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You won't be able to do very much in research unless you know how to talk about variables. A variable is any entity that can take on different values. OK, so what does that mean? Anything that can vary can be considered a variable. For instance, age can be considered a variable because age can take different values for different people or for the same person at different times. Similarly, country can be considered a variable because a person's country can be assigned a value.
Variables aren't always 'quantitative' or numerical. The variable 'city' consists of text values like 'New York' or 'Sydney'. We can, if it is useful, assign quantitative values instead of (or in place of) the text values, but we don't have to assign numbers in order for something to be a variable. It's also important to realize that variables aren't only things that we measure in the traditional sense. For instance, in much social research and in program evaluation, we consider the treatment or program to be made up of one or more variables (i.e., the 'cause' can be considered a variable). An educational program can have varying amounts of 'time on task', 'classroom settings', 'student-teacher ratios', and so on. So even the program can be considered a variable (which can be made up of a number of sub-variables).
An attribute is a specific value on a variable. For instance, the variable sex or gender has two attributes: male and female. Or, the variable agreement might be defined as having five attributes:
- 1 = strongly disagree
- 2 = disagree
- 3 = neutral
- 4 = agree
- 5 = strongly agree
Another important distinction having to do with the term 'variable' is the distinction between an independent and dependent variable. This distinction is particularly relevant when you are investigating cause-effect relationships. It took me the longest time to learn this distinction. (Of course, I'm someone who gets confused about the signs for 'arrivals' and 'departures' at airports -- do I go to arrivals because I'm arriving at the airport or does the person I'm picking up go to arrivals because they're arriving on the plane!). I originally thought that an independent variable was one that would be free to vary or respond to some program or treatment, and that a dependent variable must be one that depends on my efforts (that is, it's the treatment). But this is entirely backwards! In fact the independent variable is what you (or nature) manipulates -- a treatment or program or cause. The dependent variable is what is affected by the independent variable -- your effects or outcomes. For example, if you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones.
Finally, there are two traits of variables that should always be achieved. Each variable should be exhaustive, it should include all possible answerable responses. For instance, if the variable is "religion" and the only options are "Protestant", "Jewish", and "Muslim", there are quite a few religions I can think of that haven't been included. The list does not exhaust all possibilities. On the other hand, if you exhaust all the possibilities with some variables -- religion being one of them -- you would simply have too many responses. The way to deal with this is to explicitly list the most common attributes and then use a general category like "Other" to account for all remaining ones. In addition to being exhaustive, the attributes of a variable should be mutually exclusive, no respondent should be able to have two attributes simultaneously. While this might seem obvious, it is often rather tricky in practice. For instance, you might be tempted to represent the variable "Employment Status" with the two attributes "employed" and "unemployed." But these attributes are not necessarily mutually exclusive -- a person who is looking for a second job while employed would be able to check both attributes! But don't we often use questions on surveys that ask the respondent to "check all that apply" and then list a series of categories? Yes, we do, but technically speaking, each of the categories in a question like that is its own variable and is treated dichotomously as either "checked" or "unchecked", attributes that are mutually exclusive.
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Copyright ©2006, William M.K. Trochim, All Rights Reserved
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Last Revised: 10/20/2006
This article is written in the form of an original-research paper for the journal Sportscience. A few of the requirements for form and content are unique to Sportscience, but most are common to all good scientific journals. You can therefore use this article to help you write a research paper for any journal.
This article also exists in slightly modified form as a template for a Sportscience research article. If you intend to submit a paper to Sportscience, you should download the template from the Information for Authors page at the Sportscience site.
Whether you are submitting your article to Sportscience or to another journal, you should read my guidelines on scientific writing (Hopkins, 1999). Here are the main points from that article:
- Avoid technical terms.
- Avoid abbreviations.
- Use simple sentences.
- Avoid common errors of punctuation and grammar.
- Use the first person (I, we) rather than the passive voice.
- Link your ideas into a sensible sequence without repetitions or discontinuities.
- Get feedback on your article from colleagues.
The subheadings in this article (Background, Aim, etc.) should make it easier to write your article for Sportscience than for other journals. You will also be less likely to omit important data or important points for discussion. Please follow the instructions for each subheading. Omit or change the subheadings as necessary for a paper in another journal.
Use the first paragraph or two of the Background to explain what is known generally in the area of your study. Cite key references, but do not write an extensive review of literature; instead, direct the reader to a recent review. Then focus in on the problem that your study addresses.
State the aim in the first sentence of this section. Write a few more sentences if necessary to justify the aim and/or explain your unique approach to realizing the aim. Make it clear to the reader that your aim or your approach is interesting and that your study is therefore valuable.
State the design, then justify your choice of this design. Explain the optimum sample size, then justify the size of the sample you studied. Describe any relevant time frame. Use a figure to explain a complex design or a design involving several assays at different times.
Devote a separate paragraph to a description of the experimental and control treatments (e.g., drug, diet, training) in an experimental study.
Explain how the subjects were recruited, then list means and standard deviations of the relevant demographics (age, weight, height) plus any other relevant characteristics (recent best performances, recent training). Show ranges of characteristics only if there are unusually distant outliers in the sample. If possible, report recent best competitive performances of athletes as a percent of the world record, to make it clear what caliber of athlete the outcome of your study can be generalized to.
Show all the above characteristics for any major subgroups of subjects (e.g., males and females, non-athletes and athletes). Include the number of subjects in each subgroup. Use a table like this (tables for other journals have similar formats):
List the measures (variables) you used and explain why you chose them, as shown below. Then describe the assay for each under its own sub-subheading. Give an outline of established procedures and refer the reader to previous published accounts for details; for new procedures show enough detail to allow the reader to reproduce the procedures successfully.
Dependent (outcome) variables: list them. Explain why you chose them.
Independent (predictor) variables: list them. Explain why you chose them. For repeated-measures designs omit the obvious treatment variable, but include numeric and nominal variables you have analyzed as covariates.
Mechanism variables: list them. These are variables in repeated-measures designs that you have assayed to try to explain the effect of the treatment. Explain why you chose them.
Describe the assay for the first measure under a sub-subheading, as shown here. You may wish to group some measures under one sub-subheading, such as Training, Anthropometric, or Environmental Measures.
Describe the assay for the second measure under a sub-subheading, as shown here, and so on. When mentioning a piece of equipment, you must state the model, the manufacturer, and the city and country of origin. Include relevant information on sampling or digitizing rates and data processing that led to the measure.
Name the statistical package or program you used. Describe the statistical procedures. Finish this section with this paragraph, or something similar:
We have used means and standard deviations to represent the average and typical spread of values of variables. We have shown the precision of our estimates of outcome statistics as 95% confidence limits (which define the likely range of the true value in the population from which we drew our sample). The p values shown represent the probability of a more extreme absolute value than the observed value of the effect if the true value of the effect was zero or null. Statistically significant effects are those for which the zero or null value of the effect lies outside the 95% confidence interval (i.e., p < 0.05).
RESULTS AND DISCUSSION
Most journals have separate Results and Discussion sections. I believe this separation makes research articles more difficult to write and read.
How close to reality were your measurements? In a repeated-measures study, how reproducible were the dependent variables? How do the answers to these questions impact your findings? Address such questions about the validity and reliability of your measures here. You can also report any ancillary methodological findings. Use sub-subheadings if you wish.
Summarize the spread of values between subjects with the standard deviation, never with the standard error of the mean. Show the precision of your estimates of outcomes with confidence limits. Try not to mention p values, statistical significance, null hypotheses, type I errors, and type II errors.
State each result and discuss it immediately. Interpret the magnitude of each outcome in a qualitative way, using both your experience of the magnitudes that matter in this area of human endeavor and also any published scales of magnitudes (e.g., Cohen, 1988; Hopkins, 1998). You must interpret the observed effects and the 95% confidence limits. For example, you might have to say that you observed a moderate effect, but that the true value of the effect could be anything between trivial and very strong.
If it is more convenient to show p values than confidence limits, show the exact p value to one significant digit (for p < 0.1) or two decimal places (for p > 0.10). Do not use p < 0.05 or p > 0.05. Examples: p = 0.03; p = 0.007; p = 0.09; p = 0.74. Do not give values of test statistics (F, t, etc.).
Show data in figures rather than in tables or in text. See below for examples (Figures 1-4). Avoid repetition of data in figures, tables and text. For Sportscience articles, follow the instructions in the template on how to create figures. Paste figures and tables into the document after the paragraph where you first refer to them (other journals: tables and figures go at the end of the manuscript).
Figure 1: Informative title for a time seriesa.
Data are means. Bars are standard deviations (shown only for Groups B and C).
Figure 2: Informative title for a scattergram.
Least-squares lines are shown for each variable.
Figure 3: Informative title for a bar graph.
Data are means. Bars are standard deviations.
Figure 4: Informative title for an outcomes figure.
Data are means. Bars are 95% confidence intervals.
Do your findings apply to people in the real world if they have characteristics and behaviors different from the people in your sample? Bring together the outcomes and any technicalities in a statement that addresses this question about the generalizability of your findings to the population of subjects from which you drew your sample. Then speculate about the applicability to other populations, such as athletes of a different caliber, athletes from other sports, and non-athletes. Finish with specific justified suggestions for future research projects rather than a non-specific call for more research.
List the people who have helped you and what they did. List substantial sources of funding for the project.
There is a wide variety of styles for citing and listing references. Make sure you follow the instructions for the journal you are submitting your paper to. These references are in Sportscience style:
Cohen J (1988). Statistical power analysis for the behavioral sciences (second edition). Hillsdale, New Jersey: Lawrence Erlbaum
Hopkins WG (1998). A scale of magnitude for effect statistics. sportsci.org/resource/stats/effectmag.html: Internet Society for Sportscience
Hopkins WG (1999). Guidelines on style for scientific writing. Sportscience 3(1), sportsci.org/jour/9901/wghstyle.html (4397 words)
Check these before you submit your article.
- You have read the article on style.
- The Summary does not exceed the word limit for the journal.
- The Summary includes real data and magnitudes of effects.
- The content of the Summary is an accurate summary of the content of your article.
- The content of each section is appropriate to the section.
- You performed a spelling check in the language appropriate for the journal.
- References are in the style required by the journal.
Webmastered by Will Hopkins
Published March 1999