- Email: [email protected]
Library school faculty strengths in data processing Canadian-U.S. differences
Iqfcmnaiion Procrsstng & Managemenr Printed in the U.S.A.
Vol.21. No.
03064573185 %3.00+ .oO c 1981Pergamon Press Ltd.
2, pp 157-163, 1985
LIBRARY SCHOOL FACULTY STRENGTHS IN DATA PROCESSING CANADIAN-U.S. DIFFERENCES C. D. HURT? Graduate School of Library Science, McGill University, 3459 McTavish Street, Montreal, Quebec H3A IYI Canada
Abstract-This study examined the relative strengths of faculty expertise in the field of data processing within schools of library and information science in North America. The relative strengths of Canadian versus U.S. schools, measured by reported faculty expertise, was the particular focus of the study. Data were gathered from the directory issues of the Journal of Ed~cutio~ for~~~~a~ians~j~ for the years 19721981. Data were aggregated into two groups: (1) total faculty strength dichomotized by U.S. and Canadian schools; (2) faculty reporting expertise in data processing or automation broken down by U.S. versus Canadian. Planned comparisons using a Dunn procedure were made on each of the ten years in the study. The results of testing indicated there is a significant difference in the number of faculty reporting expertise in data processing in the two countries in four of the ten years. The differences were opposite in two of these four, however. Results of testing of overall
faculty strength indicated there was no significant difference in the numbers of faculty members during the ten year period. The major conclusion of the study was that Canadian schools of library and information science have made a concerted and successful effort to increase their over& level of expertise in data processing, but without significantly adding to their faculty levels. INTRODUCTION
The purpose of this paper was to examine the relative strengths of faculty expertise in the field of data processing in library schools in both Canada and the U.S. This study examined the strength of faculty expertise as one variable toward the measurement of overall library education strength. The particular area of data processing and automation was chosen as a pilot study before examining faculty strengths on a comprehensive level. Previous research has examined the areas of computer programming in library education, the lack of and the extent of online database searching and curricular concerns [l-4]. This paper examined changes in subject strengths of library educators in data processing and automation over time. The working hypothesis developed was that the rate of increase in the number of faculty members reporting expertise in data processing in Canadian library schools was no different from the rate of increase of faculty members reporting expertise in data processing in U.S. library schools. In a time of budgetary restraint, matched by demands for curricular revision and expansion, it is particularly important that library education be cognizant of the past and to prepare for the future. This study examined ten years of data in an effort to determine the relationship of Canadian library education to the U.S. scene in one particular area of expertise. Other areas will be explored in later papers already in progress. DATA COLLECTION
Data were obtained using the Directory Issue of the ~~~~~~1 of Ed~ca~i~~ for Lib~u~iu~s~ip for the years 1972-1981. The Directory Issue contains the names of faculty members in accredited library schools and their reported areas of expertise. An assumption was made that the areas of expertise reported were the actual areas of expertise for the faculty member. Additionally, it was assumed that the faculty members t Present address: Graduate Fenway, Boston, MA 021 IS.
School of Library and Information
Science,
Simmons College, 300 The
158
C. D. HURT
reported their actual areas of expertise and not desired areas of expertise. Data were aggregated into two groups for each of the Canadian and U.S. library schools. The first group contained the total number of faculty members reported in the Directory Issue for each year in the study. The second group consisted of all faculty members reporting expertise in data processing or automation during each year of the study. A data conversion into z scores was done to normalize the scores. DATA
ANALYSIS
The overall test of this investigation was to determine if there was a significant difference between the expertise of faculty in Canadian library schools versus U.S. library schools. Instead of attempting an overall or omnibus test for difference, a planned comparison approach was used. The hypothesis of no difference was tested using equal weighting for each of the ten comparisons made. Tests of significance were employed using a Dunn procedure [5]. The tests were run under a Type I error level ((Y)of 0.05 for the experiment. Under a decision rule written prior to initiating testing, a value of 12.931was necessary to reject the hypothesis under test [6]. The forms of the hypotheses were: HO: *1(l) = 442) = . . . = $(lO) HI: HO was false. In verbal form the hypotheses
were:
HO: There is no significant difference between the levels of faculty strength in data processing in Canadian and U.S. library schools measured on a year-by-year basis. Hl: There is at least one significant difference between the levels of faculty strength in data processing in Canadian and U.S. library schools measured on a year-by-year basis. If the hypothesis under test, HO, was rejected the result would be interpreted as there being at least one statistically significant difference between the Canadian and U.S. expertise in data processing. Because planned comparisons were being used, it was possible to be more explicit about the differences. Ten planned comparisons, corresponding to the ten year time frame of the study, were tested. Each was tested for significance, yielding more information than would the classic omnibus test of difference. A finding of significant difference in any one of the ten planned comparisons would indicate a difference but not the reasons for the difference. A failure to reject the hypothesis under test would be interpreted as there being no significant differences, on a year-by-year basis, between the faculty strengths of Canadian and U.S. library schools in the area of data processing. Such a finding would
Table 1. z Scores for data processing strength Year
2 3 4 5 6 7 8 9 10 Mean SD
Canadian -
1.03 1.03 0.27 0.65 1.03 0.11
0.49 0.49 2.02 0.87 14.7 2.63
U.S. -0.59 -0.77 - 1.29 0.63 1.59 0.63 -0.24
1.42 -0.51 -0.86 129.8 11.44
159
Library school faculty strengths in data processing Table 2. Comparison Comparison 1 2 3 4 5 6 7 8 9 10
Canadian
U.S.
+
l(- 1.03) l(- 1.03) l( -0.27) l( -0.65) l(-1.03) l(O.11) l(O.49) l(O.49) l(2.02) l(0.87)
Table =
- I(-0.59) - l(-0.77) -l(-1.29) - l(0.63) - l(l.59) - l(0.63) - I(-0.24) - l(l.42) - l(-0.51) - l( -0.86)
be equivalent to a failure to reject the hypothesis for differences such as the Chi-square test. The form of comparison used in the test was:
Value -0.44 -0.26 1.02 - 1.28 - 2.62 -0.52 0.73 -0.93 2.53 1.73
of no difference
in an omnibus test
WI = a 03 + a (u), where a was the weight assigned to the values X and Y. The form of the Dunn statistic used to test the significance of + (k) was:
where VAR(+(k))
= MSw @*(1)/n(l) + a*(Wn(2)) and
MSW = [(n(l) - l)S2(1)) + (n(2) - l)s*(2))ll[(n(l)
+ n(2)) - 21
RESULTS
The prime test in this study was a test for differences between faculty strength in the area of data processing using Canadian and U.S. library schools. Table 1 lists the z scores associated with each of the ten years in the study, broken down by Canadian and U.S. schools for total faculty strength in data processing. Summary statistics were also included. The results of the ten tests are reported in Tables 2 and 3. Table 2 lists the ten comparisons of interest and the resultant values for each. These values were only descriptive at this stage and were interpretable only as an indication of the direction of any difference. The sign of the difference indicated that, within the definitions of this table, Canadian library schools fall below U.S. schools if a negative value appears. Equal weights were given to each of the values in the comparisons. Table 3 lists the results of the Dunn two-tailed test for significance for each of the comparisons in Table 2. The Dunn procedure was analogous to performing multiple t tests for each of the comparisons. The difference between the Dunn procedure and Table 3. Dunn significance tests Comparison 1 2 3 4 5 6 7 8 1:
r Value - 1.02 - 0.60 2.53 -3.19 -6.55 - 1.21 1.70 -2.17 4.33 6.33
C. D. HURT
160
multiple r tests was that the Dunn procedure partitioned the Type I error level equally among all the comparisons. This procedure retained the sensitivity of the overall test of difference for the experiment as well as making each of the individual significance tests more conservative. The results of the Dunn tests for significant differences among the comparisons were examined in light of the decision rule written above. Under this decision rule, years 4,5,9 and 10 were significantly different. Examination of the values of the comparisons in Table 2 indicated that although the differences were indeed present, they were not different in the same direction. Years 4 and 5 (1975 and 1976) showed a negative value. Within the constraints of the weights used in Table 2, this negative sign was interpreted as a higher number of U.S. faculty reporting expertise in data processing as opposed to Canadian faculty. Years 9 and 10 (1980 and 1981) showed a positive value, which can be interpreted as more Canadian faculty reporting expertise in the area of data processing than U.S. faculty. Figure 1 is a representation of the data as a function of time. The findings reported above can be graphically demonstrated by the figure. The figure can be described as demonstrating a relative similarity with the exception of years 4, 5, 9 and 10. The differences in these four years were also demonstrably inverse. DISCUSSION
The results of the tests for significance for each of the ten comparisons showed four comparisons were significantly different. These significant differences were found to be in opposite directions with the earlier differences being negative in Canadian terms and the later differences being positive. One conclusion which can be reached was that Canadian library schools have made a concerted and successful attempt to ungrade the expertise of their faculty in the area of data processing. Two points need to be made about the data and their interpretation. First, there was the possibility of a confounding effect in the data gathering procedure. In both 1980 NR IN ORTA PROCESSING 2.0
,*’ I’
I.5
0
\
\ *
:
1.0
0. 5 @ 3 5 N
: \ ‘\ ‘\ ‘.
.. . . .
---CRNROIRN ___________-“3
Fig. 1. NR in data processing.
161
Library school faculty strengths in data processing Table 4. z Scores for total faculty Year
Canadian
2 3 4 5 6 7 8 9 10 Mean SD
- 1.36 - 1.28 -0.78 0.30 0.66 - 0.42 - 0.20 0.23 1.24 1.60 140.8 13.86
U.S. - 1.56 - 1.56 - 1.02 -0.13 0.77 0.60 0.60 0.72 0.67 0.90 1174.6 140.43
and 1981 the Directory Issue of the Journal of Education for Librarianship changed the classification system used to define areas of expertise. There was the possibility that some of the variation in faculty strength in this area may be explained by this change. The classification systems used in 1980 and 1981 were more detailed than were the previous systems. Although every effort was made to retain the consistent definition of the area of data processing and automation, this may not have occurred. To some extent, the problem may be further compounded by the fact that data processing and automation were considered by some faculty to be a popular area. There was some evidence that the popularity of areas such as management may now be greater than the current popularity of data processing and automation. Crane discusses the fashionable nature of fields in her work with the “invisible college” concept [7]. The change in classification systems together with the competition of other popular fields in which to claim expertise may have had some effect on the data gathered. The second note which should be made concerning the data was that the size of the U.S. population of library schools was decidedly larger than was the size of the Canadian population of library schools. The raw data were transformed into normalized z scores within each of the populations. This procedure placed each of the two sets of data on the same scale with the same mid-point and the same variability. Any differences were not the result of variance in number and size, but were valid differences between the two sets of data. A secondary question was posed by the results reported above. If it was the case that data processing and automation were different in terms of expertise in U.S. versus Canadian library schools, by what means has the difference been alleviated. In years 4 and 5 of the study Canadian library schools were negatively related to the level of expertise in data processing in U.S. schools. The overall level of faculty strength, the number of total faculty members available in both the Canadian and the U.S. data sets, was examined. The same set of statistical hypotheses were tested as were used above. The same decision rule was employed. Table 4 lists the z scores for each of the years
Table 5. Comparison Comparison
8 9 10
Canadian I(-1.36) 1(- 1.28) l( -0.78) l(O.30) l(O.66) I( -0.42) l(-0.20) l(0.23) l(l.24) l(l.60)
+
table U.S.
-I(-1.56) -l(-1.56) -I(-1.02) -I(-0.13) - l(O.77) - l(O.60) - l(O.60) - l(0.72) - l(O.67) - l(O.90)
=
Value 0.20 0.28 0.24 0.43 -0.11 - 1.02
- 0.80 - 0.49 0.57 0.70
162
C. D. HURT Table 6. Dunn significance tests
r Value
Comparison 1 2 3 4 5 6 7 8 9 10
0.46 0.65 0.60
1.07 - 0.28 -2.38 - 1.87 -1.14 1.43 1.75
in the study for both Canadian and U.S. schools in terms of the total number of faculty members available. Summary statistics are also included. Planned comparisons were calculated and are listed in Table 5 below. The same weights, i.e., equal, were used here as were used to test for differences in data processing strengths. Finally, the Dunn multiple testing procedure was again employed to determine if there were any significant differences. Table 6 shows the results of the tests for significance. In all cases, there was no significant difference between the number of faculty in Canadian library schools and the number of faculty in U.S. schools on a normalized scale. Figure 2 is a representation of the normalized values of the data as a function of time. These results seem to indicate that the Canadian library schools not only have made a concerted and successful effort to increase the level of expertise in data processing, but have done so without significantly adding to their faculty levels. Figure 2 indicates that the general pattern of faculty strength has been positive. Comparison of Figs. 1 and 2 indicates that, given the overall positive increase in overall faculty strength, TOTRL
FRCULTY
2. 0-f
/ 1.
5
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// 1. 0
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/: ,/
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Fig. 2. Total faculty.
91
lo
Library school faculty strengths in data processing
163
Canadian schools have added expertise in data processing, while U.S. schools have reduced the relative level of their expertise in the same area. Certainly an area which will require further study is whether the increase and decrease in relative levels of expertise is matched with increases or decreases in other fields of the classification scheme. SUGGESTED
FURTHER
RESEARCH
This study points to the need for further research in a number of areas. There is a need for replication and validation of the present study. The potential problems with the classification scheme employed by the Journal of Education for Librarianship in its Directory Issue may have caused some unintended results or the creation of an artifact. Because a significant difference was found in the two years for which the classification scheme was a potentially confounding variable, additional work needs to be done to determine if the scheme itself or the strength of data processing expertise was the cause of the finding. As suggested above, additional work needs to be done in the area of examining other fields of expertise within librarianship to determine if significant differences exist there as well. Data are currently being gathered for an examination of the levels of expertise in Canadian versus U.S. library schools on a comprehensive level. Examination of Fig. 1 seems to indicate a marked tailing off of level of expertise in data processing for the final year of the study. This trend should be examined further to determine if it is significant and to determine the causes for this large drop, particularly in the level of U.S. expertise. REFERENCES [l] R. GOEHLERT and G. SNOWDON, Computer Education for Librarianship 1980, 20, 251.
programming in library education.
Journal
of
[2] T. P. SLAVENSand M. E. RUBY, Teaching library science students to do bibliographic searches of automated data bases. RQ 1978, 18, 38. [3] B. R. BOYCE,Instruction in on-line tools at the University of Missouri. Journal of Education for Librarianship 1979, 20, 158. [4] F. W. ROPER, The integrated core curriculum; the University of North Carolina experience. Journal of Educarion for Librarianship 1978, 19, 159. [5] 0. J. DUNN, Multiple comparisons among means. J. Am. Stat. Assoc. 1961, 56, 52. [6] R. E. KIRK, Experimental Design: Procedures for the Behavioral Sciences, Table D-16, p.
551. Brooks/Cole, Belmont, California (1968). [7] D. CRANE, Invisible Colleges. Univ. of Chicago Press, Chicago (1972).
Vol.21. No.
03064573185 %3.00+ .oO c 1981Pergamon Press Ltd.
2, pp 157-163, 1985
LIBRARY SCHOOL FACULTY STRENGTHS IN DATA PROCESSING CANADIAN-U.S. DIFFERENCES C. D. HURT? Graduate School of Library Science, McGill University, 3459 McTavish Street, Montreal, Quebec H3A IYI Canada
Abstract-This study examined the relative strengths of faculty expertise in the field of data processing within schools of library and information science in North America. The relative strengths of Canadian versus U.S. schools, measured by reported faculty expertise, was the particular focus of the study. Data were gathered from the directory issues of the Journal of Ed~cutio~ for~~~~a~ians~j~ for the years 19721981. Data were aggregated into two groups: (1) total faculty strength dichomotized by U.S. and Canadian schools; (2) faculty reporting expertise in data processing or automation broken down by U.S. versus Canadian. Planned comparisons using a Dunn procedure were made on each of the ten years in the study. The results of testing indicated there is a significant difference in the number of faculty reporting expertise in data processing in the two countries in four of the ten years. The differences were opposite in two of these four, however. Results of testing of overall
faculty strength indicated there was no significant difference in the numbers of faculty members during the ten year period. The major conclusion of the study was that Canadian schools of library and information science have made a concerted and successful effort to increase their over& level of expertise in data processing, but without significantly adding to their faculty levels. INTRODUCTION
The purpose of this paper was to examine the relative strengths of faculty expertise in the field of data processing in library schools in both Canada and the U.S. This study examined the strength of faculty expertise as one variable toward the measurement of overall library education strength. The particular area of data processing and automation was chosen as a pilot study before examining faculty strengths on a comprehensive level. Previous research has examined the areas of computer programming in library education, the lack of and the extent of online database searching and curricular concerns [l-4]. This paper examined changes in subject strengths of library educators in data processing and automation over time. The working hypothesis developed was that the rate of increase in the number of faculty members reporting expertise in data processing in Canadian library schools was no different from the rate of increase of faculty members reporting expertise in data processing in U.S. library schools. In a time of budgetary restraint, matched by demands for curricular revision and expansion, it is particularly important that library education be cognizant of the past and to prepare for the future. This study examined ten years of data in an effort to determine the relationship of Canadian library education to the U.S. scene in one particular area of expertise. Other areas will be explored in later papers already in progress. DATA COLLECTION
Data were obtained using the Directory Issue of the ~~~~~~1 of Ed~ca~i~~ for Lib~u~iu~s~ip for the years 1972-1981. The Directory Issue contains the names of faculty members in accredited library schools and their reported areas of expertise. An assumption was made that the areas of expertise reported were the actual areas of expertise for the faculty member. Additionally, it was assumed that the faculty members t Present address: Graduate Fenway, Boston, MA 021 IS.
School of Library and Information
Science,
Simmons College, 300 The
158
C. D. HURT
reported their actual areas of expertise and not desired areas of expertise. Data were aggregated into two groups for each of the Canadian and U.S. library schools. The first group contained the total number of faculty members reported in the Directory Issue for each year in the study. The second group consisted of all faculty members reporting expertise in data processing or automation during each year of the study. A data conversion into z scores was done to normalize the scores. DATA
ANALYSIS
The overall test of this investigation was to determine if there was a significant difference between the expertise of faculty in Canadian library schools versus U.S. library schools. Instead of attempting an overall or omnibus test for difference, a planned comparison approach was used. The hypothesis of no difference was tested using equal weighting for each of the ten comparisons made. Tests of significance were employed using a Dunn procedure [5]. The tests were run under a Type I error level ((Y)of 0.05 for the experiment. Under a decision rule written prior to initiating testing, a value of 12.931was necessary to reject the hypothesis under test [6]. The forms of the hypotheses were: HO: *1(l) = 442) = . . . = $(lO) HI: HO was false. In verbal form the hypotheses
were:
HO: There is no significant difference between the levels of faculty strength in data processing in Canadian and U.S. library schools measured on a year-by-year basis. Hl: There is at least one significant difference between the levels of faculty strength in data processing in Canadian and U.S. library schools measured on a year-by-year basis. If the hypothesis under test, HO, was rejected the result would be interpreted as there being at least one statistically significant difference between the Canadian and U.S. expertise in data processing. Because planned comparisons were being used, it was possible to be more explicit about the differences. Ten planned comparisons, corresponding to the ten year time frame of the study, were tested. Each was tested for significance, yielding more information than would the classic omnibus test of difference. A finding of significant difference in any one of the ten planned comparisons would indicate a difference but not the reasons for the difference. A failure to reject the hypothesis under test would be interpreted as there being no significant differences, on a year-by-year basis, between the faculty strengths of Canadian and U.S. library schools in the area of data processing. Such a finding would
Table 1. z Scores for data processing strength Year
2 3 4 5 6 7 8 9 10 Mean SD
Canadian -
1.03 1.03 0.27 0.65 1.03 0.11
0.49 0.49 2.02 0.87 14.7 2.63
U.S. -0.59 -0.77 - 1.29 0.63 1.59 0.63 -0.24
1.42 -0.51 -0.86 129.8 11.44
159
Library school faculty strengths in data processing Table 2. Comparison Comparison 1 2 3 4 5 6 7 8 9 10
Canadian
U.S.
+
l(- 1.03) l(- 1.03) l( -0.27) l( -0.65) l(-1.03) l(O.11) l(O.49) l(O.49) l(2.02) l(0.87)
Table =
- I(-0.59) - l(-0.77) -l(-1.29) - l(0.63) - l(l.59) - l(0.63) - I(-0.24) - l(l.42) - l(-0.51) - l( -0.86)
be equivalent to a failure to reject the hypothesis for differences such as the Chi-square test. The form of comparison used in the test was:
Value -0.44 -0.26 1.02 - 1.28 - 2.62 -0.52 0.73 -0.93 2.53 1.73
of no difference
in an omnibus test
WI = a 03 + a (u), where a was the weight assigned to the values X and Y. The form of the Dunn statistic used to test the significance of + (k) was:
where VAR(+(k))
= MSw @*(1)/n(l) + a*(Wn(2)) and
MSW = [(n(l) - l)S2(1)) + (n(2) - l)s*(2))ll[(n(l)
+ n(2)) - 21
RESULTS
The prime test in this study was a test for differences between faculty strength in the area of data processing using Canadian and U.S. library schools. Table 1 lists the z scores associated with each of the ten years in the study, broken down by Canadian and U.S. schools for total faculty strength in data processing. Summary statistics were also included. The results of the ten tests are reported in Tables 2 and 3. Table 2 lists the ten comparisons of interest and the resultant values for each. These values were only descriptive at this stage and were interpretable only as an indication of the direction of any difference. The sign of the difference indicated that, within the definitions of this table, Canadian library schools fall below U.S. schools if a negative value appears. Equal weights were given to each of the values in the comparisons. Table 3 lists the results of the Dunn two-tailed test for significance for each of the comparisons in Table 2. The Dunn procedure was analogous to performing multiple t tests for each of the comparisons. The difference between the Dunn procedure and Table 3. Dunn significance tests Comparison 1 2 3 4 5 6 7 8 1:
r Value - 1.02 - 0.60 2.53 -3.19 -6.55 - 1.21 1.70 -2.17 4.33 6.33
C. D. HURT
160
multiple r tests was that the Dunn procedure partitioned the Type I error level equally among all the comparisons. This procedure retained the sensitivity of the overall test of difference for the experiment as well as making each of the individual significance tests more conservative. The results of the Dunn tests for significant differences among the comparisons were examined in light of the decision rule written above. Under this decision rule, years 4,5,9 and 10 were significantly different. Examination of the values of the comparisons in Table 2 indicated that although the differences were indeed present, they were not different in the same direction. Years 4 and 5 (1975 and 1976) showed a negative value. Within the constraints of the weights used in Table 2, this negative sign was interpreted as a higher number of U.S. faculty reporting expertise in data processing as opposed to Canadian faculty. Years 9 and 10 (1980 and 1981) showed a positive value, which can be interpreted as more Canadian faculty reporting expertise in the area of data processing than U.S. faculty. Figure 1 is a representation of the data as a function of time. The findings reported above can be graphically demonstrated by the figure. The figure can be described as demonstrating a relative similarity with the exception of years 4, 5, 9 and 10. The differences in these four years were also demonstrably inverse. DISCUSSION
The results of the tests for significance for each of the ten comparisons showed four comparisons were significantly different. These significant differences were found to be in opposite directions with the earlier differences being negative in Canadian terms and the later differences being positive. One conclusion which can be reached was that Canadian library schools have made a concerted and successful attempt to ungrade the expertise of their faculty in the area of data processing. Two points need to be made about the data and their interpretation. First, there was the possibility of a confounding effect in the data gathering procedure. In both 1980 NR IN ORTA PROCESSING 2.0
,*’ I’
I.5
0
\
\ *
:
1.0
0. 5 @ 3 5 N
: \ ‘\ ‘\ ‘.
.. . . .
---CRNROIRN ___________-“3
Fig. 1. NR in data processing.
161
Library school faculty strengths in data processing Table 4. z Scores for total faculty Year
Canadian
2 3 4 5 6 7 8 9 10 Mean SD
- 1.36 - 1.28 -0.78 0.30 0.66 - 0.42 - 0.20 0.23 1.24 1.60 140.8 13.86
U.S. - 1.56 - 1.56 - 1.02 -0.13 0.77 0.60 0.60 0.72 0.67 0.90 1174.6 140.43
and 1981 the Directory Issue of the Journal of Education for Librarianship changed the classification system used to define areas of expertise. There was the possibility that some of the variation in faculty strength in this area may be explained by this change. The classification systems used in 1980 and 1981 were more detailed than were the previous systems. Although every effort was made to retain the consistent definition of the area of data processing and automation, this may not have occurred. To some extent, the problem may be further compounded by the fact that data processing and automation were considered by some faculty to be a popular area. There was some evidence that the popularity of areas such as management may now be greater than the current popularity of data processing and automation. Crane discusses the fashionable nature of fields in her work with the “invisible college” concept [7]. The change in classification systems together with the competition of other popular fields in which to claim expertise may have had some effect on the data gathered. The second note which should be made concerning the data was that the size of the U.S. population of library schools was decidedly larger than was the size of the Canadian population of library schools. The raw data were transformed into normalized z scores within each of the populations. This procedure placed each of the two sets of data on the same scale with the same mid-point and the same variability. Any differences were not the result of variance in number and size, but were valid differences between the two sets of data. A secondary question was posed by the results reported above. If it was the case that data processing and automation were different in terms of expertise in U.S. versus Canadian library schools, by what means has the difference been alleviated. In years 4 and 5 of the study Canadian library schools were negatively related to the level of expertise in data processing in U.S. schools. The overall level of faculty strength, the number of total faculty members available in both the Canadian and the U.S. data sets, was examined. The same set of statistical hypotheses were tested as were used above. The same decision rule was employed. Table 4 lists the z scores for each of the years
Table 5. Comparison Comparison
8 9 10
Canadian I(-1.36) 1(- 1.28) l( -0.78) l(O.30) l(O.66) I( -0.42) l(-0.20) l(0.23) l(l.24) l(l.60)
+
table U.S.
-I(-1.56) -l(-1.56) -I(-1.02) -I(-0.13) - l(O.77) - l(O.60) - l(O.60) - l(0.72) - l(O.67) - l(O.90)
=
Value 0.20 0.28 0.24 0.43 -0.11 - 1.02
- 0.80 - 0.49 0.57 0.70
162
C. D. HURT Table 6. Dunn significance tests
r Value
Comparison 1 2 3 4 5 6 7 8 9 10
0.46 0.65 0.60
1.07 - 0.28 -2.38 - 1.87 -1.14 1.43 1.75
in the study for both Canadian and U.S. schools in terms of the total number of faculty members available. Summary statistics are also included. Planned comparisons were calculated and are listed in Table 5 below. The same weights, i.e., equal, were used here as were used to test for differences in data processing strengths. Finally, the Dunn multiple testing procedure was again employed to determine if there were any significant differences. Table 6 shows the results of the tests for significance. In all cases, there was no significant difference between the number of faculty in Canadian library schools and the number of faculty in U.S. schools on a normalized scale. Figure 2 is a representation of the normalized values of the data as a function of time. These results seem to indicate that the Canadian library schools not only have made a concerted and successful effort to increase the level of expertise in data processing, but have done so without significantly adding to their faculty levels. Figure 2 indicates that the general pattern of faculty strength has been positive. Comparison of Figs. 1 and 2 indicates that, given the overall positive increase in overall faculty strength, TOTRL
FRCULTY
2. 0-f
/ 1.
5
/
// 1. 0
/
I
/<~A\ ,---. . . . . _____________/----j~------
5
c
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Fig. 2. Total faculty.
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Library school faculty strengths in data processing
163
Canadian schools have added expertise in data processing, while U.S. schools have reduced the relative level of their expertise in the same area. Certainly an area which will require further study is whether the increase and decrease in relative levels of expertise is matched with increases or decreases in other fields of the classification scheme. SUGGESTED
FURTHER
RESEARCH
This study points to the need for further research in a number of areas. There is a need for replication and validation of the present study. The potential problems with the classification scheme employed by the Journal of Education for Librarianship in its Directory Issue may have caused some unintended results or the creation of an artifact. Because a significant difference was found in the two years for which the classification scheme was a potentially confounding variable, additional work needs to be done to determine if the scheme itself or the strength of data processing expertise was the cause of the finding. As suggested above, additional work needs to be done in the area of examining other fields of expertise within librarianship to determine if significant differences exist there as well. Data are currently being gathered for an examination of the levels of expertise in Canadian versus U.S. library schools on a comprehensive level. Examination of Fig. 1 seems to indicate a marked tailing off of level of expertise in data processing for the final year of the study. This trend should be examined further to determine if it is significant and to determine the causes for this large drop, particularly in the level of U.S. expertise. REFERENCES [l] R. GOEHLERT and G. SNOWDON, Computer Education for Librarianship 1980, 20, 251.
programming in library education.
Journal
of
[2] T. P. SLAVENSand M. E. RUBY, Teaching library science students to do bibliographic searches of automated data bases. RQ 1978, 18, 38. [3] B. R. BOYCE,Instruction in on-line tools at the University of Missouri. Journal of Education for Librarianship 1979, 20, 158. [4] F. W. ROPER, The integrated core curriculum; the University of North Carolina experience. Journal of Educarion for Librarianship 1978, 19, 159. [5] 0. J. DUNN, Multiple comparisons among means. J. Am. Stat. Assoc. 1961, 56, 52. [6] R. E. KIRK, Experimental Design: Procedures for the Behavioral Sciences, Table D-16, p.
551. Brooks/Cole, Belmont, California (1968). [7] D. CRANE, Invisible Colleges. Univ. of Chicago Press, Chicago (1972).