Statistical methods for research workers

Statistical methods for research workers

STATISTICAL METHODS FOR RESEARCH WORKERS

By Ronald A. Fisher (1925)

Posted April 2000

VI

THE CORRELATION COEFFICIENT

30. No quantity is more characteristic of modern statistical work than the correlation coefficient, and no method has been applied successfully to such various data as the method of correlation. Observational data in particular, in cases where we can observe the occurrence of various possible contributory causes of a phenomenon, but cannot control them, has been given by its means an altogether new importance. In experimental work proper its position is much less central; it will be found useful in the exploratory stages of an enquiry, as when two factors which had been thought independent appear to be associated in their occurrence; but it is seldom, with controlled experimental conditions, that it is desired to express our conclusion in the form of a correlation coefficient.

One of the earliest and most striking successes of the method of correlation was in the biometrical study of inheritance. At a time when nothing was known of the mechanism of inheritance, or of the structure of the germinal material, it was possible by this method to demonstrate the existence of inheritance, and to [p. 139] "measure its intensity"; and this in an organism in which experimental breeding could not be practised, namely, Man. By comparison of the results obtained from the physical measurements in man with those obtained from other organisms, it was established that man's nature is not less governed by heredity than that of the rest of the animate world. The scope of the analogy was further widened by demonstrating that correlation coefficients of the same magnitude were obtained for the mental and moral qualities in man as for the physical measurements.

These results are still of fundamental importance, for not only is inheritance in man still incapable of experimental study, and existing methods of mental testing are still unable to analyse the mental disposition, but even with organisms suitable for experiment and measurement, it is only in the most

favourable cases that the several factors causing fluctuating variability can be resolved, and their effects studied, by Mendelian methods. Such fluctuating variability, with an approximately normal distribution, is characteristic of the majority of the useful qualities of domestic plants and animals; and although there is strong reason to think that inheritance in such cases is ultimately Mendelian, the biometrical method of study is at present alone capable of holding out hopes of immediate progress.

We give in Table 31 an example of a correlation table. It consists of a record in compact form of the stature of 1376 fathers and daughters. (Pearson and Lee's data.) The measurements are grouped in [p. 140-141] [table] [p. 142] inches, and those whose measurement was recorded as an integral number of inches have been split; thus a father recorded as of 67 inches would appear as 1/2 under 66.5 and 1/2 under 67.5. Similarly with the daughters; in consequence, when both measurements are whole numbers the case appears in four quarters. This gives the table a confusing appearance, since the majority of entries are fractional, although they represent frequencies. It is preferable, if bias in measurement can be avoided, to group the observations in such a way that each possible observation lies wholly within one group.

The most obvious feature of the table is that cases do not occur in which the father is very tall and the daughter very short, and vice versa ; the upper right-hand and lower left-hand corners of the table are blank, so that we may conclude that such occurrences are too rare to occur in a sample of about 1400 cases. The observations recorded lie in a roughly elliptical figure lying diagonally across the table. If we mark out the region in which the frequencies exceed 10 it appears that this region, apart from natural irregularities, is similar, and similarly situated. The frequency of occurrence increases from all sides to the central region of the table, where a few frequencies over 30 may be seen. The lines of equal frequency are roughly similar and similarly situated ellipses. In the outer zone observations occur

only occasionally, and therefore irregularly; beyond this we could only explore by taking a much larger sample.

The table has been divided into four quadrants by [p. 143] marking out central values of the two variates; these values, 67.5 inches for the fathers and 63.5 inches for the daughters, are near the means. When the table is so divided it is obvious that the lower right-hand and upper left-hand quadrants are distinctly more populous than the other two; not only are more squares occupied, but the frequencies are higher. It is apparent that tall men have tall daughters more frequently than the short men, and vice versa. The method

of correlation aims at measuring the degree to which this association exists. The marginal totals show the frequency distributions of the fathers and the daughters respectively. These are both approximately normal distributions, as is frequently the case with biometrical data collected without selection. This marks a frequent difference between biometrical and experimental data. An experimenter would perhaps have bred from two contrasted groups of fathers of, for example, 63 and 72 inches in height; all his fathers would then belong to these two classes, and the correlation coefficient, if used, would be almost meaningless. Such an experiment would serve to ascertain the regression of daughter's height in father's height, and so to determine the effect on the daughters of selection applied to the fathers, but it would not give us the correlation coefficient which is a descriptive observational feature of the population as it is, and may be wholly vitiated by selection. Just as normal variation with one variate may be specified by a frequency formula in which the [p. 144] logarithm of the frequency is a quadratic function of the variate, so with two variates the frequency may be expressible in terms of a quadratic function of the values of the two variates. We then have a normal correlation surface, for which the frequency may conveniently be written in the form

In this expression x and y are the deviations of the two variates from their means, σ1 and σ2 are the two standard deviations, and ρ?is

the correlation between x and y. The correlation in the above expression may be positive or negative, but cannot exceed unity in magnitude; it is a pure number without physical dimensions. If ρ=0, the expression for the frequency degenerates into the product of the two factors

showing that the limit of the normal correlation surface, when the correlation vanishes, is merely that of two normally distributed variates varying in complete independence. At the other extreme, when p is +1 or -1, the variation of the two variates is in strict proportion, so that the value of either may be calculated accurately from that of the other. In other words, we cease strictly to have two variates, but merely two measures of the same variable quantity.

If we pick out the cases in which one variate has an assigned value, we have what is termed an array; [p. 145] the columns and rows of the table may, except as regards variation within the group limits, be regarded as arrays. With normal correlation the variation within an array may be obtained from the general formula, by giving x a constant value, (say) a, and dividing by the total frequency with which this value occurs; then we have

showing (i.) that the variation of y within the array is normal ; (ii.) that the mean value of y for that array is ρaσ2/σ1, so that the regression of y on x is linear, with regression coefficient

and (iii.) that the variance of y within the array is σ22(1-ρ2), and is the same within each array. We may express this by saying that of the total variance of y the fraction (1-ρ2) is independent of x, while the remaining fraction, ρ2, is determined by, or calculable from, the value of x.

These relations are reciprocal, the regression of x on y is linear, with regression coefficient ρσ1/σ2; the correlation ρ is thus the geometric mean

of the two regressions. The two regression lines representing the mean value of x for given y, and the mean value of y for given x, cannot coincide unless ρ=[plus or minus]1. The variation of x within an array in which y is

fixed, is normal with variance equal to σ12(1-ρ2), so that we may say that of

the variance of x the fraction (1-ρ2) [p. 146] is independent of y, and the

remaining fraction, ρ2, is determined by, or calculable from, the value of y. Such are the formal mathematical consequences of normal correlation. Much biometric data certainly shows a general agreement with the features to be expected on this assumption; though I am not aware that the question has been subjected to any sufficiently critical enquiry. Approximate agreement is perhaps all that is needed to justify the use of the correlation as a quantity descriptive of the population; its efficacy in this respect is undoubted, and it is not improbable that in some cases it affords a complete description of the simultaneous variation of the variates.

31. The Statistical Estimation of the Correlation

Just as the mean and the standard deviation of a normal population in one variate may be most satisfactorily estimated from the first two moments of the observed distribution, so the only satisfactory estimate of the correlation, when the variates are normally correlated, is found from the "product moment." If x and y represent the deviations of the two variates from their means, we calculate the three statistics s1, s2, r by the three equations

ns12 = S(x2), ns22 = S(y2), nrs1s2 = S(xy);

then s1 and s2 are estimates of the standard deviations σ1, and σ2, and r is an

estimate of the correlation ρ. Such an estimate is called the correlation coefficient, or the product moment correlation, the latter term [p. 147] referring to the summation of the product terms, xy, in the last equation.

The above method of calculation might have been derived from the consideration that the correlation of the population is the geometric mean of the two regression coefficients; for our estimates of these two regressions would be

so that it is in accordance with these estimates to take as our estimate of

which is in fact the product moment correlation.

Ex. 25. Parental correlation in stature. -- The numerical work required to calculate the correlation coefficient is shown below in Table 32.

The first eight columns require no explanation, since they merely repeat the usual process of finding the mean and standard deviation of the two marginal distributions. It is not necessary actually to find the mean, by dividing the total of the third column, 480.5, by 1376, since we may work all through with the undivided totals. The correction for the fact that our working mean is not the true mean is performed by subtracting

(480.5)2/1376 in the 4th column; a similar correction appears at the foot of the 8th column, and at the foot of the last column. The correction for the sum of products is performed by subtracting 4805x2605/1376· This correction of [p. 148] [table] [p. 149] the product term may be positive or negative; if the total deviations of the two variates are of opposite sign, the correction must be added. The sum of squares, with and without Sheppard's correction (1376/12), are shown separately; there is no corresponding correction to be made to the product term.

The 9th column shows the total deviations of the daughter's height for each of the 18 columns in which the table is divided. When the numbers are small, these may usually be written down by inspection of the table. In the present case, where the numbers are large, and the entries are complicated by quartering, more care is required. The total of column 9 checks with that of the 3rd column. In order that it shall do so, the central entry +15.5, Which does not contribute to the products, has to be included. Each entry in the 9th column is multiplied by the paternal deviation to give the 10th column. In the present case all the entries in column 10 are positive; frequently both positive and negative entries occur, and it is then convenient to form a separate column for each. A useful check is afforded by repeating the work of the last two columns, interchanging the variates; we should then find the total deviation of the fathers for each array of daughters, and multiply by the daughters deviation. The uncorrected totals, 5136.25, should then agree. This check is especially useful with small tables, in which the work of the last two columns, carried out rapidly, is liable to error.

The value of the correlation coefficient, using Sheppard's correction, is found by dividing 5045.28 [p. 150] by the geometric mean of 9209.0 and 10,392.5; its value is +.5157· If Sheppard's correction had not been used, we should have obtained +.5097. The difference is in this case not large compared to the errors of random sampling, and the full effects on the distribution in random samples of using Sheppard's correction have never been fully examined, but there can be little doubt that Sheppard's correction should be used, and that its use gives generally an improved estimate of the correlation. On the other hand, the distribution in random samples of the uncorrected value is simpler and better understood, so that the uncorrected value should be used in tests of significance, in which the effect of correction need not, of course, be overlooked. For simplicity coarse grouping should be avoided where such tests are intended. The fact that with small samples the correlation obtained by the use of Sheppard's correction may exceed unity, illustrates the disturbance introduced into the random sampling distribution.

32. Partial Correlations

A great extension of the utility of the idea of correlation lies in its application to groups of more than two variates. In such cases, where the correlation between each pair of three variates is known, it is possible to eliminate any one of them, and so find what the correlation of the other two would be in a population selected so that the third variate was constant. Ex. 26. Elimination of age in organic correlations [p. 151] with growing children. -- For example, it was found (Mumford and Young's data) in a group of boys of different ages, that the correlation of standing

height with chest girth was +.836. One might expect that part of this association was due to general growth with age. It would be more desirable for many purposes to know the correlation between the variates for boys of a given age; but in fact only a few of the boys will be exactly of the same age, and even if we make age groups as broad as a year, we shall have in each group much fewer than the total number measured. In order to utilise the whole material, we only need to know the correlations of standing

height with age, and of chest girth with age. These are given as .714

and .708.·

The fundamental formula in calculating partial correlation coefficients may be written

Here the three variates are numbered 1, 2, and 3, and we wish to find the correlation between 1 and 2, when 3 is eliminated; this is called the "partial" correlation between 1 and 2, and is designated by r12. 3, to show that variate 3 has been eliminated. The symbols r12, r13, r23, indicate the correlations found directly between each pair of variates; these correlations being distinguished as "total" correlations.

Inserting the numerical values in the above formula we find r12. 3 =·.668, showing that when age is eliminated the correlation, though still considerable, [p. 152] has been markedly reduced. The mean value given by the above-mentioned authors for the correlations found by grouping the boys by years, is .653, not a greatly different value. In a similar manner, two or more variates may be eliminated in succession; thus with four variates, we may first eliminate variate 4, by thrice applying the above formula to find r12. 4, r13. 4, and r23. 4. Then applying the same formula again, to these three new values, we have

The labour increases rapidly with the number of variates to be eliminated. To eliminate s variates, the number of operations involved, each one application of the above formula is 1/6 s(s+1)(s+2); for values of s from 1 to 6 this gives 1, 4, 10, 20, 35, 56 operations. Much of this labour may be saved by using tables of [sqrt]1-r2 such as that published by J. R. Miner.[1] The meaning of the correlation coefficient should be borne clearly in mind. The original aim to measure the "strength of heredity" by this method was based clearly on the supposition that the whole class of factors which tend to make relatives alike, in contrast to the unlikeness of unrelated persons, may be grouped together as heredity. That this is so for all practical purposes is, I believe, admitted, but the correlation does not tell us that this is so; it merely [p. 153] tells us the degree of resemblance in the actual population studied, between father and daughter. It tells us to what extent the height of the father is relevant information respecting the height of the daughter, or, otherwise interpreted, it tells us the relative importance of the factors which act alike upon the heights of father and daughter, compared to the totality of factors at work. If we know that B is caused by A, together with other factors independent of A, and that B has no influence on A, then the correlation between A and B does tell us how important, in relation to the other causes at work, is the influence of A. If we have not such knowledge, the correlation does not tell us whether A causes B, or B causes A, or

whether both influences are at work, together with the effects of common causes.

This is true equally of partial correlations. If we know that a phenomenon A is not itself influential in determining certain other phenomena B, C, D, ..., but on the contrary is probably directly influenced by them, then the calculation of the partial correlations A with B, C, D, ... in each case eliminating the remaining values, will form a most valuable analysis of the causation of A. If on the contrary we choose a group of social phenomena with no antecedent knowledge of the causation or absence of causation among them, then the calculation of correlation coefficients, total or partial, will not advance us a step towards evaluating the importance of the causes at work.

The correlation between A and B measures, on a [p. 154] conventional scale, the importance of the factors which (on a balance of like and unlike action) act alike in both A and B, as against the remaining factors which affect A and B independently. If we eliminate a third variate C, we are removing from the comparison all those factors which become inoperative when C is fixed. If these are only those which affect A and B independently, then the correlation between A and B, whether positive or negative, will be numerically increased. We shall have eliminated irrelevant disturbing factors, and obtained, as it were, a better controlled experiment. We may also require to eliminate C if these factors act alike, or oppositely on the two variates correlated; in such a case the variability of C actually masks the effect we wish to investigate. Thirdly, C may be one of the chain of events by the mediation of which A affects B, or vice versa. The extent to which C is the channel through which the influence passes may be estimated by eliminating C; as one may demonstrate the small effect of latent factors in human heredity by finding the correlation of grandparent and grandchild, eliminating the intermediate parent. In no case, however, can we judge whether or not it is profitable to eliminate a certain variate unless we know, or are willing to assume, a qualitative scheme of causation. For the purely descriptive purpose of specifying a population in respect of a number of variates, either partial or total correlations are effective, and correlations of either type may be of interest.

As an illustration we may consider in what sense [p. 155] the coefficient of correlation does measure the "strength of heredity," assuming that heredity only is concerned in causing the resemblance between relatives; that is, that any environmental effects are distributed at haphazard. In the first place, we may note that if such environmental effects are increased in magnitude, the correlations would be reduced ; thus the same population, genetically speaking, would show higher correlations if reared under relatively uniform nutritional conditions, than they would if the nutritional conditions had been

very diverse; although the genetical processes in the two cases were identical. Secondly, if environmental effects were at all influential (as in the population studied seems not to be indeed the case), we should obtain higher correlations from a mixed population of genetically very diverse strains, than we should from a more uniform population. Thirdly, although the influence of father on daughter is in a certain sense direct, in that the father contributes to the germinal composition of his daughter, we must not assume that this fact is necessarily the cause of the whole of the correlation; for it has been shown that husband and wife also show considerable resemblance in stature, and consequently taller fathers tend to have taller daughters partly because they choose, or are chosen by, taller wives. For this reason, for example, we should expect to find a noticeable positive correlation between step-fathers and step-daughters; also that, when the stature of the wife is eliminated, the partial correlation between father and daughter will be found to be lower than the total correlation. [p. 156] These considerations serve to some extent to define the sense in which the somewhat vague phrase, "strength of heredity," must be interpreted, in speaking of the correlation coefficient. It will readily be understood that, in less well understood cases, analogous considerations may be of some importance, and should if possible be critically considered.

33. Accuracy of the Correlation Coefficient

With large samples, and moderate or small correlations, the correlation obtained from a sample of n pairs of values is distributed normally about the true value ρ, with variance,

it is therefore usual to attach to an observed value r, a standard error

(1-r2)/[srqt]n-1, or (1-r2)/[sqrt]n. This procedure is only valid under the restrictions stated above; with small samples the value of r is often very different from the true value, ρ, and the factor 1-r2, correspondingly in error; in addition the distribution of r is far from normal, so that tests of significance based on the above formula are often very deceptive. Since it is with small samples, less than 100, that the practical research worker ordinarily wishes to use the correlation coefficient, we shall give an account of more accurate methods of handling the results.

In all cases the procedure is alike for total and for partial correlations. Exact account may be taken of the differences in the distributions in the two cases, [p. 157] by deducting unity from the sample number for each variate eliminated; thus a partial correlation found by eliminating three variates, and based on data giving 13 values for each variate, is distributed exactly as is a total correlation based on 10 pairs of values.

34. The Significance of an Observed Correlation

In testing the significance of an observed correlation we require to calculate the probability that such a correlation should arise, by random sampling, from an uncorrelated population. If the probability is low we regard the correlation as significant. The table of t given at the end of the preceding chapter (p. 137) may be utilised to make an exact test. If n' be the number of pairs of observations on which the correlation is based, and r the correlation obtained, without using Sheppard's correction, then we take

and it may be demonstrated that the distribution of t so calculated, will agree with that given in the table.

It should be observed that this test, as is obviously necessary, is identical with that given in the last chapter for testing whether or not the linear regression coefficient differs significantly from zero.

TABLE V.A (p. 174) allows this test to be applied directly from the value of r, for samples up to 100 pairs of observations. Taking the four definite levels [p. 158] of significance, represented by P =·.10, .05, .02, and .01, the table shows for each value of n, from 1 to 20, and thence by larger intervals to 100, the corresponding values of r.

Ex. 27. Significance of a correlation coefficient between autumn rainfall and wheat crop. -- For the twenty years, 1885-1904, the mean wheat yield of Eastern England was found to be correlated with the autumn rainfall; the correlation found was -.629. Is this value significant? We obtain in succession

For n=18, this shows that P is less than .01, and the correlation is definitely significant. The same conclusion may be read off at once from Table

V.A entered with n=18.

If we had applied the standard error,

we should have

a much greater value than the true one, very much exaggerating the significance. In addition, assuming that r was normally distributed (n = [infinity]), the significance of the result would between further exaggerated. This illustration will suffice to show how deceptive, in small samples, is the use of the standard error of the [p. 159] correlation coefficient, on the assumption that it will be normally distributed. Without this assumption the standard error is without utility. The misleading character of the formula is increased if n' is substituted for n'-1, as is often done. Judging from the normal deviate 4.536, we should suppose that the correlation obtained would be exceeded in random samples from uncorrelated material only 6 times in a million trials. Actually it would be exceeded about 3000 times in a million trials, or with 500 times the frequency supposed.

It is necessary to warn the student emphatically against the misleading character of the standard error of the correlation coefficient deduced from a small sample, because the principal utility of the correlation coefficient lies in its application to subjects of which little is known, and upon which the data are relatively scanty. With extensive material appropriate for biometrical investigations there is little danger of false conclusions being drawn, whereas with the comparatively few cases to which the experimenter must often look for guidance, the uncritical application of methods standardised in biometry, must be so frequently misleading as to endanger the credit of this most valuable weapon of research. It is not true, as the above example shows, that valid conclusions cannot be drawn from small samples; if accurate methods are used in calculating the probability, we thereby make full allowance for the size of the sample, and should be influenced in our judgment only by the value of the-probability indicated. The great increase of certainty which accrues from increasing data is [p. 160] reflected in the value of P, if accurate methods are used.

Ex. 28. Significance of a partial correlation coefficient. -- In a group of 32 poor law relief unions, Yule found that the percentage change from 1881 to 1891 in the percentage of the population in receipt of relief was correlated with the corresponding change in the ratio of the numbers given outdoor relief to the numbers relieved in the workhouse, when two other variates had been eliminated, namely, the corresponding changes in the percentage of the population over 65, and in the population itself.

The correlation found by Yule after eliminating the two variates was +.457; such a correlation is termed a partial correlation of the second order. Test its significance.

It has been demonstrated that the distribution in random samples of partial correlation coefficients may be derived from that of total correlation coefficients merely by deducting from the number of the sample, the number of variates eliminated. Deducting 2 from the 32 unions used, we have 30 as the effective number of the sample; hence

n=28

Calculating t from r as before, we find

t=2.719,

whence it appears from the table that P lies between .02 and .01. The correlation is therefore significant. This, of course, as in other cases, is on the assumption [p. 161] that the variates correlated (but not necessarily those eliminated) are normally distributed; economic variates seldom themselves

give normal distributions, but the fact that we are here dealing with rates of change makes the assumption of normal distribution much more plausible. The values given in Table V.(A) for n=25, and n=30, give a sufficient indication of the level of significance attained by this observation.

35. Transformed Correlations

In addition to testing the significance of a correlation, to ascertain if there is any substantial evidence of association at all, it is also frequently required to perform one or more of the following operations, for each of which the standard error would be used in the case of a normally distributed quantity. With correlations derived from large samples the standard error may, therefore, be so used, except when the correlation approaches [plus or minus]1; but with small samples such as frequently occur in practice, special methods must be applied to obtain reliable results.

(i.) To test if an observed correlation differs significantly from a

given theoretical value.

(ii.) To test if two observed correlations are significantly different.

(iii.) If a number of independent estimates of a correlation are

available, to combine them into an improved estimate.

(iv.) To perform tests (i.) and (ii.) with such average values. [p. 162] Problems of these kinds may be solved by a method analogous to that by which we have solved the problem of testing the significance of an observed correlation. In that case we were able from the given value r to calculate a quantity t which is distributed in a known manner, for which tables were available. The transformation led exactly to a distribution which had already been studied. The transformation which we shall now employ leads approximately to the normal distribution in which all the above tests may be carried out without difficulty. Let

z = ?{log e(1+r) - log e(1-r)}

then as r changes from 0 to 1, z will pass from 0 to [infinity]. For small values of r, z is nearly equal to r, but as r approaches unity, z increases without limit. For negative values of r, z is negative. The advantage of this transformation lies in the distribution of the two quantities in random samples. The standard deviation of r depends on the true value of the correlation, ; as is seen from the formula

Since ρ is unknown, we have to substitute for it the observed value r, and this value will not, in small samples, be a very accurate estimate of ρ. The standard error of z is simpler in form,

and is practically independent of the value of the [p. 163] correlation in the population from which the sample is drawn.

In the second place the distribution of r is skew in small samples, and even for large samples it remains very skew for high correlations. The distribution of z is not strictly normal, but it tends to normality rapidly as the sample is increased, whatever may be the value of the correlation. We shall give examples to test the effect of the departure of the z distribution from normality.

Finally the distribution of r changes its form rapidly as ρ is changed ; consequently no attempt can be made, with reasonable hope of success, to allow for the skewness of the distribution. On the contrary, the distribution of z is nearly constant in form, and the accuracy of tests may be improved by small corrections for skewness; such corrections are, however, in any case somewhat laborious, and we shall not deal with them. The simple assumption that z is normally distributed will in all ordinary cases be sufficiently accurate.

These three advantages of the transformation from r to z may be seen by comparing Figs. 7 and 8. In Fig. 7 are shown the actual distributions of r, for 8 pairs of observations, from populations having correlations 0 and 0.8; Fig.

8 shows the corresponding distribution curves for z. The two curves in Fig. 7 are widely different in their modal heights; both are distinctly non-normal curves; in form also they are strongly contrasted, the one being symmetrical,

the other highly unsymmetrical. On the contrary, in [p. 164] Fig. 8 the two curves do not differ greatly in height; although not exactly normal in form, they come so close to it, even for a small sample of 8 pairs of observations, [p. 165] that the eye cannot detect the difference; and this approximate normality holds up to the extreme limits ρ=[plus or minus]1. One additional feature is brought out by Fig. 8 ; in the distribution for ρ=0.8, although the curve itself is as symmetrical as the eye can judge of, yet the ordinate of zero error is not centrally placed. The figure, in fact, reveals the small bias which is introduced into the estimate of the correlation coefficient as ordinarily calculated; we shall treat further of this bias in the next section, and in the following chapter shall deal with a similar bias introduced in the calculation of intraclass correlations.

To facilitate the transformation we give in Table V.(B) (p. 175) the values of r corresponding to values of z, proceeding by intervals of ,01, from 0 to 3. In the earlier part of this table it will be seen that the values of r and z do not differ greatly; but with higher correlations small changes in r correspond to relatively large changes in z. In fact, measured on the z-scale, a correlation of .99 differs from a correlation .95 by more than a correlation .6 exceeds

zero. The values of z give a truer picture of the relative importance of correlations of different sizes, than do the values of r.

To find the value of z corresponding to a given value of r, say .6, the entries in the table lying on either side of .6, are first found, whence we see at once that z lies between .69 and .70; the interval between these entries is then divided proportionately to find the fraction to be added to .69. In this case we have 20/64, or .31, so that z=.6931. Similarly, in finding [p. 166] the value of r corresponding to any value of z, say .9218, we see at once that it lies between .7259 and .7306; the difference is 47, and 18 per cent of this gives 8 to be added to the former value, giving us finally r=.7267. The same table may thus be used to transform r into z, and to reverse the process. Ex. 29. Test of the approximate normality of the distribution of z. -- In order to illustrate the kind of accuracy obtainable by the use of z, let us take the case that has already been treated by an exact method in Ex. 26. A correlation of -.629 has been obtained from 20 pairs of observations ; test its significance.

For r=-.629 we have, using either a table of natural logarithms, or the special table for z, z=.7398. To divide this by its standard error is equivalent to multiplying it by [sqrt]17.· This gives -3.050, which we interpret as a normal deviate. From the table of normal deviates it appears that this value will be exceeded about 23 times in 10,000 trials. The true frequency, as we have seen, is about 30 times in 10,000 trials. The error tends slightly to exaggerate the significance of the result.

Ex. 30.·Further test of the normality of the distribution of z. -- A partial correlation +.457 was obtained from a sample of 32, after eliminating two variates. Does this differ significantly from zero? Here z =.4935; deducting the two eliminated variates the effective size of the sample is 30, and the standard error of z is 1/[sqrt]27; multiplying z by [sqrt]27, we have as a. normal variate 2.564. Table IV. shows, as before, that P is just over ·.01. There is a slight exaggeration [p. 167] of significance, but it is even slighter than in the previous example.

The above examples show that the z transformation will give a variate which, for most practical purposes, may be taken to be normally distributed. In the case of simple tests of significance the use of the table of t is to be preferred ; in the following examples this method is not available, and the only method available which is both tolerably accurate and sufficiently rapid for practical use lies in the use of z.

Ex. 31·Significance of deviation from expectation of an observed correlation coefficient. -- In a sample of 25 pairs of parent and child the

关于中国联通公司提升服务质量的对策研究剖析

中国联通分公司提升服务质量的对策研究 内容摘要：自中国21世纪加入WTO以来，国际电信运营商的即将进入，国内各大电信运营商的崛起，中国电信市场的竞争硝烟愈来愈浓。当今经济社会中，服务占有重要的地位，无论在传统的服务性行业还是制造业领域，人们都把创造增值性的服务当作竞争优势的重要手段，这种针对于服务的变革是时代的需要，是自助服务技术的广泛应用，是服务外包、业务网络化以及竞争日趋激烈的结果。越来越激烈的竟争迫使企业不能仅依赖于单纯靠生产创造出的价值。今日的顾客看重的是服务带来的增值、综合性的整体解决方案及其对服务的体验。本文为中国联通分公司面对竞争、开拓市场，从提升自身服务质量做起，赢得市场竞争等方面提供了较有力的理论依据和实践指导，具有一定的现实意义和借鉴作用。 关键词：中国联通提升服务质量对策研究

目录 内容摘要-------------------------------------------------------------1 第一章引言----------------------------------------------------------3 第二章服务质量概述--------------------------------------------------3 一、服务质量的概念-------------------------------------------------4 二、服务质量的评价方法---------------------------------------------4 1、目标顾客--------------------------------------------------------5 2、连贯性----------------------------------------------------------5 3、服务质量要素----------------------------------------------------5 3.1、功能性--------------------------------------------------------5 3.2、经济性--------------------------------------------------------5 3.3、安全性--------------------------------------------------------6 3. 4、时间性--------------------------------------------------------6 3. 5、舒适性--------------------------------------------------------6 3.6、文明性--------------------------------------------------------6 4、服务质量差距----------------------------------------------------6 三、影响服务质量的因素---------------------------------------------7 第三章中国联通海南分公司服务质量的现状分析--------------------------7 一、中国联通海南分公司概况-----------------------------------------7 二、中国联通海南分公司服务质量现状分析-----------------------------8 1、中国联通竞争优势------------------------------------------------8 2、中国联通竞争劣势-----------------------------------------------10 3、中国移动的竞争优势---------------------------------------------10 4、中国移动的竞争劣势---------------------------------------------12 三、中国联通海南分公司服务质量存在问题的原因----------------------13 第四章中国联通海南分公司提升服务质量的对策与措施-------------------13 第五章结束语-------------------------------------------------------15 参考文献------------------------------------------------------------16

中国联通客服部工作流程

中国联通分公司客服部工作流程 一、客户资料管理 1. 资料收集。在公司的日常客服工作中，收集客户资料是一项非常重要的工作，它直接关系到公司的营销计划能否实现。客服资料的收集要求客服专员每日认真提取客户信息档案，以便关注这些客户的发展动态。 2. 资料整理。客服专员提取的客户信息档案递交客服主管，由客服主管安排信息汇总，并进行分析分类，分派专人管理各类资料，并要求每日及时更新，避免遗漏。 3. 资料处理。客服主管按照负责客户数量均衡、兼顾业务能力的原则，分配给相关客服专员。客服专员负责的客户，应在一周与客户进行沟通，并做详细备案。 二、对不同类型的客户进行不定期回访 客户的需求不断变化，通过回访不但了解不同客户的需求、市场咨询，还可以发现自身工作中的不足，及时补救和调整，满足客户需求，提高客户满意度。 1、回访方式：沟通、短信业务等 2、回访流程 3、从客户档案中提取需要统一回访的客户资料，统计整理后分配到各客服专员，通过（或短信业务等方式）与客户进行交流沟通并认真记录每一个客户回访结果填写《回访记录表》（此表为回访活动的信息载体），最后分析结果并撰写《回访总结报告》，进行最终资料归档。 三、回访容： 1. 询问客户对本司的评价，对产品和服务的建议和意见； 2. 特定时期可作特色回访（如节日、电信日、促销活动期） 注意：回访时间不宜过长，容不宜过多。 四、回访规及用语 回访规：一个避免，三个必保，即： 1、避免在客户休息时打扰客户； 2、必须保证VIP客户的100%的回访； 3、必须保证回访信息的完整记录； 4、必须保证在三天之回访（最好与客户在中再约一个方便的时间）。 （1）开始：您好,我是中国联通的客服代表，请问您是××先生/小姐吗？打扰您了。（2）交流：感您选择使用我们中国联通的××服务套餐，请问您对××服务套餐满意吗？【满意】：您对我们的服务有什么建议吗？ 、【不满意/一般】：（能否告诉我您对哪方面不满意吗？/我们应改进哪方面的工作） （3）结束： 【满意】：感您的答复，您如果需要什么帮助，可随时拨打10011. 五、高效的投诉处理 完善投诉处理机制，注重处理客户投诉的规性和效率性，形成闭环的管理流程，做到有投诉即时受理，迅速有结果，处理后有回访；使得客户投诉得到高效和圆满的解决。建立投诉归档资料。 1、投诉处理工作的三个方面： （1）.为顾客投诉提供便利的渠道； （2.）对投诉进行迅速有效的处理； （3.）对投诉原因进行最彻底的分析。 2、投诉解决宗旨：挽回不满意顾客 3、投诉解决策略：短—渠道短；平—代价平；快—速度快

中国联通服务承诺

中国联通服务承诺 中国联通服务承诺为切实开展“整作风、提效能、优环境”主题活动，狠抓机关作风、提高行政效能，优化发展环境，提高服务质量、提升服务形象，切实维护消费者利益，营造让用户放心的消费环境。特向社会作出如下公开承诺：一、确保用户知情权，严格按客户服务协议执行。公司的服务协议中严禁出现“单方变更”电信服务协议的任何解释条款，杜绝不对等条款。切实维护客户权益，严格按客户服务协议执行。提供规范的用户账单、发票、详单；明确统一计费、出账时点，让用户清楚所有收费项目和内容；本篇文章来自资料管理下载。认真检查确认各类增值业务，包括SP/CP合作提供业务的收费明细项目，做到各类电信服务明码标示；对于预付费余额以及信用控制的限额必须保证用户知情。二、投诉处理“首问负责，限时办结”。落实“首问负责、限时办结”，客户投诉的答复时限当时承诺。各服务渠道执行统一的投诉处理流程和规范，切实保障用户投诉及时有效解决。承诺办结时限：一般投诉即时答复，最长不超过24小时；省级投诉平均24小时答复，最长不超过48小时；全国级投诉平均48小时答复，最长不超过72小时。三、SP业务定制必须由客户确认。有SP业务接入管理平台，对客户定制业务进行二次确认，依据客户二次确认作为与客户建立定购关系的凭证，严格杜绝强行定制发生。IVR业务做到主被叫鉴权，严控外呼业务发生。彻底清理各类低资费短信套餐，实施主叫号码鉴权，从源头上防止SP违规利用群发垃圾短信。四、公开SP业务信息，方便客户查询。在中国联通增值业务门户网站对公司所有增值业务类的业务信息进行公示，其中包括服务提供商的公司名称、业务名称、业务描述、资费描述、服务提供商的客服电话、服务接入号码等信息。五、SP服务监督热线，接受客户监督。严格按照信息产业部“谁主管、谁负责，谁接入、谁负责，谁收费、谁负责”的要求，从严惩治损害消费者行为，执行《中国联通SP服务质量监督管理办法》并进行信用等级管理，全国设立了统一的SP服务监督电话，受理客户对于SP的定制、取消、咨询和投诉，严肃查处SP/CP 违规经营行为。六、方便用户话费及时查询。公司向客户提供客服热线10011/10、营业厅、网站、短信101901查费直通车等多种渠道的话费及时查询服务。按照信产部《电信服务规范》要求，联通公司为签约用户提供最近5个月的话单清单查询。为方便客户对定制信息查询，用户所有已订制的WAP类、短信类等的移动信息服务业务都可通过手机方便地查询。本篇文章来自资料管理下载。WAP类业务用户可通过手机上网进入“互动视界”查询；短信类业务用户可发送短信“114”到“5133”和当地联通专设号码查询。七、开通全国服务质量监督举报电话。公司成立中国联通客户维权委员会。在省级分公司和总部设立服务监督举报电话，接受客户监督。客户在遇到各级服务窗口对投诉问题不能按承诺解决，或服务质量问题，可以举报。在总部、省分和地市设立面对面客户投诉争议调解接待中心，维护客户合法权益。 八、话费误差双倍返还，短信差错先行赔付对移动电话业务中存在的“错收的超长超短话单”、“不该收费的语音提示和免费电话”、“未按业务办理单据中客户选择的业务资费标准收费”、“错收的联通在信业务信息费”等四项计费误差实行双倍返还。201x年x月x日

中国联通客户服务系统技术规范书(增补部分)

中国联通客户服务系统技术规范书 增订部分 （讨论稿） 中国联通客服与呼叫中心业务部 2003年6月

目录

第一章概述 一、前言 随着电信业务市场竞争的不断加剧，市场将逐步呈现从规模扩张到规模效益型发展的转变、从提供普遍和基本服务到提供多元化、 个性化、多层次服务的转变态势，客户服务能力和水平也成为竞争的 重要因素，电信企业越来越意识到客户服务质量是企业最重要的核 心竞争力。电信企业必须以客户为中心，获取较高的客户满意度和 忠诚度，才能在竞争中立于不败之地。 客户服务中心是联通公司与客户沟通的桥梁，也是维系客户关系和保证客户体验一致性的窗口。随着客户对服务需求的不断提高， 必须扩充客户服务中心的服务手段和服务渠道，贯彻客户关系管理 的理念，体现客户服务的规范化、个性化、差异化、多样化，改善和 提高中国联通的客户服务水平和服务质量。 中国联通从2000 年启动全国各分公司的客户服务系统的集中化改造工作，经过三年的持续投入，客户服务系统的支撑平台已经实 现了规模化、集中化，在呼叫处理能力方面已经基本满足了中国联 通客户的普遍化服务需求，为中国联通客户服务质量的提升发挥着 越来越重要的作用。 中国联通深刻意识到客户服务工作必须不断适应客户对个性化、差异化服务的需求，在利用客户服务系统提升客户满意度和忠诚度 的同时，开展主动客户关怀和主动市场营销工作，逐步实现客户服务

中心由成本中心向利润中心的转变。 为了全面提升客户服务系统对客户服务工作的支撑能力，适应中国联通对未来客户服务工作的规划思路，本次规范将重点放在支持 客户差异化分层服务的本地化客户资料建立和分层服务功能、实现主 动服务的呼出功能和提高坐席代表工作效率，建立客户服务中心知识 积累的知识库管理功能、完善客户 服务质量管理的工作流管理功能上，通过对上述功能的规范来指导各 分公司客户服系统的建设工作，使客户服务工作能够有效支撑中国联 通总体战略的实现。 本规范是中国联通客户服务系统新增功能规划和建设以及未来客户服务工作的依据，各省（直辖市、自治区）公司应以本规范为指 导，进行客户服务系统新增功能的具体项目建设和服务工作的开展。 二、客服系统新功能整体描述 本规范主要包括客服系统新功能建设的六个组成部分： 客服系统本地用户资料库 用户分层服务功能 座席咨询知识库 客服运营管理系统 主动呼出系统 电子工单闭环管理系统客服系统建立本地用户资料库主要存储客户基 本资料、用户基本资料、部分帐务资料等信息，通过确定的接口

中国联通集团客户标准服务内容

附件1 中国联通集团客户标准服务内容 一、新装 （一）服务渠道：自有营业厅、集团客户经理、网格经理。 （二）受理原则 经办人凭本人有效身份证件原件、单位委托证明（加盖红章的原件）等有效登记证件复印件办理，入网协议及子用户号码表上加盖单位公章（加盖红章的原件）或提供相关资料委托客户经理代办；系统录入经办人姓名、联系电话、电子邮箱、通信地址、邮编等信息。 如业务办理需企业法人营业执照，企业未获得营业执照的政企可凭国家工商行政管理机构核发的《企业名称预先核准通知书》（原件或加盖国家工商行政管理机构材料证明章的复印件）办理业务，同时经办人出示本人的有效身份证件。领取营业执照后，再补全完整客户身份信息。 对于中小企业客户仅持外地身份证办理新装业务，应交纳一定金额的预付款或预存6-12个月套餐使用费或只允许办理预付费业务。业务受理人员核对客户有效证件后，需复印留存。若客户付款方式为单位账号银行托收，按公司营业规范要求办理。 （三）收费标准：目前移动业务入网服务不收费；固网

业务装机根据行业主管部门批准的资费标准收取手续费。 二、过户 （一）服务渠道：自有营业厅、集团客户经理、网格经理。 （二）受理原则 单位名称开户的客户：经办人持本人有效身份证件原件和单位介绍信（加盖公章），新机主（单位客户）持本人有效身份证件（有效登记证件及单位介绍信（加盖公章））办理或提供以上相关有效证件原件委托客户经理代办。 在租机/公司补贴协议期内的业务，按公司与客户签订的相关合同规定执行，原则上不办理过户业务。 受理时需按财务规定结清原用户账户中相关费用。 如果原用户账户中有积分并要求兑换，则按公司积分兑换业务规定办理；如果原用户账户中有积分但放弃兑换，则告知原用户须在业务受理单上注明同意带积分过户。 实名制预付费用户办理过户业务时，账户须处于有效期或充值期且账户余额大于0。 靓号等协议期内有特殊约定的客户不能办理过户。 新用户可以选择继承老用户业务。 新用户不能继承老用户的信用额度，需重新设定新用户的初始信用额度。 新、原机主（经办人）分别在业务受理单上签字确认，以上渠道复印留存相关证件。

中国联通提升服务能力的关键

中国联通提升服务能力的关键 [摘要]从对服务理念的认识，到实现提高客户服务的要素，实现统一、规范、一体化、精细化、差异化的一站式的服务策略，达到提高客户服务质量。 [关键词]服务理念服务能力服务质量 随着中国联通的重组、融合的完成，中国联通真正迎来了全业务的运营，服务能力的全面提升成为中国联通在服务战取胜的关键，就要提高以下要素的管理及服务水平，即：服务理念的确立、资源规划与保障、客户关系管理、服务过程管理及信息系统的支持。 1、服务理念的确立 1.1服务理念的确立是全面提升服务能力的开展前提。企业服务理念是企业客户价值的重要体现，服务理念作为服务能力提升的开展前提，服务理念的确立能够统一企业的所有人员对服务的认识，为服务能力的提升打造思想上的基础，为服务理念的提升提供保障。中国联通以为客户提供优质、精细的服务为导向的服务理念的确立，为企业的服务战略指出了既定方向，有效的推进可以减少服务理念提升过程中存在的潜在摩擦。根据全业务经营的特点制定适合统一、规范、一体化和精细化的服务标准，确立为用户提供差异化的一站式服务的服务战略，这是员工对服务理念理解与支持的保障。服务理念也是约束员工的行为准则，它清楚地向员工表述了企业对服务工作的要求标准，并对服务工作的开展，具有指导作用。统一的服务理念的建立与宣贯是企业服务能力提升、策略执行的首要因素。 1.2真正有效的服务理念的宣贯，应该是全程全网的，不仅仅局限于营业厅、客服热线、客户经理、维护主力等服务的前端界面，还包括企业内部的后台支撑和职能部门，只有前后台人员相互协作、一个标准对待客户，才能为用户提供切实有效的优质服务。 2、资源的规划与保障 2.1资源的规划与保障是服务能力提升的策略保障。根据市场及客户需求，结合现阶段中国联通的核心能力，对所拥有的内、外部资源进行规划、整合。内部资源包括：与所有服务渠道相关的人力资源、硬件资源，以及所有服务协作部门与支撑部门的相关资源、信息资源等。而外部资源是指产品链上所有与服务相关的合作伙伴。无论是对内部资源还是外部资源的规划都需要建立相应的管理模式，通过管理模式与规章制度的建立与应用，对资源的质量、数量、运行质量进行控制，从而确保资源配置的合理性和有效性。 2.2内部资源的规划与管理。内部资源的规划与管理是提升服务能力的体系化支撑。内部资源除了要配置满足需求的硬件资源外，主要就是要配置好人力

中国联通客服部工作流程

中国联通玉林分公司客服部工作流程 一、客户资料管理 1. 资料收集。在公司的日常客服工作中，收集客户资料是一项非常重要的工作，它直接关系到公司的营销计划能否实现。客服资料的收集要求客服专员每日认真提取客户信息档案，以便关注这些客户的发展动态。 2. 资料整理。客服专员提取的客户信息档案递交客服主管，由客服主管安排信息汇总，并进行分析分类，分派专人管理各类资料，并要求每日及时更新，避免遗漏。 3. 资料处理。客服主管按照负责客户数量均衡、兼顾业务能力的原则，分配给相关客服专员。客服专员负责的客户，应在一周内与客户进行沟通，并做详细备案。 二、对不同类型的客户进行不定期回访客户的需求不断变化，通过回访不但了解不同客户的需求、市场咨询，还可以发现自身工作中的不足，及时补救和调整，满足客户需求，提高客户满意度。 1、回访方式：电话沟通、短信业务等 2、回访流程 3、从客户档案中提取需要统一回访的客户资料，统计整理后分配到各客服专员，通过电话（或短信业务等方式）与客户进行交流沟通并认真记录每一个客户回访结果填写《回访记录表》（此表为回访活动的信息载体），最后分析结果并撰写《回访总结报告》，进行最终资料归档。 三、回访内容： 1. 询问客户对本司的评价，对产品和服务的建议和意见； 2. 特定时期内可作特色回访（如节日、电信日、促销活动期）注意：回访时间不宜过长，内容不宜过多。 四、回访规范及用语回访规范：一个避免，三个必保，即： 1、避免在客户休息时打扰客户； 2、必须保证VIP 客户的100%的回访； 3、必须保证回访信息的完整记录； 4、必须保证在三天之内回访（最好与客户在电话中再约一个方便的时间）。 （1）开始：您好,我是中国联通的客服代表，请问您是××先生/小姐吗？打扰您了。 （2）交流：感谢您选择使用我们中国联通的××服务套餐，请问您对××服务套餐满意吗？ 【满意】：您对我们的服务有什么建议吗？、【不满意/一般】：（能否告诉我您对哪方面不满意吗？/我们应改进哪方面的工作） （3）结束： 【满意】：感谢您的答复，您如果需要什么帮助，可随时拨打10011. 五、高效的投诉处理 完善投诉处理机制，注重处理客户投诉的规范性和效率性，形成闭环的管理流程，做到有投诉即时受理，迅速有结果，处理后有回访；使得客户投诉得到高效和圆满的解决。建立投诉归档资料。 1、投诉处理工作的三个方面： （1）.为顾客投诉提供便利的渠道； （2.）对投诉进行迅速有效的处理； （3.）对投诉原因进行最彻底的分析。 2、投诉解决宗旨：挽回不满意顾客 3、投诉解决策略：短—渠道短；平—代价平；快—速度快 六 1、投诉处理流程：

中国联通客服实习小结

中国联通客服实习小结 中国联通客服实习小结 一、对岗位实习过程的理解 年五月我到中国联通衡钢一厅做客服，刚开始我想应该和老师讲 的差不了多少吧!当我做了一两天的时间后才发现原来这个切并不简单，进入社会对我还需要一段时间而不得不让我更深切地体会到就业的压力。我们一出校门什么都不会，学校仅仅我们的避风港，不过总有一天，我们要冲出这个港湾，驶向“大海”。去过几趟杭州人才市场， 看到一张张招聘广告上的要求，我黯然伤心：一、没经验;二、没资格证;三、没胆量。虽然有了更多的实习，让我们有了一次涉及社会的体验，不过那犹如小孩子刚学会站立，还没学会走路，那么这次实习我 们就是在学走路，当然在学的一路上会摔跤、哭泣，但现在我们都挺 过去了，回头看看一路上留下了足迹，这次实习还是蛮有意义与必要的。接到公司的培训电话，我的心也就安定下来了，终于站住脚，那 时很兴奋与开心，便前去培训。培训期间，交代公司文化、公司制度、公司产品……不可否认找得又是电话营销，一个服务行业，又与专业 对口的工作。 电话是现代商人越来越常用的一种交流工具，所以，做电话营销 人员了解一下打电话的一般要求是很有用的。打电话的基本原则是简 明扼要，切忌罗嗦，既突出不了问题，又占用别人时间，从而引起别 人反感。 客服部这个分为很多组。大致分为话务组，业务处理组，投诉组，质检组，采编组。话务组很明显负责接电话，协助用户记录要求或建议。业务处理组负责帮用户开通取消业务。投诉组很明显受理投诉， 并给用户回复。质检组也就是质量检测，主要是监听话务组的录音， 当然不是每条录音都听，是随机抽取的，然后把监听情况，包括合格 和不合格的情况整理后再反馈给话务组。采编组的主要工作是负责接 收上级下达的业务，整理后，实行采编，给各组培训。

中国联通服务质量研究

中 国 联 通 服 务 质 量 研 究 目录 摘要、关键词 引言 (4) 一、中国联通简介 (4)

二、中国联通服务质量的提出 (5) 三、中国联通的服务质量下降原因 (6) 四、服务质量解决措施 (8) 五、结束语 (8) 参考文献 【摘要】：随着中国加入WTO，中国的电信市场将更加开放，国内移动通信运营商既迎来了巨大发展机遇，又面临着电信市场逐步向世界开放带来的严峻挑战。同样社会经济的快速发展，人们对电信市场的通信服务质量要求也越来越高了，不再是简简单单注重通信质量，更注重服务行为质量，这就要求中国的电信服务商要注重客户服务质量。作为中国三大通信服务商之一的中国联通，它的服务质量也越来越会受到重视。现在，人们购买通信服务，买的不仅仅是通话质量，买

的更是享受联通服务质量的过程，以及对联通公司的信任。本文首先分析在什么样的背景下提出对中国联通的服务质量的研究，接着分析产生的原因，最后根据原因针对中国联通的实际行为对比提出解决措施。 【关键词】：中国联通服务质量解决措施 [abstract]：With China's accession to the WTO, China's telecom market will be more open, the domestic mobile operators in great development opportunity already, and also faces the telecommunications market gradually to open the challenge of the world. As the rapid development of social economy, the people of the telecommun- ications market of communication service quality requirements are increasingly high, is no longer a simple, more attention to the quality of service quality, which requires the China telecom service should pay attention to quality service to customers. As one of the three communications provider in China, it is the China unicom's service quality and will be valued. Now, people buy communications services, buy just call quality, buy more enjoy the process of service quality. China unicom and China unicom's trust. This paper firstly analyzes the background in what kind of China unicom service process, then analyses the research, finally, according to the real reason for China unicom behavior and put forword to solution measures. [key word]:China Unicom Service Quality Solving Measures 引言 在新时代新经济形势的要求下，随着通信技术的快速发展，使用户在满足基本通话要求的基础上对信息沟通方面提出更高的要求，特别是在中国加入WTO 后，我国的通信服务面临着国外竞争的必将日趋激烈，这就要求中国通信服务的运营商在信息沟通方面和服务质量方面作出更好更强的服务。中国联通是中国通信服务的运营商之一，（中国移动、中国电信和中国联通是我国通信服务的三大