PROGRAMMING STRATEGIES AND THE POPULARITY OF TELEVISION PROGRAMS FOR CHILDREN

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1 PROGRAMMING STRATEGIES AND THE POPULARITY OF TELEVISION PROGRAMS FOR CHILDREN JACOB J. WAKSHLAG Indiana University BRADLEY S. GREENBERG Michigan State University This study investigated the effects of various programming strategies, commonly employed by the networks, on program popularity for children. Viewing data for prime time and Saturday morning programs were collected in the fall, winter, and spring of the season. Simple correlations supported the relationship between program popularity and the following programming strategies: counterprogramming by type, block programming by type, inheritance effects, starting time, program familiarity, and character familiarity. Regression analysis, which controlled for relationships among programming strategies, confirmed the effects of program familiarity and starting time only. The results, suggest that children are not highly adventurous viewers. On the contrary, it appears that past experience with a program coupled with availability of the child audience are overriding determinants of program popularity. This research focuses on the relationship between network television programming strategies and the popularity of programs for children. Ample anecdotal information describes program popularity and scheduling strategies. For example: A series that has been high on the popularity scale for many years may be showing clear signs of attrition, indicating it may flop if renewed one more season. Conversely, careful study of rating histories may reveal that certain program series which performed indifferently during the season had the potential of becoming hits if placed on a different evening, or at a different hour. (Brown, 1971, p. 51) Several media personnel have written descriptive accounts of the development and utilization of programming strategies. According to Shanks (1976): With increasing sophistication they realized that individual shows, though fully sponsored, could pull Jacob J. Wukshlug (Ph.D., Michigan State University, 1977) is assistant professor of telecommunications at Indiana University, Bloomington, Indiana Brudley S. Greenberg (Ph.D., University of Wisconsin, 1961) is professor of communication and telecommunication and chairman of the Department of Communication at Michigan State University, East Lansing, Michigan This study accepted for publication May 23, down the shows on either side of them or be incompatible with these shows, not only in gross numbers but in audience differences. Thus were born program flow, block booking and counter-programming. (p. 100) Discussing why shows get cancelled, Doan (1970) claims: After a show gets on the air, there is one major factor other than its inherent appeal which influences its fate. The program s night of the week and hour, and the competition it faces in that time period, help make it or break it. (p. 124) Early empirical research on program types, whether concerned with viewer behavior or preference, suffered from the confounding effects of structural variables like time of day and inheritance (audience flow) effects. When this was pointed out (Ehrenberg, 1968), other researchers used procedures which controlled for variability attributable to these structural elements, and were still able to find program-types which influenced viewing patterns and preferences. An analysis of program types which emerged from eight separate studies (Kirsch & Banks, 1962; Swanson, 1967; Wells, 1969; Rothman & Rauta, 1969; Frost, 1969; Frank, Becknell, & Clokey,

2 Wakshlag and Greenberg ; Gensch & Ranganathan, 1974; Goodhardt, Ehrenberg, & Collins, 1975) found that these types emerged repeatedly: Westerns, News and Current Affairs, Sitcoms, Variety, ActiodAdventure, Children s, Panel or Quiz, Drama, Films, and Sports. Each type was identified in both preference and viewing studies, except for News and Current Affairs, which emerged repeatedly only in preference studies. None of the program type studies cited or identified, however, were based on children s viewing and/or preferences. Rather than regenerate program types for children, this study sought to examine the merits of known program typologies for younger viewers. The two major programming strategies are commonly labeled block programming and counterprogramming. Block programming stresses the advantage of having a succession of similar type shows on a single evening. This strategy assumes that the placement of a program within a succession of similar type programs will attract a larger and more homogeneous audience than would be the case if the program were in a succession of shows different from itself. Hence, a show would be expected to do best when it is adjacent to shows of a similar type. Counterprogramming, on the other hand, stresses the advantage of scheduling shows that are different from those of competitors. Rather than compete for an audience with particular tastes, networks program to appeal to different segments of the mass audience. Counterprogramming and block programming may be employed simultaneously. One network may program a block of situation comedies while another programs a block of action-adventure shows. Therefore, blocks of programs can be counterprogrammed. Steiner (1952) and Owen, Beebe, and Manning (1974) have used the counterprogramming concept for constructing models of media economic systems and predicting viewer satisfaction. These models assume that the mass audience can be segmented into groups with different program type preferences. Probably the most widely recognized set of program types is the 36-category system used by the A.C. Nielsen Co. Because its comprehensiveness makes it very complex, Nielsen reduces the 36 into six major types for prime time programs: general drama, suspense and mystery drama (suspenselmystery, police and private detective programs), situation comedy, adventure, variety, and feature films. Recognizing these derived program types, much of their use for block programming and counterprogramming remains largely conjectural and speculative, especially for children. Further, other scheduling factors, which may be related to these two basic programming strategies, have been examined. Among the most important are: 1. Inheritence effects: The impact of popularity of adjacent shows, both before and after a program. The impact of preceding program popularity has been labeled the lead-in effect. The impact of the popularity of a later program has been labeled the lead-out effect (Goodhardt et al., 1975). 2. Channel loyalty: The propensity for people to view a particular channel. This factor has been investigated by Goodhardt et al. (1975) as an alternative to inheritance effects. Their research suggests that inheritance effects do not seem to carry much beyond immediately adjacent shows. Hence, a large audience for one show appears to affect the popularity of adjacent shows, but has minimal effects on other shows on the same evening. 3. Schedule placement: Ratings vary as a function of the total available audience. Hence, within prime time, the time a program is scheduled affects its popularity. Programs scheduled earlier in the evening, when there are a greater number available to view, will naturally have higher ratings for children. Additionally, networks often reschedule programs in order to bolster their ratings. These programs should become more popular since they are likely to be placed in spots where the networks feel the most benefits would accrue. 4. Character familiarity (spinoffs): The advantage of new shows containing characters first introduced on other successful shows. 5. Program familiarity: The benefits of a retuming, popular show as compared to a new one. Program familiarity is also considered wher

3 60 HUMAN COMMUNICATION RESEARCH / Vol. 6, No. 1, FALL 1979 block programming and counterprogramming. Placing programs adjacent to returning shows is assumed to help program ratings. Similarly, scheduling a show opposite a new show should allow it to be more popular than if it were opposite a returning show. While these effects may appear initially, they should wear off as viewers become familiar with new shows. Are children susceptible to these strategies which are apparently effective for adults? Do they respond to them as adults do, or do they respond in a different manner? For example, bedtimes impose a more serious constraint on a child s television viewing than on an adult s. This constraint could seriously dampen a later show s inheritance of the child audience from an earlier show. Thus, the effect of one programming factor on others can shed light on the way children select the programs they watch. Counterprogramming HYPOTHESES 1. Program popularity among children is positively related to the number of competing programs which differ in type from a program. 2. Program popularity among children is negatively related to the number of returning programs opposite a program. 2a. The effect of returning programs which are opposite a program diminishes over time. same type are more popular than programs which are adjacent to programs of a different ty Pe. 6. Programs which are adjacent to returning programs are more popular than programs which are adjacent to new programs. 6a. The effect of returning vs. new adjacent programs diminishes over time. Time and Schedule Placement 7. For children, the popularity of prime time shows is negatively related to the time the show starts. 8. Rescheduling is positively related to the popularity of programs for children. Spinoffs 9. Programs which are spinoffs are more popular among children than programs which use all new characters. Program Familiarity 10. Returning programs are more popular than are new programs. 1Oa. The advantage of being a returning program diminishes over time. Inheritance Effects 3. Program popularity among children is positively related to the popularity of the preceding program. 4. Program popularity among children is positively related to the popularity of the following program. Block Programming 5. Programs which are adjacent to programs of the METHODOLOGY Respondents. Respondents were students in one suburban elementary school and middle school in central Michigan during the academic year. Completed questionnaires were obtained from 300 in the fall, 286 in the winter, and 281 in the spring. Representation by sex was approximately fifty-five at each administration. There were approximately 100 fourth, sixth, and eighth graders at each administration. Data were collected in October, February, and May from intact classes. All classes were questioned on the same day.

4 Wakshlag and Greenberg 61 Selection ofprograms. Programs selected for study were those broadcast by the three major networks during the week prior to each administration of the questionnaire from p.m. Monday through Saturday, 7-11 p.m. Sunday, and 8 a.m.- 1 p.m. on Saturday (Eastern Standard Time). Program types. Program types were assigned to shows in order to construct the counterprogramming and block-programming variables. The prime time types were: action-adventure, drama, feature film, situation comedy, sports, and variety. In total, 132 programs were analyzed, 103 in prime time. Saturday morning program-types were different. Cantor (1974) suggested a content dichotomy such as adventure and comedy. Other research suggested a dichotomy relating to mode of presentation, e.g., animated vs. nonanimated. Therefore, Saturday morning programs were categorized into: animated comedy, animated adventure, nonanimated comedy, and nonanimated adventure. People usually watch a whole program. In the case of half-hour programmes, about 95% of those who watch the first quarter-hour also watch the second. With much longer programmes more substantial erosion of the audience occurs-up to about 20% of the initial viewers may be lost by the end. (p. 19) Program type block programming. This variable examined whether adjacent programs were different or the same as the target program. Two dichotomous variables were generated. The first considered whether a program s lead-in was the same type and the second considered whether the following program (lead-out) was the same type. New vs. returning programs. A program was coded as new if it became part of the network s schedule during the season analyzed. New vs. returning counterprogramming. This variable was operationally defined as the number of returning programs opposite a program. Variables Program popularity. The dependent variable for this study was program popularity. Respondents were given a checklist of network programs arranged by day of the week. They were instructed to check off only those programs which they watched every week or almost every week. Program popularity was operationally defined as the proportion of respondents who checked off the program. The unit of analysis was the program since the central concern of this project was program popularity rather than individual viewing patterns. Program type counterprogramming. This variable was operationally defined as the number of programs opposite a program which were different in type from itself (range=o- 2). A program s counterprogramming status was evaluated once at the beginning of the program and changes in opposing programs during a program s duration were not evaluated. Goodhardt et al. (1975) stated that such changes could only have minimal effects on the viewer: New vs. returning block programming, This variable concerned whether adjacent programs were new or returning. Lead-in and lead-out programs were analyzed separately. Time. Time was defined by scheduled start time. A 24-hour clock was used, an 8 a.m. program assigned a value of 8, and an 8 p.m. program a value of 20. Rescheduled programs, Rescheduled programs were those which were scheduled in a new time or day slot when compared to their scheduled position earlier in the season. Spinoffs. Programs categorized as spinoffs were those whose leading character(s) originally appeared in a different program. Analysis Simple correlations were used as the initial test relating programming strategies identified in the hypotheses to program popularity. Subsequently, multiple regression examined the significance of the

5 62 HUMAN COMMUNICATION RESEARCH / Vol. 6, No. 1, FALL 1979 TABLE 1 Correlates of Prime Time and Saturday Morning Program Popularity Among Children By Season Correlates Daypart Prime Time Saturday Morning Fall Winter Spring Fall Winter Spring Number of shows: Counterprogramming : (1) BY 5pe.02 (2) By New vs.. 00 Re turning Inheritance effects: (3) Lead-in Popularity.15 (4) Lead-out Popularity * * *.38* lo.30.43* Block Programming by Type: (5) Lead-in Same.07 (5) Lead-out Same.26*.04.32*.20.31*.48* Block programming by Returning vs. New: (6) Lead-in Returning -.13 (6) Lead-out Returning Time Placement: (7) Starting Time -. 37* -.45* -, 38* Character Familiarity : * (9) Spinoffs * * Program Familiarity (10) Returning vs. New.55*.23*.09.61*.46*.32 Note: Number in parenthesis corresponds to hypothesis number *p<.05 effect of each predictor variable (programming strategy) on program popularity for children. The three sets of data (fall, winter, and spring) were analyzed independently. For each season, separate analyses were conducted for Saturday morning and prime time. In addition, aprogram s network and its length (in hours) were included as control variables. Hypotheses concerned with the effects of new vs. returning programs over time were tested with one-way analysis of variance procedures andr tests. Changes in the popularity of rescheduled shows (Hypothesis 8) were compared to changes in the popularity of nonrescheduled shows, using at test. RESULTS Table 1 contains the zero-order correlations between the various programming variables and program popularity for prime time and Saturday

6 Wakshlag and Greenberg 63 TABLE 2 Multiple Regression Analyses for Predicting Prime Time Program Popularity Season Counterprogramming by Type Counter programming by Returning vs. New Lead-in Same as Program Lead-out Same as Program Lead-in Returning Lead-out Returning Popularity of Lead-in Popularity of Lead-out Sp ino f f s Returning vs. New Program Starting Timea Len th of Program AEX % CBSb NBC~ Constant - Fall * * Winter " Spring a R2 Adjusted R2' , Note: The absence of a regression coefficient indicates that the SPSS default value (F<.01) excluded the variable from the analysis. The slopes of these variables were essentially zero. astartirig time for prime time shows was measured as a deviation from 8 P.M. in hours. Hence 8:30=.5, 9:00 = 1.0, etc. order to control for variability attributable to the differing popularity of networks, effect coding was used (Kerlinger & Pedhazur, 1973). %orrected for shrinkage *p<.05 morning programs. Results of the multiple regression analyses for prime time shows are in Table 2, and for Saturday morning in Table 3.2 Counterprogramming. Hypothesis 1 stated that program popularity would be positively related to the number of competing programs which differ in type from the program. The correlation was significant in only one instance, for prime time programs in the spring (r=.35). However, the multiple re- gression analysis did not support that finding. In examining why counterprogramming by type emerged as a significant correlate in the spring, it was found that counterprogramming by type was significantly correlated with program starting time (-.27). In the spring, later programs (which were less popular than earlier programs) were counterprogrammed to a lesser degree than earlier programs. This indicates that the observed correlation between counterprogramming and program popu-

7 64 HUMAN COMMUNICATION RESEARCH I Vol. 6, No. 1, FALL 1979 TABLE 3 Multiple Regression Analyses for Predicting Saturday Morning Program Popularity Season F_all 'rlinter P Spring Counterprogramming by Type Counterprogramming by Returning vs. New Lead-in Same as Program Lead-out Same as Program Lead-in Returning Lead-out Returning Popularity of Lead-in Popularity of Lead-out Spinoffs Returning vs. New Program Starting Timea Length of Program ABC~ CBS NBC~ Constant * R Adjusted RZc Note: The absence of a regression coefficient indicates that the SPSS default value (F<.01) excluded the variable from the analysis. The slopes of these variables were essentially zero. astarting time for Saturday morning shows was measured as a deviation from 8 A.M. in hours. Hence 8:30=.5, 9:00=1.0, etc. order to control for variability attributable to the differing popularity of networks, effect coding was used (Kerlinger & Pedhazur, 1973). 'Corrected for shrinkage. *p<.05 larity is probably spurious, with both vzriables being influenced by starting time. The effect of counterprogramming by type was confounded with the decline in child audience size as the evening grew later. Hypothesis 1 was not supported. Hypothesis 2 stated that program popularity would be negatively related to the number of returning programs opposite a program. The correlations and regressions did not yield any support for this hypothesis. Since Hypothesis 2 was not sup- ported, Hypothesis 2a, concerning the effects of counterprogramming against returning or new shows over time, was not tested. Inheritance effects. The general hypothesis was that program popularity would be positively related to the popularity of adjacent shows. The effects of the popularity of lead-in shows (Hypothesis 3) and following shows (Hypothesis 4) were assessed independently.

8 Wakshlag and Greenberg 65 The popularity of a program s lead-in was a significant correlate of prime time program popularity for children in the winter (r=.28) and spring (r=.34) (Table 1). Again, however, the regression analyses (Table 2) failed to corroborate the simple correlations. This suggested that the popularity of a program s lead-in was related to other variables in the regression equation. Analysis of the intercorrelations among predictor variables yielded one significant correlate of a lead-in program s popularity in the spring, the time a program started (r= -.26). Later programs had weaker lead-ins than earlier ones. This latter result is of course due to the decline in audience size for later programs. The regression analysis suggests that when the effects of a program s starting time are controlled, the popularity of a lead-in has minimal if any effect upon the popularity of a later program for children. The correlations between the popularities of Saturday morning programs and their lead-ins are in Table 1. There was a significant correlation between these two variables in the winter (r=.43), again unsupported in the regression analysis. The analysis of lead-in program popularity did not support the contention that it would have a unique, statistically significant impact on a subsequent program s popularity on Saturday morning. The popularity of a program s lead-out was a significant correlate of prime time popularity in the spring (r=.38), but the effect of lead-out popularity did not remain when other variables were controlled via regression. No significant correlations emerged for the popularity of lead-out programs and Saturday morning program popularity. Hypotheses 3 and 4 on inheritance effects were not supported, largely because inheritance effects were confounded with the effect of starting time on program popularity. Block programming. Hypothesis 5 stated that programs which are adjacent to programs of the same type would be more popular than programs which are adjacent to programs of a different type. Similarity between a program and its lead-in was assessed, as were the effects of similarity of a program and its lead-out. Whether a Saturday morning program s lead-in was of the same type was a significant correlate of program popularity (r=.48) in the fall (Table 1). Regression analysis failed to support this result. Inspection of the correlations among the predictor variables yielded no single correlate of same-type lead-in which would attenuate its effect on Saturday morning program popularity. No significant correlations emerged in prime time. Whether the lead-out program was of the same type as the program itself was found to be a significant correlate of prime time program popularity in the fall (r=.26), winter (r=.32), and spring (r=.31). Regression analysis did not support these findings (Table 2). This suggested that the predictor variable was related to other variables in the regression equation which were also related to the criterion variable, program popularity. Two such variables were found-whether a program was a spinoff, and the control variable program length. Programs which were followed by same-type programs tended to be shorter and were more likely to be spinoffs. The unique effect of having a lead-out program of the same type was not observed for either prime time or Saturday morning shows. Hypothesis 5 on block programming by type was not supported, largely because of the concurrent relationship between this variable and other predictors in the regression analyses. Hypothesis 6 stated that programs adjacent to returning programs would be more popular than programs adjacent to new programs. Lead-in and lead-out effects were analyzed separately. Neither variable emerged as a correlate of program popularity. Starting time. Hypothesis 7 stated that prime time program popularity would be negatively related to the time a show starts. Starting time was a significant correlate of prime time program popularity in the fall (r=-.37), winter (r=-.45), and spring (r= -.38). Regression analysis failed to repeat this result. In the fall, starting time was significantly correlated with the control variable program length (r=.23); later programs were longer and tended to be less popular among children. In the winter, another significant correlate of program popularity, whether the lead-out program was the same type,

9 66 HUMAN COMMUNICATION RESEARCH / Vol. 6, No. 1, FALL 1979 TABLE 4 Differences in the Popularity of Returning vs. New Programs Over Time* Daypar t - Fall Winter Spring Prime Time 25.27a 7.91b 5. 9gb Saturday Morning 25.44a 7.77b 7.72b Entries with different subscripts in a row are significantly different according to t tests (p<.o5) applied subsequent to Analysis of Varience. *Cell entries reflect the popularity advantage of returning over new programs based upon the regression coefficients. was also significantly correlated to starting time (r= -.22). Programs which appeared later in the evening were more likely to be followed by different type programs than were earlier programs. Starting time was correlated with two significant correlates of program popularity in the spring. They were counterprogramming by type (r= -.27) and the popularity of the program s lead-in (r= -.26). Insertion of the three variables into a single regression equation attenuated the unique impacts of each so that none of them emerged as significant predictors of program popularity. Nevertheless, the size and consistency of the correlations between starting time and program popularity support the hypothesis. The general relationship between this audience s size and starting time suggests that the popularity of a program s lead-in is attributable to the time the lead-in program starts. The later it starts, the smaller the audience., Hence, the logical choice between these variables is starting time rather than lead-in popularity. Rescheduling shows. Hypothesis 8 posited that rescheduling shows would be beneficial to their popularity. Average changes in the popularity of rescheduled shows were compared to changes in the popularity of shows which had not been rescheduled. The mean popularity change for rescheduled shows was - 3.8%. The mean popularity change for the other shows was -3.O%. The differ- ence was opposite to the expected change but not statistically significant. Spinofls. Hypothesis 9 stated that spinoffs would be more popular than programs using new characters. Being a spinoff was significantly and positively correlated to prime time program popularity (Table 2) in the fall (.21) and spring (.24) but not in the winter (.19). Regression analyses of the same data did not yield any significant regression coefficients for the spinoff variable. In the spring, Saturday morning spinoffs did worse than programs which had no returning characters (r= -.39), contrary to the hypothesis. Since the spinoff variable did not operate in any consistent manner, and was not observed when other variables were controlled (by regression), Hypothesis 9 was not supported. Returning programs. Hypothesis 10 stated that returning programs would be more popular than new programs. Whether a show was returning or not was the strongest correlate (r= 3) of prime time program popularity among school children in the fall. A significant correlation also emerged in the winter but was considerably lower (r=.23). No significant correlation emerged in the spring. Multiple regression analyses corroborated the importance of returning shows for the fall, but not for winter. In the fall, returning programs were predicted to have an

10 Wakshlag and Greenberg 67 audience size advantage of 25.3% over new programs (Table 2). Analysis of the popularity of Saturday morning programs yielded similar results. The correlation in the fall was significant (r=.61). In the winter the correlation was lower but significant (r=.46), and not significant in the spring. Returning Saturday morning programs were estimated to have a 25.4% larger audience than new programs in the fall (Table 3), but no significant advantage in later seasons. Hypothesis 10a argues that the advantage of being a returning rather than a new program would diminish over time. The results (Table 4) indicated that, for both prime time and Saturday morning, the advantage held by a returning program was significantly greater in the fall and largely dissipated by winter, DISCUSSION While correlational evidence supported the efficacy of many of the programming strategies, multiple regression analysis, which controlled for interrelationships among predictor variables, did not confirm these findings. Significant correlations in the hypothesized direction emerged for the relationship between program popularity for children and the following variables: counterprogramming by type, lead-in inheritance effects, lead-out inheritance effects, block programming by type, starting time, program familiarity, and spinoffs.- Only program familiarity received support when other variables were controlled. As hypothesized, new programs were less popular than returning ones in the fall, and the differences diminished by winter. Discrepancies between the correlation and regression analyses are due to multicollinearity. Several predictors were interrelated. Many were observed to be related to the predictor variable of starting time. Since earlier prime time shows are more popular among children, programs which began earlier had lead-ins which were more popular than lead-ins of later programs. Therefore, the observed correlations between lead-in popularity and program popularity were attributable to starting time. The same occurred for counterprogramming by type. The data indicated that this strategy is more prevalent earlier in the evening when audiences are larger. Thus, when starting time was controlled, counterprogramming by type had no significant effect on program popularity for children. A similar process emerged for block programming. Significant correlations emerged for prime time program popularity for children and whether a program s lead-out was the same type. Programs which were followed by programs of the same type were shorter than programs which were followed by programs of different types. Since earlier shows are shorter than later shows, starting time appeared to be a major confounding variable. Thus, programs with lead-outs of the same type were not more popular than programs with lead-outs of a different type when controlling for program length and starting time. Questions may be raised concerning the appropriateness of the program typology used to construct the counterprogramming and block programming variables since it was generated from adult viewing and preference studies but applied to children s viewing. However, several significant correlations between counterprogramming and block pro- gramming by type with program popularity emerged, an indication of construct validity. Had the typology been invalid, these correlations should not have emerged. The strongest predictor of program popularity for schoolchildren was whether a program was new or returning. New programs suffer a strong disadvantage in the fall. However, by winter, the popularity of new programs is not significantly different from that of returning programs. Another predictor of program popularity for prime time programs was starting time. The variable was a significant and negative correlate of program popularity in every case and was related to other variables which had been hypothesized to be independent predictors of program popularity. One should consider the variables used in this study in combination with other variables which may be unrelated to programming strategies, for example, such factors from the child s social environment as parental influence over television viewing. This includes restrictions on viewing time and on particular programs, and sibling and parental

11 68 HUMAN COMMUNICATION RESEARCH / Vol. 6, No. 1, FALL 1979 program preferences. These influences may operate through direct control over program selection as well as a child s modeling of others program preferences. The results of this study fail to support the importance of block programming or counterprogramming on child viewers. Block programming did not increase the popularity of programs for children. Similarly, programs did not suffer when they were scheduled opposite one or more programs of the same type. Children do not seem to respond to these popular programming strategies but do seem to consider other factors, two of which (program familiarity and starting time) were identified in this study. The importance of program familiarity suggests that children are not adventurous viewers, but prefer programs which they know to be entertaining from past experience. New programs require time to catch on. Apparently, counterprogramming and block programming do little, if anything, to speed up this process. NOTES This research was supported, in part, by Grant #90- C-635 from the Office of Child Development to Michigan State University. Principal investigators for the project, Parental Mediation of Children s Social Learning from Television, are Doctors Charles K. Atkin and Bradley S. Greenberg. Additional support was made available through a research grant from the National Association of Broadcasters to the first author. Six uncodable prime time shows were: Almost Anything Goes, American Music Awards, Bugs BunnyiRoad Runner, Circus of the Lions, Sixty Minutes, and Wonderful World of Disney. The only uncodable Saturday morning show was Go-USA. The coefficients in the tables are unstandardized regression coefficients. These coefficients represent the expected change in program popularity (in percentage points) attributable to a change of one unit in a predictor variable, controlling for the other predictors in the equation. REFERENCES BESEN, S.M., & MITCHELL, B.M. Watergate and television: An economic analysis. Communication Research, 1976, 3, BOGART, L. The age of television. New York: Ungar, BROWN, L. Television: The business behind the box. New York: Harcourt, Brace, BRUNO, A.V. The network factor in TV viewing. Journal of Advertising Research, 1973, 13(5), CANTOR, M. Producing television for children. In G. Tuchman (Ed.), The Tv establishment. Englewood Cliffs: Prentice Hall, DOAN, R.K. Why shows are cancelled. In B. Cole (Ed.), Television. New York: Free Press, EHRENBERG, A.S.C. The factor analytic search for program types. Journal of Advertising Research, 1968, 8(1), FRANK, R.E., BECKNELL, J.C., & CLOKEY, J.D. Television program types. Journal of Marketing Research, 1974, 8, FROST, W.A.K. The development of a technique for TV programme assessment. Journal of the Market Research Society, 1969, l l, GENSCH, D., & RANGANATHAN, B. Evaluation of television program content for the purpose of promotional segmentation. Journal of Marketing Research, 1974, 11, GOODHARDT, G.J., EHRENBERG, A.S.C., & COLLINS, M.A. The television audience: Patterns of viewing. Lexington, Mass.: Lexington, KERLINGER, F.N., & PEDHAZUR, E.J. Multiple regression in behavioral research. New York: Holt, Rinehart & Winston, KIRSCH, A.D., &BANKS, S. Program types defined by factor analysis. Journal of Advertising Research, 1962, 2, KLEIN P. The men who run TV aren t that stupid,.. They know us better than you think. New York, 1971, NIELSEN, A.C., Co. Nielsen national TV ratings. Second February Report. Northbrook, Ill.: A.C. Nielsen, OWEN, B.M., BEEBE, J.M., &MANNING, W.G., Jr. Television economics. Lexington, Mass.: Lexington, ROTHMAN, J., & RAUTA, I. Toward a typology of the television audience. Journal of the Market Research Society, 1969, 11, SHANKS, B. The coolfire. New York: Norton, SPRAFKIN, J.N. Sex and sex role as determinants of children s television program selection and attention. Unpublished doctoral dissertation, State University of New York, STEINER, P.O. Program pattern preferences, and the workability of competition in radio broadcasting. Quarterly Journal of Economics, 1952, 66. SWANSON, C.E. The frequency structure of television and magazines. Journal ofadvertising Research, , THAYER, J.R. The relationship of various audience composition factors to television program types. Journal of Broadcasting, 1963, 7, WELLS, W.D. The rise and fall of television program types. Journal of Advertising Research, 1969, 9,