* MECHANISMS FOR DEALING WITH SHAREHOLDER-MANAGER CONFLICTS * * AGENCY VERSUS CONTRACT * * FURTHER READING:
Agency theory suggests that the firm can be viewed as a nexus of contracts (loosely defined) between resource holders. An agency relationship arises whenever one or more individuals, called principals, hire one or more other individuals, called agents, to perform some service and then delegate decision-making authority to the agents. The primary agency relationships in business are those (1) between stockholders and managers and (2) between debtholders and stockholders. These relationships are not necessarily harmonious; indeed, agency theory is concerned with so-called agency conflicts, or conflicts of interest between agents and principals. This has implications for, among other things, corporate governance and business ethics. When agency occurs it also tends to give rise to agency costs, which are expenses incurred in order to sustain an effective agency relationship (e.g., offering management performance bonuses to encourage managers to act in the shareholders' interests). Accordingly, agency theory has emerged as a dominant model in the financial economics literature, and is widely discussed in business ethics texts.
Agency theory in a formal sense originated in the early 1970s, but the concepts behind it have a long and varied history. Among the influences are property-rights theories, organization economics, contract law, and political philosophy, including the works of Locke and Hobbes. Some noteworthy scholars involved in agency theory's formative period in the 1970s included Armen Alchian, Harold Demsetz, Michael Jensen, William Meckling, and S.A. Ross.
CONFLICTS BETWEEN MANAGERS AND SHAREHOLDERS
Agency theory raises a fundamental problem in organizationself-interested behavior. A corporation's managers may have personal goals that compete with the owner's goal of maximization of shareholder wealth. Since the shareholders authorize managers to administer the firm's assets, a potential conflict of interest exists between the two groups.
SELF-INTERESTED BEHAVIOR.
Agency theory suggests that, in imperfect labor and capital markets, managers will seek to maximize their own utility at the expense of corporate shareholders. Agents have the ability to operate in their own self-interest rather than in the best interests of the firm because of asymmetric information (e.g., managers know better than shareholders whether they are capable of meeting the shareholders' objectives) and uncertainty (e.g., myriad factors contribute to final outcomes, and it may not be evident whether the agent directly caused a given outcome, positive or negative). Evidence of self-interested managerial behavior includes the consumption of some corporate resources in the form of perquisites and the avoidance of optimal risk positions, whereby risk-averse managers bypass profitable opportunities in which the firm's shareholders would prefer they invest. Outside investors recognize that the firm will make decisions contrary to their best interests. Accordingly, investors will discount the prices they are willing to pay for the firm's securities.
A potential agency conflict arises whenever the manager of a firm owns less than 100 percent of the firm's common stock. If a firm is a sole proprietorship managed by the owner, the owner-manager will undertake actions to maximize his or her own welfare. The owner-manager will probably measure utility by personal wealth, but may trade off other considerations, such as leisure and perquisites, against personal wealth. If the owner-manager forgoes a portion of his or her ownership by selling some of the firm's stock to outside investors, a potential conflict of interest, called an agency conflict, arises. For example, the owner-manager may prefer a more leisurely lifestyle and not work as vigorously to maximize shareholder wealth, because less of the wealth will now accrue to the owner-manager. In addition, the owner-manager may decide to consume more perquisites, because some of the cost of the consumption of benefits will now be borne by the outside shareholders.
In the majority of large publicly traded corporations, agency conflicts are potentially quite significant because the firm's managers generally own only a small percentage of the common stock. Therefore, shareholder wealth maximization could be subordinated to an assortment of other managerial goals. For instance, managers may have a fundamental objective of maximizing the size of the firm. By creating a large, rapidly growing firm, executives increase their own status, create more opportunities for lower- and middle-level managers and salaries, and enhance their job security because an unfriendly takeover is less likely. As a result, incumbent management may pursue diversification at the expense of the shareholders who can easily diversify their individual portfolios simply by buying shares in other companies.
Managers can be encouraged to act in the stockholders' best interests through incentives, constraints, and punishments. These methods, however, are effective only if shareholders can observe all of the actions taken by managers. A moral hazard problem, whereby agents take unobserved actions in their own self-interests, originates because it is infeasible for shareholders to monitor all managerial actions. To reduce the moral hazard problem, stockholders must incur agency costs.
COSTS OF SHAREHOLDER-MANAGEMENT CONFLICT.
Agency costs are defined as those costs borne by shareholders to encourage managers to maximize shareholder wealth rather than behave in their own self-interests. The notion of agency costs is perhaps most associated with a seminal 1976 Journal of Finance paper by Michael Jensen and William Meckling, who suggested that corporate debt levels and management equity levels are both influenced by a wish to contain agency costs. There are three major types of agency costs: (1) expenditures to monitor managerial activities, such as audit costs; (2) expenditures to structure the organization in a way that will limit undesirable managerial behavior, such as appointing outside members to the board of directors or restructuring the company's business units and management hierarchy; and (3) opportunity costs which are incurred when shareholder-imposed restrictions, such as requirements for shareholder votes on specific issues, limit the ability of managers to take actions that advance shareholder wealth.
In the absence of efforts by shareholders to alter managerial behavior, there will typically be some loss of shareholder wealth due to inappropriate managerial actions. On the other hand, agency costs would be excessive if shareholders attempted to ensure that every managerial action conformed with shareholder interests. Therefore, the optimal amount of agency costs to be borne by shareholders is determined in a cost-benefit contextgency costs should be increased as long as each incremental dollar spent results in at least a dollar increase in shareholder wealth.
MECHANISMS FOR DEALING WITH SHAREHOLDER-MANAGER CONFLICTS
There are two polar positions for dealing with shareholder-manager agency conflicts. At one extreme, the firm's managers are compensated entirely on the basis of stock price changes. In this case, agency costs will be low because managers have great incentives to maximize shareholder wealth. It would be extremely difficult, however, to hire talented managers under these contractual terms because the firm's earnings would be affected by economic events that are not under managerial control. At the other extreme, stockholders could monitor every managerial action, but this would be extremely costly and inefficient. The optimal solution lies between the extremes, where executive compensation is tied to performance, but some monitoring is also undertaken. In addition to monitoring, the following mechanisms encourage managers to act in shareholders' interests: (1) performance-based incentive plans, (2) direct intervention by shareholders, (3) the threat of firing, and (4) the threat of takeover.
Most publicly traded firms now employ performance shares, which are shares of stock given to executives on the basis of performances as defined by financial measures such as earnings per share, return on assets, return on equity, and stock price changes. If corporate performance is above the performance targets, the firm's managers earn more shares. If performance is below the target, however, they receive less than 100 percent of the shares. Incentive-based compensation plans, such as performance shares, are designed to satisfy two objectives. First, they offer executives incentives to take actions that will enhance shareholder wealth. Second, these plans help companies attract and retain managers who have the confidence to risk their financial future on their own abilitieshich should lead to better performance.
An increasing percentage of common stock in corporate America is owned by institutional investors such as insurance companies, pension funds, and mutual funds. The institutional money managers have the clout, if they choose, to exert considerable influence over a firm's operations. Institutional investors can influence a firm's managers in two primary ways. First, they can meet with a firm's management and offer suggestions regarding the firm's operations. Second, institutional shareholders can sponsor a proposal to be voted on at the annual stockholders' meeting, even if the proposal is opposed by management. Although such shareholder-sponsored proposals are nonbinding and involve issues outside day-to-day operations, the results of these votes clearly influence management opinion.
In the past, the likelihood of a large company's management being ousted by its stockholders was so remote that it posed little threat. This was true because the ownership of most firms was so widely distributed, and management's control over the voting mechanism so strong, that it was almost impossible for dissident stockholders to obtain the necessary votes required to remove the managers. In recent years, however, the chief executive officers at American Express Co., General Motors Corp., IBM, and Kmart have all resigned in the midst of institutional opposition and speculation that their departures were associated with their companies' poor operating performance.
Hostile takeovers, which occur when management does not wish to sell the firm, are most likely to develop when a firm's stock is undervalued relative to its potential because of inadequate management. In a hostile takeover, the senior managers of the acquired firm are typically dismissed, and those who are retained lose the independence they had prior to the acquisition. The threat of a hostile takeover disciplines managerial behavior and induces managers to attempt to maximize shareholder value.
STOCKHOLDERS VERSUS CREDITORS: A SECOND AGENCY CONFLICT
In addition to the agency conflict between stockholders and managers, there is a second class of agency conflictshose between creditors and stockholders. Creditors have the primary claim on part of the firm's earnings in the form of interest and principal payments on the debt as well as a claim on the firm's assets in the event of bankruptcy. The stockholders, however, maintain control of the operating decisions (through the firm's managers) that affect the firm's cash flows and their corresponding risks. Creditors lend capital to the firm at rates that are based on the riskiness of the firm's existing assets and on the firm's existing capital structure of debt and equity financing, as well as on expectations concerning changes in the riskiness of these two variables.
The shareholders, acting through management, have an incentive to induce the firm to take on new projects that have a greater risk than was anticipated by the firm's creditors. The increased risk will raise the required rate of return on the firm's debt, which in turn will cause the value of the outstanding bonds to fall. If the risky capital investment project is successful, all of the benefits will go to the firm's stockholders, because the bondholders' returns are fixed at the original low-risk rate. If the project fails, however, the bondholders are forced to share in the losses. On the other hand, shareholders may be reluctant to finance beneficial investment projects. Shareholders of firms undergoing financial distress are unwilling to raise additional funds to finance positive net present value projects because these actions will benefit bondholders more than shareholders by providing additional security for the creditors' claims.
Managers can also increase the firm's level of debt, without altering its assets, in an effort to leverage up stockholders' return on equity. If the old debt is not senior to the newly issued debt, its value will decrease, because a larger number of creditors will have claims against the firm's cash flows and assets. Both the riskier assets and the increased leverage transactions have the effect of transferring wealth from the firm's bondholders to the stockholders.
Shareholder-creditor agency conflicts can result in situations in which a firm's total value declines but its stock price rises. This occurs if the value of the firm's outstanding debt falls by more than the increase in the value of the firm's common stock. If stockholders attempt to expropriate wealth from the firm's creditors, bondholders will protect themselves by placing restrictive covenants in future debt agreements. Furthermore, if creditors believe that a firm's managers are trying to take advantage of them, they will either refuse to provide additional funds to the firm or will charge an above-market interest rate to compensate for the risk of possible expropriation of their claims. Thus, firms which deal with creditors in an inequitable manner either lose access to the debt markets or face high interest rates and restrictive covenants, both of which are detrimental to shareholders.
Management actions that attempt to usurp wealth from any of the firm's other stakeholders, including its employees, customers, or suppliers, are handled through similar constraints and sanctions. For example, if employees believe that they will be treated unfairly, they will demand an above-market wage rate to compensate for the unreasonably high likelihood of job loss.
AGENCY VERSUS CONTRACT
Although the notions of agency and contract are closely intertwined, some academics bristle at the suggestion they are essentially the same. Specifically, they point out a number of unique features of agency versus contractual relationships. There are two major sets of differences. First, agents are usually retained not for any particular or discrete set of tasks, but for a broad range of activities, which may change over time, that are consistent with basic objectives and interests set forth by the principals. In this instance principals must be concerned to some degree about agents' personal attitudes, dispositions, and other characteristics that are usually not a concern in contractual agreements. Principals hire out broad objectives to be fulfilled instead of specific tasks. Second, in an agency relationship there is typically much less independence between agent and principal than between contracting parties. Typically this also means that the principal-agent relationship is more hierarchical and power-driven than a contractual relationship, and included in this power is greater latitude for principals to reward, punish, and control agents.
A conventional view holds that agency is a special application of contract theory. However, some argue that the reverse is true: a contract is a formalized, structured, and limited version of agency, but agency itself is not based on contracts.
AGENCY AND ETHICS
Since agency relationships are usually more complex and ambiguous (in terms of what specifically the agent is required to do for the principal) than contractual relationships, agency carries with it special ethical issues and problems, concerning both agents and principals. Ethicists point out that the classical version of agency theory assumes that agents (i.e., managers) should always act in principals' (owners') interests. However, if taken literally, this entails a further assumption that either (a) the principals' interests are always morally acceptable ones or (b) managers should act unethically in order to fulfill their "contract" in the agency relationship. Clearly, these stances do not conform to any practicable model of business ethics.
A familiar real-life example is large corporations' layoff dilemma. Conventional wisdom holds that investors are rewarded when companies thin their employment rosters because operating costs are lowered, in theory leading to greater profits. This expectation is often made explicit in news reporting surrounding a downsizing episode; the reports highlight whether investors seem pleased or displeased with an announcement of a mass layoff, and the often-stated assumption is that corporate management has undertaken the layoffs in part, if not in whole, to please shareholders and enhance their wealth. In this instance it is obvious that shareholders' interests are advanced to the detriment of at least one other constituency, namely the employees. In such cases, observers question whether it is ethical to serve the principals' interests when those actions harm a large number of people, and whether the benefits shareholders receive are commensurate with the harm inflicted on the laid-off employees.
Along the same lines, others have noted that traditional agency theory makes little mention of what obligations, moral or otherwise, principals have to their agents. The emphasis lies almost exclusively on what agents should or must do for the principals, relying, in turn, on a vague assumption that principals will compensate agents adequatelyven more than adequatelyor their services. Some ethics scholars argue that principals have obligations as well. In the example above, some would argue that not only is it unethical to harm employees to obtain improvements (often marginal) in shareowners' wealth, but also that the shareholders have moral obligations directly to the employees as an extension of the ethical employer/employee relationship (i.e., not to harm them arbitrarily, among other obligations). This ethical problem is only complicated by the reality that, as noted above, principals are often institutions rather than individuals.
Meanwhile, consistent with the conventional formulation of the theory, agents are seen as having ethical duties to the principals. If managers act in self-interest rather negative assumptionnd it fails to serve the best interests of the shareholders, they may, according to some views, have fallen short on their ethical responsibilities.
In a larger sense, some see the traditional agency model as a simplistic, even deceptive, justification for traditional economic power relationships, specifically that large wealth holders can extract concessions from weaker economic beings. Certain scholars have argued that from a broader social perspective, there are many kinds of principal-agent relations, and included among these is the fact that shareholders may be seen as agents to managers, employees, and the broader society.
SEE ALSO: Fiduciary Duty
[Robert T. Kleiman]
FURTHER READING:
Bamberg, Giinter, and Klaus Spremann, eds. Agency Theory, Information, and Incentives. Berlin: Springer-Verlag, 1987.
Bowie, Norman E., and R. Edward Freeman. Ethics and Agency Theory: An Introduction. New York: Oxford University Press, 1992.
Fama, Eugene, and Michael Jensen. "Agency Problems and Residual Claims." Journal of Law and Economics 26 (1983), 327-349.
Hayne, Leland E. "Agency Costs, Risk Management, and Capital Structure." Journal of Finance, August 1998.
Jensen, Michael C., and William H. Meckling. "Theory of the Firm, Managerial Behavior, Agency Costs, and Ownership Structure." Journal of Financial Economics 3 (October 1976), 305-360.
Myers, Stewart, and Nicholas Majluf. "Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have." Journal of Financial Economics 13 (June 1984), 187-221.
Shankman, Neil A. "Reframing the Debate between Agency and Stakeholder Theories of the Firm." Journal of Business Ethics, May 1999.
Source: Encyclopedia of Business, ©2000 Gale Cengage. All Rights Reserved. Full copyright.
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Lecture Note 1: Agency Theory
This note considers the simplest possible organization: one boss (or “Principal”) and one worker (or “Agent”). One of the earliest applications of this Principal-Agent model was to sharecropping, where the landowner was the Principal and the tenant farmer the Agent, but in this course we will typically talk about more familiar organization structures. For example, we might consider a firm’s shareholders to be the
Principal and the CEO to be the Agent. One can also enrich the model to analyze a chain of command (i.e., a Principal, a Supervisor, and an Agent), or one Principal and many
Agents, or other steps towards a full-fledged organization tree.
The central idea behind the Principal-Agent model is that the Principal is too busy to do a given job and so hires the Agent, but being too busy also means that the Principal cannot monitor the Agent perfectly. There are a number of ways that the Principal might then try to motivate the Agent: this note analyzes incentive contracts (similar to profit sharing or sharecropping); later notes discuss richer and more realistic models.
Taken literally and alone, the basic Principal-Agent model may seem too abstract to be useful. But we begin with this model because it is an essential building block for many discussions throughout the course—concerning not only managing the incentives of individuals but also managing the incentives of organizational units (such as teams or divisions) and of firms themselves (such as suppliers or partners). Furthermore, this abstract model allows us to consider the nature and use of economic models more generally, as follows.
1. An Introduction to Economic Modeling
We will use several economic models in this course, so it may be helpful to begin by describing what an economic model is and what it can do. We will defer discussion of whether such models are useful until after we have a few under our belts!
An economic model is a simplified description of reality, in which all assumptions are explicit and all assertions are derived. Such a model can produce qualitative and/or quantitative predictions. A qualitative prediction is that “x goes up when y falls.” A quantitative prediction is that x = 1/y. A model’s (qualitative or quantitative) predictions are useful when they are robust within the environment(s) of interest.
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Quantitative predictions often hinge on specific assumptions from the model. If the model will be applied in one particular environment (such as a queuing model describing the lines at the Refresher Course, or the Black-Scholes model for option pricing) then the specific assumptions need to match the environment fairly closely, otherwise the quantitative predictions will not be useful in that environment. One might call this “engineering modeling” rather than “economic modeling.”
Qualitative predictions are often more robust, in two senses. First, qualitative predictions may continue to hold if one makes small changes in the model’s specific assumptions. For example, a model’s quantitative predictions might depend on whether a particular probability distribution is normal, exponential, or uniform, but the model’s qualitative predictions might hold for any single-peaked (i.e., hill-shaped) distribution, including the three mentioned above as well as others.
Qualitative predictions can also be robust in a second (and, for our purposes, more important) sense: a simple model’s qualitative predictions may be preserved even if one adds much more richness to the model. The major points we will derive from the economic models in this course are robust predictions in this latter sense. That is, adding greater richness and realism to these models will certainly change the models’ quantitative conclusions, but the major points we derive from the simple models will still be part of the package of qualitative conclusions from the richer models.
2. Pay for Performance: The Basic Principal-Agent Model
During this course we will frequently use the term “incentives.” In some settings we will mean a cash payment for a measured outcome, but in other settings our use of this term will be much broader. Lest anyone be misled or disaffected by the narrowness of the former meaning, we will start our discussion of the basic Principal-Agent model by attempting some broader definitions: let “rewards” mean outcomes that people care about
(not just dollars), let “effort” mean actions that people won’t take without rewards (not just hours worked), and let “incentives” mean links between rewards and effort (not just compensation contracts). We will refine these definitions throughout the course. For now we simply note that, according to these definitions, there are clearly lots of incentives out there, even if there are many fewer dollar-denominated incentive contracts.
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To be more precise about rewards, effort, and incentives, we turn now to the elements of the basic Principal-Agent model: (A) the technology of production, (B) the set of feasible contracts, (C) the payoffs to the parties, and (D) the timing of events.
A. The Technology of Production
In this simple model, the production process is summarized by just three variables: (1) the Agent’s total contribution to firm value (or, for now, the Agent’s
“output”), denoted by y; (2) the action the Agent takes to produce output, denoted by a; and (3) events in the production process that are beyond the Agent’s control (i.e.,
“noise”), denoted by .
(1) The Agent’s contribution to firm value, y: In the sharecropping context, the
Agent’s contribution is simply the harvest. In the CEO context, one definition of the
Agent’s contribution is the change in the wealth of the shareholders through appreciation in the firm’s stock price. For workers buried inside an organization, it is sometimes very difficult to define and measure a contribution to firm value. Later in this note we will discuss alternative objective performance measures (which sometimes raise “get what you pay for” issues); in later notes we will discuss subjective performance measures.
(2) The action the Agent takes to produce output, a: The most straightforward interpretation is that the Agent’s action is effort. This interpretation may be reasonably accurate in the sharecropping context and for low-level workers in large organizations.
For a CEO, however, one should think of “effort” not in terms of hours worked but rather in terms of paying attention to stakeholders’ interests—for example, does the CEO take actions that increase shareholder value (versus taking actions that indulge pet projects)?
Later in this note we will consider “multi-task” situations, in which the Agent can take some actions that help the Principal but also others that hurt. For example, the Agent might increase current earnings in two ways: by working hard to increase sales and cut production costs, but also by cutting R&D and marketing expenses, thereby hurting future earnings.
(3) Events beyond the Agent’s control, : In the sharecropping context, one event beyond the Agent’s control is the weather. In the CEO context, “animal spirits” in the stock market are similarly beyond the Agent’s control. For simplicity, we assume that the expected value of is zero.
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To keep the exposition simple, we will make a very specific assumption: the production function is y = a + .1
B. Contracts
We will focus on contracts in which the Agent’s total compensation for the period of the contract, denoted by w, is a linear function of output: w= s + b.y. In such a contract, s can be thought of as salary and b as the Agent’s bonus rate (so that the Agent’s bonus is b.y). We sometimes call w the Agent’s “wage,” but this should be understood to mean total compensation, not an hourly wage.
Linear contracts are simple to analyze, are observed in some real-world settings, and have an appealing property: they create uniform incentives, in the following sense.
Think of output, y, as aggregate output over (say) a year, but think of the Agent as taking lots of little actions over the course of the year—such as one per day. A non-linear contract may create unintended or unhelpful incentives over the course of the year, depending on how the Agent has done so far.
As an example of unintended or unhelpful incentives, consider a contract that pays a low wage if output is below a target level and a high wage if output meets or exceeds the target level—such as $50,000 for the year if output is below 1000 widgets, but $100,000 for the year if output meets or exceeds 1000 widgets. (In this note, we ignore future considerations such as the Agent’s reputation in the labor market or promotion prospects in the firm.) Given this contract, once the Agent reaches the target level, he or she will stop working; also, if the end of the year draws near and the Agent is still far from reaching the target level, then he or she will stop working. Alternatively, if the Agent were paid (say) a salary of s = $50,000 and a bonus rate of b = $50/widget then the Agent would earn $100,000 for producing 1000 widgets but would have constant incentives regardless of performance to date: every extra widget earns the Agent $50.
1 Some readers may wonder about the units in this production function: how can hours of effort plus inches of rain equal bushels of corn? As an antidote to this concern, consider the slightly more general production function y = g.a + h., where g is the number of bushels of corn produced per hour of effort and h similarly translates inches of rain into bushels of corn. We have simply set g = h = 1 for notational convenience. 15.903 5 R. Gibbons
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Note that a stock option creates uniform incentives on the upside, in its linear portion, but potentially unintended or unhelpful incentives if it is underwater (or even nearly so). If the option is severely underwater then there are essentially no incentives, because the Agent’s payoff is constant (at zero). More perversely, when the option is at the money, the Agent’s payoff is convex (flat below and linear above), which creates an incentive for risk-taking behavior. Similar incentives and behavior have been documented in several settings, including high-risk portfolio choices by managers of ostensibly conservative mutual funds (see Chevalier and Ellison, 1997).
C. Payoffs
The Principal receives the Agent’s total contribution to firm value, y, but has to pay the Agent’s wage, w, so the Principal’s payoff (or “profit”) is the difference between the value received and the wage paid: = y – w. For simplicity, we assume Principal is risk neutral—that is, the Principal simply wants to maximize the expected payoff, namely
E(y - w), where the notation E(x) denotes the expected value of the random variable x.
The Agent receives the wage w but has to take a costly action (e.g., supply effort) to produce any output. Let c(a) be the dollar amount necessary to compensate the Agent for taking a particular action, a. Think of a = 0 as no action at all, so c(0) = 0. (A bit more precisely, think of the action a = 0 as the action the Agent would take without any financial inducements, hence c(0) = 0.)
The Agent’s payoff (or “utility”) is the difference between the wage received and the cost of the action taken: U = w – c(a). In this note (and throughout this course), we will focus on the simple case in which the Agent is risk-neutral—that is, the Agent simply wants to maximize the expected payoff E(w) - c(a). (Since is the only uncertainty in the model and does not affect the Agent’s cost function, no expectation is necessary in the second term of the Agent’s payoff.)
We will assume that the Agent’s cost function has the (intuitive) shape shown in
Figure 1. There are really two assumptions being made here: (1) bigger actions are more costly (i.e., the cost function c(a) is increasing), and (2) a small increase in the Agent’s action is more costly starting from a big action than starting from a small action (i.e., the cost function c(a) is convex—or, equivalently, the marginal cost of actions is increasing).
The latter assumption implies that an extra five hours of work per week is tougher for someone currently working 80 hours a week than for someone currently working 40.
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Figure 1
D. The Timing of Events
Putting all the model’s elements together yields the following timing of events:
1. The Principal and the Agent sign a compensation contract (w = s + by).
2. The Agent chooses an action (a), but the Principal cannot observe this choice. 3. Events beyond the Agent’s control () occur.
4. Together, the action and the noise determine the Agent’s output (y).
5. Output is observed by the Principal and the Agent (and by a Court, if necessary). 6. The Agent receives the compensation specified by the contract.
We turn next to analyzing this basic Principal-Agent model.
3. A Risk-Neutral Agent’s Response to a Linear Contract
A risk-neutral Agent wants to choose the action that maximizes the expected value of the payoff w - c(a). Since w = s + b.y and y = a + , the Agent wants to maximize the expected value of s + b(a + ) - c(a). Since the only uncertainty in the
Agent’s payoff arises from the productivity shock , and since E() = 0, the Agent’s problem is
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Lecture Note 1: Agency Theory max a s + b.a - c(a) .
Starting from an arbitrary action a0, the marginal benefit of choosing a slightly higher action is b, and the marginal cost of choosing a slightly higher action is the slope of the cost function at a0, denoted c'(a0). Thus, if c'(a0) is less than b then it is optimal for the
Agent to choose a higher action than a0, whereas if c'(a0) is greater than b then it is optimal for the Agent to choose a lower action than a0. Therefore, the optimal action for a risk-neutral Agent to choose in response to a contract with slope b is the action at which the slope of the cost function equals b, as shown in Figure 2.
Figure 2
As an illustration, in the simple case where the cost function is the quadratic c(a) =
1
2 a2, we have that the marginal cost function is c'(a) = a and so the Agent’s optimal action in response to a contract with slope b is a*(b) = b.2
Because the slope of c(a) increases as the Agent’s action increases, a larger value of b will increase the Agent’s optimal action, a*(b). That is, steeper slopes create stronger incentives. This result is intuitive: a profit-sharing plan that gives a worker 50% of the firm’s profit is more likely to get the worker’s attention than a plan paying 1%. But does this imply that the Principal would prefer a contract with a very large value of b?
2 Note that in this model the salary s does not affect the Agent’s optimal action: the slope of the contract completely determines the Agent’s incentives; the salary is just a transfer of wealth that does not affect incentives.
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Definitely not. First, holding the Agent’s salary constant, steeper slopes create stronger incentives but also give away more of the output to the Agent. For example if b
= 1 then the Principal is sure to lose money (assuming a positive salary) because the
Agent gets all the output. But in some settings it may make sense to combine a steep slope with a negative salary: the slope does good things for the Agent’s effort, the negative salary is interpreted as a fee the Agent pays the Principal for the privilege of getting the job, and the Agent expects to recover the fee by keeping most of the output.
Most franchise contracts combine an up-front fee (s < 0) and a steep slope (b near one) in this way. To pay such a fee up front, the Agent would need plenty of wealth or access to credit. There could also be disagreements about the appropriate size for such a fee: a
Principal with a poor production process might try to claim otherwise to justify a high fee. A second potential problem with a steeper slope is that it imposes more risk on the Agent. This risk doesn’t matter if the Agent is risk-neutral, as assumed here, but would matter if the Agent were risk-averse. Interested readers can find more on the interplay between risk and incentives in an Appendix to this note (available upon request), but this material is not required elsewhere in this course.
4. Lessons and Limits of the Basic Principal-Agent Model
The basic Principal-Agent model is extremely simple. On its own it tells us only a little about managing incentives. Among its lessons are (L1) contract shape matters (e.g., linear versus kinked or jumped), (L2) steeper slopes create stronger incentives, and (L3) steeper slopes are not always better.
Beyond these simple lessons, the basic Principal-Agent model has two main values. First, the model gives us a language for expressing and analyzing abstract concepts such as reward, effort, and incentives in terms of more concrete model elements such as production (y = a + ), contract (w = s + by), and payoffs (U = w - c(a) to the
Agent and = y – w to the Principal). Second, and probably more important, the model teaches us much by what it leaves out!
One instructive exercise is to compare this stick-figure model to the rich incentive issues in the case study on Lincoln Electric (Fast and Berg, 1975). For example, Lincoln pays not only piece rates, akin to the contracts w = s + by analyzed here, but also subjective bonuses. A second useful exercise is to compare this model to the compelling
15.903 9 R. Gibbons
Lecture Note 1: Agency Theory examples of failed (or at least agonizing) incentive plans recounted by Kerr (1975), in his classic article on “Rewarding A, While Hoping for B.” To begin to address both Lincoln
Electric and Kerr’s examples, we turn next to the “multi-task” agency model, which emphasizes “get what you pay for” problems.
5. Getting What You Pay For3
In the remainder of this note, we overturn an important but unremarked assumption in the basic Principal-Agent model: instead of assuming that the Agent’s contribution to firm value can be observed by the Principal and the Agent (and also by a court if necessary to enforce the compensation contract), we now assume that the only performance measures that can be observed by the Principal and the Agent (and a court) are distortionary, in a sense made formal below. We overturn this assumption from the basic model in order to analyze situations in the spirit of Kerr’s (1975) examples, such as the following.
Business history is littered with firms that got what they paid for. At the H.J. Heinz
Company, for example, division managers received bonuses only if earnings increased from the prior year. The managers delivered consistent earnings growth by manipulating the timing of shipments to customers and by prepaying for services not yet received. At
Dun & Bradstreet, salespeople earned no commission unless the customer bought a larger subscription to the firm’s credit-report services than in the previous year. In 1989, the company faced millions of dollars in lawsuits following charges that its salespeople deceived customers into buying larger subscriptions by fraudulently overstating their historical usage. In 1992, Sears abolished the commission plan in its auto-repair shops, which paid mechanics based on the profits from repairs authorized by customers.
Mechanics misled customers into authorizing unnecessary repairs, leading California officials to prepare to close Sears’ auto-repair business statewide.
In each of these cases, employees took actions to increase their compensation, but these actions were seemingly at the expense of long-run firm value. At Heinz, for example, prepaying for future services greatly reduced the firm’s future flexibility, but the compensation system failed to address this issue. Similarly, at Dun & Bradstreet and
Sears, although short-run profits increased with the increases in subscription sizes and auto repairs, the long-run harm done to the firms’ reputations was significant (and plausibly much larger than the short-run benefit), but the compensation system again ignored the issue. Thus, in each of these cases, the cause of any dysfunctional behavior was not pay-for-performance per se, but rather pay-for-performance based on an inappropriate performance measure. (Baker, Gibbons, and Murphy, 1994)
3 This section draws on Holmstrom and Milgrom (1991) and Gibbons (1998).
15.903 10 R. Gibbons
Lecture Note 1: Agency Theory
To analyze these examples, we must stop calling y “output,” as though it could easily be measured. This label is misleadingly simple: in the basic Principal-Agent model, y reflects everything the Principal cares about, except for wages (that is, the
Principal's payoff is y - w). Therefore, I henceforth call y the Agent's “total contribution to firm value,” to emphasize that it encompasses all the Agent's actions (including mentoring, team production, and so on) and all the effects of these actions (both longand short-run). In many settings, it is very difficult to measure synergies or sabotage across Agents and/or very difficult to predict the long-run consequences of an Agent's actions based on the observed short-run contribution. To analyze such settings, I impose the following assumption (for the rest of this course!): no contract based on y can be enforced in court, including but not limited to the linear contract w = s + by.
Of course, even when contracts based on y are not available, other contracts can be enforced in court. Such contracts are based on alternative performance measures— such as the number of units produced, with limited adjustment made for quality, timely delivery, and so on. Let p denote such an alternative performance measure; the wage contract might then be linear, w = s + bp. As in the basic Principal-Agent model, a large value of b will create strong incentives, but now the Agent's incentives are to produce a high value of p, not of y. But the firm does not directly benefit from increased realizations of measured performance, p; rather, the firm benefits from increased realizations of the Agent’s total contribution to firm value, y. The essence of the incentive problem is this divergence between the Agent’s incentives to increase p and the firm’s desire for increases in y. It is no use creating strong incentives for the wrong actions. If attaching a large bonus rate b to the performance measure p would create strong but distorted incentives, then the optimal bonus rate may be quite small.
To begin to investigate these issues more formally, consider a simple extension of the basic Principal-Agent model: y = a + and p = a + . In this case the contract w = s + bp creates incentives to increase p and the induced action also increases y. But now suppose that there are two kinds of actions (or “tasks”) that the Agent can take, a1 and a2.
In this setting, the contract w = s + bp creates incentives that depend on the bonus rate b and on the way the actions a1 and a2 affect the performance measure p. For example, if y
= a1 + a2 and p = a1then a contract based on p cannot create incentives for a2 and so misses this potential contribution to y. Alternatively, if y = a1 and p = a1 + a2 then a contract based on p creates an incentive for the Agent to take action a2, even though a2 is
15.903 11 R. Gibbons
Lecture Note 1: Agency Theory irrelevant to the Agent’s total contribution to firm value. Finally, in an extreme case such as y = a1 + and p = a2 + , the contract w = s + bp creates no value at all.
All of the examples above are special cases of the “multi-task” agency model described in the next section (which itself can easily be extended to include more actions and other enrichments). Compared to the basic Principal-Agent model, the chief departure in the multi-task model is the introduction of a fifth element of the model: in addition to the technology of production, the contract, the payoffs, and the timing of events, we now also require a technology of performance measurement.
6. The Multi-Task Agency Model4
Suppose that the technology of production is y = f1a1 + f2a2 + , the technology of performance measurement is p = g1a1 + g2a2 + , the contract is w = s + bp, and the payoffs are = y - w to the Principal and U = w - c(a1, a2) to the Agent. To keep things simple, assume that E() = E() = 0 and c(a1, a2) = 1
2 a1
2 + 1
2 a2
2 , but notice that the latter assumption rules out the potentially important case where the actions compete for the Agent’s attention (i.e., increasing the level of one action increases the marginal cost of the other).
The timing of events in this model is essentially the same as in the basic
Principal-Agent model, except that it is modified to incorporate the new distinction here between y and p:
1. The Principal and the Agent sign a compensation contract w = s + bp
(which we take to be linear for the reasons discussed above—namely, analytical simplicity and constant incentives).
2. The Agent chooses actions (a1 and a2) but the Principal cannot observe these choices.
3. Events beyond the Agent’s control (and ) occur.
4 This section draws on Feltham and Xie (1994), Datar, Kulp, and Lambert (2001), and Baker
(2002).
15.903 12 R. Gibbons
Lecture Note 1: Agency Theory
4. The actions and the noise terms determine the Agent’s total contribution to firm value (y) and measured performance (p).
5. Measured performance is observed by the Principal and the Agent (and by a
Court, if necessary).5
6. The Agent receives the compensation specified by the contract, as a function of the realized value of p.
In this setting, the (risk-neutral) Agent chooses the actions a1 and a2 to maximize the expected payoff E(w) - c(a1, a2) and so must solve the following problem: max a1,a2 s + b.(g1a1 + g2a2) - 1
2 a1
2 - 1
2 a2
2 .
The Agent’s optimal actions are therefore a1*(b) = g1b and a2*(b) = g2b, analogous to the special case of the basic Principal-Agent model where c(a) =
1
2 a2 and so a*(b) = b.
To finish analyzing this model we must determine the optimal level of b. It turns out that there is a single value of b that is efficient. That is, if the parties were to sign a contract with a slope other than this efficient value then they could both be made better off by switching to a contract with the efficient slope (and a new value of s chosen to make both parties better off after the switch).
To derive the efficient value of b, note that the Principal’s expected payoff from the contract w = s + bp is
E() = E(y - w) = f1a1*(b) + f2a2*(b) - s - bg1a1*(b) - bg2a2*(b) , where the Agent’s optimal actions in response to the contract have been included in the calculation of the Principal’s expected payoff. Similarly, the Agent’s expected payoff from the contract w = s + bp is
E(U) = E(w) - c(a1, a2) = s + b[g1a1*(b) + g2a2*(b)] - 1
2 a1*(b)2 - 1
2 a2*(b)2.
The sum of these expected payoffs is the expected total surplus, E(+ U):
5 In this model, no one ever observes the Agent’s total contribution, even though the Principal eventually receives the payoff y - w. See Lecture Note 2, on relational contracts and subjective performance assessment, for a more realistic approach to this issue.
15.903 13 R. Gibbons
Lecture Note 1: Agency Theory
E(y) - c(a1, a2) = f1a1*(b) + f2a2*(b) - 1
2 a1*(b)2 - 1
2 a2*(b)2.
The efficient value of b is the value that maximizes this expected total surplus. A little math shows that this efficient value of b is b*  f1g1 f2g2 g1 2 g2
2 .
This expression for the efficient value of b may not seem very helpful!
Fortunately, it can be restated using Figure 3, which plots both the coefficients f1 and f2 from the technology of production and the coefficients g1 and g2 from the technology of performance measurement. (The figure is drawn assuming that f1, f2, g1, and g2 are all positive, but this is not necessary.) This figure happens to represent a case in which g1 is larger than f1 but f2 is larger than g2. In such a case, paying the Agent on p will create stronger incentives than the Principal wants for a1 but weaker incentives than the
Principal wants for a2.
Figure 3
There are two important features in Figure 3: scale and alignment. To understand scale, imagine that g1 and g2 were both much larger than f1 and f2. Then the Agent could greatly increase p by choosing high values of a1 and a2 but these actions would result in a much smaller value of y (ignoring the realizations of the noise terms for the moment). As a result, the efficient contract should put a small bonus rate on p, as will emerge below.
To understand alignment, imagine first that the f and g vectors are closely aligned—they lie almost on top of one another (even if one is longer than the other). In this case the
15.903 14 R. Gibbons
Lecture Note 1: Agency Theory incentives created by paying on p are valuable for increasing y. Alternatively, imagine that the f and g vectors are badly aligned—for example, they might be orthogonal to each other (e.g., f1 = 0 and g2 = 0, so that y depends on only a2 and p depends on only a1). In this second case the incentives created by paying on p are useless for increasing y.
It turns out that scale and alignment are hiding in the expression for b* derived above. With a little more math we can rewrite that efficient slope as b*  f1 2 f2
2
g1
2 g2
2
cos() , where is the angle between the f and g vectors, as shown in Figure 3. Dusting off the
Pythagorean Theorem reminds us that f1
2 f2
2 is the length of the f vector, and correspondingly for g1
2 g2
2 , so f1
2 f2
2 g1
2 g2
2 reflects scaling. For example, if g is much longer than f (as considered above) then the efficient contract should put a small weight on p, as shown in this second expression for b*. Recall also that cos(0) = 1 and cos(90) = 0, so cos() reflects alignment. For example, if the f and g vectors are closely aligned then cos() is nearly 1 so b* is large, whereas if the f and g vectors are almost orthogonal then cos() is nearly 0 so b* is small.
Let me note immediately that I have never seen a real incentive contract (or any other business document) that involves a cosine! In this sense, the formula for the efficient slope, b*, illustrates the distinction between quantitative and qualitative predictions, as described in Section 1 above. That is, the two key ideas in the formula are not the Pythagorean Theorem and the cosine of an angle, but rather are scale and alignment – qualitative ideas that will persist as important factors in the determination of the efficient slope in many variations on and extensions of the simple model analyzed here. 7. Lessons and Limits of the Multi-Task Agency Model
The multi-task agency model delivers two important lessons, beyond the three lessons of the basic Principal-Agent model: (L4) objective performance measures typically cannot be used to create ideal incentives, and (L5) efficient bonus rates depend on scale and alignment. Furthermore, we can now dispel a persistent confusion about what makes a good performance measure, as follows.
15.903 15 R. Gibbons
Lecture Note 1: Agency Theory
One might be tempted to say that p is a good performance measure if it is highly correlated with y. But what determines the correlation between p and y? That is, what would cause p and y to move together if we watched them over time? Given technologies such as y = f1a1 + f2a2 + and p = g1a1 + g2a2 + , one important part of the answer involves the two variables we have not discussed thus far—the noise terms and .
Simply put, p and y will move together over time if the noise terms are highly correlated, regardless of the Agent’s actions. For example, suppose that p is a division’s accounting earnings and y is the firm’s stock price: both are hit by business-cycle variations (noise terms), but earnings reflect only the short-run effects of current actions while the stock price incorporates both short- and long-term effects of current actions. Thus, the earnings and the stock price might be highly correlated because of their noise terms, even though paying on the former creates distorted incentives for the latter (namely, incentives to ignore the long-run effects of current actions). Put more abstractly, y and p will be highly correlated if y = a1 + and p = a2 +, but in this case p is clearly a lousy performance measure. This argument leads to the central conclusion of the multi-task agency model: p is a valuable performance measure if it induces valuable actions, not if it is highly correlated with y. In short, alignment is more important than noise.
Although the multi-task agency is an important improvement on the basic
Principal-Agent model, even the multi-task model clearly omits important issues. For example, in this model there would be no effort if b = 0, but in the real world we sometimes see great effort even if there is no direct link between pay and performance. In some contexts, such efforts may be inspired by intrinsic motivation, rather than the extrinsic motivation analyzed in economic models. But even economic models can begin to address incentives in settings that have no formal incentive contract such as w = s + bp, as follows.
In Lecture Note 2 (“Relational Contracts”), we move beyond the static models discussed here to a dynamic model of “relational contracts” that are enforced by the parties’ concerns for their reputations rather than by the power of a court. In this dynamic model the Agent’s contribution to firm value is observed by the Agent and the Principal but not by a court. In this sense, the Agent’s performance can be subjectively assessed but not objectively measured. The model explores the extent to which the Principal can credibly promise to pay a bonus based on such a subjective assessment of the Agent’s contribution to firm value. We have already mentioned that such subjective bonuses are used at Lincoln Electric, where they complement piece-rate compensation based on
15.903 16 R. Gibbons
Lecture Note 1: Agency Theory objective performance measures. In later cases we will see that such subjective bonuses are very important in many other settings, both literally (e.g., in investment banking) and by analogy (e.g., in promotion decisions).
References
Baker, George. 2002. “Distortion and Risk in Optimal Incentive Contracts.” Journal of
Human Resources 37: 728-751.
Baker, George, Robert Gibbons, and Kevin J. Murphy. 1994. “Subjective Performance
Measures in Optimal Incentive Contracts,” Quarterly Journal of Economics 109:
1125-56.
Chevalier, Judith, and Glen Ellison. 1997. “Risk Taking by Mutual Funds as a Response to Incentives.” Journal of Political Economy 105:1167-1200.
Datar, Srikant, Susan Kulp, and Richard Lambert. 2001. “Balancing Performance
Measures.” Journal of Accounting Research 39: 75-92.
Fast, Norman, and Norman Berg. 1975. “The Lincoln Electric Company.” Harvard
Business School Case #376-028.
Feltham, Gerald and Jim Xie. 1994. “Performance Measure Congruity and Diversity in
Multi-Task Principal/Agent Relations.” The Accounting Review 69: 429-53.
Gibbons, Robert. 1998. “Incentives in Organizations.” Journal of Economic Perspectives
12: 115-32.
Holmstrom, B., and P. Milgrom. 1991. “Multitask Principal-Agent Analyses: Incentive
Contracts, Asset Ownership, and Job Design,” Journal of Law, Economics, and
Organization 7: 24-52
Kerr, Steven. 1975. “On the Folly of Rewarding A, While Hoping for B.” Academy of
Management Journal 18: 769-83.

Research design
From Wikipedia, the free encyclopedia
Jump to: navigation, search | This article's tone or style may not reflect the encyclopedic tone used on Wikipedia. See Wikipedia's guide to writing better articles for suggestions. (January 2013) |
A research design encompasses the methodology and procedure employed to conduct scientific research. The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g.: descriptive-longitudinal case study), research question and hypotheses, independent and dependent variables, experimental design if applicable, data collection methods and a statistical analysis plan. * |
[edit] Design types and sub-types
There are many ways to classify research designs, but sometimes the distinction is artificial and other times different designs are combined. Nonetheless, the list below offers a number of useful distinctions between possible research designs. * Descriptive (e.g.: case-study, naturalistic observation, survey) * Correlational (e.g.: case-control study, observational study) * Semi-experimental (e.g.: field experiment, quasi-experiment) * Experimental (Experiment with random assignment) * Review (Literature review, Systematic review) * Meta-analytic (Meta-analysis)
Sometimes a distinction is made between ‘’fixed’’ and ‘flexible’’ or, synonymously, ‘’quantitative’’ and ‘’qualitative’’ research designs.[1] However, fixed designs need not be quantitative, and flexible design need not be qualitative. In fixed designs, the design of the study is fixed before the main stage of data collection takes place. Fixed designs are normally theory driven; otherwise it’s impossible to know in advance which variables need to be controlled and measured. Often, these variables are measured quantitatively. Flexible designs allow for more freedom during the data collection process. One reason for using a flexible research design can be that the variable of interest is not quantitatively measurable, such as culture. In other cases, theory might not be available before one starts the research.
[edit] Grouping
The choice of how to group participants depends on the research hypothesis and on how the participants are sampled. In a typical experimental study, there will be at least one ‘’experimental’’ condition (e.g.: ‘’treatment’’) and one ‘’control’’ condition (‘’no treatment’’), but the appropriate method of grouping may be depend on factors such as the duration of measurement phase and participant characteristics: * Cohort study * Cross-sectional study * Cross-sequential study * Longitudinal study
[edit] Confirmatory versus exploratory research
Confirmatory research tests a priori hypotheses - outcome predictions that are made before the measurement phase begins. Such a priori hypotheses are usually derived from a theory or the results of previous studies. The advantage of confirmatory research is that the result is more meaningful, in a sense that it is much harder to claim that a certain result is statistically significant. The reason for this is that in confirmatory research, one ideally strives to reduce the probability of falsely reporting a non-significant result as significant. This probability is known as α-level or type I error. Loosely speaking, if you know what you are looking for, you shou==Examples of fixed designs==
In an experimental design, the researcher actively tries to change the situation, circumstances, or experience of participants (manipulation), which may lead to a change in behavior or outcomes for the participants of the study. The researcher randomly assigns participants to different conditions, measures the variables of interest and tries to control for confounding variables. Therefore, experiments are often highly fixed even before the data collection starts.
In a good experimental design, a few things are of great importance. First of all, it is necessary to think of the best way to operationalize the variables that will be measured. Therefore, it is important to consider how the variable(s) will be measured, as well as which methods would be most appropriate to answer the research question. In addition, the statistical analysis has to be taken into account. Thus, the researcher should consider what the expectations of the study are as well as how to analyse this outcome. Finally, in an experimental design the researcher must think of the practical limitations including the availability of participants as well as how representative the participants are to the target population. It is important to consider each of these factors before beginning the experiment.[2] Additionally, many researchers employ power analysis before they conduct an experiment, in order to determine how large the sample must be to find an effect of a given size with a given design at the desired probability of making a Type I or Type II error.
To read more about experimental research designs, See Experiment.
[edit] Non-experimental research designs
Non-experimental research designs do not involve a manipulation of the situation, circumstances or experience of the participants. Non-experimental research designs can be broadly classified into three categories. First, relational designs, in which a range of variables is measured. These designs are also called correlational studies, because correlational data are most often used analysis. It is important to clarify here that correlation does not imply causation, and rather identifies dependence of one variable on another. Correlational designs are helpful in identifying the relation of one variable to another, and seeing the frequency of co-occurrence in two natural groups (See correlation and dependence). The second type is comparative research. These designs compare two or more groups on one or more variable, such as the effect of gender on grades. The third type of non-experimental research is a longitudinal design. A longitudinal design examines variables such as performance exhibited by a group or groups over time. See Longitudinal study. ld be very confident when and where you will find it; accordingly, you only accept a result as significant if it is highly unlikely to have been observed by chance.
Exploratory research on the other hand seeks to generate a posteriori hypotheses by examining a data-set and looking for potential relations between variables. It is also possible to have an idea about a relation between variables but to lack knowledge of the direction and strength of the relation. If the researcher does not have any specific hypotheses beforehand, the study is exploratory with respect to the variables in question (although it might be confirmatory for others). The advantage of exploratory research is that it is easier make new discoveries due to the less stringent methodological restrictions. Here, the researcher does not want to miss a potentially interesting relation and therefore aims to minimize the probability of rejecting a real effect or relation, this probability is sometimes referred to as β and the associated error is of type II. In other words, if you want to see whether some of your measured variables could be related, you would want to increase your chances of finding a significant result by lowering the threshold of what you deem to be significant.
Sometimes, a researcher may conduct exploratory research but report it as if it had been confirmatory (HARKing); this is a questionable research practice bordering fraud.
[edit] Examples of fixed designs
In an experimental design, the researcher actively tries to change the situation, circumstances, or experience of participants (manipulation), which may lead to a change in behavior or outcomes for the participants of the study. The researcher randomly assigns participants to different conditions, measures the variables of interest and tries to control for confounding variables. Therefore, experiments are often highly fixed even before the data collection starts.
In a good experimental design, a few things are of great importance. First of all, it is necessary to think of the best way to operationalize the variables that will be measured. Therefore, it is important to consider how the variable(s) will be measured, as well as which methods would be most appropriate to answer the research question. In addition, the statistical analysis has to be taken into account. Thus, the researcher should consider what the expectations of the study are as well as how to analyse this outcome. Finally, in an experimental design the researcher must think of the practical limitations including the availability of participants as well as how representative the participants are to the target population. It is important to consider each of these factors before beginning the experiment.[3] Additionally, many researchers employ power analysis before they conduct an experiment, in order to determine how large the sample must be to find an effect of a given size with a given design at the desired probability of making a Type I or Type II error.
To read more about experimental research designs, See Experiment.
[edit] Non-experimental research designs
Non-experimental research designs do not involve a manipulation of the situation, circumstances or experience of the participants. Non-experimental research designs can be broadly classified into three categories. First, relational designs, in which a range of variables is measured. These designs are also called correlational studies, because correlational data are most often used analysis. It is important to clarify here that correlation does not imply causation, and rather identifies dependence of one variable on another. Correlational designs are helpful in identifying the relation of one variable to another, and seeing the frequency of co-occurrence in two natural groups (See correlation and dependence). The second type is comparative research. These designs compare two or more groups on one or more variable, such as the effect of gender on grades. The third type of non-experimental research is a longitudinal design. A longitudinal design examines variables such as performance exhibited by a group or groups over time. See Longitudinal study.
[edit] Examples of flexible research designs
[edit] Case study
See also: Case study
In a case study, one single unit is extensively studied. that can be a case of a person, organization, group or situation. Famous case studies are for example the descriptions about the patients of Freud, who were thoroughly analysed and described.
Bell (1999) states “a case study approach is particularly appropriate for individual researchers because it gives an opportunity for one aspect of a problem to be studied in some depth within a limited time scale”.[4]
[edit] Ethnographic study
See also: Ethnography
This type of research is involved with a group, organization, culture, or community. Normally the researcher shares a lot of time with the group.
[edit] Grounded Theory study
See also: Grounded Theory
Grounded theory research is a systematic research process that works to develop "a process, and action or an interaction about a substantive topic".[5]
[edit] References 1. ^ Robson, C. (1993). Real-world research: A resource for social scientists and practitioner – researchers. Malden: Blackwell Publishing. 2. ^ Adèr, H. J., Mellenbergh, G. J., & Hand, D. J. (2008). Advising on research methods: a consultant's companion. Huizen: Johannes van Kessel Publishing. ISBN 978-90-79418-01-5 3. ^ Adèr, H. J., Mellenbergh, G. J., & Hand, D. J. (2008). Advising on research methods: a consultant's companion. Huizen: Johannes van Kessel Publishing. ISBN 978-90-79418-01-5 4. ^ Bell, J. (1999). Doing your research project. Buckingham: OUP. 5. ^ Creswell, J.W. (2012). Educational research: Planning, conducting, and evaluating quantitative

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The purpose of this guide is to provide advice on how to develop and organize a research paper in the social sciences.
Last Updated: Jan 22, 2013URL: http://libguides.usc.edu/writingguidePrint Guide RSS Updates * Purpose of Guide * Types of Research Designs * 1. Choosing a Topic * 2. Preparing to Write * 3. The Abstract * 4. The Introduction * 5. The Literature Review * 6. The Methodology * 7. The Results * 8. The Discussion * 9. The Conclusion * 10. Proofreading Your Paper * 11. Citing Sources * Annotated Bibliography * Giving an Oral Presentation * Grading Someone Else's Paper * How to Manage Group Projects * Writing a Book Review * Writing a Field Report * Writing a Research Proposal * Acknowledgements

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Introduction
Before beginning your paper, you need to decide how you plan to design the study.
The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you can use, not the other way around!
General Structure and Writing Style
The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem as unambiguously as possible. In social sciences research, obtaining evidence relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe a phenomenon. However, researchers can often begin their investigations far too early, before they have thought critically about about what information is required to answer the study's research questions. Without attending to these design issues beforehand, the conclusions drawn risk being weak and unconvincing and, consequently, will fail to adequate address the overall research problem.
Given this, the length and complexity of research designs can vary considerably, but any sound design will do the following things: 1. Identify the research problem clearly and justify its selection, 2. Review previously published literature associated with the problem area, 3. Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem selected, 4. Effectively describe the data which will be necessary for an adequate test of the hypotheses and explain how such data will be obtained, and 5. Describe the methods of analysis which will be applied to the data in determining whether or not the hypotheses are true or false.
Action Research Design
Definition and Purpose
The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out (the action in Action Research) during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and the cyclic process repeats, continuing until a sufficient understanding of (or implement able solution for) the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.
What do these studies tell you? 1. A collaborative and adaptive research design that lends itself to use in work or community situations. 2. Design focuses on pragmatic and solution-driven research rather than testing theories. 3. When practitioners use action research it has the potential to increase the amount they learn consciously from their experience. The action research cycle can also be regarded as a learning cycle. 4. Action search studies often have direct and obvious relevance to practice. 5. There are no hidden controls or preemption of direction by the researcher.
What these studies don't tell you? 1. It is harder to do than conducting conventional studies because the researcher takes on responsibilities for encouraging change as well as for research. 2. Action research is much harder to write up because you probably can’t use a standard format to report your findings effectively. 3. Personal over-involvement of the researcher may bias research results. 4. The cyclic nature of action research to achieve its twin outcomes of action (e.g. change) and research (e.g. understanding) is time-consuming and complex to conduct.
Case Study Design
Definition and Purpose
A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about a phenomenon.
What do these studies tell you? 1. Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships. 2. A researcher using a case study design can apply a vaiety of methodologies and rely on a variety of sources to investigate a research problem. 3. Design can extend experience or add strength to what is already known through previous research. 4. Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and extension of methods. 5. The design can provide detailed descriptions of specific and rare cases.
What these studies don't tell you? 1. A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things. 2. The intense exposure to study of the case may bias a researcher's interpretation of the findings. 3. Design does not facilitate assessment of cause and effect relationships. 4. Vital information may be missing, making the case hard to interpret. 5. The case may not be representative or typical of the larger problem being investigated. 6. If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your intepretation of the findings can only apply to that particular case.
Causal Design
Definition and Purpose
Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.
Conditions necessary for determining causality: * Empirical association--a valid conclusion is based on finding an association between the independent variable and the dependent variable. * Appropriate time order--to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable. * Nonspuriousness--a relationship between two variables that is not due to variation in a third variable.
What do these studies tell you? 1. Causality research designs helps researchers understand why the world works the way it does through the process of proving a causal link between variables and eliminating other possibilities. 2. Replication is possible. 3. There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
What these studies don't tell you? 1. Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent]. 2. Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven. 3. If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and therefore to establish which variable is the actual cause and which is the actual effect.
Cohort Design
Definition and Purpose
Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed." * Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof. * Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
What do these studies tell you? 1. The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies on cohort designs. 2. Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect. 3. Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.]. 4. Either original data or secondary data can be used in this design.
What these studies don't tell you? 1. In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables. 2. Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings. 3. Because of the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.
Cross-Sectional Design
Definition and Purpose
Cross-sectional research designs have three distinctive features: no time dimension, a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure diffrerences between or from among a variety of people, subjects, or phenomena rather than change. As such, researchers using this design can only employ a relative passive approach to making causal inferences based on findings.
What do these studies tell you? 1. Cross-sectional studies provide a 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time. 2. Unlike the experimental design where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena. 3. Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time. 4. Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling. 5. Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound. 6. Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population. 7. Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
What these studies don't tell you? 1. Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult. 2. Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical contexts. 3. Studies cannot be utilized to establish cause and effect relationships. 4. Provide only a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen. 5. There is no follow up to the findings.
Descriptive Design
Definition and Purpose
Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.
What do these studies tell you? 1. The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject. 2. Descriptive research is often used as a pre-cursor to more quantitatively research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively. 3. If the limitations are understood, they can be a useful tool in developing a more focused study. 4. Descriptive studies can yield rich data that lead to important recommendations. 5. Appoach collects a large amount of data for detailed analysis.
What these studies don't tell you? 1. The results from a descriptive research can not be used to discover a definitive answer or to disprove a hypothesis. 2. Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated. 3. The descriptive function of research is heavily dependent on instrumentation for measurement and observation.
Experimental Design
Definition and Purpose
A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental Research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.
What do these studies tell you? 1. Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “what causes something to occur?” 2. Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects. 3. Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study. 4. Approach provides the highest level of evidence for single studies.
What these studies don't tell you? 1. The design is artificial, and results may not generalize well to the real world. 2. The artificial settings of experiments may alter subject behaviors or responses. 3. Experimental designs can be costly if special equipment or facilities are needed. 4. Some research problems cannot be studied using an experiment because of ethical or technical reasons. 5. Difficult to apply ethnographic and other qualitative methods to experimental designed research studies.
Exploratory Design
Definition and Purpose
An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to. The focus is on gaining insights and familiarity for later investigation or undertaken when problems are in a preliminary stage of investigation.
The goals of exploratory research are intended to produce the following possible insights: * Familiarity with basic details, settings and concerns. * Well grounded picture of the situation being developed. * Generation of new ideas and assumption, development of tentative theories or hypotheses. * Determination about whether a study is feasible in the future. * Issues get refined for more systematic investigation and formulation of new research questions. * Direction for future research and techniques get developed.
What do these studies tell you? 1. Design is a useful approach for gaining background information on a particular topic. 2. Exploratory research is flexible and can address research questions of all types (what, why, how). 3. Provides an opportunity to define new terms and clarify existing concepts. 4. Exploratory research is often used to generate formal hypotheses and develop more precise research problems. 5. Exploratory studies help establish research priorities.
What these studies don't tell you? 1. Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large. 2. The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. 3. The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value in decision-making. 4. Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.
Historical Design
Definition and Purpose
The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute your hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, logs, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.
What do these studies tell you? 1. The historical research design is unobtrusive; the act of research does not affect the results of the study. 2. The historical approach is well suited for trend analysis. 3. Historical records can add important contextual background required to more fully understand and interpret a research problem. 4. There is no possibility of researcher-subject interaction that could affect the findings. 5. Historical sources can be used over and over to study different research problems or to replicate a previous study.
What these studies don't tell you? 1. The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem. 2. Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts. 3. Interpreting historical sources can be very time consuming. 4. The sources of historical materials must be archived consistentally to ensure access. 5. Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources. 6. Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity. 7. It rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.
Longitudinal Design
Definition and Purpose
A longitudinal study follows the same sample over time and makes repeated observations. With longitudinal surveys, for example, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study and is sometimes referred to as a panel study.
What do these studies tell you? 1. Longitudinal data allow the analysis of duration of a particular phenomenon. 2. Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments. 3. The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time]. 4. Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
What these studies don't tell you? 1. The data collection method may change over time. 2. Maintaining the integrity of the original sample can be difficult over an extended period of time. 3. It can be difficult to show more than one variable at a time. 4. This design often needs qualitative research to explain fluctuations in the data. 5. A longitudinal research design assumes present trends will continue unchanged. 6. It can take a long period of time to gather results. 7. There is a need to have a large sample size and accurate sampling to reach representativness.
Observational Design
Definition and Purpose
This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.
What do these studies tell you? 1. Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe (data is emergent rather than pre-existing). 2. The researcher is able to collect a depth of information about a particular behavior. 3. Can reveal interrelationships among multifaceted dimensions of group interactions. 4. You can generalize your results to real life situations. 5. Observational research is useful for discovering what variables may be important before applying other methods like experiments. 6. Observation researchd esigns account for the complexity of group behaviors.
What these studies don't tell you? 1. Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and difficult to replicate. 2. In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups. 3. There can be problems with bias as the researcher may only "see what they want to see." 4. There is no possiblility to determine "cause and effect" relationships since nothing is manipulated. 5. Sources or subjects may not all be equally credible. 6. Any group that is studied is altered to some degree by the very presence of the researcher, therefore, skewing to some degree any data collected (the Heisenburg Uncertainty Principle).
Philosophical Design
Definition and Purpose
Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways: * Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative? * Epistemology -- the study that explores the nature of knowledge; for example, on what does knowledge and understanding depend upon and how can we be certain of what we know? * Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
What do these studies tell you? 1. Can provide a basis for applying ethical decision-making to practice. 2. Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research. 3. Brings clarity to general guiding practices and principles of an individual or group. 4. Philosophy informs methodology. 5. Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse. 6. Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics). 7. Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
What these studies don't tell you? 1. Limited application to specific research problems [answering the "So What?" question in social science research]. 2. Analysis can be abstract, argumentative, and limited in its practical application to real-life issues. 3. While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation. 4. There are limitations in the use of metaphor as a vehicle of philosophical analysis. 5. There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.
Sequential Design
Definition and Purpose
Sequential research is that which is carried out in a deliberate, staged approach [i.e. serially] where one stage will be completed, followed by another, then another, and so on, with the aim that each stage will build upon the previous one until enough data is gathered over an interval of time to test your hypothesis. The sample size is not predetermined. After each sample is analyzed, the researcher can accept the null hypothesis, accept the alternative hypothesis, or select another pool of subjects and conduct the study once again. This means the researcher can obtain a limitless number of subjects before finally making a decision whether to accept the null or alternative hypothesis. Using a quantitative framework, a sequential study generally utilizes sampling techniques to gather data and applying statistical methods to analze the data. Using a qualitative framework, sequential studies generally utilize samples of individuals or groups of individuals [cohorts] and use qualitative methods, such as interviews or observations, to gather information from each sample.

What do these studies tell you? 1. The researcher has a limitless option when it comes to sample size and the sampling schedule. 2. Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method. Useful design for exploratory studies. 3. There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce extensive. 4. Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed.
What these studies don't tell you? 1. The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more sample can be difficult. 2. Because the sampling technique is not randomized, the design cannot be used to create conclusions and interpretations that pertain to an entire population. Generalizability from findings is limited. 3. Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.
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BOX 2.1 EXAMPLES OF RESEARCH QUESTIONS FROM
QUALITATIVE STUDIES
How can we explain the extent and nature of football hooliganism at different football clubs and in different countries, and variations therein over time?
(Spaaij, 2006)
Do psychiatric/mental health nurses provide meaningful caring response to suicidal people, and if so, how? (Cutcliffe, Stevenson, Jackson & Smith, 2006)
What perceptions about information systems/information technology (IS/IT) are held by government authorities and how are they reacting to problems they have in IS/IT initiatives in government? (Lee & Kim, 2007)
What are undergraduate medical students’ perceptions and experiences of teaching in relation to gender and ethnicity? (Lempp & Seale, 2006)
What are young people’s experiences of crime, disorder and risk-taking in the night-time economy and in what ways are drinking patterns, attitudes to drinking alcohol and the effects of binge drinking related to these experiences?
(Engineer et al., 2003)
How do unaccompanied minor refugee youths, who grew up amidst violence and loss, cope with trauma and hardships in their lives? (Goodman, 2004)
How has the meaning which women attribute to sedatives and their use been influenced by information which they have gathered from their daughters, people in their social networks or other sources? (Haafkens, 1997)
The research question is often broad and encompassing, and is usually divided into multiple sub-questions that further structure the research. Formulating sub-questions is difficult for a number of reasons:
They must use the terminology that fits the chosen approach or tradition
(see Chapter 1).
They need to fall under the umbrella of the overall research question, confining the research to topics described in the overall question and allowing you to make a contribution to the knowledge in a particular scientific area.
Choosing a setting
In selecting a setting,Morse and Field (1996) use the principle of maximization.This means that a location should be determined where the topic of study manifests itself most strongly.They conducted research on pain and the way in which nurses offered consolation and comfort.They considered investigating this topic in the context of childbirth, but decided not to.They reasoned that pain might be maximally present
Purposive sampling
Composing the sample in qualitative research is different from the common sampling approach used in quantitative research.There has even been an objection to the use of the term ‘sample’ since it carries connotations that some find undesirable for qualitative research.These connotations have to do with the following. In quantitative research it is paramount that statistical representation is implemented.The probability that the case falls within the sample is determined by chance, and the sample reflects the proportional distribution of relevant population characteristics. Based on the findings in this randomly selected sample, probabilistic assertions may be made about the entire population, commonly referred to as generalization or statistical inference
(see Chapter 9). Statistical rules and procedures are used to make such assessments.
Although both procedures – random sampling and statistical inferences – do not apply to qualitative research, the term ‘sample’ is widely used in qualitative research terminology.A sample consists of the cases (units or elements) that will be examined and are selected from a defined research population. In qualitative research the sample is intentionally selected according to the needs of the study, commonly referred to as ‘purposive sampling’ or ‘purposeful selection’.The cases are specifically selected because they can teach us a lot about the issues that are of importance to the research
(Coyne, 1997).
All samples in qualitative research have some features in common (Curtis, Gesler,
Smith &Washburn, 2000).The samples are often small, although that is not a fixed rule. Cases are studied intensively, and each case typically generates a large amount of information.Generally samples are not predetermined, and selection is sequential, interleaved with data collection and analysis. Sampling strategies in qualitative research typically aim to represent a wide range of perspectives and experiences, rather than to replicate their frequency in the wider population (Ziebland &
McPherson, 2006).
Two types of purposive sampling can typically be distinguished in qualitative research (Curtis et al., 2000). One form of purposive sampling is suitable for qualitative research, which is informed a priori by an existing body of social theory on which the research questions are based. In this case the sample selection is driven by a theoretical framework which guides the research from the onset. Many different sampling strategies can be found, for instance drawing a unique case, a critical case, an extreme case or a typical case, drawing for maximum variation and homogeneous drawing (Miles & Huberman, 1994).
The other form is theoretical sampling, designed to generate theory which is grounded in the data, rather than established in advance of the fieldwork.Theoretical sampling is based on the grounded theory approach (see Chapter 1). Theoretical sampling is defined as ‘the process of data collection for generating theory whereby the analyst jointly collects, codes, and analyses his data and decides which data to collect next and where to find them, in order to develop his theory as it emerges’
(Glaser, 1978: 36).
Examples of both types of sampling are given below.The first type, purposive sampling in research that already starts with a theoretical framework, is illustrated by the study of Spaaij (2006) into football hooliganism.He chose to examine this phenomenon in more than one football club (multiple case study design). In order to make a proper selection of cases, he reviewed the literature and consulted experts on the subject. Because Spaaij had a rather strong theoretical impetus in his research, his sample was chosen beforehand. Box 2.6 describes how and why Spaaij selected football
Lee, R.M. (1993). Doing research on sensitive topics. London: Sage.
Mason, J. (2002). Qualitative researching (2nd ed.). London: Sage.
Maxwell, J.A. (2004). Qualitative research design. An interactive approach (2nd ed.).
Thousand Oaks, CA: Sage.
Punch, K.F. (2005). Introduction to social research. Quantitative and qualitative approaches (2nd ed.). London: Sage.