Writing Good Quantitative Research Questions

Note: this is an excerpt from How to Write a Research Paper, Proposal, or Thesis eBook. Buy it now!

There are three main types of questions that a researcher can ask when writing a quantitative study. They are:

  • Causal
  • Descriptive
  • Predictive

Causal Questions
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)?

Descriptive Questions
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.

He predicted lots that he didn’t have empirical evidence for… we know how that turned out.

Predictive Questions
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).

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How to Write a Research Paper eBook Now Available!

Hey all, haven’t posted in a bit because I’ve been writing an eBook that combines all the research based knowledge that I have here on MCT and a TON more.

It’s titled: How to Write a Research Paper, Proposal, or Thesis.

Here is the over view of everything contained in the ~40 page eBook. It contains detailed sections on how to write:

    • Title
    • Abstract
    • Introduction
    • Analysis/Discussion
    • Background/Literature Review
    • Select and define your concept
    • Study purpose and Relevance
    • How to write good qualitative and quantitative research questions
    • Research design
    • Data collection (surveys, analysis, etc.)
    • Ethics, Budget
    • Brief overview and quick guide on citing sources
    • How to describe your results and write your conclusion
    • Witty commentary and general jack-assery.

Update: 4/1/13 (Not an April fools joke!) – The eBook is now free – thanks to everyone who supported it! Grab it:
Download the eBook here

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Mass Comm Theory Resources Page Launched!

Hello fellow communicators! Just a quick note – I’ve launched a Resources page, see it in the nav above ↑ or click the link. Basically this is a list of various resources to help you research better, formulate better ideas, write better, and generally become a better seeker of knowledge.

On the resource page you’ll find:

  • Links to various books on Amazon that can seriously aid you in your work (full disclosure: I’m an amazon affiliate and if you click through and buy one I get about a nickel. Oh yeah!)
  • Article links – free places to download theory related articles (I don’t host these articles… don’t sue me.
  • eBooks covering all sorts of topics related to this blog (research, theory, etc.)
    • I’m currently writing an eBook titled “How to Write a Research Paper” it covers all of the research related content on the blog and has a ton more content that will only be available through the eBook. I’ll sell it for dirt cheap so everyone can afford a copy (I’m thinking about 2.99 right now just to cover my time). If you’d like to be notified when it comes out drop me an email and let me know at gavin.s.davie@gmail.com and you’ll get 1 email from me when the eBook is ready for download, that’s it. I won’t abuse the address. [UPDATE: It’s now available here.]

I’ll always be updating the page, so bookmark it and come back often, and of course the easiest way to stay up to date is to subscribe via email or RSS.

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How to Define Your Concept a.k.a. Concept Explication [Part 2]

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.

  1. 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:
    1. 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:
      1. 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.”
      2. 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.
    2. 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:
      1. 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.
      2. 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.
    3. 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:
      1. 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).
      2. 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.
    4. 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.
  2. 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:
    1. 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.
  3. 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.
  4. 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.

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How to Define Your Concept a.k.a. Concept Explication [Part 1]

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:

  1. 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:
    1. Intelligence is what an I.Q. test measures. Ok, but this still tells me nothing about what intelligence actually is.
    2. Communication development is a nation’s daily newspaper circulation per capita. What? I sort of get it, but still very unclear.
    3. Consensus consists of a majority vote. Right, but what does it mean? 51%? More? Does it apply to other situations?
  2. 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:
    1. 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).
    2. Legal controls on the press include laws against libel, sedition, obscenity, blasphemy… (There is actually a much longer list that sadly expands).
  3. 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:
    1. 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.
    2. 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.
    3. 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.
  4. 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:
    1. 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.
    2. 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.
    3. 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.

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Qualitative Media Analysis: Altheide’s Approach

Today we’ll be diving into Altheide’s approach to qualitative media analysis. This approach is comprehensively postulated in his book: Altheide, D. L. (1996). Qualitative Media Analysis. Thousand Oaks, CA: Sage. If you plan on doing an Altheide analysis, I HIGHLY recommend you pick it up.

The main premise of the approach is the study of documents or content. Documents are studied to understand culture, they can be conceptualized as the process and the array of objects, symbols, and meanings that make up social reality shared by members of a society. For our purposes, a large part of culture consists of documents. A document can be defined as any symbolic representation that can be recorded or retrieved for analysis.

Examples:

  • News article
  • Book
  • TV Show
  • Film
  • Magazine
  • Newpaper

Ethnographic Content Analysis

Ethnographic content analysis is oriented to documenting and understanding the communication of meaning, as well as verifying theoretical relationships. A major difference, however, is the reflexive and highly interactive nature of the investigator, concepts, data collection and analysis. Altheide’s method tends to hold almost a dual focus on the ethnographic approach as well as a straight content analysis. Unlike in qualitative content analysis, in which the protocol is the instrument, the investigator is continually central in ethnographic content analysis, although protocols may be used in later phases of the research. As with all ethnographic research, the meaning of a message is assumed to be reflected in various modes of information exchange, format, rhythm, and style.

Process of Qualitative Document Analysis

Problem & Unit of Analysis

  • Select your specific problem to be investigated.
  • Become familiar with the process and context of the information source.
  • Become familiar with several examples of relevant documents, noting particularly the format. Select a unit of analysis.

Constructing a Protocol (p. 25)

  • List several items of categories (variables) to guide data collection a draft a protocol.
  • Test the protocol by collecting data from several documents.
  • Revise the protocol and select several additional cases to further refine the protocol.

Determine Themes and Frames

Overlapping concepts that aim to capture the emphasis and meaning are frame, theme, and discourse. These are related to communication formats which, in the case of the mass media, refer to selection, organization, and presentation of information.

  • Arrive at a sampling rationale and strategy – examples: theoretical, opportunistic, cluster, stratified random (Note that this will usually be theoretical sampling)
  • Theoretical Sampling
  • Stratified Random Sampling

Collecting the Data

  • Collect the Data, using preset codes, if appropriate, and many descriptive examples.

Data Analysis

  • Perform data analysis, including conceptual refinement and data coding (p. 41).

Finally, compare and contrast “extremes” and “key differences” within each category or item. Next, combine brief summaries with an example of the typical case as well as the extremes. Then integrate the findings with your interpretation and key concepts.

The most important thing to note about the Altheide method is that it is incredibly through, some might argue that is is too through, but either way the time and amount of data collected is staggering. This article is meant to serve a a very brief overview of his method and to give you and to give you an idea of his rigorous approach to the analysis of documents should you be so inclined to undertake this method. One major advantage of using this method is that while it is more time consuming from a data gathering standpoint, the entire method has been validated and stored in his book in meticulous detail.

If you do decide to go down the road of Atheide’s Media Analysis, get the book, it’s a handy guide and walks you through every step of the process.

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Theory Words & Definitions

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.

a primer in theory construction by paul davidson reynolds

Fig. 1: Sweet, delicious mass comm theory… and a book.

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.

Good. Good. Let the empirical testing flow through you.

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.

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