Writing the title and abstract can be the easiest and most frustrating part of writing a research paper. There are two major things to keep in mind when writing your title and abstract:
Be clear and concise. You want everyone to know exactly what your paper is about simply by reading the title.
Write the title, abstract (and introduction) last. This may seem a little strange to a lot of people but it makes the most sense to write them once you understand what you studied, what your results were, and what you want your audience to take away from reading it.
Writing Your Title
The title should describe what you are studying and to what effect. For example, my thesis was called: The Hero Soldier: Portrayals of Soldiers in War Films (You can access it here if you are interested) This title hits all the main points:
What: soldiers as heroes
Where: war ﬁlms
Concept: the way they are portrayed
This covers the basics and only the basics, don’t include your research methods, your results, or your pet’s name (seriously). Hit the main points that people will:
Be searching for (Google, Library Databases, etc.)
Catch their attention
Tell the audience exactly what the study is about
That’s all. I didn’t call my thesis: A Qualitative In-depth Analysis of the Conception of the the Hero as Portrayed by Soldiers in War Films. I could have, because that’s what it is about, but it impedes comprehension. It needs to be to the point and convey exactly what that person will read.
Writing a Good Abstract
Outside the title the abstract is the only place where someone can get a quick overview of your study, think of the title as the abstract-lite, without the conclusions or big words. Basically your abstract should only be a paragraph long (that’s 3-4 sentences MAX!). Don’t ramble on for 15 sentences. There are only a few basic things you need to cover in your abstract:
What you are studying + why it’s important
How you are studying it (method)
What you learned/found/argue and its signiﬁcance
That’s it! The point of an abstract is to summarize your entire paper in a paragraph so someone looking at it can get a brief idea what it is about and determine if they want to keep reading the entire paper. If you can’t write a brief and succinct abstract then you clearly don’t know what your own paper is about.
Writing a Good Introduction
The introduction should cover the same topics as your abstract but in a bit more detail.
You also need to include:
Overview of the study methods
Theoretical framework (if you have one)
The reasons why the study has value to the research area you’re contributing to
If you’ve ﬁnished your research be sure to give us a good idea about your ﬁndings
Many times, when beginning any writing project it is suggested that you start with a “hook” to get your reader interested in your topic, this is not necessary in a research paper. It can however, add to your paper. It’s acceptable but not required. After covering everything mentioned above, provide a one paragraph roadmap of your paper. This gives us an idea of how you will attack the rest of the document we are about to read. For example:
“In the following pages I will ﬁrst discuss the relevant literature and previously
conducted studies that relate to my study about goldﬁsh and their love for beer. Second, I then outline the method by which the research was conducted, followed last by a discussion of the results as well as future implications of the goldﬁsh/beer relationship.” You’ll notice that I use “I” in that statement. It is perfectly acceptable to use “I” from time to time in a paper as long as you don’t overuse it.
Protip: Don’t write your introduction ﬁrst. As it is a preview of the study it’s usually best to write your introduction and abstract last.
There are three main types of questions that a researcher can ask when writing a quantitative study. They are:
Causal questions are exactly what they sound like – a question that tries to compare two or more phenomena and determine (or at least suggest) a relationship between the two (or more).
For example: “Does reading the How To Write A Research Paper eBook increase the average research paper grades in a class?” (The answer, of course, is yes. Everyone gets an A++).
Quantitative questions rely on an independent variable or one that remains the same (the students reading the eBook, in the example above). These questions often involve the manipulation of an independent variable and the comparison of the outcome of this manipulation.
Generally the script for a causal question follows this formula:
Does the ________________ (change) in _________________ (independent variable) produce change (increase, decrease, not affect) the _______________ (a dependent variable)?
Once again, these are pretty much what you would expect them to be: descriptive research questions ask “how often?”, “how much?”, or “what is the change over time or in a different situation?” questions.
Generally the script for a descriptive question follows this formula:
How often do ________________ (participants) do ________________ (variable being studied) at ________________ (research site)?
For example: “How often do college students need to use the bathroom during a test?” (Obviously the research site is implied here – at college).
Many times descriptive questions involve the degree or existence of relationship that exists between two or more variables. The script for a descriptive relationship question usually follows the below formula:
What is the relationship between ______________ (variable) and ____________ (variable) for _________________ (participants)?
For example: “How often do college students need to use the bathroom during a test as compared to during a normal class?”
Descriptive questions usually lead to further questions that your study was never meant to answer and it is a BIG MISTAKE to suggest so. In the example above one could deduce that if college students use the bathroom quite a bit more during tests that they may be cheating, or just more nervous, but you don’t know that! Don’t speculate until the very end and say exactly that: “This could mean may different things. However, more study is required to determine the reason(s).” The answer “why” is an entirely different study and almost always a qualitative one.
Predictive questions are questions that try to predict (no way!) whether one or more variables can be used to predict an outcome. Predictive questions and studies are always highly controversial, be sure to cover all your bases when trying to predict something, more often than not there are about 3,000 variables that come together to create an outcome and trying to link only a few of those to always get the same outcome can be a huge mistake (especially in social science).
Generally the script for a predictive question follows this formula:
Does ________________ (cause variable) lead to/create _____________ (outcome variable) in ________________ (setting)?
For example: “Does the color of a person’s hair lead to higher grades in school?”
As a general suggestion, especially early on, stay away from predictive studies. They can be some of the most fun, but more often than not people get far too excited and overstep the bounds of their study. For example, in answering the above question, you come to the conclusion that yes, people with black and very dark brown hair always get higher grades in school. But unless you explore the ALL possible variables you can’t claim that. Maybe IQ changes someone’s genes and smarter people always have darker hair. Maybe due to the “dumb blonde” stereotype teachers always give preferential treatment to non-blondes. You just don’t know – be very careful in these types of studies. (Obviously the example was meant to be humorous, but you get the point).
This is part 2 (the final part) of a series, be sure to read part 1 as well.
A key to research that can be used and repeated is the careful definition of the major concepts in the study. A hazy definition of a concept may enter into relationships with other variables, but since the concept was ill-defined the meaning of those relationships can be no better than ill-defined. The process by which concepts are defined for scientific purposes is called explication, that’s your ten-dollar-impress-your-grad-professor word of then day. Also in academia the word often substitutes for the word “explanation” becase it sounds much, much cooler. So, now that the intro is covered, let’s jump into part 2!
Author’s note: This post is based on a handout from my grad work and the monograph, “Fundamentals of Concept Formation in Empirical Science,” by Carl G. Hempel (1952) – citation at the end of the post.
Apply Defining Criteria: By this point in your defining you should have culled down your thinking to a few definitions. The more specific you can get, the better. Analyze them by means of these criterions:
Specificity, or how specific the definition currently is , in terms both of details of observation and lack of sentences linking the various elements of the concept (the fewer the better). The more general the definition the worse off you are. Examples:
It is more useful to record that Jim-Bob “watched Channel 13 from 7 to 9 p.m. yesterday evening” than to say he “watched TV last night.”
A definition of “dissonance” as “any cognitive discrepancy” is less helpful than an extended definition that catalogs the various kinds of cognitions that can be discrepant with one another, the various means by which they might be that way, etc.
Non-reification, Ok we’re getting a bit more complex here. Nothing insane, just pay attention. Avoid giving names to attributes that you might imagine exist, but that cannot be observed. You may think that there is a key factor that has not been observed, but that could be given empirical meaning by careful research. If this is the case, you are proposing a hypothetical construct (the hypothesis being that it does, in fact, exist). If you really need to do this, the first task of your research should then be a “validity check” on its existence. When you provide evidence of a hypothetical construct, it attains the more secure status of as a variable. If a hypothetical construct remains unobserved, it is considered a reification (see, took me a while, but we got around to the definition), and other researchers are unlikely to be persuaded by your reference to it. The important thing is to recognize that status of all elements of your definition, and to design research that will demonstrate their empirical content. Examples:
Some common reifications in communication research are terms “catharsis,” “dissonance,” “group cohesiveness,” “coorientation,” and “attitude.” So far, none of these things has ever been observed, yet they hold important positions in certain theoretical formulations. The danger is that they may not exist, except in the minds of the theoreticians.
By careful research, some hypothetical constructs that have gradually been converted into variables include “empathy,” “understanding,” “learning,” and “conformity.” However, these concepts are tied to very specific operational definitions, and when they are used to cover other kinds of situations they are simply reified terms.
Invariance of usage. This is a simple one – the same person should use a term consistently. Sadly, this isn’t always the case. Some writers use the same term to refer to different things at different times. Even more common is the switching levels of analysis without making any terminological distinction. Examples:
Marshall McLuhan jumps from discussion of individual differences in perception to statements about national character, historical epochs, and other macroscopic concepts (no surprise there, McLuhan was a bit all over the place).
The term “generation” is a term used appropriately for analyzing families and other kinship systems. It can be is misapplied to differences between age groups in society as a whole in the notion of a generation gap.
Inter-observer invariance – the measure of scientific usage would be that everyone uses the concept to mean the same thing. This level of agreement is practically impossible to achieve. But it is a useful goal to strive for, and careful application of the concept criteria and explanation can move you toward that goal.
Set boundaries. Perhaps the most important step in explication is to decide on clear boundaries for your concept. In meaning analysis, this is simply a matter of considering whether of not to include various lower concepts in your definition. In empirical analysis, boundaries are set by understanding which conditions are necessary and/or sufficient, and which are neither necessary or sufficient. In both cases, this stage of explication consists of stripping the concept of extra meanings. Examples:
A study shows that the strength of an expressed opinion can be increased by reinforcing it through social approval. The author’s conclusion is that reinforcement is a necessary condition for opinion formation. A later study demonstrates that there are conditions under which opinions change without reinforcement. So the definition is watered-down, in that reinforcement becomes a sufficient condition, rather than a necessary one. Finally, it is found that in some instances opinions shift in a direct opposite to the pattern of reinforcement. So, the element of reinforcement is eliminated from the definition of opinion formation, because it is neither necessary or sufficient.
Tentatively define. Try to develop a satisfactory definition via empirical analysis. You may find that it is surprisingly brief and simplified. Simpler is better as long as you are satisfied that it covers what you want the concept to mean, and does not cover anything else. If an empirical definition eludes you, more research may be needed. So turn to meaning analysis and work on a list of lower concepts. Keep in mind, though, that this is an intermediary stage in the development of your concept.
Define operationally. For each element of each concept that you retain in your final definition, you must specify at least one operational definition. The more specific the better, and the more carefully each operation is linked to your conceptual definition by clear reduction statements the better. It is not necessary to attempt to list all operational definitions; indeed, if your concept is not trivial, it will be impossible to list them all. But it is necessary to demonstrate that each element of your definition is amenable to observation in real world experiences. Operational definition consists of stating the observable indicators of the attributes (properties or relations) involved, so that someone else can “know one when he sees one.” Operational definitions might be contrived in the form of interview questions, experimental manipulations, unobtrusive observations, content categories, etc. The key to this final stage of explication is that all your reasoning and linkages be spelled out explicitly, so that someone else reading your work will know what you have done, what you think it represents conceptually, and why.
In the early stages of planning a research project, it is unnecessary to reduce operational definitions to precise terms. What is needed is to demonstrate conclusively that you can do so when the time comes to design an empirical study.
This was Part 2 and the thrilling conclusion to: How to Define Your Concept a.k.a. Concept Explication, be sure to read Part 1.
Citation: Hempel, C. G. (1952). Fundamentals of concept formation in empirical science. Chicago: University of Chicago Press.
A key to research that can be used and repeated is the careful definition of the major concepts in the study. A hazy definition of a concept may enter into relationships with other variables, but since the concept was ill-defined the meaning of those relationships can be no better than ill-defined. The process by which concepts are defined for scientific purposes is called explication, that’s your ten-dollar-impress-your-grad-professor word of then day. Also in academia the word often substitutes for the word “explanation” becase it sounds much, much cooler.
Author’s note: This post is based on a handout from my grad work and the monograph, “Fundamentals of Concept Formation in Empirical Science,” by Carl G. Hempel (1952) – citation at the end of the post.
So, before we can begin defining our concept, we need to choose what we will be studying…
Selecting the Concept: You have to start with at least a basic idea of what you want to study, or a commonly used label that might be an interesting object of analysis (don’t know what that is? Check out the theory words & definitions post). In the beginning of your quest about the only thing you can choose is what you want to focus on. Your thinking about that concept or focal variable should change quite a bit as you study it. Keep in mind that you should try to select a concept that is amenable to empirical observation, and likely to fit into relationships that are important for mass comm and communication theory. Avoid using operational definitions from other people’s research. You can make your best contribution by a fresh start that might lead to innovative studies.
Literature Review: Once you have decided roughly what your focus is to be (focal variable!!), scour research journals, books, articles, etc. in search of studies that have dealt with it (DO NOT use Wikipedia, a Department Chair clubs a baby seal every time you do). Your goal is to locate the various definitions that have been used. Keep a running list of all the ways that the concept has been defined for research purposes and where. A spreadsheet or Google Doc can be very handy for this. You can ignore purely abstract definitions, those where the concept is given a meaning that doesn’t seem to relate to the real world or any place where your term is used and no definition is provided. There will undoubtedly be cases where your concept has been given some other name – keep track of those too. It is the empirical usage or main idea of the concept that is truly important, not the label that is put on it. However, be sure to note in your writing that the concept can go by different names.
Definition Levels: Sort out the various definitions you have found, into one of the three basic types:
Nominal Definition:When a set of operational procedures is given an arbitrary name without any “reduction statements” linking the name to the measure, the definition is a nominal one. This is the most common type of definition in mass comm and communication research and, sadly, the least useful. It can usually be spotted by the obvious gap between the label and the measure (or definition). Examples:
Intelligence is what an I.Q. test measures. Ok, but this still tells me nothing about what intelligence actually is.
Communication development is a nation’s daily newspaper circulation per capita. What? I sort of get it, but still very unclear.
Consensus consists of a majority vote. Right, but what does it mean? 51%? More? Does it apply to other situations?
Real Definition – Meaning Analysis: A much more useful type of definition is to express the meaning of a top level term by listing the lower level concepts that compose it. The lower terms are less complex in that they are more clearly tied to actual definitions. This list of lower concepts is expandable and replaceable usually – new items can be added and others may be removed. Any changes of this sort change the meaning of the concept. Examples:
Mass media are newspapers, books, magazines, radio, television… (Note that this list is clearly able to go on and on, however depending on what you add, can change the meaning).
Legal controls on the press include laws against libel, sedition, obscenity, blasphemy… (There is actually a much longer list that sadly expands).
Real definition – Empirical analysis: This form of definition is the listing of the necessary and sufficient conditions for observation of the concept. This is the most useful type of definition for scientific purposes since changes in the lower concepts do not change the nature of the higher concept. In a way, these definitions are hypotheses, subject to modification as we learn more about the concept. In mass comm and communication research, this type of definition is rare, and frankly, awesome to come across. Some cursory efforts, as examples:
Communication requires that a symbol be transmitted by one person and received by a second person, and a signal (represented by the symbol) must be shared, at least in part, by the transmitter and the receiver.
Information seeking consists of a person undertaking some action to increase his [or her] input of a specific type of communication content; that he [she] be, to some extent, uncertain what content he [she] will receive; and that his [her] action is to some extent motivated by uncertainty.
In both these cases you can see how clearly we’ve defined the term. It’s not 100% there but we’re way past giving examples or listing things that are part of it.
Level of Analysis: The next step is to distinguish between two kinds of attributes that are called property terms and relational terms. A property term is an attribute that is observable for one person or object (or, you know, a property of that object), in isolation from other persons or objects. A relational term is only observable in the interaction of two persons, or the comparison of two objects, or in some similar two-unit relationship (like a relationship, not rocket science here). Most of the attributes we are interested in for communication research are relational in nature. Strangely, they are often described as if they were properties, in that only one person, say, is observed at a time. This kind of anomaly is a serious error in research procedure. Early in the process of explication (admit it, it sounds cooler) you should decide whether your concept is a property or a relation. Any further work with the concept should stick to whichever level of analysis you have decided on. Examples:
Income is a property, but socioeconomic status is a relational term. So if you are interested in SES but have data only on income, you should be treating that data as relational. Easy cheesy.
Information seeing can be thought of as a property of an individual. But it may be relational to other forms of behavior. For instance, it preempts other forms of communication, in that a person can only do one thing at a time. So your explication might well lead you into defining a whole typology of forms of communication, which are mutually exclusive. This is very frequent in social research, and provides a rich source of hypotheses.
It should be clear that such concepts as obedience, power, I.Q., liberalism, relevance, and knowledge are relational for most purposes. It should be clear. It isn’t always that way.
Stay tuned for Part 2 and the thrilling conclusion to: How to Define Your Concept a.k.a. Concept Explication coming soon to a mass communication blog near you (this one, in case that was confusing).
Citation: Hempel, C. G. (1952). Fundamentals of concept formation in empirical science. Chicago: University of Chicago Press.
There are a lot of terms that get thrown around in the academic lexicon, sometimes they align with those you’ll find in a dictionary, sometimes they don’t. So I thought I’d outline a good handful for you here that will be helpful as you wade through some sweet, delicious mass comm theories (Fig. 1). This article is based on Reynolds’s book: a primer in theory construction (a must have for aspiring theorists), citation at the end of the article, as well as from a grad class I took.
Object of analysis: The system whose properties we are trying to explain. The research problem should determine what attributes of the system we are interested in. If the attributes are those of the individual person (e.g., a personality characteristic, attitude change), then it probably belongs to cognitive theory. If the attributes are those of a group of persons (e.g., community status, rate of diffusion), then it lies in the social systems realm. Societies, communities, large organizations, and primary groups are types of social systems.
Concepts: The most basic elements in theory, they are the attributes of the object that we are trying to explain and those that we are using to provide the explanation. They are abstractions from reality. We also use them in everyday life, of course, but research concepts are supposed to be more precise. Concepts are interesting to researchers only when they vary; we call a concept that can be observed to have different values a variable (as contrasted to a constant). Often called constructs because scientific concepts are carefully constructed from observation.
Conceptual definition: Each concept in a theoretical system (a collection of interrelated theoretical statements) should have a clear and unambiguous definition that is consistently used by the individual theorist and in agreement with the way other theorists define the concept. But that is seldom the case in social science. Careful definition of concepts is where we must begin with theory building (Normally I hate italics, but dammit, that sentence is important, write it down!).
Postulates: Ideas, biases, and strategies of a particular theorist that help to explain why his theory is constructed as it is and why he does the kind of research he does (nothing to do with posteriors). Theses statements are more abstract than assumptions or theoretical statements and not usually testable. They may represent statements about human nature, causation, the nature of data, and the broad type of causal forces in society – in short, what’s important to look at and how you should do it.
Assumptions: These are statements about the concepts used in the theory. Assumptions are taken for granted in the theory being tested. They are not investigated, but the falsification of that theoretical statement may result in the revision of the assumption in the future. Assumptions (or revised assumptions) may serve as hypotheses in subsequent research. Two or more assumptions provide the premises from which the theoretical statements (and hypotheses) are derived through logic.
Theoretical Statement: The statement specifying the relation between two or more concepts (variables). Reynolds calls these relational statements and distinguishes these from existence statements that include postulates, definitions and assumptions. Other people call theoretical statements axioms, theorems or propositions. Seriously, the label doesn’t matter, just so we know what we’re referring to.
Relations: (No not that kind, get your mind out of the gutter) The connection between concepts can be stated in a number of forms: that one variable causes another, that two variables are associated, and more complicated relations are possible.
Operational definitions: The set of procedures a researcher uses to measure (or manipulate as in experiments) a given concept. These should follow clearly and logically from the conceptual definition of the concept. These are less abstract than conceptual definitions. They tell us “how to measure it,” ideally using more than one method.
Explication: The process by which conceptual and operational definitions are connected. This is done either by analysis using the logical criteria of definition or through empirical analysis using research data to clarify measurement to distinguish the concept from other concepts. Abstract concepts often need to be broken down into two or more lower order (less abstract) concepts before they can be translated into hypotheses. Basically a fancy way of saying “explain.”
Measurement: The assignment of values to objects on the basis of rules relevant to the concept being measured. Reynolds describes four levels of measurement: nominal, ordinal, interval, and ratio. The quality of measurement is assessed by reliability and validity. Speaking of reliability…
Reliability: The stability and precision of measurement of a variable. Stability overtime is called test-retest reliability (i.e., do those scoring high at one time also score high at a second point in time). A second form, equivalence, looks at the level of agreement across items (internal consistency) or forms, or between coders doing the measurement.
Validity: The degree to which you’re really measuring what you think you’re measuring. There are two different approaches: you find external independent evidence (e.g., a criterion group known to possess the characteristic) against which to compare your measurement (pragmatic validity), or you look at the extent to which the empirical relationships of the concept to other concepts fit your theory (construct validity).
Hypothesis: A statement of the relationship between two or more operational definitions. It should be capable of being stated in an “if, then” form, and is less abstract than theoretical statements, assumptions, and postulates. The type of research you are doing will largely dictate how to phrase your hypothesis.
Dependent Variables + Independent Variables: The dependent variable is the “effect” that we are seeking to explain; the independent variable is the presumed “cause” of that effect. We often say “x” is the independent variable that is the cause of the dependent variable “y,” (the effect). There are various names for third variables: extraneous variable, intervening variable, mediating variable, etc. that alter the relationship between the independent and dependent variables.
Empirical testing: A good theory must be capable of being tested by observation in the “real world.” Most frequently, statistics are used to make this test. Note that we test theory indirectly through hypotheses and operational definitions. It is made even more indirect by the fact that we test the null hypothesis: the statistical hypothesis of no difference – that the relationship is not strong enough to reject chance. If the data is judged to be not strong enough to reject the null hypothesis, then we have falsified the theoretical statement. If the observations are judged sufficient to reject the null hypothesis, then theory merely remains viable or tenable.
Type I and Type II errors: One of the problems of doing research is that you can be wrong in the inferences you make from research evidence. You can be wrong if you decide to reject the null hypothesis and say that the result is consistent with your theory. That’s a type I error. If your results don’t look very supportive and you decide you can’t reject the null hypothesis, you can be wrong too. In that case you incorrectly gave up on your research hypothesis (indirectly falsifying your theory), but there really was support in the “real world” and your research wasn’t good enough to detect it. That is a type II error.
Causality: As you may know by now, this is a “can of worms.” It’s probably better to think of establishing causality between two variables as something that we move toward than to think of it as being capable of being “discovered” through an experiment. Realize that it is better to think in terms of various types of causes than to look for “the cause” of something. To work toward causality, three conditions have to be met: There has to be an association (correlation) between the two variables; a time order has to be established such that the presumed cause precedes the effect; and other explanations have to be ruled out, such as that some third variable causes both of the two variables of interest. If this last is the case, then we say the relationship we thought was causal was really spurious.
Necessary condition: A situation that must be present for some effect to take place. This is one type of cause. Sometimes a necessary condition describes the level of a third variable that is essential for the relationship between two other variables to hold. In this case the third variable can also be called a contingent condition. Third variables that make the relationship stronger or weaker but don’t totally limit its domain (are not necessary conditions) are called contributory conditions.
Sufficient condition: A situation that if present is enough to produce all effects. This implies that there are no contingent conditions. Experiments are probably more suited to finding sufficient conditions than are nonexperimental sample surveys and other methods. Social scientists would like to find necessary and sufficient conditions, but that is a goal, not an immediate reality.
Models and paradigms: Social scientists sometimes find it useful to employ simplified versions of reality to gain insight and to illustrate their theoretical ideas. A model is a conceptual structure borrowed from some field of study other than the one at hand; it needs not include causal statements, but it does specify structural relationships among variables. A paradigm is a conceptual structure designed specifically for the field of application; it also specifies structural relationships. When a model or paradigm incorporates causal statements, it is usually called a theory. Models and paradigms can be assessed on the basis of their usefulness in helping us construct valid theory.
Reynolds, P. D. (1971). A primer in theory construction. Indianapolis, IN: Bobbs-Merrill Educational Publishing.