Research Glossary

Think of this as an encyclopedia of personnel research. I will be adding to it continually throughout the semester. If you have suggestions about items to add or changes in the definitions, please email me at or fill out the comments form -- I would really appreciate it!


Applied Research
1. The kind of research usually performed by consultants or HR professionals. Typically motivated by the need to solve a specific problem in a particular organization. 2. Contrasts with basic research.
Basic Research
1. The kind of research usually done by academics. Typically tries to uncover universal relationships among variables. This kind of research generally has implications for solving particular problems in specific organizations, but is primarily motivated by a generic desire to understand how things work. 2. Contrasts with applied research.
Objects or entities whose behavior or characteristics we study. Usually, the cases are persons. But they can also be groups, departments, organizations, etc. They can also be more esoteric things like events (e.g., meetings), utterances, pairs of people, etc. In the context of sampling, cases are also called elements.
While the goal of research is to understand what causes what, this is a very difficult goal to achieve. Strictly speaking, it is impossible. In fact, the notion of causality is just a theory itself. However, on a day-to-day basis, we assume that causality does exist and that we can discover it through a combination of inductive and deductive work. In general, laboratory experiments are the only way to ascertain causality.
Cluster Sampling
A multi-stage sampling scheme in which the population is first divided into clusters, then a sample of these clusters is chosen via simple random sampling, and then a simple random sample of population elements is selected within the chosen clusters. This differs from stratified sampling in that in stratified sampling, all strata are sampled, whereas in cluster sampling we take a sample of clusters, not all clusters. Cluster sampling is used when it is difficult to construct a sampling frame for the entire population, or when it is too costly to visit randomly chosen population elements.
Construct (noun)
A variable in a theory. Sometimes carries the connotation of something that cannot be observed directly, or which we suppose to exist but has not been measured yet. Similar in this sense to a latent variable. Intelligence is a construct that is used to explain competence.
Cross-Sectional Study
A cross-sectional study is where we collect data only once from each unit of analysis. For example, if we want to examine the effects of age on attitude towards abortion, we collect attitude data from people of all ages, then see if there is a correlation between age and attitude. This is the opposite of a longitudinal study, where you take a set of young people, then measuring their attitude towards abortion every few years as they get older.
The outcome of measurement. The set of values or codes that record what was observed, such as the blood pressure of 100 people.
Descriptive Study
A descriptive study is similar to an exploratory study in that we do not attempt to test hypotheses. Often, they are used in settings where a theory of how variables are related is already in place, but specific values for each of the variables are needed for specific cases in order to take some action. For example, if an organization is considering adopting a new benefits package that costs more but has new features that might be attractive, the organization needs to know what the needs of the employees are to determine whether the package makes sense for them. For example, if the main feature of the more expensive package is a domestic partner program (that's where homosexual partners of employees are entitled to health insurance just like heterosexual spouses), it makes sense to find out how many gay & lesbian employees the firm has.
Elements of a Population
In the context of sampling, elements are cases -- units of observation. They are the things being sampled. In general, elements are persons.
Exploratory Phase
The exploratory phase of a study is where you try to figure out (usually qualitatively) what is going on. There are three basic objectives: (1) learn the lingo of your respondents; (2) learn the background context within which everything happens; and (3) develop a set of testable theories about what is going on
Exploratory Study
Also known as "inductive" or "theory-building". In this kind of study, we don't begin with a theory. Instead, we collect data that, after analysis, we will use to develop a theory. After we develop the theory, we might then design a study to test the theory.
In the context of theory construction, is the property of a theory to possibily be shown false. There are several ways to be non-falsifiable. One is to be circular (tautological). For example, to explain why people do dumb things, you could theorize that it is because they are dumb. If you then define dumb as a person who does dumb things, the theory is circular. Another way is to appeal to things that can't be measured. For example, to explain why people go to the movies, we could theorize that they want to. But there is no way to measure whether they want to that would avoid circularity.
Field Experiment
A field experiment is a study in which you make changes in the independent variable to see how it affects the dependent variable, but otherwise you leave everything in its natural state. For example, an ethologist (someone who studies animal behavior), might put sugar water at different distances from a bee nest in order to observe how the difference in distance affects the dance that the bees do on returning home to communicate where food is.
Field Study
A field study is a study in which the researcher goes to a research site and observes and asks questions, but does not change anything. It's like a naturalist observing wildlife without doing anything like setting out food to attract animals, or making obstacles to see how the animals react.
Haphazard Sampling
A non-probability sampling scheme in which population elements are chosen based on convenience (e.g., choosing your friends to make up a sample of college students).
1. A postulated relationship between a pair of variables. The reason for expecting the variables to be related should come from a theory. 2. Any theory-based prediction about some measurable data.
Interval level of measurement
1. A level of measurement in which the ratios of measured values are not meaningful, which is to say that they do not correspond to similar relationships among the objects measured. The classic example of interval measurement is the measurement of temperature using Fahrenheit and Centigrade scales. If it is 80F in Tulsa and 40F in Juneau, you cannot say it is twice as hot in Tulsa. Here is one clue that this is not meaningful. Suppose we measured the temperature in Centigrade instead of Fahrenheit. The formula for converting Fahrenheit to Centigrade is C=(F-32)*5/9. So in Tulsa it is 27 C and in Juneau it is 4 C. Now it looks like Tulsa is 4 times as hot as Juneau! Yet Fahrenheit and Centigrade are perfectly equivalent and equally valid measuring scales. So you know ratios are not meaningful in interval measurement. 2. The ratios of differences (intervals) among measured values is meaningful. For example, suppose it is 70 F in L.A. and 50 F in San Francisco. The difference in temperature between Tulsa and Juneau (40 F) is twice as much as the difference in temperature between L.A. and San Francisco (20 F). This statement is still true if we measure the temperatures in Centigrade, so interval measurements preserve ratios of differences in measured values.
Intervening Variable
An intervening or intermediary variable is one that is affected by the independent variable and in turn affects the dependent variable.
Judgment Sampling
A non-probability sampling scheme in which you make use of special expertise to select elements for the study. Typically, this is used to obtain a balance of viewpoints or to select knowledgable respondents.
Laboratory Experiment
A lab experiment is a study in which you make changes in the independent variable, and then control all the other variables so that only the variable of interest could possibly affect the outcome. For example, if you are interested in the effects of seeing an inspirational film on taking a math test, you can recruit some experimental subjects to come to your theatre, then randomly assign half to see the film, and the other half to see some other film, then give them the test right after the films.
Level of measurement
At the simplest, level of measurement refers to what kinds of arithmetic relationships among the numeric values of the data actually reflect some kind of similar relationship among the objects themselves. Although there are many different kinds of measurement, in this course we will pretend that there are just 3 levels of measurement: ordinal, interval, and ratio.
Longitudinal Study
A longitudinal study is where we follow the units of analysis (say, employees) over time, and measure key variables at different points in time. For example, we might measure morale before and after a promotion.
The generation of data. A process of assigning numbers (or codes) to things such that certain specifiable relationships among the things are reflected in certain relationships among the numbers. For example, when we measure the mass of objects, we assign a number to each object, known as its weight. If the number assigned to object A is 10 and to object B is 20, we can say that object B has twice as much mass as object A. This preservation of ratios works for the way we measure mass, but it doesn't work for the way we (usually) measure temperature. Exactly which kinds of relationships between the numbers is actually reflective of relationships among the objects is what defines the level of the measurement.
Moderator Variable
A moderator variable is one that modifies the relationship between two other variables. If variable X modifies the relationship between variables Y and Z, then there is an interaction between X and Y. In a regression, the interaction between a pair a variables is tested by including the product of the two variables as an additional independent variable.
Non-Probability Sampling
Any sampling scheme in which the probability of a population element being chosen is unknown. There are four basic kinds: haphazard, quota, judgement, and snowball.
In the context of sampling, population refers to the universe of all possible cases. If you are studying the members of IBM, it is the set of all members of IBM. Can be used in contrast to sample.
1. A summary value calculated from a population (as opposed to a sample). Contrasts with statistic.
Probability Sampling
Any sampling scheme in which the probability of choosing each individual is the same (or at least known, so it can be readjusted mathematically to be equal). Also called random sampling. Probability samples are more costly to obtain, but are more accurate, and they allow the researcher to calculate the amount of error she can expect. There are three major kinds of probability sampling: simple random sampling (SRS), stratified sampling, and cluster sampling.
A formal, written, set of closed-ended and open-ended questions that are asked of every respondent in the study. The questions may be self-administered, or interviewer-administered. A source of data.
Quota Sampling
A sampling scheme similar to stratified sampling in which you first divide the population into classes (such as males and females) and then obtain a haphazard sample within each class.
A subset of population elements. In some usages, contrasts with population.
The practice of choosing a subset of population elements to study instead of the entire population. In general, we sample because (a) it's cheaper; (b) in some cases the population is theoretically infinite. There are two basic kinds of sampling: probability and non-probability.
Sampling Error
A difference, due to sampling, between a population parameter and the corresponding sample statistic. For example, the average age of a population might be 25 years, but a given sample might yield an average of 26 because, by chance, more old people were selected than the population proportion.
Sampling Frame
The sampling frame is a specific list of names (or other identifying codes) of the cases to be sampled. Usually, this is supposed to be the same as the population. For example, when you study IBM, you start by obtaining a list of all IBM employees. This is the sampling frame. If your list is not complete (e.g., it omits top management), your results may not be valid in the sense of generalizing to all of IBM.
Sampling Ratio
The sampling ratio is the size of the sample divided by the size of the population.
Simple Random Sampling (SRS)
A sample in which the population is first divided into strata (classes of elements). Within each stratum, each element has an equal chance of being chosen for the sample.
Snowball Sampling
A non-probability sampling scheme in which you begin by sampling one person, then ask that person for the names of other people you might interview, then interview them and obtain a list of people from them, and so on.
A statistic generally refers to a summary value calculated from a sample. For example, we might compute the average age of all respondents. The term contrasts with population parameter.
Stratified Sampling
A sample in which each element in the population has an equal chance of being chosen for the sample.
A general explanation of how something works. A theory says what is related to what and why. A theory is, in part, a collection of related hypotheses. However, a theory also contains a sense of process and mechanism -- a sense of understanding of why and how the variables are related the way they are. Desirable characteristics of a theory include: falsifiability, parsimony, truth, fertility, generality, surprise, and a sense of process or mechanism.
Theoretical Framework
A theoretical framework is a theoretical perspective. It can be simply a theory, but it can also be more general -- a basic approach to understanding something. Typically, a theoretical framework defines the kinds of variables that you will want to look at.
Time Allocation Studies
A technique for determining how much time people spend doing different activities. Basically what you do is arrive at specific locations at random intervals and record what everyone is doing. Or, you don't arrive at a particular location but instead go find a specific individual and record what that individual is doing. For example, your theory might require you to know how much time the manager spends on a set of key activities, such as writing reports, talking in meetings, or socializing.
1. Characteristics, attributes, or qualities of cases. For example, if the cases are persons, the variables could be sex, age, height, weight, feeling of empowerment, math ability, etc. 2. Anything we measure. 3. Constructs in a theory.

Revised: June 24, 1997 Go to Home Page