Encyclopedia of SociologyVol._1

DEPRESSION

scales try to understand the negative comparisons.

An example of this type of measure is one where individuals are asked about the circumstances (interactions with people, idle thoughts, etc.), the dimensions (social skills, intelligence), the gender, and the type of relationship with the comparison target, and the individual’s mood before and after the interaction. To get at the least-accessible level of thoughts, those that are believed to store, organize, and direct the processing of personally relevant information, researchers have used measures like the Stroop color-word task. Individuals are asked to name the color of the ink in which a word is printed but to ignore the meaning of the word itself. Slower response rates are thought to indicate greater effort to suppress words that are highly descriptive of the self. For example, depressed individuals take longer to name the color in which words like ‘‘sad’’ and ‘‘useless’’ are printed compared to the color for positive words.

In health, clinical, and counseling research and evaluation settings, the two most common measures of depression are the Beck Depression

Inventory (BDI) and the Center for Epidemiological Studies Depression Scale (CESD). The BDI was designed to measure ‘‘symptom-attitude categories’’ associated with depression (Beck 1967). These include, among others, mood, pessimism, and sense of failure as well as somatic preoccupation. Many of the items reflect Beck’s belief in the relevance of negative cognitions or self-evalua- tions in depression. Each item includes a group of statements that reflect increasing levels of one of these symptom-attitude categories. The test taker is asked to choose the statement within each item that reflects the way he or she has been feeling in the past week. The items are scored on a scale from 0–3, and reflect increasing levels of negativity. A sample item includes 0 = ‘‘I do not feel like a failure,’’ to 3 = ‘‘I feel I am a complete failure as a person.’’ The CESD is a twenty-item scale, is a widely used measure of depressive symptomatology, and has been shown to be valid and reliable in many samples. Participants are asked to best describe how often they felt or behaved during the previous week, in a variety of ways reflective of symptoms of depression, using a scale ranging from 0 (Rarely or none of the time[less than 1 day]) to 3 (Most or all of the time [5–7 days]). For example, participants are asked how often their sleep was restless or they felt that everything they did was an

effort. Other self-report measures include the Minnesota Multiphasic Personality Inventory Depression Scale (MMPI-D), the Zung Self-Rating Depression Scale (SDS), and the Depression Adjective Check List (DACL). Complete descriptions of these scales can be found in Constance Hammen (1997).

TREATMENT

As can be expected, the type of treatment depends on the type of depression and to some extent the favored theory of the health-care provider (physician, psychologist, or therapist) one goes to for treatment.

Biologically based treatments. The most common treatment for depression that is thought to have a physiological basis is antidepressant medication. Based on biological theories suggesting that depression results from low levels of the monoamines serotonin, norepinephrine, and dopamine, antidepressant medications act to increase the levels of these chemicals in the bloodstream. These drugs work by either preventing the monoamines from being broken down and destroyed (referred to as monoamine oxidase (MOA) inhibitors and tricyclics) or by preventing them from being removed from where they work (referred to as selective serotonin inhibitors [SSRIs]). Elavil, Norpramin, and Tofranil are the trade names of some MAO inhibitors. Prozac, Paxil, Zoloft, and

Luvox are examples of SSRIs.

Although these medications have been proven to be effective in reducing depression, they also have a variety of side effects and need to be taken only under medical supervision. For example, tricyclics also cause dry mouth, constipation, dizziness, irregular heartbeat, blurred vision, ringing in the ears, retention of urine, and excessive sweating. Some of the SSRIs were developed with an eye toward reducing side effects and are correspondingly more often prescribed. Unfortunately, they are most commonly associated with prescription drug overdoses resulting in many thousands of deaths a year.

Several medicinal herbs have antidepressant effects. The most powerful is St. John’s wort, a natural MAO inhibitor. In addition, ginkgo and caffeine may also help. Although much more research remains to be done, studies to date support

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the effectiveness of such alternative medicine. For example, a group of researchers in Texas, in collaboration with German scientists, surveyed studies including a total of 1,757 outpatients with mainly mild or moderately severe depressive disorders, and found that extracts of St. John’s wort were more effective than placebos (i.e., inactive pills) and as effective as standard antidepressant medication in the treatment of depression. They also had fewer side effects than standard antidepressant drugs (Linde et al. 1996).

In extreme cases of depression when drugs have been tried and found to not have an effect, and when the patient does not have the time to wait for drugs to take an effect (sometimes up to two or three weeks), electroconvulsive therapy

(ECT) is recommended. ECT involves passing a current of between 70 and 130 volts through the patient’s head after the administration of an anesthetic and muscle relaxant (to prevent injury from the convulsion caused by the charge). ECT is effective in treating severe depression although the exact mechanisms by which it works have not been determined.

Cognitive treatments. Cognitive therapists focus on the thoughts of the depressed person and attempt to break the cycle of negative automatic thoughts and negative self-views. Therapy sessions are well structured and begin with a discussion of an agenda for the session, where a list of items is drawn up and then discussed one by one. The therapist then tries to identify, understand, and clarify the misinterpretations and unrealistic expectations held by the client. Therapists use several techniques to identify these thoughts including asking direct questions, asking the client to use imagery to evoke the thoughts, or role-playing.

Identifying these thoughts is a critical part of cognitive therapy and clients are also asked to keep daily diaries to list automatic thoughts when they occur as they are often unnoticed by depressed individuals. The client is then asked to provide a written summary of the major conclusions from the session to solidify what has been achieved and finally, the therapist prescribes a

‘‘homework assignment’’ designed to help the client practice skills and behaviors worked on during the session. Behavioral therapy is closely related to cognitive therapy and involves training the client to have better social skills and behaviors that enable them to develop better relationships with others.

Which are more effective treatments: cognitive or biological? A large National Institute of

Mental Health study suggests that there is little difference in the effectiveness of the two therapies although the two treatments seem to produce different effects over time. Patients who received cognitive therapy were less likely to have a return of depression over time as compared to patients with biological therapy, although the small sample size used in this study precludes a definite answer to this question. Both therapies have been found to be effective, and it is likely that one is better with some forms of depression than the other, depending on how long the person has been depressed and the exact nature of his or her symptoms. In general both treatments, whether cognitive or biological, are recommended to be continued for a short time after the depressed episode has ended in order to prevent relapse.

CONCOMITANTS

A wide body of research has documented the links between depression and a wide variety of other factors. It is both a component of many other psychological disorders as well as something that follows many other disorders. In fact, some studies have shown that out of all the people at a given time with depression, only 44 percent of them display what can be called ‘‘pure’’ depression, whereas the others have depression and at least one other disorder or problem. The most common of these associated problems are anxiety, substance abuse, alcoholism, and eating disorders (see Hammen 1997 for more details). Given the symptoms of depression, individuals with the disorder also experience associated social problems including strained relationships with spouses, family, and friends, and in the workplace. Most alarming perhaps is that the children of depressed parents (especially mothers) are especially at risk for developing problems of their own.

Depression has also been linked to positive factors although not always with good results. For example, there is some evidence that depression is linked to creativity. Artists tend to suffer more than their share of depression according to psychiatrists at Harvard medical school, who charted the psychological histories of fifteen mid-twenti- eth-century artists. They found that at least half of

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them, including artists like Jackson Pollock and Mark Rothko, suffered from varying degrees of depression (Schildkraut and Aurora 1996). Many of these artists eventually committed suicide, which is perhaps one of the most significant and dangerous results of depression. At least 15 percent of people with depression complete the act of suicide, but an even higher proportion will attempt it. Consequently, individuals with severe cases of depression may experience many suicide-related thoughts and sometimes need constant surveillance.

Depression is often seen in patients with chronic or terminal illnesses and in patients who are close to dying. For example, depression is a common experience of AIDS patients, and is related to a range of factors such as physical symptomatology, number of days spent in bed, and in the perceived sufficiency of social support. Depression has also been linked to factors that influence mortality and morbidity. Higher depressed mood has been significantly associated with immune parameters pertinent to HIV activity and progression: lower levels of CD4 T cells, immune activation, and a lower proliferative response to PHA (a natural biological reaction that is essential to good health). Depression is also a critical variable with respect to compliance with treatment, especially in HIV-positive women of low-socioeconomic status.

Depression is strongly related to the number and duration of stressors experienced, or chronic burden. Chronic burden, defined by Leonard Pearlin and Carmi Schooler (1978) as ongoing difficulties in major social roles, including difficulties in employment, marriage, finances, parenting, ethnic relations, and being single/separated/divorced contributes to depression and increases vulnerability to health problems by reducing the ability of the body to respond to a physiological challenge, such as mounting an immune response to a virus. Related to chronic burden, many aspects of depression are concomitants of low-so- cioeconomic status, traditionally measured by education, income, and occupation. Research showing clear social-class differences in depression also suggest the contribution of the stress of poverty, exposure to crime, and other chronic stressors that vary with social class. Jay Turner, Blair Wheaton, and David Lloyd (1995) found that individuals of low-socioeconomic status were exposed to more chronic strain in the form of life difficulties in

seven domains (e.g., parenting, relationships, and financial matters) than individuals of high-socioe- conomic status, which could account for higher levels of depression.

The influence of culture is one factor that has not been sufficiently studied in the context of depression. To date, most clinical-disorder classification systems do not sufficiently acknowledge the role played by cultural factors in mental disorders. The experience of depression has very different meanings and forms of expression in different societies. Most cases of depression worldwide are experienced and expressed in bodily terms of aching backs, headaches, fatigue, and a wide assortment of symptoms that lead patients to regard this condition as a physical problem (Sarason and Sarason 1999). Only in contemporary Western societies is depression seen principally as an internal psychological experience. For example, many cultures tend to view their mental health problems in terms of physical bodily problems. That is, they tend to manifest their worries, guilt feelings, and strong negative emotions (such as depression) as physical complaints. This could be because bodily complaints do not carry the stigma or negative social consequences that psychological problems do, and are correspondingly easier to talk about.

Although not an essential part of aging, many people over age sixty-five develop clinical depression. Surveys suggest that only about 5 percent of healthy elderly people living independently suffer depression at any given moment, but more than 15 percent experience depression at some point during their elderly years, and the condition tends to be more chronic than in younger people. In addition, some 25 percent of elderly individuals experience periods of persistent sadness that last two weeks or longer, and more than 20 percent report persistent thoughts of death and dying. The likelihood of depression varies with the situation the person is in, and is more likely when the elderly person is away from his or her family in a novel setting. For example, some 20 percent of nursing home residents are depressed. Depression is also antagonized by serious medical conditions that elderly men and women may have. Correspondingly, depression is commonly associated with illnesses like cancer, heart attack, and stroke. Depression often goes undiagnosed and untreated in the elderly and is something that caregivers (spouse,

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children, family, and friends) should be especially watchful for given the relationship between depression and suicide.

CONCLUSIONS

Many people still carry the misperception that depression is either a character flaw, a problem that happens because of personal weaknesses, or is completely ‘‘in the head.’’ As described above, there are psychological, physiological, and societal components to depression. Most importantly, it is something that can and should be treated. There are too few people who see a doctor when they recognize symptoms of depression or think of getting medical treatment for it. Depression is so prevalent that it is often seen as a natural component of life events like pregnancy and old age, and depressed mothers and elderly men and women often do not get the attention they need. Today, much more is known about the causes and treatment of this mental-health problem, with the best form of treatment being a combination of medication and psychotherapy. Depression need not be ‘‘the end.’’

REFERENCES

Abraham, Karl 1968 ‘‘Notes on the Psychoanalytic Investigation and Treatment of Manic-Depressive Insanity and Allied Conditions’’ (1911). In K. Abraham, ed., Selected Papers of Karl Abraham. New York: Basic Books.

Abramson, Lauren, Y., G. I. Metalsky, and L. B. Alloy 1989 ‘‘Hopelessness Depression: A Theory-Based Subtype of Depression.’’ Psychological Review 96:358–372.

American Psychiatric Association 1994 Diagnostic and Statistical Manual of Mental Disorders: DSM-IV, 4th ed. Washington, D.C. : American Psychiatric Association.

Beck, Aaron T. 1967 Depression: Clinical, Experimental and Theoretical Aspects. New York: Harper and Row.

Beckham, E. E., and W. R. Leber (eds.) 1995 Handbook of Depression: An Updated Review and Integration, 2nd ed. New York: Guilford.

Bowlby, John 1988 A Secure Base: Parent-Child Attachment and Healthy Human Development. New York: Basic Books.

Brown, G. W., and T. O. Harris 1978 Social Origins of Depression. London: Free Press.

Endler, Norman S. 1990 Holiday of Darkness: A Psychologist’s Personal Journey Out of His Depression. New York: John Wiley and Sons.

Freud, S. 1957 ‘‘Mourning and Melancholia’’ (1917). In J. Strachey, ed., The Standard Edition of the Complete Psychological Works of Sigmund Freud, vol 14. London: Hogarth.

Gotlib, Ian H., and Constance L. Hammen 1992 Psychological Aspects of Depression: Toward a Cognitive-Inter- personal Integration. New York: John Wiley and Sons.

Hammen, Constance 1997 Depression. Hove, East Sus-

sex, Eng.: Psychology Press.

Honig, A., and H. M. van Praag (eds.) 1997 Depression: Neurobiological, Psychopathological, and Therapeutic Advances. New York: John Wiley and Sons.

Linde K., G. Ramirez, C. D. Mulrow, A. Pauls, W. Weidenhammer, and D. Melchart 1996 ‘‘St. John’s Wort for Depression: A Meta-Analysis of Randomized Clinical Trials.’’ British Medical Journal, 313, 253.

Nemeroff, Charles B. 1998 ‘‘The Neurobiology of Depression.’’ Scientific American 278:42–49.

Nolen-Hoeksema, Susan 1995 ‘‘Epidemiology and Theories of Gender Differences in Unipolar Depression.’’ In M. V. Seeman, ed., Gender and Psychopathology, 63– 87. Washington, D.C.: American Psychiatric Press.

———, and J. S. Girgus 1994 ‘‘The Emergence of Gender Differences in Depression During Adolescence.’’

Psychological Bulletin 115:424–443.

Pearlin, L. I., and C. Schooler 1978 ‘‘The Structure of Coping.’’ Journal of Health and Social Behavior 19:2–21.

Sarason, Irwin G., and Barbara R. Sarason 1999 Abnormal Psychology: The Problem of Maladaptive Behavior, 9th ed. Upper Saddle River, N.J.: Prentice-Hall.

Schildkraut, J. J., and A. Otero (eds.) 1996 Depression and the Spiritual in Modern Art: Homage to Miro, 112–130. New York: John Wiley and Sons.

Seligman, Martin E. P. 1975 Helplessness: On Depression, Development and Death. San Francisco: Freeman.

Styron, William 1990 Darkness Visible: A Memoir of Madness. New York: Random House.

Turner, R. J., B. Wheaton, and D. A. Lloyd 1995 ‘‘The Epidemiology of Social Stress.’’ American Sociological Review 60:104–125.

REGAN A. R. GURUNG, PH. D.

DESCRIPTIVE STATISTICS

Descriptive statistics include data distribution and the summary information of the data. Researchers use descriptive statistics to organize and describe the data of a sample or population. The characteristics of the sample are statistics while those of the

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population are parameters. Descriptive statistics are usually used to describe the characteristics of a sample. The procedure and methods to infer the statistics to parameters are the statistical inference. Descriptive statistics do not include statistical inference.

Though descriptive statistics are usually used to examine the distribution of single variables, they may also be used to measure the relationship of two or more variables. That is, descriptive statistics may refer to either univariate or bivariate relationship. Also, the level of the measurement of a variable, that is, nominal, ordinal, interval, and ratio level, can influence the method chosen.

DATA DISTRIBUTION

To describe a set of data effectively, one should order the data and examine the distribution. An eyeball examination of the array of small data is often sufficient. For a set of large data, the aids of tables and graphs are necessary.

Tabulation. The table is expressed in counts or rates. The frequency table can display the distribution of one variable. It lists attributes, categories, or intervals with the number of observations reported. Data expressed in the frequency distribution are grouped data. To examine the central tendency and dispersion of large data, using grouped data is easier than using ungrouped data.

Data usually are categorized into intervals that are mutually exclusive. One case or data point falls into one category only. Displaying frequency distribution of quantitative or continuous variables by intervals is especially efficient. For example, the frequency distribution of age in an imaginary sample can be seen in Table 1.

Here, age has been categorized into five intervals, i.e., 15 and below, 16–20, 21–25, 26–30, and 31– 35, and they are mutually exclusive. Any age falls into one category only. This display is very efficient for understanding the age distribution in our imaginary sample. The distribution shows that twenty cases are aged fifteen or younger, twenty-

five cases are sixteen to twenty years old, thirty-six cases are twenty-one to twenty-five years old, twenty cases are twenty-six to thirty years old, and nineteen cases are thirty-one to thirty-five years old. To compare categories or intervals and to

compare various samples or populations, the reporting percent or relative frequency of each category is important. The third column shows the percent of sample in each interval or category. For example, 30 percent of the sample falls into the range of twenty-one to twenty-five years old. The fourth column shows the proportion of observation for each interval or category. The proportion was called relative frequency. The cumulative frequency, the cumulative percent, and the cumulative relative frequency are other common elements in frequency tabulation. They are the sum of counts, percents, or proportions below or equal to the corresponding category or interval. For instance, the cumulative frequency of age thirty shows 101 persons or 84.2 percent of the sample age thirty or younger.

The frequency distribution displays one variable at a time. To study the joint distribution of two or more variables, we cross-tabulate them first. For example, the joint distribution of age and sex in the imaginary sample can be expressed in Table 2.

This table is a two-dimensional table: age is the column variable and sex is the row variable. We call this table a ‘‘two-by-five’’ table: two categories for sex and five categories for age. The marginal frequency can be seen as the frequency distribution of the corresponding variables. For example, there are fifty seven men in this sample. The marginal frequency for age is called column frequency and the marginal frequency for sex is called row frequency. The joint frequency of age and sex is cell frequency. For example, there are seventeen women twenty-one to twenty-five years old in this sample. The second number in each cell is column percentage; that is, the cell frequency divided by the column frequency and times 100 percent. For example, 47 percent in the group of twenty-one to twenty-five year olds are women. The third number in each cell is row percentage; that is, the cell frequency divided by the row frequency. For example, 27 percent of women are twenty-one to twenty-five years old. The marginal frequency can be seen as the frequency distribution of the corresponding variables. The row and column percentages are useful in examining the distribution of on variable conditioning on the other variable.

Charts and Graphs. Charts and graphs are efficient ways to show data distribution. Popular

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Age Distribution of an Imaginary Sample

Codes	Frequency	Percent	Relative	Cumulative	Cumulative
			Frequency	Frequency	Percent
15 and below	20	16.7	.17	20	16.7
16–20	25	20.8	.21	45	37.5
21–25	36	30.0	.30	81	67.5
26–30	20	16.7	.17	101	84.2
31–35	19	15.8	.16	120	100
Total	120	100	1.0

Table 1

graphs for single variables are bar graphs, histograms, and stem-and-leaf plots. The bar graph shows the relative frequency distribution of discrete variables. A bar is drawn over each category with height of the bar representing the relative frequency of observations in that category. The histogram can be seen as a bar graph for the continuous variable. By connecting the midpoints of tops of all bars, a histogram becomes the frequency polygon.

Histograms effectively show the shape of the distribution.

Stem-and-leaf plots represent each observation by its higher digit(s) and its lowest digit. The value of higher digits is the stem while the value of the final digit of each observation is the leaf. The stem-and-leaf plot conveys the same information as the bar graph or histogram. Additionally, it tells the exact value of each observation. Despite providing more information than bar graphs and histograms, stem-and-leaf plots are used mostly for small data.

Other frequently used graphs include line graphs, ogives, and scatter plots. Line graphs and ogives show the relationship between time and the variable. The line graph usually shows trends. The ogive is a form of a line graph for cumulative relative frequency or percentage. It is commonly used for survival data. The scatter plot shows the relationship between variables. In a two-dimen- sional scatter plot, x and y axises label values of the data. Conventionally, we use the horizontal axis (x- axis) for the explanatory variable and use the vertical axis (y-axis) for the outcome variable. The plain is naturally divided into four areas by two axises. For continuous variables, the value at the joint point of two axises is zero. When the x-axis

goes to the right or y-axis goes up, the value ascends; when the x-axis goes to the left or y-axis goes down, the value descends. The data points, determinated by the joint attributes of the variables, are scattered in four areas or along the axises.

SUMMARY STATISTICS

We may use measures of central tendency and dispersion to summarize the data. To measure the central tendency of a distribution is to measure its center or typicality. To measure the dispersion of a distribution is to measure its variation, heterogeneity, or deviation.

Central Tendency. Three popular measures of the central tendency are mean, median, and mode. The arithmetic mean or average is computed by taking the sum of the values and dividing by the number of the values. It is the balanced point of the sample or population weighted by values. Mean is an appropriate measure for continuous (ratio or interval) variables. However, the information might be misleading because the arithmetic mean is sensitive to the extreme value or outliers in a distribution. For example, the ages of five students are 21, 19, 20, 18, and 20. The ages of another five students are 53, 9, 12, 13, and 11. Though their distributions are very different, the mean age for both groups is 19.6.

Median is the value or attribute of the central case in an ordered distribution. If the number of cases is even, the median is the arithmetic average of the central two cases. In an ordered age distribution of thirty-five persons, the median is the age of the eighteenth person, while, in a distribution of thirty-six persons, the median is the average age of

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Age

Sex	15 and below	16–20	21-25	26–30	31-25	Total
Male	9	12	19	9	8	57
	45.0%	48.0%	52.8%	45.0%	42.1%	47.5%
	15.8%	21.1%	33.3%	15.8%	14.0%
Female	11	13	17	11	11	63
	55.0%	52.0%	47.2%	55.0%	57.9%	52.5%
	17.5%	20.6%	27.0%	17.5%	17.5%
	20	25	36	20	19	120
Total	16.7%	20.8%	30.0%	16.7%	15.8%	100%

Table 2

the seventeenth and eighteenth persons. The median, like mean, can only tell the value of the physical center in an array of numbers, but cannot tell the dispersion. For example, the median of 21,

30, 45, and 100 is 27.5 and the median of 0, 27, 28, and 29 is also 27.5, but the two distributions are different. The mode is the most common value, category, or attribute in a distribution. Like the median, the mode has its limitations. For a set of values of 0, 2, 2, 4, 4, 4, 4, 5, and 10, the mode is four. For a set of values of 0, 0, 1, 4, 4, 4, 5, and 6, the mode is also four. One cannot tell one distribution from the other simply by examining the mode or median alone. The mode and median can be used to describe the central tendency of both continuous and discrete variables, and values of mode and median are less affected by the extreme value or the outlier than the mean.

One may also use upper and lower quartiles and percentiles to measure the central tendency.

The n percentile is a number such that n percent of the distribution falls below it and (100−n) percent falls above it. The lower quartile is the twenty-fifth percentile, the upper quartile is the seventy-fifth percentile, and the median is the fiftieth percentile. For example, the lower quartile or the twentyfifth percentile is two and the upper quartile or the seventy-fifth percentile is seven for a set of values of 1, 2, 3, 4, 5, 6, 7, and 8. Apparently, the upper and lower quartiles and the percentiles can provide more information about a distribution than the other measures of the central tendency.

Dispersion. The central tendency per se does not provide much information on the distribution. Yet the combination of measures of central tendency and dispersion becomes useful to study a

distribution. The most popular measures of dispersion are range, standard deviation, and variance. Range is the crude measure of a distribution from the highest value to the lowest value or the difference between the highest and the lowest values.

For example, the range for a set of values of 1, 2, 3,

4, and 5 is one to five. The range is sensitive to the extreme value and may not provide sufficient information about the distribution. Alternatively, the dispersion can be measured by the distance between the mean and each value. The standard deviation is defined as the square root of the arithmetic mean of the squared deviation from the mean. For example, the standard deviation for a set of values of 1, 2, 3, 4, and 5 is 1.44. We take the square root of the squared deviation from the mean because the sum of the deviation from the mean is always zero. The variance is the square of the standard deviation. The variance is two in the previous array of numbers. The standard deviation is used as a standardized unit in statistical inference. Comparing with standard deviation, the unit of the variance is not substantively meaningful. It is, however, valuable to explain the relationship between variables. Mathematically, the variance defines the area of the normal curve while the standard deviation defines the average distance between the mean and each data point. Since they are derived from the distance from the mean, standard deviation and variance are sensitive to the extreme values.

The interquartile range (IQR) and mean absolute deviation (MAD) are also commonly used to measure the dispersion. The IQR is defined as the difference between the first and third quartiles. It is more stable than the range. MAD is the average

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absolute values of the deviation of the observations from the mean. As standard deviation, MAD can avoid the problem that the sum of the deviation from the mean is zero, but it is not as useful in statistical inference as variance and standard deviation.

Bivariate Relationship. One may use the covariance and correlation coefficients to measure the direction and size of a relationship between two variables. The covariance is defined as the average product of the deviation from the mean between two variables. It also reports the extent to which the variables may vary together. On average, while one variable deviates one unit from the mean, the covariance tells the extent to which the corresponding value of the other variable may deviate from its own mean. A positive covariance suggests that, while the value of one variable increases, that of the other variable tends to increase. A negative covariance suggests that, while the value of one variable increases, that of the other variable tends to decrease. The correlation coefficient is defined as the ratio of the covariance to the product of the standard deviations of two variables. It can also be seen as a covariance rescaled by the standard deviation of both variables. The value of the correlation coefficient ranges from −1 to 1, where zero means no correlation, −1 means perfectly negatively related, and 1 means perfectly positively related. The covariance and correlation are measures of the bivariate relationship between continuous variables. Many measures of association between categorical variables are calculated using cell frequencies or percentages in the cross-tabula- tion, for example, Yule’s Q, phi, Goodman’s tau,

Goodman’s gamma, and Somer’s d. Though measures of association alone show the direction and size of a bivariate relationship, it is statistical inference to test the existence of such a relationship.

RELATIONSHIPS BETWEEN GRAPHS AND

SUMMARY STATISTICS

The box plot is a useful tool to summarize the statistics and distribution. The box plot is consisted of a rectangular divided box and two extended lines attached to the ends of the box. The ends of the box define the upper and lower quartiles. The range of the distribution on each side is shown by an extended line attached to each quartile. A line

dividing the box shows the median. The plot can be placed vertically or horizontally. The box plot became popular because it can express the center and spread of the data simultaneously. Several boxes may be placed next to one another for comparison.

The order of mode, median, and mean is related to the shape of the distribution of a continuous variable. If mean, median, and mode are equal to each other, the shape of the histogram approximates a bell curve. However, a uniform distribution, in which all cases are equally distributed among all values and three measures of the central tendency are equal to each other, has a square shape with the width as the range and the height as the counts or relative frequency. In a bimodal distribution, two modes are placed in two ends of the distribution equally distanced from the center where the median and the mean are placed. We seldom see the true bell-curved, uniform, and bimodal distributions. Most of the distributions are more or less skewed to the left or to the right. If the mean is greater than median and the median is greater than mode, the shape is skewed to the right. If the mean is smaller than the median, and the median is smaller than the mode, the shape is skewed to the left. The outliers mainly lead the direction.

The shape and direction of the scatter plot can diagnose the relationship of two variables. When the distribution directs from the upper-right side to the lower-left side, the correlation coefficient is positive; when it directs from the upper-left side to the lower-right side, the correlation coefficient is negative. The correlation of a loosely scattered plot is weaker than that of a tightly scattered plot.

A three-dimensional scatter plot can be used to show a bivariate relationship and its frequency distribution or a relationship of three variables.

The former is commonly seen as a graph to examine a joint distribution.

Descriptive statistics is the first step in studying the data distribution. In omitting this step, one might misuse the advanced methods and thus be led to wrong estimates and conclusions. Some summary statistics such as standard deviation, variance, mean, correlation, and covariance, are also essential elements in statistical inference and advanced statistical methods.

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REFERENCES

Agresti, Alan, and Barbara Finlay 1997 Statistical Methods for the Social Sciences, 3rd ed. New York: Simon & Schuster.

Blalock, Jr., Hubert M. 1979 Social Statistics, rev. 2nd ed. New York: McGraw-Hill.

Johnson, Allan G. 1988 Statistics. New York: Harcourt

Brace Jovanovich.

Wonnacott, Thomas H., and Ronald J. Wonnacott 1990 Introductory Statistics. 5th ed. New York: John Wiley & Sons, Inc.

DAPHNE KUO

DEVIANCE

See Alienation; Anomie; Criminalization of

Deviance; Deviance Theories; Legislation of

Morality.

DEVIANCE THEORIES

Since its inception as a discipline, sociology has studied the causes of deviant behavior, examining why some persons conform to social rules and expectations and why others do not. Typically, sociological theories of deviance reason that aspects of individuals’ social relationships and the social areas in which they live and work assist in explaining the commission of deviant acts. This emphasis on social experiences, and how they contribute to deviant behavior, contrasts with the focus on the internal states of individuals taken by disciplines such as psychology and psychiatry.

Sociological theories are important in understanding the roots of social problems such as crime, violence, and mental illness and in explaining how these problems may be remedied. By specifying the causes of deviance, the theories reveal how aspects of the social environment influence the behavior of individuals and groups. Further, the theories suggest how changes in these influences may yield changes in levels of deviant behaviors. If a theory specifies that a particular set of factors cause deviant behavior, then it also implies that eliminating or altering those factors in the environment will change levels of deviance. By developing policies or measures that are informed

by sociological theories, government agencies or programs focused on problems like crime or violence are more likely to yield meaningful reductions in criminal or violent behavior.

Despite their importance, deviance theories disagree about the precise causes of deviant acts. Some look to the structure of society and groups or geographic areas within society, explaining deviance in terms of broad social conditions in which deviance is most likely to flourish. Others explain deviant behavior using the characteristics of individuals, focusing on those characteristics that are most highly associated with learning deviant acts. Other theories view deviance as a social status conferred by one group or person on others, a status that is imposed by persons or groups in power in order to protect their positions of power.

These theories explain deviance in terms of differentials in power between individuals or groups.

This chapter reviews the major sociological theories of deviance. It offers an overview of each major theory, summarizing its explanation of deviant behavior. Before reviewing the theories, however, it may prove useful to describe two different dimensions of theory that will structure our discussion. The first of these, the level of explanation, refers to the scope of the theory and whether it focuses on the behavior and characteristics of individuals or on the characteristics of social aggregates such as neighborhoods, cities, or other social areas. Micro-level theories stress the individual, typically explaining deviant acts in terms of personal characteristics of individuals or the immediate social context in which deviant acts occur.

In contrast, macro-level theories focus on social aggregates or groups, looking to the structural characteristics of areas in explaining the origins of deviance, particularly rates of deviance among those groups.

Theories of deviance also vary in relation to a second dimension, causal focus. This dimension divides theories into two groups, those that explain the social origins of norm violations and those explaining societal reactions to deviance.

Social origin theories focus on the causes of norm violations. Typically, these theories identify aspects of the social environment that trigger norm violations; social conditions in which the violations are most likely to occur. In contrast, social reaction theories argue that deviance is often a matter of

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DEVIANCE THEORIES

social construction, a status imposed by one person or group on others and a status that ultimately may influence the subsequent behavior of the designated deviant. Social reaction theories argue that some individuals and groups may be designated or labeled as deviant and that the process of labeling may trap or engulf those individuals or groups in a deviant social role.

These two dimensions offer a four-fold scheme for classifying types of deviance theories. The first, macro-level origin theories, focus on the causes of norm violations associated with broad structural conditions in the society. These theories typically examine the influences of such structural characteristics of populations or communities like the concentration of poverty, levels of community integration, or the density and age distribution of the population on areal rates of deviance. The theories have clear implications for public policies to reduce levels of deviance. Most often, the theories highlight the need for altering structural characteristics of society, such as levels of poverty, that foster deviant behavior.

The second, micro-level origin theories focus on the characteristics of the deviant and his or her immediate social environment. These theories typically examine the relationship between a person’s involvement in deviance and such characteristics as the influence of peers and significant others, persons’ emotional stakes in conformity, their beliefs about the propriety of deviance and conformity, and their perceptions of the threat of punishments for deviant acts. In terms of their implications for public policy, micro-level origin theories emphasize the importance of assisting individuals in resisting negative peer influences while also increasing their attachment to conforming lifestyles and activities.

A third type of theories may be termed microlevel reaction theories. These accord importance to those aspects of interpersonal reactions that may seriously stigmatize or label the deviant and thereby reinforce her or his deviant social status. According to these theories, reactions to deviance may have the unintended effect of increasing the likelihood of subsequent deviant behavior. Because labeling may increase levels of deviance, micro-level reaction theories argue that agencies of social control (e.g. police, courts, correctional systems) should adopt policies of ‘‘nonintervention.’’

Finally, macro-level reaction theories emphasize broad structural conditions in society that are associated with the designation of entire groups or segments of the society as deviant. These theories tend to stress the importance of structural characteristics of populations, groups, or geographic areas, such as degrees of economic inequality or concentration of political power within communities or the larger society. According to macro-level reaction theories, powerful groups impose the status of deviant as a mechanism for controlling those groups that represent the greatest political, economic, or social threat to their position of power. The theories also imply that society can only achieve reduced levels of deviance by reducing the levels of economic and political inequality in society.

The rest of this article is divided into sections corresponding to each of these four ‘‘types’’ of deviance theory. The article concludes with a discussion of new directions for theory—the development of explanations that cut across and integrate different theory types and the elaboration of existing theories through greater specification of the conditions under which those theories apply.

MACRO-LEVEL ORIGINS OF DEVIANCE

Theories of the macro-level origins of deviance look to the broad, structural characteristics of society, and groups within society, to explain deviant behavior. Typically, these theories examine one of three aspects of social structure. The first is the pervasiveness and consequences of poverty in modern American society. Robert Merton’s (1938) writing on American social structure and Richard Cloward and Lloyd Ohlin’s (1960) subsequent work on urban gangs laid the theoretical foundation for this perspective. Reasoning that pervasive materialism in American culture creates unattainable aspirations for many segments of the population, Merton (1964) and others argue that there exists an environmental state of ‘‘strain’’ among the poor. The limited availability of legitimate opportunities for attaining material wealth forces the poor to adapt through deviance, either by achieving wealth through illegitimate means or by rejecting materialistic aspirations and withdrawing from society altogether.

According to this reasoning, deviance is a byproduct of poverty and a mechanism through

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