Pre-putt routines and putt outcomes of collegiate golfers.
These aforementioned aspects of putting suggest that the skill of putting is difficult to master (Pelz, 2000). Efforts of simplifying the process include the development of numerous different types of putters, and ways to actually hold the putter (Rotella, 2001). One facet of putting similar to other sports is the preparation time prior to the execution of a shot which is classified as a pre-performance routine (Cohn, 1990). However, research specifically addressing the effectiveness of pre-putt routines has yet to be explored to its limits.
Singer (2002) suggested that one important function of pre-performance routines is to aid athletes in the self-regulation of "arousal level, thoughts, performance expectancy, and attentional focus" (p.359). Singer also suggested that self-regulation is thought to be particularly important in self-paced events in which the performer has the opportunity to prepare for the movement. Routines can help an athlete mentally prepare for upcoming movements by enhancing concentration. For instance, it has been argued that pre-performance routines help athletes transfer their attention from task irrelevant thoughts to task relevant thoughts (Weinberg & Gould, 2007). Pre-performance routines increase the likelihood that individuals will not be internally or externally distracted before and/or during performance. They also noted that pre-performance routines help structure the athlete's thought processes and emotional states, keeping the focus of attention in the present and on task-related cues (Weinberg & Gould, 2007).
Research has shown that individuals possess a limited attentional capacity (Lewis & Linder, 1997). However, motor behavior research also suggests that once a skill has been learned, the attentional demands towards performance are decreased (Schmidt & Wrisberg, 2000). For instance, Backman and Molander (1991) found that expert putters who attended to the technical aspects of the putting stroke negatively affected their performance. Specifically, it is possible that attention to skill execution may be counterproductive to performance and may manifest with undue pre-performance routines (Beilock, Afremow, Rabe, & Carr, 2001; Lewis & Linder, 1997). Due to the limited attentional capacities, there may be a tendency of counterproductive over-thinking, an increased susceptibility to distractions, and/or heightened emotions (Krane & Williams, 1987). Golfers who focus on the possible repercussions of poor performance are likely to experience unsuitable levels of emotional anxiety (Beilock et al., 2001). Thus, the implementation of suitable pre-performance routines should help lessen the effects of negative stimuli and enhance the prospects of successful performance (Cohn, Rotella & Lloyd, 1990).
Specific research has revealed results that suggest pre-performance routines increase the likelihood of successful outcomes in basketball free throw shooting (Czech, Ploszay, & Burke, 2004; Gayton, Cielinski, Francis-Keniston, & Hearns, 1989; Lobmeyer & Wasserman, 1986). For instance, Lobmeyer and Wasserman found that free-throw pre-shot routines in basketball significantly contributed to the accuracy of the shot. An experimental study by Gayton et al. (1989) examined shooters' outcomes with alternating between implementing pre-shot routines. Results revealed that the percentage of made free throws was significantly higher during the pre-shot routine condition than the control condition. However, the researchers manipulated pre-shot routines by adding or removing dribbles, thus, introducing the extraneous variable of practice which may have been a limitation to the study. More recent research by Czech and colleagues (2004) also examined the importance of pre-shot routines in basketball free-throw shooting. They observed the maintenance of routines that were classified as behaviors repeated 90% of the time or more in between shots. Results revealed that the group maintaining the same pre-shot routine for the duration of the study shot a higher percentage than those not maintaining the same pre-shot routine. Results of this study greatly added to the external validity in that it is thought to be the first study of pre-performance routines that observed athletes during actual competition.
Research in the area of golf has also shown routines to be an effective tool for minimizing distracting thoughts. Boutcher and Crews (1987) studied the effects of attentional pre-shot routines on a golf putting task. Twelve collegiate golfers were randomly assigned to four groups, two control groups and two experimental pre-shot routine groups. The purpose of the attentional training in the experimental group was an attempt to prevent golfers from placing any unnecessary focus on a specific aspect of the skill. The two control groups (male and female) practiced the putting skill without the use of a pre-shot routine. It was found that the routine group actually increased the time period (i.e., took longer) between the addressing of the golf ball and the impact of striking the ball. It was also found that the variability of the putting task decreased, although only the female routine group showed improved putting performance. The authors concluded that the routines seemed to provide a means of effectively controlling the mental and physiological states before the performance of closed-skills. Although the golfers were instructed to go through a routine and only one of the groups showed improvement, the introduction of a routine has the potential to help golfers improve their putting accuracy (Cohn et al., 1990).
McCann, Lavallee, and Lavallee (2001) also examined the efficacy of implementation of pre-shot routines. The authors examined the effect of pre-shot routines on wedge shots and observed the effects of novice golfers' pre-shot routines on the accuracy of wedge shots from distances of 40 meters, 50 meters, and 60 meters. Participants were randomly assigned into two main groups" novice golfer and non-golfer. They were then separated into three subgroups based on skill level and intervention type (routine/no routine or practice/non-practice). Those golfers in the cognitive-behavior performance routine groups were issued a handout of a scripted performance routine and shown two demonstrations. Results revealed the non-golfer routine group significantly improved their shot dispersion following a 3-week long period of learning and implementing a pre-performance routine, suggesting pre-performance routines have a positive impact on golfers wedge shot accuracy to a given target.
The effects of consistency of routines have also been examined in past literature. Boutcher and Zinsser (1990) conducted an observational study of pre-putt routines of beginning and elite level golfers performing six 4-feet and six 12-feet putts. Cardiac, respiratory, and behavioral patterns of putting were measured. Researchers found that elite golfers had longer, more meticulous pre-putt behaviors than those of beginning golfers. The elite-level golfers demonstrated more consistent behavior during their pre-putt routines by exhibiting their dominant routine (i.e., one exhibited most often) on 62% of their putt attempts. Beginning golfers did not show noticeably consistent behaviors when performing their pre-putt routines as they exhibited their dominant routine on only 35% of their putt attempts. Results indicate that expert or near expert golfers who maintain consistency amongst their putting routines will most likely have improved putting success.
Pre-shot routines and the effect of duration on outcome were examined by Crews and Boutcher (1987) as they observed 12 players on the Ladies Professional Golf Association (LPGA) tour. The authors analyzed the pre-shot routines of both full-swing and putting strokes for 12 holes during tournament play. Observational results indicated that all players were extremely consistent with regards to time and behaviors. It was observed that the more successful golfers used longer time periods for the full shot and putting routines, suggesting that the chances of holing a putt are improved by taking longer during the pre-shot routine. Yet, the authors did not account for difficulty of shots and it is not possible to determine if this affected pre-performance routines.
Kingston and Hardy (2000) utilized a case-study design aimed at developing more systematic and consistent pre-shot routines. The 10-week intervention phase consisted of developing holistic goals and cue words for an expert golfer. Results were significant regarding lessening the variability of the timing of putting routines. However, results did not correlate with improved putting performance. The authors utilized self-report measures on the participant's perception of performance and point out that self-reporting was problematic due to the inability of controlling for extraneous factors such as difficulty of putts. As a result, the collection of actual performance data and duration of putting routines is still warranted.
Past research has accentuated the value and importance of pre-performance routines. A common theme has been that routines are most effective when maintained across a series of given tasks (Boutcher & Zinsser, 1990; Crews & Boutcher, 1987; Czech et al., 2004; Lidor & Singer, 2000). Routines have been reported to organize thoughts and focus attention before the execution of a sport-specific movement (Boutcher & Crews, 1987). Nonetheless, the lack of applied research within the area of putting and pre-performance routines still needs to be addressed (Mack, 2001).
Pre-putt routines are individualized and vary across participants. Putting has been reported by players as very "feel" oriented and much of golfers' preparation for a putt attempt is based on this notion (Penick & Shrake, 1992). Despite this, researchers have yet to examine the effects of pre-performance routine duration on the outcome of putting attempt in an applied setting. The aim of the current study was to investigate the duration of pre-putt routines of highly skilled golfers and the accuracy of putts.
Direct observation occurred during two NCAA Division I golf tournaments. One-hundred and sixty-seven male collegiate golfers from 30 Division I Universities across various conferences participated in the tournaments combined and due to the team system within college tournaments, were presumed to be experienced (< 5 handicap). Both collegiate golf tournaments consisted of 54-holes of play with the format consisting of 36-holes during day one and 18-holes of golf for day two.
Data collection took place during the fall 2007 at two separate collegiate golf tournaments in the Midwestern United States. The first tournament for direct observation was coded as tournament A. The second was coded as tournament B.
Design and Procedures
Prior to data collection, the researchers established a hierarchical selection criteria based on the following: (1) A par-three hole was observed to ensure similar approach shots from the same teeing ground; (2) the length of the hole was as close to 175 yards as possible, since the first hole used for observation determined the length of subsequent distances; (3) the green of the hole was relatively fiat, thus eliminating the variation of the difficulty of putts. Both golf holes used for observation were par-three's measuring between 158 and 183 yards.
During tournament A, the ideal green for data collection that satisfied the aforementioned selection criteria was a par-three measuring 173 yards. The distance from tee to green was measured to the center of the green and each day, the hole location was changed to a different portion of the green. Thus, there were two distances for each hole. The first day's length was 168 yards and the second day's length measured 180 yards.
The green at the tournament B course used for data collection that satisfied the selection criteria was a par-three hole similar in yardage measuring 175 yards. The first day's length measured 158 yards. The second day's length measured 183 yards. Both holes featured a water hazard bordering the right side of the green.
Speed of Green
To help ensure the consistency of measurements, the speeds of the greens were measured using a Stimpmeter (Pelz, 2000). To obtain this measurement, a ball was placed onto the Stimpmeter while it is flat on the ground and then raised until the ball rolled onto the green. The distance (in feet) that the ball rolled (green speed) was taken in the morning before play and after play was finished. The speeds of the greens were similar in that the tournament A course measured 10 in the morning and 10.5 in the afternoon on both days. The green speed at the tournament B course measured nine feet in the morning and 9.5 feet in the afternoon both days. Golf literature states that a deviation of speed in greens less than .5 feet is considered similar green speed (Brede, 1990).
The weather for the tournament A was sunny with a temperature in middle 80 degrees for both days. The wind was out of the North at approximately 10 miles per hour both days. The weather for the tournament B was overcast for much of the first day with temperatures in the lower 50 degrees. The wind was out of the north/northwest at 10 miles per hour. The second day at tournament B consisted of sunny conditions with temperatures in the lower 70 degrees. The wind speed and direction was minimal for the second day of the tournament. Weather conditions for each tournament day were retrieved from The Weather Channel website (www.weather.com).
One-hundred and forty total putts from both collegiate golf tournaments were directly observed and recorded. The primary variable of duration of putting routines initiated when the player removed the marker from behind the ball and timing finished at the beginning of the execution of the putting stroke. These two criteria were selected due to their consistency amongst the routines of each player. Data were not recorded if a player did not complete either of these tasks. Observers recorded data using a standardized stopwatch to ensure the consistency of the measurement.
Putts between .91 meters (3 feet) and 3.04 meters (10 feet) in length were recorded because these distances normally yield the highest percentage of makeable putts (Pelz, 2000). The distances were then separated into two categories for data analysis purposes: .91 meters (3 feet) to 1.82 meters (6 feet) in length and 1.82 meters (6 feet) to 3.04 meters (10 feet) in length. Distances were separated for the potential effect of length on the outcome of the putt. Putts that could be considered "tap-ins" (i.e. less than three feet from the hole) were not included in the observation and data collection process. This was due to the limited skill demands required for making shorter distance putts, as well as the absence of a pre-putt routine (Cohn et al., 1990). Lastly, putts outside of 3.04 meters (10 feet) were not recorded due to the lower percentage of made putts (Cassidy, Morgan, & Cherry, 2006; Pelz, 2000;).
Additional putting data were recorded for each participant's analysis; grouping order, score, and putting order. Participants were pre-organized into groups of three by the tournament committee and name and school were recorded for identification purposes. Group number was also recorded to examine if the time of day affected the duration of putting routines or outcomes of putts. Putts for scores of birdie, par, or bogey were also recorded. Putts for a score of double bogey (5) or higher were recorded, but not used for analysis. Putting order was recorded to determine if the order of putting affected the outcome of the routine. Thus, each player who had a putt from a distance inside the circle of measurement was assigned a number based on the proximity from the hole. For instance, if all three players were eligible for observation, the player furthest from the hole was number 1, the next closest was number 2, and the closest was number 3. If a player was attempting his first putt then he was labeled as X. 1 (X = number given based on proximity). For example, the furthest player from the hole attempting his first putt would be labeled 1.1.
The following steps were taken to ensure internal validity regarding recorded distances and minimize potential observation error (Kazdin, 1998). First, maintaining rigid measurements was essential. Prior to the beginning of each day's play, markers were placed at .91 meters, 1.82 meters, and 3.04 meters at five different points around the hole to establish the distance category circles. Second, three separate observers recorded data during the study. At least two observers' recorded separate measurements at all times and were approximately 20 feet from the green. Data collection procedures were unobtrusive, and participants were unaware of any data collection methods performed by the researchers. Third, observers utilized standard stopwatches to record time and to help ensure a standard use of measurement, the fastest time recorded was utilized.
Lastly, observers conferred with each other after each group left the putting green and determined the distance category for every putt that may have been of a questionable length. During the tournament, any distance not unanimously agreed upon was not recorded. During all recorded observations, researchers were in agreement 95% of the time. There was one incidence of disagreement and the data were not used. These measures helped ensure inter-scorer reliability (Kazdin, 1998).
To test the relationship between putting routine duration and putt outcome, four separate logistic regression analysis using SPSS were performed on the data from both tournaments (Tabachnik & Fidell, 2001). The outcome variable of interest was the outcome of the putt (make/miss). Model building was conducted, in which an initial set of independent variables (time) was included in the analysis, after which those with large p-values were removed and the logistic regression analysis was conducted again. Because this study was exploratory in nature, it was felt that such model building was appropriate so as to remove potential predictors that were clearly unrelated to the outcome variable of making the putt. Three logistic models were developed, one for the combined tournament data and one for each separate tournament. The fourth logistic regression included participants in both tournaments who had at least three recordable putts (a subset of the total sample), thus a within-subjects logistic regression model was used. In order to account for the repeated measurements on the same participants, general estimating equations (GEE) were used for parameter estimation with this latter model (Tabachnik & Fidell, 2001). By using GEE, the standard error estimates for the parameters of interest were calculated properly, accounting for correlations due to multiple occurrences in the data set by the same golfers.
Data were analyzed linking the duration of participants putting routine to the outcome variable (making or missing the putt). Within the recorded distances of .91 meters (3 feet) to 3.04 meters (10 feet) there were a total of 46 made putts and 17 missed putts from tournament A (see Table 1).
Descriptive data of duration of putting routines revealed the mean for the made putts was 21.90 seconds SD = [+ or -] 6.69, while means for the missed putts was 27.33 seconds SD = [+ or -] 8.76. For the entirety of tournament A, the observed hole was .39 strokes over par. There were 20 birdies, 150 pars, 56 bogeys and 23 double bogeys.
The logistic regression revealed significant relationships with the likelihood of making the putt were obtained for routine duration, [chi square] (3, N= 63) [beta] =-0.098, p<.05. The negative slope value ([beta] = -0.098) for time reveals that players with longer duration routines had lower probability of making the putt (see Table 2). A significant relationship also existed regarding distance and outcome of the putt during tournament A, [chi square] (3, N=63) [beta] =-1.768, p<.05. The negative slope value ([beta] = - 1.768) for distance reveals that players attempting putts from longer distances from the hole (i.e., 3-6 feet, 6-10 feet) had a lower probability of making the putt.
Within the recorded distances, there were 41 made putts and 36 missed putts. Descriptive data illustrates that the average time for the made putts was 25.69 seconds SD = [+ or -] 8.55, while the average time for the missed putts was 24.35 seconds SD = [+ or -] 7.97. During tournament B, the observed hole played .23 strokes over par. There were 30 birdies, 183 pars, 80 bogeys and 18 double bogeys (see Table 3).
A logistical regression revealed no statistically significant relationship between routine duration and the likelihood of making the putt from tournament B, [chi square] (3, N=77) [beta] =.014, p<.05. The only significant predictor in the logistic regression analysis was distance, [chi square] (3, N=77) [beta] = - 1.016, p<.05. Putts further away from the hole were less likely to be made (see Table 4).
Combining both tournament data, the logistic regression revealed a non-significant relationship between routine duration and outcome [chi square] (3, N =1 40) [beta] =-0.037, p<.05 (see table 5). Similar to both previous models, the predictor of distance was a significant factor for this model [chi square] (3, N=140) [beta] =-1.286, p<.05 indicating that the further from the hole, the less likely the player is to make the putt.
A total of 15 players participated in both tournaments and had three or more recordable putts, and thus were included in the within-subjects model. Within the recordable distances, there were 30 made putts and 13 missed putts. The logistic regression model using General Estimating Equations (GEE) revealed that routine duration was a significant factor [chi square] (3, N=43) [beta] =-0.009, p<.05 related to the probability of players making the putt (Table 6). Players showing less deviation in their routine duration across the multiple putt attempts were more likely to make the putt. Also, the further from the hole, the less likely the player was to make the putt [chi square] (3, N--43) [beta] =-0.516, p<.05.
Pre-performance routines have been noted across a wide range of sports (Cohn, 1990; Cornelius, 2002; Weinberg & Gould, 2007). However gaps still exist in that pre-performance routines have not been extensively analyzed in applied settings and golf in particular. To date, research has yet to look at pre-performance routines and putting in the specific manner of the present study.
In this study, an attempt was made to examine the duration of pre-putt routines of elite level golfers and the accuracy of putt attempts. There is a lack of research concerning the times of pre-putt routines, but similar research (Boutcher & Zinsser, 1990) suggested that longer duration of routines would yield more made putts. However, results from this study are counter to previous studies (Boutcher & Crews, 1987; Boutcher & Zinsser, 1990). Results from the analyses lend support that the longer pre-performance routine duration, the lower the probability of making the putt. Distance was also shown to be a significant factor that the longer the putt, the less likely the player was going to make the putt. Although, results from the current study are not conclusive and whereas significance was shown for the within-subjects design, the between subjects design revealed significance for only tournament A.
Putting routines are individualized tasks and it raises the question of whether the temporal consistency correlates with the behavioral consistency of routines (Penick & Shrake, 1992). As a result, the within-subjects design attempted to bridge the gap of previous putting routine research (Wrisberg, Cassidy, Morgan, & Cherry, 2009).
Results from the within-subjects design revealed that players who maintained their preparation time (i.e., pre-putt routines) were more likely to make the putt. It is thought that the consistency of pre-putt routines would produce less conscious processing through the movement resulting in more consistent behaviors and outcomes (Kingston & Hardy, 2001). Juxtaposed is that deviation in temporal consistency resulted in a decrease in putting performance. Although no specific behavioral data were collected, it can be assumed that the near-expert level of the participants (handicap <5) yielded established and near "automatic" pre-shot routines (Boutcher & Crews, 1987). As a result, changes in the duration of pre-putt routines and not necessarily obvious behavioral changes caused the decrease in performance. These results were similar to past research in basketball free-throw shooting (Czech et al., 2004) suggesting that players who repeated their dominant routine at least 90% of the time were more likely to make the shot.
Due to the consistency of behavioral routines of elite level golfers, perhaps the deviation in duration of putting routines was the reflection of varied attentional processing. Past research has revealed the importance of attention in self-paced skills and revealed that conscious control over the movement results in decreasing performance (Baumeister, 1984, Boutcher & Crews, 1987). Results of Beilock, Bertenthal, McCoy, and Carr (2004) suggest that experienced golfers (handicap < 8) performed better with less time compared to when they were instructed to take as much time as they needed. Thus, it is plausible in the within-subjects design that deviations in duration of routines would lead to counterproductive over-thinking resulting in a disruption of automaticity.
Due to the "real" world setting of the data collection, statistical analyses helped control for threats to internal validity (Kazdin, 1998). Putting order, distance, group number, and score were not shown to be significant among any of the models thus ruling out alternative hypotheses. First, no differences in duration of routines were reported regarding putting order. Participants did not appear to alter their routine with respect to whether they putted first, second, or third. This aspect also reportedly filled in gaps presented by similar research (Wrisberg et al., 2009).
Interestingly, although distance did reveal significance regarding made/missed putts, it did not play a significant role in the duration of the routines. Routine duration from the shorter distances of .91-1.82 meters did not significantly differ from observed routines in the longer 1.82-3.04 meter range. In addition, putting for a specific score (birdie, par, or bogey) nor the time of day (group number), significantly impacted the duration of the pre-putt routines. Although past studies (Jackson & Backer, 2001) lend support that task difficulty results in longer preparation times, results from the current study did not appear to be the case.
In light of the current analyses and the idiosyncratic nature of putting routines, assessing causal relationships between duration and success, may warrant mixed methodologies. In the current study, the variance of results from the between-subject design does not control for intersubject error. Research has advocated intraindividual approaches of data collection that that are sensitive to within-subject fluctuations (Robazza, Bortoli, & Hanin, 2004). For instance, past state anxiety research by Sonstroem and Bernardo (1982) and Burton (1988) both employed repeated measures methodology to help control for interpersonal differences. Thus, utilizing within-subject design methodologies for naturalistic observation appears to offer greater advantages. Examining specific participants across several tournaments consistent with the current within-subject design may yield more meaningful results than between-subject designs. Specifically, Gentner, McGraw, Gonzalez, and Czech (2009) as well as Wrisberg and colleagues (2009) suggest that prolonged observations across several tournaments that examine both temporal patterns and behavioral tendencies may reveal more concrete findings. Whereas, the present study looked at elite level golfers (< 5 handicap), within-subject observation could examine professional golfers in natural settings across 72-hole tournaments as opposed to 54-holes. Combining the aforementioned methodologies along with qualitative interviews may reveal further insight into the underlying processes of pre-putt routines (see Wrisberg et al., 2009 for a detailed discussion of naturalistic observation in sport settings).
Results of the between-subjects design are inconclusive, thus suggesting the need for further applied research. Within the natural settings of the current study, there may have been some extraneous variables present with the data collection. First, whereas the researchers attempted to satisfy all of the selection criteria for greens observed, the continuity of the putting surfaces may have been a limitation to this study. The first putting green at tournament A for observation was extremely flat on all sides of the hole for both days compared to the hole location at tournament B. During tournament B, the back side of the second day hole location provided more slope and it is possible routines differed according to the difficulty of this putt encountered. However, it should be noted that location of putts were observed and no participant putted from this vantage point. Nonetheless, the processing time for the putting routines may still have varied.
Second, another limitation may have resulted from the participants observed. Tournament B had more overall players than tournament A, which may have led to more total observed putts. There were 77 putts, 41 made and 36 missed, within the recordable distances for tournament B compared to 63 putts, 46 made and 17 missed, for tournament A. Similarly, the quality of the tournament field could have played a role in the lack of significance for tournament B. Tournament B received a weaker tournament ranking of 142 compared to the stronger ranking of 177 for tournament A (www.golfstat.com). This may explain why the observed hole at tournament A played somewhat easier (.23 strokes over par) compared to the hole at tournament B (.39 strokes over par).
Last, weather conditions may have influenced data collection during the first day of tournament B. The temperature was between 50-55 degrees, which was approximately 20 degrees colder than the second day of the tournament. This could have affected the data by influencing players to display a routine that was longer or shorter than their normal duration. Again, future within-subject methodologies that include qualitative explorations could help discern participants' interpretations of putting routines in adverse or changing weather patterns.
Whereas results from the current applied study reveal a possible correlation between routine duration and outcome, conclusions should be tempered with ideations of future research. The within-subject design revealed that temporal consistencies are of importance to the likelihood of making the putt. However, the between-subject design from tournament A and B yielded varying results emphasizing the best way to assess naturalistic observation is through intrasubject analysis. Identifying a duration range that correlate with more made putts can potentially be applied to various coaches and performers. Thus, it is important that sport psychology consultants and other professionals are aware of and continue naturalistic oberservation studies of pre-putt routines.
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Robert J. Bell, Kyle E. Cox, and W. Holmes Finch
Ball State University
Address Correspondence to: Robert J. Bell, Ph.D, HP 222-E, Ball State University, Muncie, IN, 47306. Email: firstname.lastname@example.org. Phone: 765-285-3286.
Table 1. Tournament A Putting Routine Duration Made/Missed Mean N Std.Dev Made 21.9 46 6.69 Miss 27.33 17 8.76 Table 2. Tournament A Results Predictor B SE B [e.sup.B] Sig Time -0.098 .048 .906 .038 * Distance -1.768 .704 .171 .012 * Putt # 1.146 .782 3.146 .143 Note: * P <.05 Table 3. Tournament B Putting Routine Duration Made/Missed Mean N Std. Dev Made 25.67 41 8.55 Miss 24.35 36 7.97 Table 4. Tournament B Results Predictor [beta] SE [beta] [e.sup.B] Sig Time .014 .032 1.014 .662 Distance -1.016 .504 .362 .044 * Putt # .541 .364 1.717 .137 Note: * P<.05 Table 5. Combined Tournament Results Predictor [beta] S E [beta] [e.sup.B] Sig Time -.037 .027 .963 .162 Distance -1.286 .437 8.672 .003 * Putt # 0.541 .341 1.718 .112 Note: * P <.05 Table 6. Within Subjects Design: Generalized Estimating Equation [chi L 95% U 95% Parameter [beta] S. E. square] C.I. C.I. Sig (Intercept) .901 .178 25.480 .551 1.250 .000 * Distance -.516 .132 15.170 -.775 -.256 .000 * Time -.009 .005 4.019 -.018 .000 .045 * Note: * P<.05
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|Author:||Bell, Robert J.; Cox, Kyle E.; Finch, W. Holmes|
|Publication:||Journal of Sport Behavior|
|Date:||Aug 14, 2010|
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