual scheme. One exercise you can try is to have students attempt to identify the concepts/constructs in the hypothetical construct…job interest, and discuss which elements are truly measurable…and how.

Language of Research An operational definition defines a variable in terms of specific measurement and testing criteria. Operationalization is a process of quantifying variables for the purpose of measuring their: occurrence, strength and frequency. A variable is used as a synonym for the construct being studied. A variable is something that takes on different values or categories; e.g., gender. A constant is something that cannot vary, a single value or category; e.g., male and female

Operational Definitions
How can we define the variable “class level of students”? Freshman Sophomore Junior Senior < 30 credit hours 30-50 credit hours 60-89 credit hours > 90 credit hours Operational definitions are definitions stated in terms of specific criteria for testing or measurement. The specifications must be so clear that any competent person using them would classify the objects in the same way. If a study of college students required classifying students by class level, a definition of each category would be necessary. Students could be grouped by class level based on self-report, number of years in school, or number of credit hours completed. Credit hours is the most precise measure.

A Variable Is the Property Being Studied
Event Act Characteristic Trait Attribute In practice, the term variable is used as a synonym for the property being studied. In this context, a variable is a symbol of an event, act, characteristic, trait, or attribute that can be measured and to which we assign categorical values. The different types of variables are presented on the following slides.

Categorical versus Quantitative Variables
Categorical Variables varies by type or kind e.g., gender, religion, college major, method of therapy Quantitative Variables varies by degree or amount e.g., reaction time, height, age, anxiety level

Religious affiliation
Types of Variables Dichotomous Male/Female Employed/ Unemployed Discrete Ethnic background Educational level Religious affiliation For the purposes of data entry and analysis, we assign numerical values to a variable based on that variable’s properties. Dichotomous variables have only two values that reflect the absence or presence of a property. Variables also take on values representing added categories such as demographic variables. All such variables are said to be discrete since only certain values are possible. Continuous variables take on values within a given range or, in some cases, an infinite set. Continuous Income Temperature Age

Independent and Dependent Variable Synonyms
Independent Variable (IV) Predictor Presumed cause Stimulus Predicted from… Antecedent Manipulated Dependent Variable (DV) Criterion Presumed effect Response Predicted to…. Consequence Measured outcome Exhibit 3-2 Exhibit 3-2 presents the commonly used synonyms for independent and dependent variables. An independent variable is the variable manipulated by the researcher to cause an effect on the dependent variable. The dependent variable is the variable expected to be affected by the manipulation of an independent variable.

Independent versus Dependent Variables
Independent Variable (IV) presumed to cause changes in another variable variable manipulated by the researcher Dependent Variable (DV) the presumed effect or outcome of the study variable that is measured by the researcher variable that influenced by the IV

Relationships Among Variable Types

Mediating and Moderating Variables
Mediating Variable occurs between two other variables in a causal chain also called intervening variable e.g., anxiety causes distraction (mediating variable) which affects memory Moderating Variable qualify a causal relationship as dependent on another variable e.g., the impact of anxiety on memory depends on level of fatigue (moderating variable)

Relationships Among Variable Types

Moderating Variables (MV)
The introduction of a four-day week (IV) will lead to higher productivity (DV), especially among younger workers (MV) The switch to commission from a salary compensation system (IV) will lead to increased sales (DV) per worker, especially more experienced workers (MV). The loss of mining jobs (IV) leads to acceptance of higher-risk behaviors to earn a family-supporting income (DV) – particularly among those with a limited education (MV). Moderating variables are variables that are believed to have a significant contributory or contingent effect on the originally stated IV-DV relationship. Whether a variable is treated as an independent or as a moderating variable depends on the hypothesis. Examples of moderating variables are shown in the slide.

Extraneous Variables a variable that competes with the IV in explaining the DV. sometimes called third variables or confounding variables. Extraneous variables are variables that could conceivably affect a given relationship. Some can be treated as independent or moderating variables or assumed or excluded from the study. If an extraneous variable might confound the study, the extraneous variable may be introduced as a control variable to help interpret the relationship between variables.

Extraneous Variables (EV)
With new customers (EV-control), a switch to commission from a salary compensation system (IV) will lead to increased sales productivity (DV) per worker, especially among younger workers (MV). Among residents with less than a high school education (EV-control), the loss of jobs (IV) leads to high-risk behaviors (DV), especially due to the proximity of the firing range (MV). Extraneous variables are variables that could conceivably affect a given relationship. Some can be treated as independent or moderating variables or assumed or excluded from the study. If an extraneous variable might confound the study, the extraneous variable may be introduced as a control variable to help interpret the relationship between variables. Examples are given in the slide.

Language of Research A proposition is a statement about observable phenomena that may be judged as true or false. A hypothesis is a proposition formulated for empirical testing.

Propositions and Hypotheses
Brand Manager Jones (case) has a higher-than-average achievement motivation (variable). Brand managers in Company Z (cases) have a higher-than-average achievement motivation (variable). Generalization A proposition is a statement about observable phenomena that may be judged as true or false. A hypothesis is a proposition formulated for empirical testing. A case is the entity or thing the hypothesis talks about. When the hypothesis is based on more than one case, it would be a generalization. Examples are provided in the slide.

Descriptive Hypothesis
Hypothesis Formats Descriptive Hypothesis In Detroit, our potato chip market share stands at 13.7%. American cities are experiencing budget difficulties. Research Question What is the market share for our potato chips in Detroit? Are American cities experiencing budget difficulties? A descriptive hypothesis is a statement about the existence, size, form, or distribution of a variable. Researchers often use a research question rather than a descriptive hypothesis. Examples are provided in the slide. Either format is acceptable, but the descriptive hypothesis has three advantages over the research question. Descriptive hypotheses encourage researchers to crystallize their thinking about the likely relationships. Descriptive hypotheses encourage researchers to think about the implications of a supported or rejected finding. Descriptive hypotheses are useful for testing statistical significance.

Relational Hypotheses
Correlational Young women (under 35) purchase fewer units of our product than women who are older than 35. The number of suits sold varies directly with the level of the business cycle. Causal An increase in family income leads to an increase in the percentage of income saved. Loyalty to a grocery store increases the probability of purchasing that store’s private brand products. A relational hypothesis is a statement about the relationship between two variables with respect to some case. Relational hypotheses may be correlational or explanatory (causal). A correlational hypothesis is a statement indicating that variables occur together in some specified manner without implying that one causes the other. A causal hypothesis is a statement that describes a relationship between two variables in which one variable leads to a specified effect on the other variable.

The Role of Hypotheses Guide the direction of the study
Identify relevant facts Suggest most appropriate research design Provide framework for organizing resulting conclusions This slide presents the functions served by hypotheses.

Characteristics of Strong Hypotheses
Adequate A Strong Hypothesis Is Testable The conditions for developing a strong hypothesis are more fully developed in Exhibit 3-4. Better than rivals

Language of Research A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. A model is a representation of a system constructed to study some aspect of that system.

Theory within Research
Exhibit 3-5 What is the difference between theories and hypotheses? Theories tend to be complex, abstract, and involve multiple variables. Hypotheses tend to be simple, limited-variable statements involving concrete instances. A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. To the degree that our theories are sound and fit the situation, we are successful in our explanations and predictions. The product life cycle, shown in Exhibit 3-5, is an example of a theory.

The Role of Reasoning Exhibit 3-7:
Business models are developed through the use of inductive and deductive reasoning. As illustrated in Exhibit 3-7, a business model may originate from empirical observations about market behavior based on researched facts and relationships among variables. Inductive reasoning allows the modeler to draw conclusions from the facts or evidence in planning the dynamics of the model. The modeler may also use existing theory, managerial experience or judgment, or facts.

A Model within Research
Exhibit 3-6 A model is a representation of a system constructed to study some aspect of that system or the system as a whole. Models versus Theories a model’s role is to represent or describe A theory’s role is to explain. Models in business research may be descriptive, predictive, and normative. Descriptive models are used for complex systems because they allow for the visualization of numerous variables and relationships. Predictive models forecast future events and facilitate business planning. Normative models are used for control, because they indicate necessary actions. Exhibit 3-6, shown in the slide, is a distribution network model called a maximum flow model used in management science. In this example, a European manufacturer of automobiles needs an increased flow of shipping to its Los Angeles distribution center to meet demand. However the primary distribution channel is saturated and alternatives must be sought. Models allow researchers to specify hypotheses that characterize present or future conditions: the effect of advertising on consumer awareness or intention to purchase, brand switching behavior, an employee training program, or other aspects of business.

The Scientific Method Direct observation Clearly defined variables
Clearly defined methods Empirically testable Elimination of alternatives Statistical justification Self-correcting process Good business research is based on sound reasoning because reasoning is essential for producing scientific results. This slide introduces the scientific method and its essential tenets. The scientific method guides our approach to problem-solving. An important term in the list is empirical. Empirical testing denotes observations and propositions based on sensory experiences and/or derived from such experience by methods of inductive logic, including mathematics and statistics. Researchers using this approach attempt to describe, explain, and make predictions by relying on information gained through observation. The scientific method is described as a puzzle-solving activity.

Researchers Encounter problems State problems Propose hypotheses
Deduce outcomes Formulate rival hypotheses Devise and conduct empirical tests Draw conclusions The steps followed by business researchers to approach a problem are presented in the slide.

Measurement Select measurable phenomena Develop a set of mapping rules
Measurement in research consists of assigning numbers to empirical events, objects or properties, or activities in compliance with a set of rules. This slide illustrates the three-part process of measurement. Text uses an example of auto show attendance. A mapping rule is a scheme for assigning numbers to aspects of an empirical event. Apply the mapping rule to each phenomenon

Language of Research Measurement in research consists of assigning numbers to empirical events, objects or properties, or activities in compliance with a set of rules. This slide illustrates the three-part process of measurement.

Language of Research Classification means that numbers are used to group or sort responses. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio.

Types of Scales Nominal Ordinal interval Ratio
Students will be building their measurement questions from different types of scales. They need to know the difference in order to choose the appropriate type. Each scale type has its own characteristics. Ratio

Levels of Measurement Classification Nominal Ordinal interval Ratio
This is a good time to ask students to develop a question they could ask that would provide only classification of the person answering it. Classification means that numbers are used to group or sort responses. Consider asking students if a number of anything is always an indication of ratio data. For example, what if we ask people how many cookies they eat a day? What if a business calls themselves the “number 1” pizza in town? These questions lead up to the next slide. Does the fact that James wears 23 mean he shoots better or plays better defense than the player donning jersey number 18? In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. Mapping rules have four characteristics and these are named in the slide. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio. Ratio

Nominal Scales Mutually exclusive and Collectively exhaustive
categories Exhibits only classification Nominal scales collect information on a variable that can be grouped into categories that are mutually exclusive and collectively exhaustive. For example, symphony patrons could be classified by whether or not they had attended prior performances. The counting of members in each group is the only possible arithmetic operation when a nominal scale is employed. If we use numerical symbols within our mapping rule to identify categories, these numbers are recognized as labels only and have no quantitative value. Nominal scales are the least powerful of the four data types. They suggest no order or distance relationship and have no arithmetic origin. The researcher is restricted to use of the mode as a measure of central tendency. The mode is the most frequently occurring value. There is no generally used measure of dispersion for nominal scales. Dispersion describes how scores cluster or scatter in a distribution. Even though LeBron James wears #23, it doesn’t mean that he is better player than #24 or a worse player than #22. The number has no meaning other than identifying James for someone who doesn’t follow the Cavs.

Language of Research Nominal scales collect information on a variable that can be grouped into categories that are mutually exclusive and collectively exhaustive. The counting of members in each group is the only possible arithmetic operation when a nominal scale is employed. Nominal scales are the least powerful of the four data types. They suggest no order or distance relationship and have no arithmetic origin. Examples: gender, religious affiliation, college major, hair color, birthplace, nationality

Levels of Measurement Classification Nominal Classification Ordinal
Order interval Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. You can ask students to develop a question that allows them to order the responses as well as group them. This is the perfect place to talk about the possible confusion that may exist when people order objects but the order may be the only consistent criteria. For instance, if two people tell them that Pizza Hut is better than Papa Johns, they are not necessarily thinking precisely the same. One could really favor Pizza Hut and never considering eating another Papa John’s pizza, which another could consider them almost interchangeable with only a slight preference for Pizza Hut. This discussion is a perfect lead in to the ever confusing ‘terror alert’ scale (shown on the next slide)…or the ‘weather warning’ system used in some states to keep drivers off the roads during poor weather. Students can probably come up with numerous other ordinal scales used in their environment. Ratio

Implies greater than or less than
Ordinal Scales Characteristics of nominal scale Order Implies greater than or less than Ordinal data require conformity to a logical postulate, which states: If a is greater than b, and b is greater than c, then a is greater than c. Rankings are examples of ordinal scales. Attitude and preference scales are also ordinal. The appropriate measure of central tendency is the median. The median is the midpoint of a distribution. A percentile or quartile reveals the dispersion. Nonparametric tests should be used with nominal and ordinal data. This is due to their simplicity, statistical power, and lack of requirements to accept the assumptions of parametric testing.

Levels of Measurement – Ordinal Scales
Variables with attributes we can logically rank order. Examples: socioeconomic status, level of conflict, prejudice, conservativeness, hardness Ordinal level of measurement refers to categories for which there is an order but little else E.g. Level of education where 1=did not complete high school, 2=high school graduate, 3=some post secondary, 4=post secondary degree

Levels of Measurement – Ordinal Variables
Higher values indicate higher levels of the variables but the distances between categories are not always equal Where to break the categories depends on theory or follows established procedure E.g. Please circle the response that corresponds most closely to your own opinion: The United Nations keeps the world safe. Strongly Agree Strongly Disagree

Levels of Measurement Nominal Classification Ordinal Classification
Order interval Classification Distance Order In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. Mapping rules have four characteristics and these are named in the slide. Classification means that numbers are used to group or sort responses. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio. Ratio

Levels of Measurement – Interval Variables
Variables for which the actual distance between attributes has meaning. Examples: temperature, (Fehrenheit), IQ score

Levels of Measurement Nominal Classification Ordinal Classification
Order interval Classification Distance Order In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. Mapping rules have four characteristics and these are named in the slide. Classification means that numbers are used to group or sort responses. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio. Ratio Classification Distance Order Natural Origin

Ratio Scales Characteristics of nominal, ordinal, interval scales
Absolute zero Examples Weight Height Number of children Ratio data represent the actual amounts of a variable. In business research, there are many examples such as monetary values, population counts, distances, return rates, and amounts of time. All statistical techniques mentioned up to this point are usable with ratio scales. Geometric and harmonic means are measures of central tendency and coefficients of variation may also be calculated. Higher levels of measurement generally yield more information and are appropriate for more powerful statistical procedures.

Levels of Measurement – Ratio Variables
Variables whose attributes meet the requirements of a interval measure, and has a true zero point. Examples: temperature (Kelvin), age, length of time, number of organizations, number of groups, number of As received in college

Variables & Scales

Variables & Scales A measure can be regrouped (recoded) to form a measure at a lower but not a higher level of measurement E.g. interval can become ordinal or nominal; ordinal can become nominal; Income that is measured in exact dollar amount (ratio) can be regrouped into categories of $25000 0 to $25000 = 1 $25001 to $50000 = 2 $50001 and over = 3 The variable is now an ordinal level variable

From Investigative to Measurement Questions
Exhibit 11-4 While Exhibit 11-3 summarized the characteristics of all the measurement scales. Exhibit 11-4, shown in the slide, illustrates the process of deciding which type of data is appropriate for one’s research needs.

Hypotheses (Some Types)
Null Alternative Directional Other

Null Hypothesis The absence of a relationship or difference in the results; any relationship or difference is due to chance or sampling error Example: There is no statistically significance difference between _____ and ____ regarding ______.

Alternative/Directional
Expresses a relationship between the variables under study Alternative: points a direction and requires “assumption” that is specified and objective Expresses a relationship between the variables under study Directional: points a direction and requires evidence via literature

Not: prove, accept, rejection (a finality to such verbs)
Hypotheses Support Not supported Not: prove, accept, rejection (a finality to such verbs)

Example of Hypothesis Null Alternative Directional
No relationship exists between levels of funding and staffing and the existence of a formal marketing plan. Alternative Formal marketing plans exist in institutions with greater levels of funding and staffing. Directional Lower funding and staffing results in lower levels of marketing planning.

Terms used in research  Hypothesis Formats
A descriptive hypothesis is a statement about the existence, size, form, or distribution of a variable. Researchers often use a research question rather than a descriptive hypothesis. Example: Descriptive Hypothesis In Riyadh, our dates market share stands at 33%. Research Question What is the market share for our dates in Riyadh?

Descriptive hypothesis versus research question. Either format is acceptable, but the descriptive hypothesis has three advantages over the research question. Descriptive hypotheses encourage researchers to crystallize their thinking about the likely relationships. Descriptive hypotheses encourage researchers to think about the implications of a supported or rejected finding. Descriptive hypotheses are useful for testing statistical significance.

A relational hypothesis is a statement about the relationship between two variables with respect to some case. Relational hypotheses may be correlational or explanatory (causal). A correlational hypothesis is a statement indicating that variables occur together in some specified manner without implying that one causes the other. A causal (explanatory) hypothesis is a statement that describes a relationship between two variables in which one variable leads to a specified effect on the other variable.

Example: Correlational Young women (under 35) purchase fewer units of our product than women who are older than 35. Causal An increase in family income leads to an increase in the percentage of income saved.

Terms used in research  Hypothesis  A Strong Hypothesis
A Strong Hypothesis has the following characteristics: Adequate Testable Better than rivals

What is a conceptual framework?
A written or visual presentation that: “explains either graphically, or in narrative form, the main things to be studied – the key factors, concepts or variables - and the presumed relationship among them”. (Miles and Huberman, 1994, P18)

What inputs go into developing a conceptual framework?
Experiential knowledge of student and supervisor: Technical knowledge. Research background. Personal experience. Data (particularly for qualitative). Literature review: Prior ‘related’ theory – concepts and relationships that are used to represent the world, what is happening and why. Prior ‘related’ research – how people have tackled ‘similar’ problems and what they have learned. Other theory and research - approaches, lines of investigation and theory that are not obviously relevant/previously used.

How might a conceptual framework be developed?
The pieces of the conceptual framework are borrowed but the researcher provides the structure. To develop the structure you could: Identify the key words used in the subject area of your study. Draw out the key things within something you have already written about the subject area – literature review. Take one key concept, idea or term at a time and brainstorm all the other things that might be related and then go back and select those that seem most relevant. Whichever is used it will take time and a number of iterations and the focus is both on the content and the inter-relationships.

Thinking Like a Researcher Language of Research

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