Deduction & Induction

In logic, we often refer to the two broad methods of reasoning as the deductive and inductive approaches.

Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a "top-down" approach. We might begin with thinking up a theoryabout our topic of interest. We then narrow that down into more specifichypotheses that we can test. We narrow down even further when we collect observations to address the hypotheses. This ultimately leads us to be able to test the hypotheses with specific data -- a confirmation (or not) of our original theories.

Inductive reasoning works the other way, moving from specific observations to broader generalizations and theories. Informally, we sometimes call this a "bottom up" approach (please note that it's "bottom up" and not"bottoms up" which is the kind of thing the bartender says to customers when he's trying to close for the night!). In inductive reasoning, we begin with specific observations and measures, begin to detect patterns and regularities, formulate some tentative hypotheses that we can explore, and finally end up developing some general conclusions or theories.
These two methods of reasoning have a very different "feel" to them when you're conducting research. Inductive reasoning, by its very nature, is more open-ended and exploratory, especially at the beginning. Deductive reasoning is more narrow in nature and is concerned with testing or confirming hypotheses. Even though a particular study may look like it's purely deductive (e.g., an experiment designed to test the hypothesized effects of some treatment on some outcome), most social research involves both inductive and deductive reasoning processes at some time in the project. In fact, it doesn't take a rocket scientist to see that we could assemble the two graphs above into a single circular one that continually cycles from theories down to observations and back up again to theories. Even in the most constrained experiment, the researchers may observe patterns in the data that lead them to develop new theories.

Ethics in Research and Research Fallacies

Ethics in Research

We are going through a time of profound change in our understanding of the ethics of applied social research. From the time immediately after World War II until the early 1990s, there was a gradually developing consensus about the key ethical principles that should underlie the research endeavor. Two marker events stand out (among many others) as symbolic of this consensus. The Nuremberg War Crimes Trial following World War II brought to public view the ways German scientists had used captive human subjects as subjects in oftentimes gruesome experiments. In the 1950s and 1960s, the Tuskegee Syphilis Study involved the withholding of known effective treatment for syphilis from African-American participants who were infected. Events like these forced the reexamination of ethical standards and the gradual development of a consensus that potential human subjects needed to be protected from being used as 'guinea pigs' in scientific research.

By the 1990s, the dynamics of the situation changed. Cancer patients and persons with AIDS fought publicly with the medical research establishment about the long time needed to get approval for and complete research into potential cures for fatal diseases. In many cases, it is the ethical assumptions of the previous thirty years that drive this 'go-slow' mentality. After all, we would rather risk denying treatment for a while until we achieve enough confidence in a treatment, rather than run the risk of harming innocent people (as in the Nuremberg and Tuskegee events). But now, those who were threatened with fatal illness were saying to the research establishment that they wanted to be test subjects, even under experimental conditions of considerable risk. You had several very vocal and articulate patient groups who wanted to be experimented on coming up against an ethical review system that was designed to protect them from being experimented on.

Although the last few years in the ethics of research have been tumultuous ones, it is beginning to appear that a new consensus is evolving that involves the stakeholder groups most affected by a problem participating more actively in the formulation of guidelines for research. While it's not entirely clear, at present, what the new consensus will be, it is almost certain that it will not fall at either extreme: protecting against human experimentation at all costs vs. allowing anyone who is willing to be experimented on.

Ethical Issues

There are a number of key phrases that describe the system of ethical protections that the contemporary social and medical research establishment have created to try to protect better the rights of their research participants. The principle of voluntary participation requires that people not be coerced into participating in research. This is especially relevant where researchers had previously relied on 'captive audiences' for their subjects -- prisons, universities, and places like that. Closely related to the notion of voluntary participation is the requirement of informed consent. Essentially, this means that prospective research participants must be fully informed about the procedures and risks involved in research and must give their consent to participate. Ethical standards also require that researchers not put participants in a situation where they might be at risk of harm as a result of their participation. Harm can be defined as both physical and psychological. There are two standards that are applied in order to help protect the privacy of research participants. Almost all research guarantees the participants confidentiality -- they are assured that identifying information will not be made available to anyone who is not directly involved in the study. The stricter standard is the principle of anonymitywhich essentially means that the participant will remain anonymous throughout the study -- even to the researchers themselves. Clearly, the anonymity standard is a stronger guarantee of privacy, but it is sometimes difficult to accomplish, especially in situations where participants have to be measured at multiple time points (e.g., a pre-post study). Increasingly, researchers have had to deal with the ethical issue of a person's right to service. Good research practice often requires the use of a no-treatment control group -- a group of participants who do not get the treatment or program that is being studied. But when that treatment or program may have beneficial effects, persons assigned to the no-treatment control may feel their rights to equal access to services are being curtailed.

Even when clear ethical standards and principles exist, there will be times when the need to do accurate research runs up against the rights of potential participants. No set of standards can possibly anticipate every ethical circumstance. Furthermore, there needs to be a procedure that assures that researchers will consider all relevant ethical issues in formulating research plans. To address such needs most institutions and organizations have formulated an Institutional Review Board (IRB), a panel of persons who reviews grant proposals with respect to ethical implications and decides whether additional actions need to be taken to assure the safety and rights of participants. By reviewing proposals for research, IRBs also help to protect both the organization and the researcher against potential legal implications of neglecting to address important ethical issues of participants.

Two Research Fallacies

A fallacy is an error in reasoning, usually based on mistaken assumptions. Researchers are very familiar with all the ways they could go wrong, with the fallacies they are susceptible to. Here, I discuss two of the most important.

The ecological fallacy occurs when you make conclusions about individuals based only on analyses of group data. For instance, assume that you measured the math scores of a particular classroom and found that they had the highest average score in the district. Later (probably at the mall) you run into one of the kids from that class and you think to yourself "she must be a math whiz." Aha! Fallacy! Just because she comes from the class with the highest average doesn't mean that she is automatically a high-scorer in math. She could be the lowest math scorer in a class that otherwise consists of math geniuses!

An exception fallacy is sort of the reverse of the ecological fallacy. It occurs when you reach a group conclusion on the basis of exceptional cases. This is the kind of fallacious reasoning that is at the core of a lot of sexism and racism. The stereotype is of the guy who sees a woman make a driving error and concludes that "women are terrible drivers." Wrong! Fallacy!

Both of these fallacies point to some of the traps that exist in both research and everyday reasoning. They also point out how important it is that we do research. We need to determine empirically how individuals perform (not just rely on group averages). Similarly, we need to look at whether there are correlations between certain behaviors and certain groups (you might look at the whole controversy around the book The Bell Curve as an attempt to examine whether the supposed relationship between race and IQ is real or a fallacy.

Hypotheses

An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study. Not all studies have hypotheses. Sometimes a study is designed to be exploratory. There is no formal hypothesis, and perhaps the purpose of the study is to explore some area more thoroughly in order to develop some specific hypothesis or prediction that can be tested in future research. A single study may have one or many hypotheses.
Actually, whenever I talk about an hypothesis, I am really thinking simultaneously about two hypotheses. Let's say that you predict that there will be a relationship between two variables in your study. The way we would formally set up the hypothesis test is to formulate two hypothesis statements, one that describes your prediction and one that describes all the other possible outcomes with respect to the hypothesized relationship. Your prediction is that variable A and variable B will be related (you don't care whether it's a positive or negative relationship). Then the only other possible outcome would be that variable A and variable B are not related. Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis. Sometimes we use a notation like H_A or H₁ to represent the alternative hypothesis or your prediction, and H_O or H₀ to represent the null case. You have to be careful here, though. In some studies, your prediction might very well be that there will be no difference or change. In this case, you are essentially trying to find support for the null hypothesis and you are opposed to the alternative.
If your prediction specifies a direction, and the null therefore is the no difference prediction and the prediction of the opposite direction, we call this a one-tailed hypothesis. For instance, let's imagine that you are investigating the effects of a new employee training program and that you believe one of the outcomes will be that there will be less employee absenteeism. Your two hypotheses might be stated something like this:


The null hypothesis for this study is:


H_O: As a result of the XYZ company employee training program, there will either be no significant difference in employee absenteeism or there will be a significant increase.
which is tested against the alternative hypothesis:
H_A: As a result of the XYZ company employee training program, there will be a significantdecrease in employee absenteeism.

In the figure on the left, we see this situation illustrated graphically. The alternative hypothesis -- your prediction that the program will decrease absenteeism -- is shown there. The null must account for the other two possible conditions: no difference, or an increase in absenteeism. The figure shows a hypothetical distribution of absenteeism differences. We can see that the term "one-tailed" refers to the tail of the distribution on the outcome variable.
When your prediction does not specify a direction, we say you have a two-tailed hypothesis. For instance, let's assume you are studying a new drug treatment for depression. The drug has gone through some initial animal trials, but has not yet been tested on humans. You believe (based on theory and the previous research) that the drug will have an effect, but you are not confident enough to hypothesize a direction and say the drug will reduce depression (after all, you've seen more than enough promising drug treatments come along that eventually were shown to have severe side effects that actually worsened symptoms). In this case, you might state the two hypotheses like this:


The null hypothesis for this study is:


H_O: As a result of 300mg./day of the ABC drug, there will be no significant difference in depression.
which is tested against the alternative hypothesis:
H_A: As a result of 300mg./day of the ABC drug, there will be a significant difference in depression.

The figure on the right illustrates this two-tailed prediction for this case. Again, notice that the term "two-tailed" refers to the tails of the distribution for your outcome variable.
The important thing to remember about stating hypotheses is that you formulate your prediction (directional or not), and then you formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case. When your study analysis is completed, the idea is that you will have to choose between the two hypotheses. If your prediction was correct, then you would (usually) reject the null hypothesis and accept the alternative. If your original prediction was not supported in the data, then you will accept the null hypothesis and reject the alternative. The logic of hypothesis testing is based on these two basic principles:

• the formulation of two mutually exclusive hypothesis statements that, together, exhaust all possible outcomes
• the testing of these so that one is necessarily accepted and the other rejected

OK, I know it's a convoluted, awkward and formalistic way to ask research questions. But it encompasses a long tradition in statistics called the hypothetical-deductive model, and sometimes we just have to do things because they're traditions. And anyway, if all of this hypothesis testing was easy enough so anybody could understand it, how do you think statisticians would stay employed?

Relationships and Variable in Resarch

Types of Relationships

A relationship refers to the correspondence between two variables. When we talk about types of relationships, we can mean that in at least two ways: the nature of the relationship or the pattern of it.

The Nature of a Relationship

While all relationships tell about the correspondence between two variables, there is a special type of relationship that holds that the two variables are not only in correspondence, but that one causes the other. This is the key distinction between a simple correlational relationship and a causal relationship. A correlational relationship simply says that two things perform in a synchronized manner. For instance, we often talk of a correlation between inflation and unemployment. When inflation is high, unemployment also tends to be high. When inflation is low, unemployment also tends to be low. The two variables are correlated. But knowing that two variables are correlated does not tell us whether one causes the other. We know, for instance, that there is a correlation between the number of roads built in Europe and the number of children born in the United States. Does that mean that is we want fewer children in the U.S., we should stop building so many roads in Europe? Or, does it mean that if we don't have enough roads in Europe, we should encourage U.S. citizens to have more babies? Of course not. (At least, I hope not). While there is a relationship between the number of roads built and the number of babies, we don't believe that the relationship is a causal one. This leads to consideration of what is often termed the third variable problem.

 In this example, it may be that there is a third variable that is causing both the building of roads and the birthrate, that is causing the correlation we observe. For instance, perhaps the general world economy is responsible for both. When the economy is good more roads are built in Europe and more children are born in the U.S. The key lesson here is that you have to be careful when you interpret correlations. If you observe a correlation between the number of hours students use the computer to study and their grade point averages (with high computer users getting higher grades), youcannot assume that the relationship is causal: that computer use improves grades. In this case, the third variable might be socioeconomic status -- richer students who have greater resources at their disposal tend to both use computers and do better in their grades. It's the resources that drives both use and grades, not computer use that causes the change in the grade point average.

Patterns of Relationships

We have several terms to describe the major different types of patterns one might find in a relationship. First, there is the case of no relationship at all. If you know the values on one variable, you don't know anything about the values on the other. For instance, I suspect that there is no relationship between the length of the lifeline on your hand and your grade point average. If I know your GPA, I don't have any idea how long your lifeline is.
Then, we have the positive relationship. In a positive relationship, high values on one variable are associated with high values on the other and low values on one are associated with low values on the other. In this example, we assume an idealized positive relationship between years of education and the salary one might expect to be making.

On the other hand a negative relationship implies that high values on one variable are associated with low values on the other. This is also sometimes termed aninverse relationship. Here, we show an idealized negative relationship between a measure of self esteem and a measure of paranoia in psychiatric patients.
These are the simplest types of relationships we might typically estimate in research. But the pattern of a relationship can be more complex than this. For instance, the figure on the left shows a relationship that changes over the range of both variables, a curvilinear relationship. In this example, the horizontal axis represents dosage of a drug for an illness and the vertical axis represents a severity of illness measure. As dosage rises, severity of illness goes down. But at some point, the patient begins to experience negative side effects associated with too high a dosage, and the severity of illness begins to increase again.

Variables

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 'gender' consists of two text values: 'male' and 'female'. 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: maleand 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 independentand 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.

Basic concepts of Research Methodology

Research has been difined as the manupulation of things or concepts for the purpose of genralising to extend, correct or verify knoeledge whether that knowledge aids in contruction of ttheory are in the practice often are.

Five Big Words

Research involves an eclectic blending of an enormous range of skills and activities. To be a good social researcher, you have to be able to work well with a wide variety of people, understand the specific methods used to conduct research, understand the subject that you are studying, be able to convince someone to give you the funds to study it, stay on track and on schedule, speak and write persuasively, and on and on.

Here, I want to introduce you to five terms that I think help to describe some of the key aspects of contemporary social research. (This list is not exhaustive. It's really just the first five terms that came into my mind when I was thinking about this and thinking about how I might be able to impress someone with really big/complex words to describe fairly straightforward concepts).

I present the first two terms -- theoretical and empirical -- together because they are often contrasted with each other. Social research is theoretical, meaning that much of it is concerned with developing, exploring or testing the theories or ideas that social researchers have about how the world operates. But it is also empirical, meaning that it is based on observations and measurements of reality -- on what we perceive of the world around us. You can even think of most research as a blending of these two terms -- a comparison of our theories about how the world operates with our observations of its operation.

The next term -- nomothetic -- comes (I think) from the writings of the psychologist Gordon Allport. Nomothetic refers to laws or rules that pertain to the general case (nomos in Greek) and is contrasted with the term "idiographic" which refers to laws or rules that relate to individuals (idios means 'self' or 'characteristic of an individual ' in Greek). In any event, the point here is that most social research is concerned with the nomothetic -- the general case -- rather than the individual. We often study individuals, but usually we are interested in generalizing to more than just the individual.

In our post-positivist view of science, we no longer regard certainty as attainable. Thus, the fourth big word that describes much contemporary social research is probabilistic, or based on probabilities. The inferences that we make in social research have probabilities associated with them -- they are seldom meant to be considered covering laws that pertain to all cases. Part of the reason we have seen statistics become so dominant in social research is that it allows us to estimate probabilities for the situations we study.

The last term I want to introduce is causal. You've got to be very careful with this term. Note that it is spelledcausal not casual. You'll really be embarrassed if you write about the "casual hypothesis" in your study! The term causal means that most social research is interested (at some point) in looking at cause-effect relationships. This doesn't mean that most studies actually study cause-effect relationships. There are some studies that simply observe -- for instance, surveys that seek to describe the percent of people holding a particular opinion. And, there are many studies that explore relationships -- for example, studies that attempt to see whether there is a relationship between gender and salary. Probably the vast majority of applied social research consists of these descriptive and correlational studies. So why am I talking about causal studies? Because for most social sciences, it is important that we go beyond just looking at the world or looking at relationships. We would like to be able to change the world, to improve it and eliminate some of its major problems. If we want to change the world (especially if we want to do this in an organized, scientific way), we are automatically interested in causal relationships -- ones that tell us how our causes (e.g., programs, treatments) affect the outcomes of interest.

Types of Questions

There are three basic types of questions that research projects can address:

1.     Descriptive.When a study is designed primarily to describe what is going on or what exists. Public opinion polls that seek only to describe the proportion of people who hold various opinions are primarily descriptive in nature. For instance, if we want to know what percent of the population would vote for a Democratic or a Republican in the next presidential election, we are simply interested in describing something.

2.     Relational.When a study is designed to look at the relationships between two or more variables. A public opinion poll that compares what proportion of males and females say they would vote for a Democratic or a Republican candidate in the next presidential election is essentially studying the relationship between gender and voting preference.

3.     Causal.When a study is designed to determine whether one or more variables (e.g., a program or treatment variable) causes or affects one or more outcome variables. If we did a public opinion poll to try to determine whether a recent political advertising campaign changed voter preferences, we would essentially be studying whether the campaign (cause) changed the proportion of voters who would vote Democratic or Republican (effect).

The three question types can be viewed as cumulative. That is, a relational study assumes that you can first describe (by measuring or observing) each of the variables you are trying to relate. And, a causal study assumes that you can describe both the cause and effect variables and that you can show that they are related to each other. Causal studies are probably the most demanding of the three.

Time in Research

Time is an important element of any research design, and here I want to introduce one of the most fundamental distinctions in research design nomenclature: cross-sectional versus longitudinal studies. Across-sectional study is one that takes place at a single point in time. In effect, we are taking a 'slice' or cross-section of whatever it is we're observing or measuring. A longitudinal study is one that takes place over time -- we have at least two (and often more) waves of measurement in a longitudinal design.

A further distinction is made between two types of longitudinal designs: repeated measures and time series. There is no universally agreed upon rule for distinguishing these two terms, but in general, if you have two or a few waves of measurement, you are using a repeated measures design. If you have many waves of measurement over time, you have a time series. How many is 'many'? Usually, we wouldn't use the term time series unless we had at least twenty waves of measurement, and often far more. Sometimes the way we distinguish these is with the analysis methods we would use. Time series analysis requires that you have at least twenty or so observations. Repeated measures analyses (like repeated measures ANOVA) aren't often used with as many as twenty waves of measurement.

Types of Wholesalers

The Ten Types of Wholesalers:

It is hard to define what a wholesaler is because there are so many different wholesalers doing different jobs.  Some of their activities may even seem like manufacturing.  As a result, some wholesalers call themselves "manufacturer and dealer."  Some like to identify themselves with such general terms as merchant, jobber, dealer, or distributor.

Definition:  "Wholesaling is concerned with the activities of those persons or establishments which sell to retailers and other merchants, and/or to industrial, institutional, and commercial users, but who do not sell in large amounts to final consumers."  So, wholesalers are firms whose main function is providing 'wholesaling activities.'

It is important for a marketing manager to understand the role wholesalers play in a distribution system, what functions they provide, and the strategies they use.  Each of the ten major types of wholesalers is discussed below:

1. Merchant wholesalers.  These wholesalers own (take title to) the products they sell.  For example, a wholesale lumber yard that buys plywood from the producer is a merchant wholesaler.  It actually owns - takes title to - the plywood for some period of time before selling to its customers.  About four out of five wholesaling establishments in the United States are merchant wholesalers - and they handle about 59 percent of wholesale sales.  Merchant wholesalers often specialize by certain types of products or customers and they service relatively small geographic areas.  And several wholesalers may be competing for the same customers.  For example, about 3,000 specialized food wholesalers compete for the business of restaurants, hotels, and cafeterias across the United States.

2. General merchandise wholesalers.  These are service wholesalers who carry a wide variety of nonperishable items such as hardware, electrical supplies, plumbing supplies, furniture, drugs, cosmetics, and automobile equipment.  These wholesalers originally developed to serve the early retailers - the general stores.  Now, with their broad line of convenience and shopping products, they serve hardware stores, drugstores, electric appliance shops, and small department stores.

3. Single-line (or general-line) wholesalers.  These are service wholesalers who carry a narrower line of merchandise than general merchandise wholesalers.  For example, they might carry only food, wearing apparel, or certain types of industrial tools or supplies.  In consumer products, they serve the single- and limited-line stores.  In business products, they cover a wide geographic area and offer more specialized service.

4. Specialty wholesalers.  These are service wholesalers who carry a very narrow range of products - and offer more information and service than other service wholesalers.  A consumer products specialty wholesaler might carry only health foods or oriental foods instead of a full line of groceries.  Or a specialty wholesaler might carry only automotive items and sell exclusively to mass-merchandisers.  Specialty wholesalers often know a great deal about the final target markets in their channel.  For example, Advanced Marketing is the leading wholesale supplier of books to membership warehouse clubs.  The company offers hardcover best sellers, popular paperbacks, basic reference books, cookbooks, and travel books.  Consumers in different geographic areas are interested in different kinds of books and that affects what books will sell in a particular store.

5. Cash-and-carry wholesalers.  These wholesalers operate like service wholesalers - except that the customer must pay cash.  Some retailers, such as small auto repair shops, are too small to be served profitably by a service wholesaler.  So service wholesalers set a minimum charge - or just refuse to grant credit to a small business that may have trouble paying its bills.  Or the wholesaler may set up a cash-and-carry department to supply the small retailer for cash on the counter.  The wholesaler can operate at lower cost because the retailers take over many wholesaling functions.  And using cash-and-carry outlets may enable the small retailer to stay in business.  These cash-and-carry operators are especially common in less-developed nations where very small retailers handle the bulk of retail transactions.

6. Drop-shippers.  These wholesalers own (take title to) the products they sell - but they do not actually handle, stock, or deliver them.  These wholesalers are mainly involved in selling.  They get orders - from wholesalers, retailers, or other business users - and pass these orders on to producers.  Then the producer ships the order directly to the customers.  Because drop-shippers do not have to handle the products, their operating costs are lower.  Drop-shippers commonly sell products so bulky that additional handling would be expensive and possibly damaging.

7. Truck wholesalers.  These wholesalers specialize in delivering products that they stock in their own trucks.  By handling perishable products in general demand - tobacco, candy, potato chips, and salad dressings - truck wholesalers may provide almost the same functions as full-service wholesalers.  Their big advantage is that they deliver perishable products that regular wholesalers prefer not to carry.  Some truck wholesalers operate 24 hours a day, every day - and deliver an order within hours.

8. Mail-order wholesalers.  These wholesalers sell out of catalogs that may be distributed widely to smaller industrial customers or retailers who might not be called on by other middlemen.  These wholesalers operate in the hardware, jewelry, sporting goods, and general merchandise lines.  For example, Inmac uses a catalog to sell a complete line of 3,000 different computer accessories and supplies.  Inmac's catalogs are printed in six languages and distributed to business customers in the United States, Canada, the United Kingdom, Germany, Sweden, the Netherlands, and France.  Many of these customers - especially those in smaller towns - don't have a local wholesaler.

9. Producers' cooperatives.  These wholesalers operate almost as full-service wholesalers - with the "profits" going to the cooperative's customer-members.  Cooperatives develop in agricultural markets where there are many small producers.  Examples of such organizations are Sunkist (citrus fruits), Sunmaid Raisin Growers Association, and Land O' Lakes Creameries, Inc.  Successful producers' cooperatives emphasize sorting - to improve the quality of farm products offered to the market.  Some also brand these improved products - and then promote the brands.  For example, the California Almond Growers Exchange has captured most of the retail market with its Blue Diamond brand.

10. Rack jobbers.  These wholesalers specialize in nonfood products sold through grocery stores and supermarkets - and they often display them on their own wire racks.  Most grocers don't want to bother with reordering and maintaining displays of nonfood items (housewares, hardware items, and books and magazines) because they sell small quantities of so many different kinds of products.  Rack jobbers are almost service wholesalers - except that they usually are paid cash for what is sold or delivered.

Note that producers who take over wholesaling activities are not considered wholesalers.  However, when producers set up branch warehouses at separate locations, these establishments basically operate as wholesalers.  In fact, they're classified as wholesalers by the U.S. Census Bureau and by government agencies in many other countries.