Media Research
                    Chong Ho Yu, Ph.D.s



Today many instructional programs are delivered through high tech media such as multimedia, hypertext, World Wide Web, and video conferencing. The following is a brief outline of procedures for media evaluation.

First of all, you need a very detailed plan. Many people do not plan ahead, rather they claim that they are doing Exploratory Data Analysis (EDA). Actually what they do is EDC-exploratory data collection, which does not exist in any school of research methodology.

This write-up is an attempt to answer some frequently asked questions and to address several common misconceptions in media evaluation. Readers are required to have some familiarity with statistical concepts and procedures. If you do not understand the details, you may try to underestand the overall framework first, and then fill in the details later by checking out the books cited in the reference section.

Determine instructional theory

A well-designed instructional package should be theory-laden. It is important to notice that the mission of media research is not only to evaluate the product, but also the ideas behind the product. Five years from now, state of the art technology that you have presently will be laughable. Technologies just come and go, but sound instructional theories have a longer life. In a similar vein, Lockee, Burton, and Cross (1999) made it clear that media evaluation and media research are fundamentally different:

"Evaluation is practical and concerned with how to improve a product or whether to buy and use a product. Studies that compare one program or media against another are primarily evaluation. Evaluation seeks to find the programs that 'work' more cheaply, efficiently, quickly, effectively, etc. Research, on the other hand tends to be more concerned with testing theoretical concepts and constructs or with attempting to isolate variables to observe their contributions to a process or outcome." (p.36)

There is nothing new under the sun. The ideas of many high tech instructional packages could be traced back to traditional pedagogy. Today some instructional designers conduct research to compare the effect of static webpages with that of interactive webpages. Interactivity does not necessarily happen in Web-based instruction (WBI). In a classroom setting there are one-way lectures and forum-style discussions. Moreover, today some researchers investigate the effect of hyperlinking on WBI. Actually, before the age of the Internet, hyperlinking was implemented and studied on standalone hypertext systems such as HyperCard and Authorware. Other ideas such as self-paced learning, collaborative learning, and multimedia learning could also be found in conventional instructional media.

Therefore, the inferences drawn from media research should go beyond a particular product or even a particular medium. For instance, the concern should not be "Can Web-based Instruction enhance learning?" Rather it should be "Can interactivity, hyperlinking, multimedia, self-paced learning, and collaborative environment on Web-based Instruction enhance learning?" Even if later the technology is outdated, those ideas could be transferred to another medium and the research findings on those ideas could still carry on as principles and guidelines for instruction and research

Determine target audience, audience segments, and sample size

  • Population and Sample: Both imagined target audience (population) and actual target audience (sample) should be defined. The former plays a crucial role in developing evaluation while the latter is important to the development of instructional objectives. In order to enhance the generalizability of the evaluation, it is desirable to obtain two more or samples with slightly different backgrounds such as engineering majors, business majors, and humanities majors. Lindsay and Ehrenberg (1993) state that many researchers focus on how to analyze a single set of data, rather than how to handle and interpret many data sets. As a result, findings based upon one sample may not be replicated in another sample and thus generalization to a broader population is limited. To remediate this shortcoming, cross-validation within the same sample group or replication of the study with different samples are helpful.

  • Audience segments: Today many high tech instructional media stratifies various audience segments. For example, a multimedia program comprised predominately of graphics may be geared toward "visual-oriented learners" while a text-based program may be tailored for "text-oriented learners." A linear program may be suitable for "structural learners" whereas a non-linear hypertext program may be aimed at "exploratory learners." Some Web-based instruction programs may be targeted at computer novices while others are designed for computer literates. In most cases, the same program with different options are delivered to all types of audiences. In order to evaluate the effect of the program on different types of learners or the Aptitude Treatment Interaction (ATI), learners' attributes/aptitudes such as "visual/text-oriented," "structural/exploratory-oriented," and "computer literary" should be clearly defined and discriminant analysis (Eisenbeis , 1972) should be conducted to classify learners.

    However, an evaluator may run a risk of ill-defining learners' attributes and creating an unreliable or invalid instrument resulting in mis-classification. It is advisable either to use naturally ocurring (organismic) types like grade, gender, and race for segmentation, or to adopt well-defined learner cognitive styles and well-established tests such as Myers-Briggs Type Indicator (MBTI) to study treatment effect by audience segment (e.g. Jones, 1994).

  • Sample size: The sample size should be determined by the power, the effect size, and the alpha level.

    • Power is the probability of detecting a true significant difference (Cohen, 1988).

    • Effect size is the numerical difference between the control group and the treatment group in terms of the control group's standard deviation (Glass, McGraw, & Smith, 1981).

    • Alpha level is the cut-off for determining statistical significance.

    A larger sample size is not necessarily better than a smaller sample size if the study is over-powered i.e. When you have a very large sample, you may prove anything you want but the so-called "significant difference" is questionable.

    Nevertheless, the problem of low power, small effect size, and small effect size is more common in social sciences than the problem of large sample size. Proper sample size, power, and effect size should be calculated based upon the methods introduced by Cohen and Glass.

Define instructional objectives

Objectives drive both instructional design and evaluation. Change in cognition and change in motivation are two major categories of instructional objectives:

  • Cognition: The cognition aspect is often referred to as the increase of knowledge and skills. Basically, it may involve:

    • Rote learning such as memorization of concepts/declarative knowledge (what) and memorization of procedural knowledge (how)

    • High level cognitive skills such as logical reasoning (why) and problem solving (why and how)

    It is a major pitfall that many evaluators give a one-shot performance test right after the treatment and draw a conclusion about the effectiveness of the program from the test. Indeed, a follow-up like repeated measures is essential due to two reasons:

    • Regarding memorization of content material, a good instruction program should enable learners to retain the information in their long term memory, not just the short term memory.

    • Regarding high level cognitive skills, it takes time for the learners to digest the materials and develop reasoning logics and problem solving skills. For instance, research competence may be evaluated a few months after students complete a statistics course.

  • Motivation: Many instructional designers expect the effect of ongoing lifelong learning from the students. Essentially this is about motivation. There are two major aspects of motivation, namely,

    • Motivation on self: It poses a question like "Could the instruction enhance the learners' self-image so that they will be confident enough to take more intellectual challenges?"

    • Motivation on subject: It is concerned with "Could the instruction stimulate their intellectual curiosity on the subject matter so that they will further their learning through other resources in the future?"

    Depending on the target audience, some instructional programs may stress the former while others concentrate on the latter. For instance, one of my colleagues used the World Wide Web as a medium of instruction for high school drop-outs. For this program ego-building is more important than acquiring knowledge of the subject matter.

  • Mixed Objectives: The above objectives should be clearly defined so that the researcher does not measure cognitive skills that are affected by motivation, and vice versa. For example, it is a common mistake that an instructor "evangelizes" the merits of a new instructional medium while the objectives of the instruction are concerned with cognition rather than motivation.

    In this case, the evaluator receives contaminated data and may not know whether the improvement in performance is caused by the medium, the instructional design, or by strong motivation i.e. if the students believe that the treatment can help them, they will try harder to learn.

    Nevertheless, it does not mean that an instructional designer cannot put both cognition and motivation into the objectives. If both objectives are included, specific measurement procedures are required to filter the data.

Define media properties

The instructional designer should ask what specific properties in the medium can enhance learning. For example, some engineering professors connect a scanning probe microscope with the Internet to provide an opportunity of visually exploring and manipulating the subatomic world in a real time manner. There are at least three features in this medium:

  • Visualization

  • Exploration

  • Real time manipulation to the real object

Define mental constructs

With reference to cognitive psychology, the instructional designer should ask what psychological constructs of the learner will be affected by the above media properties. In this example, we can map the following changes in mentalities to the preceding media properties:

  • Visual thinking: The learner will develop imagery of the subatomic world and map the images with concepts.

  • Exploratory thinking: The learner will adopt exploration as a learning tool and ask "what-if' questions for problem-solving.

  • Control: The learner will try to manipulate the subject matter to advance his/her knowledge.

Define physical and behavioral outcomes

The instructional designer should ask how he/she expects the learner to behave after the above psychological changes occur. These outcomes should align with the instructional objectives. For instance, those engineering professors may define the objectives within the cognitive domain such as "the learner is able to classify various macrostructures and measure particles by nano-size scale. The tangible outcome would be "the learner is able to examine the stability and precision of a microchip in a semiconductor fab."

Develop testable hypotheses

Someone may argue whether it is necessary to develop a hypothesis. A school of research methodology suggests that no hypothesis should be pre-determined and the inquiry should be data-driven i.e. let a story emerges from the data. There is no such thing as "no hypothesis." In research one can starts with a vague and loose, or clear and form hypothesis. For example, once a doctoral student insisted that there wasn't any hypothesis in his study on computer-mediated communication. Actually, he must at least hypothesized that CMC has certain instructional values, otherwise, why bother?

Testable hypotheses based upon both mental constructs and physical outcomes should be formed. A testable hypothesis should be specific enough and stand a chance to be falisfied.

The rationale of hypothesis testing could be explained in the perspective of Principle of Falsification, introduced by prominent philosopher of science, Karl Popper (1959). According to Popper, conclusive verification of hypotheses is not possible, but conclusive falsification is possible. The validity of knowledge is tied to the probability of falsification. For example, a very broad and general statement such as "Humans should respect and love each other" can never be wrong and thus does not bring us any insightful knowledge. The more specific a statement is, the higher the possibility that the statement can be negated. If the statement has a high possibility of falsification and can stand "the trial of fire," then we can confirm its validity.

Quantification such as the assertion that "the mean of population A is the same as the population B" is a high degree of specification. Following the Popperian logic, the mission of a researcher is to falsify a specific statement rather than to prove that it is right. Therefore, we test the hypothesis by attempting to reject it.

The following are some examples of testable/falsifiable hypotheses:

  • The learner can understand the subatomic world better if he/she can visualize it.

  • The learner can understand the subatomic world better if he/she can explore it.

  • The learner can understand the subatomic world better if he/she manipulate the real object in real time rather than by simulation.

  • The learner can function in a semiconductor fab if he/she understands microstructures through scanning probe microscopy.

The following are examples of untestable/non-falsifiable hypotheses:

  • Good instructional design and proper application of media can lead to effective learning.

  • Good web design can lead to good use of navigation.

Not only the preceding hypotheses are vague (What is good web design? What is effective learning?), but also the test results do not carry any practical value. Welkowitz, Ewen, and Cohen (1982) used a funny example to illustrate this problem. Suppose a researcher hypothesizes that college education cultivates students' intelligence. He/she set the null hypothesis as "The mean IQ of college graduates is 68" and the alternate hypothesis as "The mean IQ of college graduates is more than 68." No doubt he/she could reject the null hypothesis, but his/her finding does not contribute anything to educational research. By the same token, in the first example, the null hypothesis would be "Good instructional design and proper application of media do not lead to effective learning." Needless to say, this null hypothesis will be rejected and the alternate is always right!

The above examples are not falsifiable because they are always right. But some hypotheses cannot even allow us to find out whether they are right or wrong. For instance, a Freudian psychologist may use a smoker's childhood experience to explain his vice: The patient smokes a lot because he sucked his mother's breasts when he was a baby. But another patient who was fed by cow milk during his infantry also consumes a lot of cigarettes now. Then the psychologist said that the absence of his mother's breasts drives him to seek for compensation from sucking cigarettes! Instructional psychologists should avoid this type of untestable theories.

If possible, a researcher should state the alternate hypothesis as a directional hypothesis for a one-tailed test rather than as a non-directional hypothesis for a two-tailed test. Compare the following two hypotheses:

  • There is a significant difference between the test scores of the control group and that of the treatment group.

  • The test scores of the treatment group is significantly higher than that of the control group.
Needless to say, the first hypothesis is in a "safer" position because better performance in either the control group or the treatment group is considered a significant difference. On the other hand, the second hypothesis is in a riskier position, thereby has a higher probability of falsification.

Design experiment

Design of experiments (DOE) is a process to control the environment for testing variables. To understand how DOE works, one must understand basic concepts such as factors, levels, within-subjects, between-subjects, variance control, main effects, and interaction effects. Although they appear to be simple, I found that they are most problematic to many people. For instance, when the design should be an one-way ANOVA with 4 levels, one may misinterpret it as a four-way ANOVA.

DOE should follow the methodologies recommended by Campbell and Stanley (1963), Cook and Campbell (1979), and Maxwell and Delaney (1990). To better conceptualize the design, the evaluator should draw a grid of the experiment with factors and levels. For example, a basic experimental design is a pretest-posttest administered to a control group and a treatment group as the following. This example has two factors and each factor has two levels.



The design can be more complex. For instance, if you want to discriminate the learner segment, introduce more versions of the treatment and measure the subjects repeatedly, the grid may look like the following:

PretestTest right after
the treatment
Test after
a week later
ControlVisual-oriented learners

Text-oriented learners

Wed-based instruction
with dominated text
Visual-oriented learners

Text-oriented learners

Wed-based instruction
with dominated graphics
Visual-oriented learners

Text-oriented learners

In brief, a grid is helpful to visualize the experiment, especially when you have a complex design with many factors and levels. At the early development of experimental methodology, quite a few experiments were applied to argiculture. In those argicultural research, a grid was used not only in conceptualizing and visualizing the experiments, but also in the actual implementation. The picture below shows a 5 x 5 Latin square laid out at Bettgelert Forest in 1929. The experiment was to study the effect of exposure on Sitka spruce, Norway spruce, Japaneses larch, Pinus contorta and Beech (UCLA Statistics, 1999).

Latin Square design

There are software packages for visualizing experimental design. Examples of such software packages are SPSS's Trial Run, SAS's JMP, and SAS's ADX Besides visualization, these software packages could list all possible options of experimental design according to your input. The following figure is an example of a research design made with JMP.

It is very tempting for an evaluator to adopt an extremely complex design such as a "4 X 4 X 4 X 4 X 4 all wheels drive factorial design" in attempt to answer all questions. However, the rules of KISS (Keep It Simple, Stupid) or KISBUTT (Keep It Simple Based Upon T-Tests) should be applied due to the following reasons:

  • In a very complex design the evaluator may eventually find one or two significant differences out of many variables. If I shoot continuously with a M16, I can eventually hit someone, of course. In other words, from a complex model, you may always find some support to your theory. Thus, it does not have a high degree of falsifiability, the Popperian principle that was introduced earlier.

  • According to the principle of parismony, given that all other things being equal, a simpler model with fewer variables is better than a more complex model. A very complex design such as one involving four-way interactions may not lead to interpretable results for practical use.

So, keep the design simple with a few variables. However, in the preceding section it was mentioned that the measurement instrument should be long enough to achieve high reliability. Are a simple design with a few variables and a long measurement instrument with many items contradictory? Not at all. The evaluator could put down 50 items in a test, but it does not mean he/she will have 50 variables for the final analysis. For instance, there are hundreds of questions in GRE, but GRE measures only three mental constructs: Verbal intelligence, quantitative intelligence, and logical reasoning.

The preceding example is a long instrument with pre-determined mental constructs. But what if the evaluator is not sure what the underlying mental constructs are. In this case, he/she could conduct a factor analysis to collapse many items into just a few variables. For more information on factor analysis please consult Comrey and Lee (1992).

Develop measurement instrument

Measurement instruments should be developed for both mental constructs and physical outcomes. The instrument must comply to the standards of validity and reliability specified by American Psychological Association in Standards for Educational and Psychological Measurement (1985).

The rule of thumb is: Write a long test and run a pilot study (or several pilot studies). First, the longer the test is, the more reliable it is. Second, after the pilot study you could throw out poorly-written items from the long test and retain a shorter version. Nunnally (1978) suggested that the initial item pool should contain twice as many items as desired in the final instrument. If you want to keep the same length, you could replace those bad items with better items. In brief, if you start with a longer test, you have more options later. Readers may consult Crocker and Algina (1986) for the details of test construction. For a summary of the concepts please read my write-up on reliability and validity. For a guideline of the procedure of computing a reliability coefficient, please read my SAS write-up.

Further readings

In 1999, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference. The mission of this committee is to develop a guideline of proper practice in psychological research. The report was published in American Psychologist and also available online (Wilkinson & Task Force, 1999). Like this article, the report covers every step of research such as selecting supporting theories, posting research questions, formulating hypotheses, designing the experiment, selecting the target population and sample, measuring the subjects, choosing statistical procedures, and many others. The suggestions are crystalized from many great masters in the field. No one will get fired for buying from IBM. Similarily, no research will go wrong for following APA.


American Psychological Association. (1985). Standards for Educational and Psychological Measurement. Washington D.C.: Author.

Campbell, D. & Stanley, J. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand-McNally.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, N.J. : L. Erlbaum Associates.

Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis. Hillsdale, N.J. : L. Erlbaum Associates.

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin Company.

Crocker, L. M., & Algina, J. (1986). Introduction to classical and modern test theory. New York : Holt, Rinehart, and Winston.

Eisenbeis, R. A. (1972). Discriminant analysis and classification procedures: theory and applications. Lexington, Mass., Heath.

Glass, G. V., McGraw, B., & Smith, M. L. (1981). Meta-analysis in social research. Beverly Hills, Calif. : Sage Publications.

Jones, P. (1994). Computer use and cognitive style. Journal of Research on Computing in Education, 26, 514-522.

Lindsay, R. M., and Ehrenberg, A. S. C. (1993). The Design of Replicated Studies, The American Statistician, 47, 217-228.

Lockee, B. B.; Burton, J. K., Cross, L. H. (1999). No comparison: Distance education finds a new use for 'no significance difference.' Educational Technology Research and Development, 3, 32-42.

Maxwell, S. E., & Delaney, H. D. (1990). Design experiments and analyzing data: A model comparison perspective. Belmont, CA: Wadsworth Publishing company.

Nunnally, J. C. (1978). Psychometric theory (2 nd ed.). New York: McGraw-Hill.

Popper, K. R. (1959). Logic of scientific discovery. London : Hutchinson.

UCLA Statistics. (1999). History of statistics. [On-line] Available URL:

Welkowitz, J., Ewen, R. B., & Cohen, J. (1982). Introductory statistics for the behavioral sciences. San Diego, CA: Harcourt Brace Javanovich, Publishers.

Wilkinson, L, & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594?04. [On-line] Available URL:



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