Theory of Planned Behavior

The Theory of Planned Behavior
It’s Easy, Fun, and Popular!

This chapter is about the Real Big Winner in persuasion theory.  Virtually everyone who’s been to college has heard of this one.  It’s like the periodic chart of elements in chemistry, MyDearAuntSally in mathematics, veni, vedi, vici in Latin, and “Let’s Go, Mountaineers” on game day.  If you’ve been to college, you know this like the best deal on Beer Night.

A little history and background . . . the Theory of Planned Behavior (TpB) came into being as the Theory of Reasoned Action (TRA) in the late 1960s.  In the 1960s most college campuses were better known for riots, protests, teach-ins, free love, rock ‘n roll, and illegal substances, but in some departments some people were actually doing serious thinking.  Martin Fishbein at the University of Illinois was one of those guys.  Dr. Fishbein and one of his students, Icek Aizen who got his doctorate and became Dr. Aizen, were sitting around one day thinking about why people did what they did and they came up with this theory they called Reasoned Action.  They did a bunch of interesting studies (to me any way) and wrote several books explaining it.  They got really famous and if they had done something like this in Economics or Medicine they would have probably won a Nobel Prize.  Really.  You don’t have to go through a research library and get a doctoral degree like I did to see the impact they’ve had.  Get your favorite online search engine and type in the key terms “theory reasoned action” or “theory planned behavior”  You’ll get so many hits you’ll think you’ve typed in an exotic slang term for sex.  Last time I did this, Google returned over 5 million hits.

Now, as good as the Theory of Reasoned Action is, Dr. Aizen thought it could be improved and he created an extension to it called the Theory of Planned Behavior.  There’s a lot of to and fro – which is how academics fight; they don’t throw punches, they just to and fro – about the distinctions between the TRA and TpB and you can make some professors really angry about the topic which is kind of fun to do if you’ve had a couple of drinks and you’re tired of talking about structural equation models and elaboration moderators and cognitive production systems.  Oops, I just dropped an olive.

In this chapter we’ll focus on TpB because if you know it, you know everything about TRA and we kill two birds with one stone.  (Is it okay nowadays to write about killing birds with stones?  Aren’t the PETA people petitioning on this?  And what about the gun control crowd?  Aren’t they also anti-stone?)

To understand the Theory of Planned Behavior just consider the title.  “Theory” means a set of concepts that are linked together in a pattern of sequence (beginning to end) and hierarchy (top to bottom).  “Planned” is just that, an activity that is thoughtful, goal oriented, but not perfect or absolute.  And “Behavior” is the stuff you can see people do, the actions of everyday life.  So, TpB will give us a small list of conceptual tools that describe and explain how people behave in planned ways.

Fun, Popular, and Easy

Here are the fundamental concepts of TpB:  Behavior, Intention, Attitude, Norm, and Control.  And here’s a low tech diagram that shows the sequence and hierarchy:

TpB

This diagram shows that things called Attitude, Norm, and Control combine to move something called Intention, which in turn will move Behavior.  To understand this diagram we need to read it backwards from right to left.

Let’s start with Behavior or Action (they’re synonyms and in this chapter they mean exactly the same thing; I will write one or the other to avoid monotony).  Action is the concrete observable behavior that people perform.  They smile at others.  They vote on Election Day.  They choose this brand over that brand then purchase it.  They drive their cars.  They flirt with that gorgeous blonde in the Lair.  They point and click.  They tap.  They rap.  Actions all, behavior, too.

Now, what predicts the actions people perform?  This is the $64,000 question (although with inflation we really should call this the $439,706 question).  If you can answer this question, whether for $64,000 or for $439,706, you are way ahead of the game.  Seriously, think about this one for a minute.  If you are pretty good at figuring the likely actions of other people, you can have a pretty good time in life.  Anybody who has a good answer to this question is going to find it easier to get famous, make a lot of money, or to be more virtuous about it, and will also find it easier to be a better person.

According to TpB, the best predictor of action is intention.  The best internal, psychological (you can’t see it, but it’s going on inside) predictor of behavior (you can see it, it’s going on outside) is intention.

Intention.

Intention is the best predictor of behavior.

Thus, to understand what people are going to do, you need to understand what they intend to do.  (Intend:  plan, expect, propose, aim at, hope for, etc.)   Or actions follow intentions.

Let’s think about this for a moment.  Intentions predict actions.

Really?

What about reflexes?  If you’ve ever taken that obnoxious glaucoma test at the eye doctor, you know all about reflex behavior.  The optometrist has you rest your chin on that little black cup and soothingly asks you to “just look into the light.”  Then they blow that strong puff of air directly into your trusting eyeball.  Even when you know it is coming, you simply cannot control your behavior in response to the puff of air.  You blink (and twitch, gasp, and in my case, curse under my breath) and there’s just not much you can do to prevent this non-volitional, non-voluntary action.

What about classical conditioning?  Ding-dong, baby.  There’s no intention in that one.  What about reinforcement?  Zap!  You don’t think about the shock, you just run from it.  What about habit?  You just do because you’ve always done it, no thinking required.

Bang, we’ve already generated several obvious exceptions to the claim that intentions predict actions.  So, no, intentions do not always predict actions.  They don’t predict actions that are conditioned, reinforced, or habituated.

But, now consider all the actions people perform that are not conditioned, reinforced, or habituated.

Vote.  Marry.  Buy.  Advocate.  Choose.  Any action that requires some thought, planning, consideration, value, tradeoff.

That’s quite a list and an interesting list at that.  All of these actions are ones that we volitionally select.  That means we think about them, ruminate, weigh, consider pros and cons, we run them against our values.  Intentional actions are the most important and interesting actions that people perform.  These are the ones that really count in the world and show us for whom we truly are.  So the fact that there are some actions that are unintentional (habits, ding-dongs, carrots and sticks) doesn’t mean that intentional behaviors aren’t worth understanding.

TpB explains volitional behaviors we perform.  A volitional behavior is one that we have or had conscious control over.  It is a behavior we intend to perform, a behavior we know we are going to do.  By contrast, non-volitional behaviors are action we cannot rationally control.   Now, TpB will not explain those kind of behaviors, but if the action is something you planned to do, TpB maps out the crucial variables that drive your voluntary action.

Back to the opera . . .

The best predictor of volitional action is intention.  Now, what’s intention?

Intention is a psychological concept.  You can’t open up the body and find the intention organ.  It is an element in the cognitive world created in our brains.  This intention reflects the outcome of our thinking, consideration, and rumination about who we are and what we want to do in the world.

After careful thinking, we intend to reduce the amount of calories we eat and exercise more.

After thoughtful consideration, we intend to attend every damn class, read all the chapters, take good notes, and love our professors.

After long rumination, we . . . are sick of the parallel structure in this guy’s writing and wish he would get back to the point!

Intentions reflect the willful, planful likelihood of performing actions in given circumstances.  Intentions express the probability, not the certainty, that a behavior will occur in the future.

Okay, now all of this intention -> behavior stuff makes sense.  For volitional behaviors, it makes good common sense that if you know what others intend to do, you can make a pretty good prediction about what they will do.  So, what predicts intentions?

According to the Theory of Planned Action, three psychological variables are the best predictor of intentions:  Attitudes, Norms, and Control.

Attitudes express what is good or bad about the action.

Norms express who we think approves or disapproves of us doing the action.

Control expresses what makes it easier or harder to do the action.

Consider the low tech diagram:

Attitude (good-bad)
Norm (approves-disapproves)   -> Intention  ->  Action
Control (easier-harder)

Reading from left to right, if you think it is good, other people approve of you doing it, and it is easy, then you will have a stronger intention to perform the action.  If you think it is bad, other people disapprove, and it is hard, then you will have a weaker intention to perform.

Consider this idea as an equation:

Behavior = Intention.

Intention = Attitude (good-bad) + Norm (approves-disapproves) + Control (easier-harder).

Let’s run an example through this equation.  Consider the action of “exercising more often – at least three times a week.”

What’s good about this?

“I do feel better physically, mentally, and emotionally when I exercise.  Experts say you live longer and are healthier.  I might meet interesting new people, like that great looking blonde I saw at the Rec Center.”

What’s bad about this?

“I don’t look good in Spandex.”

Who approves?

“Come on.  Who doesn’t approve?  Everyone says you should do this.”

Who disapproves?

“Well, when I see myself in the mirror wearing Spandex, I disapprove, but that doesn’t count here.  The approval-disapproval thing is about other people.  So, really, nobody disapproves.”

What makes it hard to exercise?

“Time control.  Like I’m busy and where do I make time for this?”

What makes it easier to exercise?

“If I don’t over do it and just kind of ease into a routine.  If I joined a club or a group or some kind of social thing, it would be easier.”

Doesn’t this sound like the conversation you’ve had in your own head?  You think a bunch of thoughts and after all that thinking you somehow arrive at a conclusion which in this case we’ll call an intention.  If you were to take the time to write down all of these thoughts you have in your head when you have this conversation, you’d find that they pretty much fall into those three categories of attitude (good-bad), norm (approve-disapprove), and control (easy-hard).

If, on balance, you have more thoughts that are “good,” “approved,” and “easier” then you’ve got a favorable intention to perform.  And, contrariwise, if you have more thoughts that are “bad,” “disapproved,” and “harder” then you’ve got an unfavorable intention to perform.

The Theory in Action

I participated in a fun long term research program that aimed at changing diet to reduce consumption of fats.  Believe it or not, if all Americans switched from drinking higher fat milk (2% or more) to low fat (1% or Less!), as a country we’d hit the USDA guidelines for total fat consumption.  Stated another way, Americans drink a lot of milk and milk contains a lot of fat.  You can read the gory details in the publications to verify this astonishing fact.  More interesting here is how you can get a fairly important health effect with a simple behavior:  Just move your hand from a higher fat milk carton a few inches to a lower fat milk carton.

Across highly similar West Virginia towns, we ran a series of four combinations of a treatment versus a control.  The treatment and control towns were far away from each other and outside of each other’s (small) media markets.  We also drew random samples of people in each community for pre and post testing, so we had a quasi-experimental, pre-post design, repeated in our four different comparisons between a treatment town or a control town.

In each treatment, the “persuasive message” was always the same (based on extensive message testing also called formative research).  What we varied was the channel that carried the message.  We created three types:  Ads, PR, and Education.  Ads were purchased and clearly identified as advertising placed in local TV, radio, and papers.  PR comprised events we staged to draw free local news coverage in TV, radio, and papers.  Education was the standard face-to-face community organizing at meetings, churches, associations, etc.  Here’s how the treatment towns got different combinations of channels.

Treatment 1 (paid ads, PR, and community education)
Treatment 2 (paid ads and PR)
Treatment 3 (PR and education)
Treatment 4 (paid ads)

The key behavior outcome was switching.  At pretest we asked participants in treatment or control to tell us what kind of milk they used.  At posttest we asked the same participants the same question and computed “switching” as the difference between pre and post.  (This is one helluva difficult way to measure switching.  Most folks would dispense with the pretest and just ask at posttest whether you’d switched in the past couple of weeks.  If you don’t see the difference between defining switching as our pre post test versus just asking, you are not a good researcher and will someday face embarassing public proof of it.)

Let’s get to the results.

Milk Combined Results

According to a really useful meta analysis by Leslie Snyder and Mark Hamilton, normally a health and safety campaign will produce about a 9% improvement (treatment rate versus the control rate).  Across our 4 tests, our average improvement (the mean of the Difference column percentages) was 19.4%, twice as large as the meta average.  And, more interestingly, there is clear variation in switching depending upon the combination of channels.  The combination of ads and PR in Treatments 1 and 2 produced a nearly 30% improvement compared to the other two combinations that produced an average 9% improvement.  And, if you look at the last column, Reception Rate, you see why.  We got more reception with the paid/PR combination than with the others.  Thus, our campaign produced average results that were twice as good (19% versus 9%) as the meta and we could enhance that advantage to a tripling (30% versus 9%) through smart Reception planning.

With Treatment 1 we also collected supermarket milk sales data at three times:  pre, post, and six months later.  Control milk sales did not vary practically or statistically over the 3 periods.  Treatment sales showed a big increase pre to post in low fat milk sales (yeah, baby) that were maintained six months after we ended the campaign.

Clearly the campaign worked and now we can ask why.  During one of the experiments (treatment 1) we also collected data on Theory of Reasoned Action factors.  (We didn’t use Control because it obviously had no impact on the volitional behavior of switching.  Therefore we didn’t do TpB.)  Our message testing indicated that people’s decision to switch was driven by their Attitude (cost, taste, enjoyment) and not their Norms (what other people think they should do).  As a result, our campaign messages offerred strong Arguments that focused on Atttiude (e.g. low fat doesn’t cost any more than whole), but no Norm Arguments (your friends drink it and you should, too).  So, we expected a path model to show that the Treatment changed Attitude (not Norm) which changed Intention which changed Behavior.

Here’s the model with path regression weights (which for our purposes are essentially correlation coefficient ranging from 0 to 1.00).

Milk 1 TRA Path

If you were sick that day in algebra class when they taught structural equation models, let me explain.  The campaign (Treatment) changed people’s Attitudes about switching (.31), but had no impact on their Norms (.00).  Attitudes then had large impact on Intention (.48) while again Norms played no role (.00).  Intention to switch then led to large changes in self reported behavior (.56).  Exactly what we predicted.  The overall model (MR = .71) explained over 50% of the variance and had a Goodness of Fit index of .999.  Hey, if you can find a better structural equation model, buy it!

Here’s another way to display the TRA changes.

Milk TRA Means

There was a “large” difference in Intention to switch (3.2 versus 2.2), a “medium” difference in Attitude (41.8 versus 35.5) and a “small” difference in Norm.  In most published research, that small Norm difference would be the biggest effect the campaign would produce, but with this campaign, it is a piddling effect that is largely due to the strength of the other variables.  In other words, if you dropped Intention and Attitude from the analysis and just looked at Norm, it would not even be statistically significant.

However, every time I think about it, I want milk and cookies.

Fine Points About Attitude, Norm, and Control . . .

If you read the original sources on TpB the first thing you’ll realize is that Attitudes, Norms, and Control are theoretically more complex than I’m showing here.  Each of these components is actually composed of two smaller elements.  If you want to go on into an Exciting Career as a social scientist you really need to know this.  If you have a different career path, these nuggets of gold may be shiny, but you’re probably looking for something else and that’s fine.  Just know that things are a little more complicated than this quick (shallow, facile, glib, clever, sigh, the things colleagues say about my work) overview.

A TpB “Attitude” is the same thing described as “attitude” in the Basics chapter.  You’ll recall that I defined an attitude as an evaluation of an object of thought.  We used a mental yardstick as a metaphor.  Well, Attitude is also an evaluation, but we’re using a different metaphor – good to bad – with this.  When you “evaluate” you are expressing what is “good” or “bad” about the object of thought.

The concept of Norm is a massive idea in social science and we’re gliding on skates on the surface of a vast field.  You can take entire college courses devoted to the study of Norms (i.e. the rules of society).  I’m presenting it here as the simple notion of approval versus disapproval from others.  Just take a moment right now to consider how much of our life is shaped by the opinions of others.  We dress to please, impress, or attract good opinion.  We talk about topics that we think will please, impress, or attract.  Virtually every act of social behavior (actions in the presence of other people) is done with an eye toward garnering approval and avoiding disapproval.  Can there be any doubt that this external force is crucial in shaping our intentions and actions?

Control is another one of those social science concepts that is defined well enough so that most people can understand it and use it for both the real world practice and research studies.  It is also vague enough that people can use the term and mean something else.  For example, there is another related field of study on a term called, “self-efficacy.”

Show Me the Numbers

How good is TRA or TpB?  I mean, empirically.  If you do the thing right with a TACTful behavior and properly measure Intention, Attitude, Norm, and/or Control, what kind of prediction do you get?  Frankly, the numbers for a well done TpB model are insanely good.  Let’s take a look.

First, some caveats, fine points, and nuances.  The impact of attitude, norm, or control is going to vary widely depending upon the behavior in question.  For example, in my own research on health behaviors, we found that for the behavior of switching from high fat to low fat milk (1% or Less!), attitude was the only predictor of intention and behavior while for physical activity (Wheeling Walks!; we like exclamation points), attitude and control, but not norms were significant predictors.  Other researchers have reported similar variations.  Thus, while attitude, norm, and control play theoretically important roles for the whole theory, in practice with specific behaviors, the relative weight of each component is modulated.  Thus, in trying to understand how well TpB works, we need to focus on the “downstream” outcomes of intention and behavior, because the “upstream” predictors of attitude, norm, and control will vary in very predictable ways.

With this perspective we exclaim again:  Show me the numbers!  Perhaps the best answer comes from a 2001 meta analysis by Armitage and Conner.  They combined and summarized the TpB results from 185 published studies.  (If you are not in the research business you have no idea how much effort 185 published studies represents.  This is a huge number.)  They found that the average Windowpane effect of attitude, norm, and control on behavior was almost 25/75 and the effect on intention was nearly 20/80.  These are large effects and demonstrate the empirical power of the theory.  Thus, fun, popular, and easy account for large amounts of variation in both intention and behavior.

Given this kind of proven success, you can see why I make TpB a major element the Standard Model.  If you want to change a volitional behavior, you’ve got to find the fun, popular, and easy factors that drive it.  Then, use the ELM to design persuasion that changes the fun or popular or easy factor and you’re off to the races.

Implementation Intentions: Another Look at Intentional Action

Both TRA and TpB focus on volitional action that is goal directed.  You know you want to do it.  Thus, your intention to act is a great predictor of future action.  TRA and TpB try to predict intention with attitude and norms and/or control.  And, clearly, it works pretty well. But, if you think about it, you could try to ignore the fun, popular, and easy and just head for the intention.  What could you do to strengthen and activate the intention to act?

Consider this.  Intention refers to a goal state.  We intend to eat fewer calories and exercise more often.  We intend to vote on Election Day.  We intend to finish this chapter then do something really fun!

But to have a goal does not mean that you have a plan for achieving it.  There’s a difference between the goal and how you get there.  Peter Gollwitzer proposes a direct attack:  Implementation Intentions.  The label accurately describes the concept.  How do you intend to implement (attain, plan for, aim at) actions to achieve your goal?

The manipulation of implementation intentions procedes quite simply.  Just write or say, “Here’s how I intend to do it.”  Participants are encouraged to develop detail in the plan with specifics (next Tuesday at 10am in my living room I will do floor exercises for 20 minutes while watching a fitness DVD) and sometimes to develop coping plans to handle unexpected barriers or problems.  But the key manipulation is simple:  Just tell me specifically what you’re going to do to reach your goal.

Such planning may be provided by the experimentor (the participants are given common scripts that contain specific details) or self-generated by the participant (with coaching or without).  Furthermore, this approach has been tested with many different intentions and behaviors ranging from writing a report for school to weight loss, control of problem drinking, exercise, and smoking cessation; across days, months, and years of planning and action; and with a wide variety of populations from university students, to community dwelling adults, hospitalized schizophrenics, and heroin addicts in treatment.

Best of all, this simple act of explicit planning works.  Gollwitzer and Sheeran conducted a meta analysis of 94 implementation intention studies and found an average d effect size of .65 on goal attainment which translates into a medium Windowpane of 35/65.  Thus, if you want to make intentions alone drive behavior, focus on the implementation of the intention, the plan for achieving the goal.

By rough comparison, you’ll recall from a meta analysis of TpB studies (Show Me the Numbers), the Windowpane effect size for behavior was 25/75, a large effect.  Thus, generally speaking TpB is a stronger predictor of volitional action than Implementation Intentions (II).  However, II is a bit easier to execute.  Just have your target express an action plan with details.  That simple manipulation will produce the behavior change you want.

II rolls along like a research freight train, attracting new adherents demonstrating new applications.  My blog has several recent studies on helping kids resist tobacco, doing better in college, and cell phone II apps for increasing physical activity.  Check them out.

As a practical persuasion play, II is as simple to execute as most Cues yet it operates as a Central Route move with all the advantages of persistence, resistance, and prediction.  Of course, it helps if your targets are already leaning in the direction of that behavior change as in our Stages of Change analysis (precontemplation, contemplation, preparation, action, or maintenance).  An II play with targets in precontemplation (what’s an STD?) will not work as well as with targets in preparation (making a list and checking it twice).  But, we understand that and it’s an inherent limitation for every persuasion attempt.

So, to generate higher rates of change for a volitional action, just get your receivers to express a plan.  Make them make it concrete, observable, real.  Details count!  I also see ImpInt as a High WATT strong Argument approach.  The demand for planning makes the receivers go High WATT and they then produce their own strong Arguments – the actual detailed actions they will perform – and they engage in that “long conversation” in the head as they do all this planning.  No wonder it works.

Doing TpB

I find TpB to be an incredibly useful descriptive, planning tool for persuasion.  It helps me identify the best areas of development for changing a volitional action.  For a serious intervention, TpB is a great way to do message testing and formative research that helps me determine which factors are most important, then dig down to specific kinds of attitude (fun), norm (popular), or control (easy) beliefs I need to address.  In my research with the milk studies, research showed that attitude, but not norm or control drove purchase decisions.  In other TpB research I’ve done, it turned at that control was the dominant factor (physical exercise).

Thus, the most effective use of TpB is to do the whole nine yards of it.  (You can visit Icek Ajzen’s website for a great manual on this – see it in References and Recommended Readings or hit the Standard Model section of the Primer.)  Whether for a prosocial health and safety campaign or a new ad campaign for the latest HiTechWonderDevice, understanding attitude, norm, and control is a powerful template.

And, on a more simple and practical basis, TpB can be incredibly useful.  You want to your teen agers to clean up after themselves?  Figure out how to make that volitional action fun, popular, and easy.  Want to motivate your employees to follow a new policy?  Look for the fun, popular, and easy plays.

Hey, TpB works.  Take advantage of it.

Outro

That’s about it for TpB.  Here are the key words:  Behavior, Intention, Attitude, Norm, and Control.  Define each.  Generate examples for each.

Now, arrange them in sequence.

You’ve got a famous theory and a good answer to the $439,706 question.

References And Recommended Readings

Armitage, C. & Conner, M. (2001).  Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40, 471-499.

Booth-Butterfield, S., & Reger, B. (2004).  The message changes belief and the rest is theory:  The “1% Or Less” milk campaign and reasoned action.  Preventive Medicine, 39, 581-588.

Gollwitzer, P. & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. In M. Zanna, (Ed). Advances in Experimental Social Psychology, Vol 38, 69-119. San Diego, CA, US: Elsevier Academic Press.

Snyder, L. and Hamilton, M. (2002). A meta-analysis of U.S. health campaign effects on behavior: Emphasize enforcement, exposure, and new information, and beware the secular trend.  In R. Hornik (Ed), Public health communication: Evidence for behavior change. (pp. 357-383). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.

Wootan, M., Reger-Nash, B., Booth-Butterfield, S., Cooper, L. (2005) The cost-effectiveness of 1% Or Less media campaigns promoting low-fat milk consumption. Preventing of Chronic Disease [serial online]:  http://www.cdc.gov/pcd/issues/2005/oct/05_0019.htm

Aizen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Springer series in social psychology (pp. 11-39). Berlin: Springer.

Aizen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Aizen, I. (1988). Attitudes, personality, and behavior. Milton-Keynes, England: Open University Press & Chicago, IL: Dorsey Press.

Visit Dr. Aizen’s website at:  http://www.people.umass.edu/aizen/tpb.html.

Okay, about his last name, Aizen.  First, it is pronounced “Eye-Zen.”  Second, it is sometimes spelled, “Ajzen.”  Altogether it is “Eye-Zak” “Eye-Zen.”  Icek Aizen.  Pronounced just like it looks.

If you’re really motivated and yearn for the great weariness of much knowledge, here are the references to Reasoned Action by Dr. Fishbein and Dr. Aizen.

Fishbein, Martin (1967). Readings in Attitude Theory and Measurement, New York, NY: Wiley.

Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27, 41-57.

Fishbein, M. (1967). Attitude and the prediction of behavior. In M. Fishbein (Ed.), Readings in attitude theory and measurement (pp. 477-492). New York: Wiley.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co.

Yes, those references are from the 1960s.  And if you want to read them you’ll actually have to go to a library, search the shelves, find the book, open it and read it like Abraham Lincoln and I did when we were learning.  Amazingly enough, books often contain the same great ideas you find on the Internet!  Or from another angle, just because it is old, unfamiliar, and not digitized does not mean it is out of date.  In fact, pound for pound of book versus byte for byte of file, books are more likely to contain truth than are computer files.  Consider that you are the first generation in human history to grow up without books as your primary source of knowledge.  So what?  Books or files, how can it matter?

Well, the file you’re reading right now seems a lot like an academic textbook except that the writing style is flip, friendly, and fast.  There’s another big exception:  No one has reviewed this file prior to its publication.  Virtually everything on the real page (book, magazine, journal, etc.) went through a review and editorial process that vetted the content.  Talk to any writer and you’ll hear the laments of all those great ideas that never made it to print because of those lying reviewers or damn editors who killed the manuscript.  You really need to be careful about what you read on the Internet, including this Primer.  Determine the credibility of the writer.  Do periodic searches on key terms and see what other sources are saying.

Finally, I am sad to report that Dr. Fishbein recently passed away.