A short complaint today. I am reading student papers in which they are reporting on a psychology experiment the students designed and performed in groups. This assignment is the final project in a senior capstone course, and draws on many skills the students have learned over their 4 years as Psychology majors.
One component of the report is to prepare an APA-style Results section. Students have to deal with their own data, figure out how best to display summaries of the data in tables and graphs, and run appropriate statistical tests. Since these are experimental studies, the appropriate statistical tests are usually t-tests or analyses of variance.
It is not uncommon for student projects like this to fail to find differences between groups or conditions in an experiment. Even if the underlying hypothesis has merit, the experiment may be inadequate to test the hypothesis due to the limitations of student projects - not enough time to research the best dependent variables, not enough resources to run enough participants, not enough control to limit nuisance variables that obscure main effects.
From my perspective, that's understandable, and it provides the students plenty to speculate on in their Discussion sections.
What's frustrating, though, is that students don't know how to report the lack of significant effects in their Results section. All too often I see vague and misleading statements such as "The results were not significant" or "The data were insignificant".
Let's take a specific example. Imagine an experiment in which 8-year old and 10-year old children are given a logic puzzle, with the hypothesis that 10-year old children will have developed sufficient cognitive skills to perform the puzzle faster than 8-year old children. The independent variable is Age with two levels (8, 10). The dependent variable is time to complete the puzzle in seconds. Twelve children of each age are tested.
The results indicate that 8-year old children solve the puzzle in an average of 67 seconds. The 10-year old children solve the puzzle in an average of 53 seconds. Despite the difference in means, there is quite a bit of variability in each group, and the t-test provides a p-value of 0.09, higher than the usual threshold of p < 0.05; not significant.
But what is it that is not significant? Can we say "the data are not significant"? That phrase implies that the data are meaningless, but the data aren't meaningless. We now know it takes about a minute for kids to complete this particular logic puzzle. The t-test doesn't test the significance of the data; if it did, we should throw up our hands and say "I have no idea how long it takes kids to complete this puzzle. Maybe an hour, maybe a day, maybe they take one look at it and know the answer immediately."
Can we say "the results are not significant"? If "results" just means "data", then my explanation above indicates this phrasing is wrong. But if by results we mean "8-year old and 10-year old children solve this puzzle in about the same amount of time" that result may indeed be significant in the sense that we didn't expect it to be that way, so we've learned something new. Maybe we've learned that 8-year old children have surprising cognitive ability. Maybe we've learned that 10-year old children haven't developed substantially in 2 years on this particular puzzle. Maybe we've learned this puzzle isn't a sensitive index of the cognitive skills we're interested in. Any one of these conclusions might indeed be important, interesting, worthwhile. If you say the result is insignificant, that's like saying the whole stupid experiment was a waste of time and nothing new was learned.
So what do we say then? To describe the t-test result correctly, you need to know what a t-test does. The t-statistic compares the difference in two means relative to a measure of within-group variability. If the absolute value of the t-statistic is large, the p-value will be small, and the conclusion will be "statistically significant". What is statistically significant? The difference between the two means being compared. Not the data. Not the results. The difference between the means. Likewise, if the absolute value of the t-statistic is small, the p-value will be large, and the conclusion is "not statistically significant" (a better phrase than "insignificant"). What is not significant? Not the data. Not the results. The difference between the means.
And so the correct way to write this is: "We found no difference between 8-year old and 10-year old children in solving times on this puzzle (t(22) = 1.78, p = 0.09, n.s.)."
If the design had instead included 3 groups, let's say 8-, 10-, and 12-year old children, and the means were as follows: 67 seconds, 53 seconds, and 58 seconds (12-year old children fell in the middle, unexpectedly), we would instead have to run an analysis of variance. If our p-value came out to be p = 0.14, we would say this is "not significant." But again, what isn't significant?
An ANOVA relies on an F-ratio, which is a comparison of the variability between groups relative to the variability within groups. If the F-ratio is large, the p-value will be small, and we will conclude that the independent variable "affects" the dependent variable. If the F-ratio is small, the p-value will be large, and we will not be able to conclude that the independent variable affects the dependent variable. Remembering that the dependent variable is puzzle solving time, and the independent variable is Age with 3 levels (8, 10, 12), we can correctly summarize this as:
"There was no significant main effect of age on solving time (F(2,33) = 2.07, p = 0.14, n.s.)."
A less specific description ("the results were insignificant") is just plain wrong.
Thursday, May 4, 2017
Monday, December 1, 2014
Capital letter: Stand down!
Two of the classes I teach are Neuropsychology and Physiological Psychology. As a result, we inevitably deal with brain areas in both courses and with diseases and syndromes in Neuropsychology. Generally I assign some sort of paper in each class, and one of the recurring errors that I haven't yet commented on in this blog is a tendency for students to capitalize words that don't require capitalization.
For example, I might get a sentence such as the following:
On one hand, I get the confusion. I remember very well being confused about what should be capitalized when I was younger. On the other hand, we did capitalize more when I was younger because we didn't have easy access to typographic tricks like italics. Additionally, there's really no excuse for this mistake - one just needs to examine the text book for the course, or my lecture slides, to learn from the template that you do not capitalize these words.
That's probably what bothers me the most about this error - it is so easy to avoid. In my experience, though, more than half of my students don't naturally use published work as templates for the rules that should guide their own writing. That's unfortunate, because checking how others do it is a great way to avoid mistakes.
Finally, it should be noted that there is one exception to this rule: when the syndrome, disease, or brain area is named for a person. That's an unfortunate oddity of our language, I suppose, but in those instances the syndrome, disease, or brain area "inherits" the proper noun status of the person's name. Thus we have Down's syndrome, Alzheimer's disease, and Heschl's gyrus. We have Korsakoff's syndrome, Parkinson's disease, and Purkinje cells. Even in those cases, however, it is only the first word - the eponym - which receives the capital letter, not the word syndrome, disease, gyrus, or cell. And without an eponym, use lower case - for generalized seizures, encephalitis lethargica, amygdala, cerebral achromatopsia, influenza, nucleus of the solitary tract, And So On And So On.
For example, I might get a sentence such as the following:
Surgeons removed H.M.'s Hippocampus, which unexpectedly caused Anterograde Amnesia.I would then slash through the H in hippocampus, and both As in anterograde amnesia. My usual margin note would be "Only capitalize proper nouns," though I suspect many students don't know what a proper noun is, or more likely, assume that the names of syndromes, diseases, and brain regions are proper nouns.
On one hand, I get the confusion. I remember very well being confused about what should be capitalized when I was younger. On the other hand, we did capitalize more when I was younger because we didn't have easy access to typographic tricks like italics. Additionally, there's really no excuse for this mistake - one just needs to examine the text book for the course, or my lecture slides, to learn from the template that you do not capitalize these words.
That's probably what bothers me the most about this error - it is so easy to avoid. In my experience, though, more than half of my students don't naturally use published work as templates for the rules that should guide their own writing. That's unfortunate, because checking how others do it is a great way to avoid mistakes.
Finally, it should be noted that there is one exception to this rule: when the syndrome, disease, or brain area is named for a person. That's an unfortunate oddity of our language, I suppose, but in those instances the syndrome, disease, or brain area "inherits" the proper noun status of the person's name. Thus we have Down's syndrome, Alzheimer's disease, and Heschl's gyrus. We have Korsakoff's syndrome, Parkinson's disease, and Purkinje cells. Even in those cases, however, it is only the first word - the eponym - which receives the capital letter, not the word syndrome, disease, gyrus, or cell. And without an eponym, use lower case - for generalized seizures, encephalitis lethargica, amygdala, cerebral achromatopsia, influenza, nucleus of the solitary tract, And So On And So On.
Monday, April 28, 2014
Here's "looking at" you, kid
Just a short post today on the issue of colloquialisms.
It should be obvious, I think, but when writing virtually any kind of college-level paper, and particularly a technical paper such as a report of an experiment - avoid colloquialisms.
There's one in particular that shows up again and again in student papers, and that's "look at" or "looked at". Here's an example:
Even better are studied or tested. These verbs correctly imply that the scientists are setting up special conditions that allow inferences to be made about how variables influence one another. Any one can "look at" something, but it takes special care to examine something, and it takes a very particular arrangement to study something or to test an idea.
And in this case, the more precise word also provided economy. "Look at" is a two-word phrase and requires an ugly and cognitively demanding preposition. Test, examine, and study are clean, precise, single-word active verbs that do the job better.
It is true that one should write as naturally as possible, reduce complexity in long sentences, and avoid unnecessary jargon. But in following such advice, one must also avoid colloquialisms and choose the most precise word or phrase.
It should be obvious, I think, but when writing virtually any kind of college-level paper, and particularly a technical paper such as a report of an experiment - avoid colloquialisms.
There's one in particular that shows up again and again in student papers, and that's "look at" or "looked at". Here's an example:
In another study, Kahn and Wansink (2004) looked at how variety of food influences consumption in children and adults.Normally I'm in favor of using plain language rather than going for the $20 word, but in this case the plain language is imprecise and does a disservice to the scientists you are citing. The proper way to write this sentence is:
In another study, Kahn and Wansink (2004) tested the effect of food variety on food consumption in children and adults.I've cleaned up the second half of the sentence here, but the important change is replacing "looked at" with tested. Other replacements for "looked at" include examined or studied. Although one definition of examined is pretty close to "looked at", examined at least implies looking at something closely. It implies that some form of analysis is taking place, a process that is not implied by the verb phrase "look at".
Even better are studied or tested. These verbs correctly imply that the scientists are setting up special conditions that allow inferences to be made about how variables influence one another. Any one can "look at" something, but it takes special care to examine something, and it takes a very particular arrangement to study something or to test an idea.
And in this case, the more precise word also provided economy. "Look at" is a two-word phrase and requires an ugly and cognitively demanding preposition. Test, examine, and study are clean, precise, single-word active verbs that do the job better.
It is true that one should write as naturally as possible, reduce complexity in long sentences, and avoid unnecessary jargon. But in following such advice, one must also avoid colloquialisms and choose the most precise word or phrase.
Friday, March 28, 2014
When? Not then!
Here's a pretty specific comment, though it's related to my empty phrases post of a couple of years ago. I have noticed that students have a habit of starting a sentence with the word "When" in instances where the word does not apply.
The error occurs most commonly in a Results section of a lab report. Here's an example:
So the first sin in this sentence is that it's inaccurate. Now let's make it accurate:
In other cases, there's simply no "when" involved at all. Imagine this comment relevant to an image comparing the brain sizes of several different primates:
In many cases, the psychology experiments we conduct - even if they must obviously occur on some specific days on the calendar - are nonetheless attempting to describe things that are true of the world. Thus, returning to the original example above, crafting a sentence using the word "when" at least tacitly implies that the result being described is limited to a particular moment in time. If the "experimental group" was denied sleep for 48 hours, then hopefully the result of the experiment is teaching us a fact about the world - sleep deprivation is bad for memory - not just describing what happened to some participants at one instant in time that has no relevance to other people at other moments in time. The word "when" trivializes the process.
The error occurs most commonly in a Results section of a lab report. Here's an example:
When we looked at the data, the control group remembered twice as many words as the experimental group.There are variations on this theme ("When looking at Figure 2...," "When a t-test was conducted..."). In the example quoted above, the control group remembered twice as many words as the experimental group. When did they do this? Not "when" the researchers "looked at the data", but rather "when" the experiment was conducted.
So the first sin in this sentence is that it's inaccurate. Now let's make it accurate:
When the experiment was conducted, the control group remembered twice as many words as the experimental group.Now it's accurate, but the second sin becomes apparent: that information is irrelevant. Of course that's "when" the the groups did the remembering, but did you really think you had to tell me that? Is there any possibility of misunderstanding this timing of events? The answer is no - and so the entire clause becomes an "empty phrase" that lends no information to the sentence which isn't already strongly implied by the context.
In other cases, there's simply no "when" involved at all. Imagine this comment relevant to an image comparing the brain sizes of several different primates:
When looking at the brain sizes of various primates (Figure 1), humans have a much larger brain proportionate to their body than their nearest relatives.Again, the statement is inaccurate, since it implies that humans didn't have a larger proportionate brain size until someone looked at a figure. But worse, it isn't even possible to rewrite the sentence using the word "when" that is in any way accurate. The brain size phenomenon isn't a thing that occurred at some instant in time, it is a thing that is true of the world.
In many cases, the psychology experiments we conduct - even if they must obviously occur on some specific days on the calendar - are nonetheless attempting to describe things that are true of the world. Thus, returning to the original example above, crafting a sentence using the word "when" at least tacitly implies that the result being described is limited to a particular moment in time. If the "experimental group" was denied sleep for 48 hours, then hopefully the result of the experiment is teaching us a fact about the world - sleep deprivation is bad for memory - not just describing what happened to some participants at one instant in time that has no relevance to other people at other moments in time. The word "when" trivializes the process.
Wednesday, December 11, 2013
Writing Results sections right
A common task is writing a lab report (often in APA style) that consists of an Introduction, Method, Results, and Discussion section. Good Introductions and Discussion sections are very hard to write, and really take a lifetime of practice. Method and Results sections, by contrast, are more formulaic and therefore are easier to master - and yet, when my students make their first attempt at these sections, they make a number of common rookie mistakes. Today I'd like to focus on the Results section.
1. Just say it, don't tell me you're going to say it
"After collecting the data, the Control group averaged 124.5 correct."
As I've posted before, there is no need to state the obvious. Of course you got this average "after collecting the data." This problem shows up any number of ways. "Our results indicated that the control group did worse that the experimental group" is another popular one. Just kill the extra words.
2. Results are not significant or insignificant, but certain comparisons might be
I can't count the number of times a student has written some version of "Our results were insignificant," though the number would probably be proportional to the number of hairs that used to be in my head that are now ripped out of my head and collecting on the floor around my desk chair.
When we run statistical tests, we are assessing whether some difference between groups, or some relationship between variables, or some other kind of comparison, is likely (or not likely) to have occurred by chance alone. We use the (perhaps unfortunate) term "statistical significance" to indicate when the data are not likely to have occurred by chance alone.
Imagine I have two groups, a Control and an Experimental group, and I have conducted a t-test which resulted in a non-significant p-value. That "result" - that the Control and Experimental groups don't differ, may be important. It may be reliable. It may be replicable. To say "Our results were insignificant" is, therefore, potentially misleading, and it's definitely vague.
The right way to describe the t-test would be: "The difference between the Control and Experimental groups was not significant (t(18) = 0.563, p = 0.58)." When describing a statistical result, always describe what your statistics is testing. It's a t-test, so it's testing whether a difference between two groups is a reliable difference. You aren't testing a "result" - you're testing a difference.
Likewise "Our ANOVA was not significant" and "Our correlation was significant" aren't very informative. "There was no main effect of temperature on our measure of aggressiveness" and "Level of anxiety was significantly correlated with blood pressure readings," on the other hand, correctly describe the statistical results.
3. Figures and statistical tests should match
There's an art to making figures, but it's an important art. Figures are usually the heart of a Results section, so much so that I make my figures first and build the text of the Results around the key figures.
Students often don't know what to graph, but the recipe for making a graph is actually quite simple. Your hypothesis is a prediction about how some independent variable will affect some dependent variable. ("We hypothesized that reaction time in a visual search task will increase as a function of carbon dioxide levels.") Your design was based on the hypothesis. ("We tested people in 4 conditions: the atmospheric carbon dioxide level in the testing room was set at 0.04%, 0.08%, 0.16%, or 0.32%.") Your statistical analysis is based on your design. ("We conducted a one-way ANOVA on reaction times with carbon dioxide level as a between-subjects factor.") Your key figure - it should be obvious - will have the dependent variable on the vertical axis ("Average reaction time, ms") and the independent variable on the horizontal axis (the four carbon dioxide level groups).
Students sometimes seem to get overwhelmed with what to do once they've got a spreadsheet full of data. The cure for this is to plan your graphs before you start your experiment. If you have a good experimental design, the graphs you'll need in your report should be obvious from the very beginning.
3a. Figures are figures, tables are tables
Every year I get a student or two who refers me to "Table 1" when he or she really wants me to look at "Figure 1". I don't know why.
3b. Refer to figures early and often, but parenthetically
Frequently students will refer to figures at the end of the Results almost like an afterthought. "The data are graphed in Figure 1." As soon as the figure is relevant - as soon as looking at the figure would help a reader understand what you're saying - it should be referenced in the text. Whenever I can, I try to refer to the first Results figure at the end of the first sentence of my Results section. "Somewhat consistent with our expectation, reaction times were longer at the highest carbon dioxide level, but were relatively equivalent at the three lower levels (see Figure 1)." Notice that I refer to the figure at the end of that sentence - the text should not just refer to the figure, it should also describe the pattern of the data that the figure graphically represents. Don't just say "The relationship of reaction time to carbon dioxide level is shown in Figure 1." Tell the reader what the data were and refer to the figure parenthetically so they can also see the data pattern for themselves.
4. Inferential statistics aren't data - statistics assess the reliability of patterns in the data
Students have a way of losing their data when they start doing inferential statistics like ANOVA and t-test. Imagine a Results section that starts out like this: "An ANOVA indicated that there was a significant main effect (F(3,36) = 5.797, p = 0.002)." That's a terrible first sentence, because I have no idea what actually happened in the experiment, I only know what happened after the experiment when my student was studying a computer printout from his or her statistics program. Better would be:
"Somewhat consistent with our expectation, reaction times were longer at the highest carbon dioxide level, but were relatively equivalent at the three lower levels (see Figure 1). An ANOVA indicated that the main effect of carbon dioxide level on reaction times was significant (F(3,36) = 5.797, p = 0.002). A post-hoc test (Tukey-HSD) indicated that the differences between the highest carbon dioxide group and the other 3 groups was significant (all p-values < 0.05) but that the 3 other groups did not differ from one another." All the statistics are dutifully included, but I also now know what was measured (reaction time), what was manipulated (carbon dioxide levels), and what the pattern of the data was (one group's reaction times were higher than the other three).
5. Units are always important
This should go without saying, but if a number has a unit, always report the unit. "The control group averaged 375.6" is wrong. "The control group averaged 375.6 ms" is much better.
6. A final bit of advice
Ideally, your Results section should be perfectly understandable if every figure and statistic was removed. Figures and statistics are super-important - you'd never really want to remove them - but the point is that you shouldn't "lead" with them either. Your Results section isn't really about figures, F-values, and p-values; it's really about what happened to your subjects. Some of them scored high. Some scored low. Some were fast, some were slow. Your reader must have a clear picture of the pattern of the data, and this usually requires you to use plain, every day language.
Wherever you can, stick the stats and the references to figures in parentheses, and then make sure your sentences still make sense without the parentheses. Sometimes you won't be able to do this, but you might put your statistics or figure descriptions in a separate sentence, and then verify that your paragraphs make sense without those sentences. Yes, you need the statistics. You need the figures. But these do not replace a description of the pattern of data obtained in your experiment.
1. Just say it, don't tell me you're going to say it
"After collecting the data, the Control group averaged 124.5 correct."
As I've posted before, there is no need to state the obvious. Of course you got this average "after collecting the data." This problem shows up any number of ways. "Our results indicated that the control group did worse that the experimental group" is another popular one. Just kill the extra words.
2. Results are not significant or insignificant, but certain comparisons might be
I can't count the number of times a student has written some version of "Our results were insignificant," though the number would probably be proportional to the number of hairs that used to be in my head that are now ripped out of my head and collecting on the floor around my desk chair.
When we run statistical tests, we are assessing whether some difference between groups, or some relationship between variables, or some other kind of comparison, is likely (or not likely) to have occurred by chance alone. We use the (perhaps unfortunate) term "statistical significance" to indicate when the data are not likely to have occurred by chance alone.
Imagine I have two groups, a Control and an Experimental group, and I have conducted a t-test which resulted in a non-significant p-value. That "result" - that the Control and Experimental groups don't differ, may be important. It may be reliable. It may be replicable. To say "Our results were insignificant" is, therefore, potentially misleading, and it's definitely vague.
The right way to describe the t-test would be: "The difference between the Control and Experimental groups was not significant (t(18) = 0.563, p = 0.58)." When describing a statistical result, always describe what your statistics is testing. It's a t-test, so it's testing whether a difference between two groups is a reliable difference. You aren't testing a "result" - you're testing a difference.
Likewise "Our ANOVA was not significant" and "Our correlation was significant" aren't very informative. "There was no main effect of temperature on our measure of aggressiveness" and "Level of anxiety was significantly correlated with blood pressure readings," on the other hand, correctly describe the statistical results.
3. Figures and statistical tests should match
There's an art to making figures, but it's an important art. Figures are usually the heart of a Results section, so much so that I make my figures first and build the text of the Results around the key figures.
Students often don't know what to graph, but the recipe for making a graph is actually quite simple. Your hypothesis is a prediction about how some independent variable will affect some dependent variable. ("We hypothesized that reaction time in a visual search task will increase as a function of carbon dioxide levels.") Your design was based on the hypothesis. ("We tested people in 4 conditions: the atmospheric carbon dioxide level in the testing room was set at 0.04%, 0.08%, 0.16%, or 0.32%.") Your statistical analysis is based on your design. ("We conducted a one-way ANOVA on reaction times with carbon dioxide level as a between-subjects factor.") Your key figure - it should be obvious - will have the dependent variable on the vertical axis ("Average reaction time, ms") and the independent variable on the horizontal axis (the four carbon dioxide level groups).
Students sometimes seem to get overwhelmed with what to do once they've got a spreadsheet full of data. The cure for this is to plan your graphs before you start your experiment. If you have a good experimental design, the graphs you'll need in your report should be obvious from the very beginning.
3a. Figures are figures, tables are tables
Every year I get a student or two who refers me to "Table 1" when he or she really wants me to look at "Figure 1". I don't know why.
3b. Refer to figures early and often, but parenthetically
Frequently students will refer to figures at the end of the Results almost like an afterthought. "The data are graphed in Figure 1." As soon as the figure is relevant - as soon as looking at the figure would help a reader understand what you're saying - it should be referenced in the text. Whenever I can, I try to refer to the first Results figure at the end of the first sentence of my Results section. "Somewhat consistent with our expectation, reaction times were longer at the highest carbon dioxide level, but were relatively equivalent at the three lower levels (see Figure 1)." Notice that I refer to the figure at the end of that sentence - the text should not just refer to the figure, it should also describe the pattern of the data that the figure graphically represents. Don't just say "The relationship of reaction time to carbon dioxide level is shown in Figure 1." Tell the reader what the data were and refer to the figure parenthetically so they can also see the data pattern for themselves.
4. Inferential statistics aren't data - statistics assess the reliability of patterns in the data
Students have a way of losing their data when they start doing inferential statistics like ANOVA and t-test. Imagine a Results section that starts out like this: "An ANOVA indicated that there was a significant main effect (F(3,36) = 5.797, p = 0.002)." That's a terrible first sentence, because I have no idea what actually happened in the experiment, I only know what happened after the experiment when my student was studying a computer printout from his or her statistics program. Better would be:
"Somewhat consistent with our expectation, reaction times were longer at the highest carbon dioxide level, but were relatively equivalent at the three lower levels (see Figure 1). An ANOVA indicated that the main effect of carbon dioxide level on reaction times was significant (F(3,36) = 5.797, p = 0.002). A post-hoc test (Tukey-HSD) indicated that the differences between the highest carbon dioxide group and the other 3 groups was significant (all p-values < 0.05) but that the 3 other groups did not differ from one another." All the statistics are dutifully included, but I also now know what was measured (reaction time), what was manipulated (carbon dioxide levels), and what the pattern of the data was (one group's reaction times were higher than the other three).
5. Units are always important
This should go without saying, but if a number has a unit, always report the unit. "The control group averaged 375.6" is wrong. "The control group averaged 375.6 ms" is much better.
6. A final bit of advice
Ideally, your Results section should be perfectly understandable if every figure and statistic was removed. Figures and statistics are super-important - you'd never really want to remove them - but the point is that you shouldn't "lead" with them either. Your Results section isn't really about figures, F-values, and p-values; it's really about what happened to your subjects. Some of them scored high. Some scored low. Some were fast, some were slow. Your reader must have a clear picture of the pattern of the data, and this usually requires you to use plain, every day language.
Wherever you can, stick the stats and the references to figures in parentheses, and then make sure your sentences still make sense without the parentheses. Sometimes you won't be able to do this, but you might put your statistics or figure descriptions in a separate sentence, and then verify that your paragraphs make sense without those sentences. Yes, you need the statistics. You need the figures. But these do not replace a description of the pattern of data obtained in your experiment.
Saturday, June 16, 2012
Get Rid Of The Mini-Me Algorithm
The Problem
Today's insight may be specific to a certain kind of writing assignment, though I'm not sure - it may prove to be more general. I asked my students to summarize a chapter from a book. The summaries were to be 5-6 double-spaced pages, meaning that the chapters they were summarizing had about 10 times as many words.
The problem was that most of the summaries used exactly the same outline as the target chapter. My students seemed to be adopting a "Mini-Me" algorithm for writing their summaries (Mini-Me being the character in the Austin Powers movie franchise who was a smaller version of the main character). Rather than summarize the chapter, my students dutifully went page by page in the chapter, summarizing each topic the author discussed in the target article, and in the exact same order.
I can certainly understand this impulse. This "algorithm" (an algorithm is a programmatic series of steps guaranteed to complete a task) ensures that every component of the chapter will be mentioned in the summary, and therefore the student won't get marked off for leaving out something critical. Unfortunately, there are several major drawbacks to this approach:
1. The resulting summary is very dry - the student feels like he or she must include all the terms and jargon, and has space for little else.
2. The resulting summary is stilted. Ideas don't flow, because, to fit 20 printed pages into 5 double-spaced pages, all transitions, explications, and synthetic observations must be removed.
3. The resulting summary is unoriginal. The student uses no original thought in the design of the paper - the organization is completely set by the target chapter - and the student has no space for any other original contributions. Worse, the professor grading the paper is invited to think that the student doesn't necessarily even understand the target chapter, since the paper is merely a dehydrated version of the target chapter.
4. The resulting summary is more difficult to understand than the target chapter. One goal of a summary should be to simplify a complex topic and distill it down to the most important concepts presented in a lively, coherent, and clear manner. By adopting the Mini-Me algorithm, the summary includes not just the most important concepts but also a number of less important concepts and without space to clarify which is which.
Proof That This Is Bad Writing
Here's the proof that this is bad writing. Imagine the author of the target chapter had been told: sorry, you can't use 10,000 words for this topic. You can only use 1250 words.Do you think that the author would write the chapter in exactly the same way? Would he or she include all of the same topics, in exactly that order, and just reduce the number of words written about each topic?
That's hard to believe. The author would probably first ask himself or herself, what do I absolutely need to get across? What elements of this chapter are useful not only for specialists, but for anyone who might read this chapter? What examples do I use that are the most memorable? What concepts are the most general?
Following that brainstorming session, the author would then tear up the long version of the chapter and start on a fresh page. It would be a completely re-envisioned document - a completely new chapter. Nowhere in the author's writing method would Mini-Me appear.
The Solution
Fortunately, there's an easy solution (and in a coincidence that is no coincidence, it is a solution I offered in my previous blog post, Don't Quote!). Here is the best algorithm for writing a chapter summary:
Read - Reflect* - Write* - Rewrite
The stages with the asterisks mean that you can't read during these stages. You shouldn't even consult any notes, except perhaps the barest outline. That is, when you are reflecting, don't read. And when you are writing, don't read.
Read the chapter. Take notes. Read your notes. Reread the chapter. Get yourself to a point where you understand the chapter. You certainly won't have every single detail in your head, but what you should have is the gist of the chapter. You should have the most important concepts. You will remember a couple of really memorable examples. (Go back and read the third paragraph under Proof That This Is Bad Writing, and see if the list of things you know aren't exactly the things you would need to know to write a good 1250 word chapter summary.) This is the reflection stage - figure out what are the highlights, then start thinking about how you would communicate those highlights to a reader.
When you begin to write, do not refer back to the target article. This will ensure that you don't write in the same sequence as the target article, and it will reduce the likelihood that you will include unnecessary details. You won't be able to take any direct quotes (almost always a good thing to avoid). Your writing more lively and interesting, because you will probably best-remember the most interesting aspects of the target chapter. You will only be able to write about what you truly understand, which was probably the professor's goal in assigning the summary in the first place. You will be forced to include original content in the form of how you organize the summary, make transitions between points, and justify the importance of the concepts you are including.
At the rewriting stage, you are allowed to review your notes and the target chapter. There is much less danger now that you will shove in unnecessary details or undo the good work of organizing that you have already done. These additional details and clarifications will now be forced to fit your paper, which is a lot better than the Mini-Me model, in which your paper is forced to fit the details of the target chapter.
And finally, a word about rewriting. I think another of the Mini-Me algorithm's failures is that it is so sequential. If you write as you flip the pages of the target chapter, it feels like, when you get to the end, you are done. What's to go back and rewrite? You've already assured yourself that you've "gotten everything." The Mini-Me algorithm is so bad that you can actually convince yourself that rewriting is unnecessary. By rewriting I do not mean tearing up and starting again. I mean reflecting on each paragraph in your first draft and asking yourself if it is clear, complete, and flows well with the previous and succeeding paragraphs. This stage is critical to good writing; any algorithm that omits this stage should be suspect.
Friday, April 27, 2012
Stop Quoting! (You Can Quote Me On That)
The Problem
Many of my students seem to have learned along the way that quoting articles is a good idea. I'm not sure exactly where this comes from, although three possibilities occur to me.
Quoting To Avoid Plagiarism
Maybe a student picked up the quoting habit in this way. First, the student turns in a paper in which, through inexperience, he or she plagiarizes some published writing. The helpful teacher informs the student "If you are going to use someone else's words, you must put those words in quotation marks and cite the source." From then on, the student now has a license to use other people's words without being accused of plagiarism! The teacher, grateful that the student has learned the importance of quotation marks and citations, declines to ruin this success by pointing out that, while the quotation and citation has been done correctly, the result is a lousily written paper.
Quoting Out Of Inferiority Complex
When a student is asked to summarize some published piece of writing, it is a rather daunting task. How can I, a college undergraduate, be expected to write something more clearly and thoroughly than the published author of the target article, when that person has a PhD, 20 years of writing experience, and knows the material ten times better than I do? Faced with that realization, the student decides he or she can't win. But if you can't do better than the author, you can at least do as well as the author - by saying exactly what the author said. The student paper then becomes summary by collage - summary by cut and paste. Unfortunately, this isn't a summary at all.
Failure To Discriminate Two Types Of Quotes
On the other hand, it may be that the student got good advice from a teacher along the way, perhaps in an English class, that quoting provides a useful starting point to analysis. That's correct. The truth is: there are times when quotations are a great idea. But just because quotations are a great idea sometimes doesn't alter the fact that quotations are a terrible idea at other times.
A quote is a good idea if you want to comment on something the author said - often to critique it. A quote is a bad idea if you are quoting the author to replace some of your own words. To put this another way, if you quote the author and then continue on from the quote without any further comment, you've quoted inappropriately.
This point is subtle enough that I will have to make up an example. Here's a case where a quote is appropriate:
In their article, the authors defined aversion as "a decrease in the amount of the stimulus consumed" (Authorson et al., p. 7). However, many authors reserve the term aversion for a negative hedonic evaluation, and use the term avoidance to indicate a reduction in intake.
In the example above, the student is commenting on the author's choice of words, and so it is necessary to first document the author's word choice with a quote. The quote is necessary here, otherwise the student's comment makes no sense. However, if the student wasn't going to comment on the word choice, and just wanted to report the dependent variable, the quote would be inappropriate.
The Tip: Don't Have The Source Material Sitting Next To You When You Write
So how do you avoid filling your paper with quotes? My advice is to avoid the temptation completely by writing your paper without having the work you are summarizing sitting next to you. This might sound scary, and that's right. It is fear that makes you quote in the first place - feeling like you don't know the article well enough, feeling like you can't phrase things as well as the authors, feeling like you need to borrow some of the article's "sparkle" for your own paper. If you have the source sitting next to you, you will give into that fear and your paper will turn out lousy as a result.
So read your target article carefully, take notes, highlight it, whatever you normally do. Maybe even make an outline of what you want to write. Then, bury everything. The article. Your notes. Your outline. Go and write your summary without consulting anything but your own brain. If you can't do this, you aren't ready to write the paper anyway because you don't know the material as well as you think you do.
Once you've written that draft, of course go back to your notes, your outline, and the source material. You probably did forget something, you probably didn't say everything perfectly, and who knows, you might even need to get some numbers or even words out of that source material. But with a draft in hand all written in your own words, it is much easier to resist the temptation of over-quotation. An added bonus is that if you ever have to recall the information later (like on a closed-notes exam) you will have learned it much better than if you cut and pasted.
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