Scheduled Overtime and Labor Productivity_ Quantitative Analysis

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SCHEDULED OVERTIME AND LABOR PRODUCTIVITY:

QUANTITATIVE ANALYSIS

By H. Randolph Thomas1 and Karl A. Raynar2

Note. Discussions open until November 1, 1997. To extend theclosing date one month, a written

request must be filed with the ASCE Manager of Journals. The manuscript for this paper was

submitted for review and possible publication on April 29, 1996. This paper is part of the JournalofConstruction Engineering and Management, Vol. 123, No. 2, June, 1997. CASCE, ISSN 0733-

9364/97/0002-0181-0188/$4.00 + $.50 per page. Paper No. 13135.

1Prof. of Civ. Engrg., Pennsylvania Transp. Inst., Pennsylvania State Univ., 203 Res. Ofc. Build.,

University Park, PA 16802.

2Res. Assoc., Pennsylvania Transp. Inst., Pennsylvania State Univ., 106 Res. Ofc. Build., UniversityPark, PA.

Abstract This paper describes a study of 121 weeks of labor productivity data from four industrial projects. Theobjective is to quantify the effects of scheduled overtime. First, it describes how the data were collected, processed, andanalyzed. The results show losses of efficiency of 10-15% for 50- and 60-h work weeks. The results compare favorably t

other published data including the Business Roundtable (BRT) curves. Therefore, it was concluded that the BRT curve isreasonable estimate of losses that may occur on average industrial projects. Second, this paper addresses the reasonsfor efficiency losses. For this analysis disruptions in three categories-resource deficiencies, rework, and managementdeficiencies-were analyzed. The analyses showed that the disruption frequency, which is the number of disruptions per100 work hours, worsened as more days per week were worked. This led to the conclusion that losses of efficiency arecaused by the inability to provide materials, tools, equipment, and information at an accelerated rate.

TRODUCTION

heduled overtime has been the subject of controversy since the Business Roundtable (BRT) publishedertime study in the early 1970s. It was reissued again in 1983 as p of the Construction Industry Cost

ectiveness project ("Scheduled" 1980). Some argue that scheduled overtime can be used without losor efficiency [Construction Indus Institute (CII) 1988], and others argue that when an overtime schedulplied, labor efficiency automatically suffers There are numerous disagreements about the extent offficiencies and misunderstandings regarding how overtime schedules affect labor output.

BJECTIVES

e objectives of the present paper are to detail the result of a comprehensive study to measure the effehedule overtime on construction labor efficiency and to define the relationship between scheduled oved various types o disruptions. The objectives are then to document how much loss of productivity one pect and to question why inefficiencies occur. The emphasis in this paper is on labor work hours rathe
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n costs.

EFINITIONS

his paper, the term "scheduled overtime" refers to a planned decision by project management tocelerate the progress of the work by scheduling more than 40 work hours per week for an extended petime for much of the craft work force. This term is in contrast to "spot overtime," which is appliedoradically for a limited number of workers. A "disruption" is an event that is known or has been reporteliterature to adversely affect labor productivity. Examples include lack of materials, lack of tools or

uipment, congestion, and accidents. "Efficiency" is the relative loss of productivity compared to someseline period. A value less than unity means performance is less than the baseline period. "Laboroductivity" is the work hours during a specified time frame divided by the quantities. The time frame caly, weekly, or the entire project (cumulative). This measure is commonly called the unit rate.

ACKGROUND

comprehensive review of the literature related to scheduled overtime has been published by Thomas992). This paper reported the literature to be very sparse-dated to the late 1960s and earlier-based onall sample sizes and largely developed from questionable data sources. While there appears to be a

mber of sources, this is an illusion because many of the articles and publications quote other sources oviding no new data or insight. Where the data source is known, other pertinent information, such as thvironmental conditions, quality of management and supervision, and the labor situation, is unknown. Thrious graphs and data that have been published serve to suggest an upper bound on the losses ofciency that might be expected. The literature offers no guidance as to what circumstances may lead tses of efficiency. With respect to loss of efficiency as a function of time, there are very few articles or

ports that show how efficiency is supposed to deteriorate over long periods of time.

OW OVERTIME AFFECTS LABOR PRODUCTIVITY

detailed representation of the factor model is shown in Fig. 1. The model shows that the conversion ofuts (work hours) to outputs (quantities) is a function of the work method or conversion technology. Vartors affect the efficiency with which inputs are converted to outputs. These impediments are divided in

o categories: the work to be done, and the work environment. The work to be done refers to the physicmponents of the work. The work environment portion shows 10 variables that can be influential. Theseroot causes of loss of efficiency. While there can be many other factors, these 10 are the most comm

ese factors impede or enhance the efficiency with which inputs or work hours are converted to output oantities.


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ertime is an indirect factor that causes disruptions in the work environment. In extreme cases overtimentribute to ripple effects. This is because the view is that overtime (exclusive of fatigue) itself does notproductivity losses. If it did, the losses would be automatic, which most professionals agree is not the tead, a scheduled overtime situation causes other variables to be activated. Consider the followinguation where project management decides to go from a work week consisting of four 10-h days to sixys. The labor component is thus increased by 50%. What else happens? Does the work get finished 5ter? To function efficiently the entire system must respond to the increase in work hours. Materials mu

ade available 50% faster; equipment will be used 50% more; and the project staff must respond to 50%ore questions. Everything is accelerated. If a project is behind schedule because of one or more of thevironment factors in Fig. 1, an overtime schedule will only make matters worse. It is this theory, the cauk between overtime and disruptions, that is being examined in this paper.

VERALL ASPECTS OF STUDY

e study had several unique aspects that are different from most previous studies (CII 1994). Theseerences are summarized in the following. The smallest man-power unit that produces completed outpcrew. Therefore, the focus is on an average crew. The study includes crews of electricians and/or pip

ers from four active construction projects. The work of the crews involves bulk installations only, such able, conduit, and piping. Since the stage of construction can affect labor productivity, the study specifi

cluded the early phase of the work and the startup phase. Most previous studies have relied on cumulaoductivity data. In this study, unit data are summarized daily and weekly. Cumulative data are also use

ATA COLLECTION PHASE

fine Study Parameters

his paper, only the electrical and piping crafts are studied. The rationale is that these crafts representajority of the work that is most likely to be affected by scheduled overtime. The work performed by thesafts was further narrowed to crews performing production-related work. For electricians the production

ated work studied was the installation of conduit, cable and wire, terminations and splices, and junctioxes. For piping, the work studied was pipe erection and the installation of supports and valves. Crewsrforming other kinds of work were not considered for study.

oject selection is also an important element for removing other potential influences. The labor environmould be tranquil, and there should not be an inordinate number of changes. Experimental, unique, or poanaged projects should be avoided. In this study, each of these criteria was met. None of the projects dy experienced labor problems, jurisdictional disputes, labor shortages, or other factors that may havuenced the results.

e study duration was sufficient to include a straight-time and an overtime schedule. The performance o

w on an overtime schedule was compared to the same crew on a straight-time schedule. In this studyget duration of 14 weeks was planned. The actual durations on the four projects studied ranged from 16 weeks.

oject Descriptions

this study, productivity data were collected from four active construction projects as shown in Table 1. Tre a total of 151 weeks of data. The projects were constructed in the 1989-92 time frame. Each wasnstructed in a tranquil labor environment and was well managed. None experienced any unusual diffict would have caused progress to fall behind schedule. Each project was completed in a timely fashion

ertime schedule was used to maintain schedule, not to attract labor. The manufacturing and paper mill


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ojects were existing facilities where old systems and equipment were removed and new ones installedngestion was a concern in each facility. The process plant was a spacious, outdoor, grassroots facilitd the refinery involved the rebuilding of parts of an existing facility. With respect to owner involvement,sign, and construction management, the four projects were considered average industrial projects.

ocedures

e data collection effort was independent of the cost reporting system. A procedures manual wasveloped for this purpose (Thomas and Rounds 1991). Site personnel collected the data. The philosopd evolution of the procedures manual are explained elsewhere (Thomas et al. 1989). The data collectort was organized around the completion of eight forms. Seven forms were completed daily. The formicited information about the work hours, crew size, absenteeism, the quantities installed, and thenditions in which the work was done. Selected information requested on each form is as follows:

rm number 1 -manpower/labor pool: crew size, crew composition (skflled and unskilled), and absente

1. Form number 2-quantity measurement: measured units completed for each subtask2. Form number 3-design features/work content: work type and design details3. Form number 4-environmental/site conditions: temperature, humidity, and weather events4. Form number 5-management practices: delays, material and equipment availability, congestion,

sequencing, and rework5. Form number 6-construction methods: length of work day, overtime schedule, and working foreper6. Form number 7-project organization: size of project work force, other site support personnel, and

number of forepersons7. Form number 8-project features: type of project, approximate cost, and approximate planned dura

e type of data recorded was continuous, integer, and binary. An example of continuous data is the quaconduit, i.e., 22.8 m (74.6 ft). Integer data included the crew size, i.e., nine tradespeople. Binary variab

e on values of 0 or 1, depending on whether a particular condition is present. For example, if aeasurable portion of the work hours were affected by the lack of materials, a 1 would be recorded; if not variable would be recorded as 0.

hough the data forms are more detailed than shown here, every effort was made to streamline the datlection process. Following an initial familiarization period, data collection typically took about 30 min p

ew per day.

ATA PROCESSING PHASE

e purpose of the data processing phase was to normalize the productivity data to the estimated daily


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oductivity had the crews been installing the same item of work, screen the data for unusual peculiaritied normalize the data so that performance is related to a baseline productivity when a straight-timehedule was being worked.

lculate Conversion Factors

s known that the installation of different sized components require different labor resources. For exam1.6mm (4-in.) conduit requires more work hours per foot to install than a 19.1-mm (0.75-in.) conduit.ferences such as these exist for all items included in the study. These differences are accounted for b

ng conversion factors. The logic is explained elsewhere and is as follows (Thomas and Napolitan 199

e first step is to define a standard item. In theory the choice of an item is irrelevant. In practice, it is usected as an item that occurs frequently. In this study, the standard item for electrical work was 50.8-mm) galvanized rigid steel (GRS) conduit, and for piping it was 63.5-mm (2 1/2in.), schedule 40, butt weldrbon steel spools.

his investigation the estimate of conversion factors was based on unfactored unit rates that were obtam standard estimating manuals. For electrical work the Means and Richardson manuals and a manuam a construction company were consulted. For piping work the Means, Richardson, Page & Nations,

manual from the same construction company were used. The use of multiple estimating manualsecludes the factors from being influenced by one source.

ing the data from a single estimating manual, conversion factors for each item are calculated as

ere i = item number; and j = manual number. Once conversion factor values have been calculated for anuals and items, multiple regression techniques can be used to develop a mathematical relationship ch grouping of like items. Groups are for conduit, cable, pipe, valves, and so on. The group regressio

uation was used to estimate the conversion factor for each item in the group.

practice, the conversion factor shows how much more or less difficult an item is to install compared to ndard item (Sanders and Thomas 1990). The theory behind conversion factors is that of earned valuen be easily verified that irrespective of the mix of quantities installed, the conversion factor does not a hours earned in a given time frame.

nversion factors are analogous to monetary exchange rates. For example, a mix of marks, yen, andunds can be exchanged for an equivalent amount (or value) of pounds or another currency such as do

e utility of the conversion factor approach is that the productivity of crews doing a variety of work can hir output expressed as an equivalent output of a single standard item. Thus, the productivity of all crewn be calculated for the same standard item during each time period regardless of the work performedewise, crews from different projects can have their productivities calculated for the standard item, met the data from multiple projects can be combined into a single database because all the productivityues represent installing the same item of work.

illustrate how the conversion factors are calculated, consider the items listed in Table 2. 'Me standard50.8mm (2-in.) GRS conduit. The conversion factors in the last column are calculated using (1), where t rate for the standard item is 0.584 work hours/m (0.178 work hours/ft).


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lculate Equivalent Quantities

e equivalent quantities are the number of units of the standard item that will yield the swne number ofrned hours as was actually earned by installing nonstandard items. Practically speaking, it is the mostimate of the quantity of the standard item that would have been completed for the same set of worknditions. The equivalent quantity is calculated using (2)

ere i = denotes the item being installed; and k = total number of i tems installed during work day 1.

ppose on a given day a crew installs the quantities listed in the first two columns in Table 3. The convetors in Table 3 are used in (2) to calculate the equivalent quantities. As shown, the crew did the equiva61.0-m (200-ft) of 50.8-mm (2-in.) GRS conduit.

e work hours earned are determined by multiplying the quantities installed by the unit rate from Table 2homas and Kramer 1987). For the actual installed quantities in Table 3, the crew earned 35.6 work ho earned hours are calculated based on the equivalent quantity of 61.0 m (199.95 ft), the unit rate of 0.5rk hours/m (0.178 work hours/ft) for the standard item [50.8-mm (2-in.) GRS conduit] from Table 2 is ud the earned work hours are also equal to 35.6. Therefore, the value of the work in terms of earned hosame; it is simply expressed in a different way. If a different standard item is chosen, the earned wor

urs will still be 35.6.

fining Baseline


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define the baseline the nominal hours per week were used. For example, if the crew worked 37.5 h dweek, that was considered a 40-h week.

e of the difficulties in examining overtime data is that, in practice, it can be difficult to identify a periode where a straight-time schedule was used followed by an overtime schedule. Work schedules areected by weather, and managers strive to ensure that workers have ample time away from the job. It isrequent that one would see an extended overtime schedule lasting 10-12 weeks as presented in the Bdy (1980).

riations in work schedule make it difficult to define a baseline. Since there were no data for five eight-ys, a four to 10 schedule was used as the.baseline. In determining the weeks to use, consideration waen to consistency of work hours, crew size, and number of days worked per week. For the baseline w work hours and quantities were determined. The baseline values were then calculated for each crewng the following equation:

e calculated baseline values are summarized in Table 4.

nal Data Screening

hen examining the weekly productivity values, one must be cognizant of outliers. However, simply remoreme data points would be improper since they are, to some extent, the focus of this study. Some initficulties with data collection are noted for projects 9,181, 9,183, and 9,185. Accordingly, the first weekta for these three projects have been discarded. This leaves a total of 148 weeks of data. All subsequalyses have been performed on this reduced data set.

lculate Performance Factors

r each data set, performance factors were calculated using (4)

performance factor value greater than unity means that performance that week was better than therformance during the baseline period. The use of performance factors allows data sets from variousurces to be combined. In this instance the 11 sets were combined, and all analyses were done on the

rformance factors.


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ATA ANALYSES: HOW MUCH

s section explains the results of the data analyses. It relies on daily, weekly, and cumulative performantors. The approach used is to examine the influence of hours per day and then to perform other analystermine if they support or contradict the initial investigation. Tlere was insufficient dispersion of data toestigate the influence of hours per day.

AYS PER WEEK

e initial analysis was to determine the influence of days per week on labor performance using weeklyrformance factors. The analysis was done on work weeks of two, three, four, five, and six days. Workeks shorter than four days usually were shortened because of bad weather. There was one seven dayrk week, and it was discarded.

e weekly performance factor values were analyzed to determine if there were changes in the performator that were correlated to the number of days worked per work week. The results of this analysis are

Table 5. The efficiency is calculated by dividing the average weekly performance factor by the averagekly performance factor for a 40-h (fourday) work week or 0.98. The statistical significance of the resus evaluated using an analysis of variance (ANOVA) test. The level of significance, which ranges betw

000 and 1.000, was calculated to be 0.046. If it is hypothesized that an independent variable producestistically significant differences in a dependent variable, then the level of significance is the calculatedue at which the null hypothesis Ho, which there is no difference, would be rejected (Devote 1991). In

mpler terms the level of significance is the maximum probability that chance or randomness produced served results when, in fact, the null hypothesis is true. The level of significance is also called the p-vaue near 0.000 means a highly significant relationship. The use of the level of significance highlights theference between the approaches of theoretical or classical and applied statistics. A brief discussion iovided in Appendix I.

e efficiencies for two-, three-, four-, five-, and six-day work weeks are shown in Fig. 2. The reducedciency for the two- and three-day work weeks was caused by bad weather. 'Me five- and six-day workeks are of particular interest. These schedules showed greater variability in performance factor valuen the other schedules.


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nopsis of Initial Investigation

e initial investigation based on 120 weeks of work showed that there was, on average, about a 10-15s of productivity when working longer than a normal 40-h or four day work week. The loss of efficiency

e- and six-day work weeks (50- and 60-h work weeks) was about the same. The remaining analyses aort to support the initial determination that there are productivity losses when working an overtimehedule.

ertime Duration

examining performance as a function of the duration of the overtime schedule, cumulative performancetors were calculated and comparisons were made against the curves from the BRT study (1980). Themparisons are limited for two reasons. First, most crews worked an overtime schedule for three weeks compared to the BRT curves that extend for 12 weeks. Second, there was some inconsistency in th

ertime schedule. For example, a crew may work five days one week, six days the next, and then returne-day work week.

examining the efficiency trends for 50- and 60-h weeks, it was evident that most crews follow the genewnward trend established in the BRT study; however, not all crews follow this trend (BRT 1980). It mayssible that overtime schedules lasting three to four weeks or less can be used with minimal loss ofciency. However, no other data from this study could be identified to support this conclusion. For longertime schedules, fatigue probably increases. Fig. 3 shows the average of all crews working a 50-h wBRT curve (1980), and the results from several references reporting overtime efficiency as a function

e (Adrian 1988; Haneiko and Henry 1991; Overtime 1989). From this analysis one concludes that the

rve is probably a good representation of the industry average of overtime efficiency, but individual woray vary.


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riations Caused by Schedule Changes

vertime causes negative impacts, one would expect that when going from a straight-time schedule to ertime schedule, most of the time there would be a decline in performance. That is, the performance fauld decrease. Likewise, when coming off of an overtime schedule, one might expect an increase inrformance.

s aspect was investigated by calculating the change in performance when there was a schedule chans analysis showed considerable variability. The frequent changing of the schedule to and from overtimay be more detrimental than intuition may suggest. Subsequent research suggests that the change inhedule is more likely to be caused by variations in the workload (Thomas et al. 1995). Thus, frequentcelerations and decelerations are detrimental to efficiency.

ATA ANALYSES: WHY

e previous analyses investigated the effects of an overtime schedule on labor efficiency, i.e., how muc impact. Negative effects were shown to have occur-red. The analyses that follow investigate the quewhy negative impacts occur. Understanding the "why" question is necessary for one to manage anertime schedule.

sruptive Events

sruptions are defined as the occurrence of events that are known or have been reported in the literatuversely affect labor productivity. In this analysis only the four-, five-, and six-day work weeks werealuated, thus negating most of the weather disruptions that affected the results in Fig. 2. The rationale oring weather disruptions is that they are unrelated to overtime schedules. The disruption types were

ganized into three categories as follows:

1. Resources


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Material availabilityTool availabilityEquipment availabilityInformation availability

2. Rework

ChangesRework

3. Management

CongestionOut-of-sequence workSupervisoryMiscellaneous

he factor model (see Fig. 1) is a valid representation of labor productivity, then one would expect to seore frequent occurrences of disruptions and a simultaneous worsening of productivity. Conversely, if thno worsening of productivity, there should be little change in the frequency of occurrence of disruptions

lationship of Performance to Disruptions

test the previous hypothesis, a statistical analysis was performed to assess the influence of disruptiorformance. The daily performance factor values for days with and without disruptions were compared analysis of variance test. It was found that the efficiency on days when disruptions occurred was redu

an average of 73% of what it would have been if there had been no disruption. The level of significancs calculated as 0.098. From this analysis the likelihood of randomly observing the differences inrformance factor values for the subsets of disruptions and no disruptions is less than 10%, which maye to conclude that there is a causal relationship between lower performance and the presence of

ruptions.

lationship of Performance and Disruptions to Weekly Schedule

eekly disruption frequencies were calculated using the following equation:

e disruption frequency represents the number of weekly disruptions based on a 10-person crew worki-h day or every 100-work hours. Thus, by using the disruption frequency, a shortened work week can bmpared to a longer work week.

e weekly disruption frequencies were averaged according to the number of days worked per week. Tults are summarized in Table 6 and are shown in Fig. 4. As can be seen, as the work week lengthensruption frequencies increase. The six-day work week involves weekend work, and the nature of the wng performed may explain the reduction in disruption frequency for the longer work week.


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sruption by Type

e type of disruption was also analyzed. The data are summarized in Table 7 and are shown graphicall. 5. The research showed that the number of disruptions caused by changes and rework varied accohe days worked per week with no consistent pattern. Management-related disruptions (congestion, oquence work, supervision, and miscellaneous) were more for the five-day per week schedule than for

er schedules. Disruptions caused by lack of resources (materials, equipment, tools, and information)reased consistently with the number of days worked per week.

pact of Disruptions

sruption impacts were also assessed by calculating a disruption index, which is the ratio of therformance factor on days when a specific type of disruption occurred divided by the average performator on days when no disruption occurred. These average daily values are summarized in Table 8. Only

ost significant disruptions are shown. As can be seen, rework has the greatest impact on performance


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mparison to Previous Studies

ost of the previous studies show single efficiency values for a particular work schedule (Thomas 1992any cases, these values show greater losses of efficiency than the values shown in this study. From rea

reports and articles, one learns little or nothing about the origin of the data, and except for one or twodies, no differentiation is made between short- and long-term effects. Some data were known to be fr

ojects that were involved in contract disputes.

s study examined mainly short-term overtime effects, e.g., three to four weeks and less. On average,oductivity losses of about 10-15% were observed. The trends are consistent with the curves publishedRT ("Scheduled" 1980). The research also suggested that it may be possible to work overtime for thre

r weeks without losses of productivity, although the likelihood is small. This observation is somewhatnsistent with an earlier study published by CII (1988). Based on the analysis of disruptions, there arereasing difficulties in providing resources as the overtime schedule becomes more intensive. It

4heorized that on projects where there are few resource problems when working straight time, the lossciency can range from 0 to 15%. The overtime losses can exceed 15% if the project is already behin

hedule because of other problems, such as incomplete design, numerous changes, work in an operatvironment, or labor unrest. Under those circumstances, the values shown elsewhere in the literature m

ore realistic.


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ONCLUSIONS

a result of this study, several important conclusions can be formulated. These are summarized in theowing.

ere is little doubt that scheduled overtime results in a loss of productivity. Since the projects studied diperience labor problems, material shortages, and other major disruptive event, it is concluded that therve is a reasonable estimate of the minimum loss of productivity. For projects experiencing worseninggrees of distress and disruption, the loss of productivity will probably be greater. While it is possible to

rform some limited scope of work for a few weeks with no loss of productivity, the likelihood of doing sall. Consecutive overtime schedules lasting longer than three to four weeks will lead to productivity losm fatigue.

s study has shown scheduled overtime to be a resource problem. A causal relationship betweenruptions and losses of efficiency was shown. It was also shown that as more days per week are workre are increasing difficulties in providing resources, i.e., materials, equipment, tools, and information.

erefore, it is concluded that the major reason for losses of productivity during a period of scheduledertime is the inability to provide resources at an accelerated rate. The factor model was shown to be aid representation of overtime productivity.

CKNOWLEDGMENTS

s work was sponsored by the Construction Industry Institute (CII) under the guidance of the Scheduledertime Task Force. Their support and assistance in this research is gratefully acknowledged andpreciated.

PPENDIX 1. THEORETICAL VERSUS APPLIED STATISTICS

e classical or theoretical approach to hypothesis testing is to define an acceptable level of significanc

value a priori, use the data set to compute an F-ratio and a -value, and reject the null hypothesis H, if tmputed (x-value exceeds the preselected value. This approach may be inadequate because it saysthing about whether the computed value of the test statistic just barely fell into the rejection region orceeded the critical value by a large amount.

e applied statistician approaches the hypothesis testing problem in a slightly different way. No pass/faue is selected in advance. Instead, the data are analyzed, and the smallest (x-value at which the null

pothesis Ho would be rejected is computed. This statistic is called the p-value or level of significance. value conveys much about the strength of evidence against Ho and allows an individual decision-makeaw a conclusion without imposing a particular a on others, who might wish to draw their own conclusio

e level of significance has other practical implications as well. The level of significance is the maximumobability that chance or randomness produced the observed differences when, in fact, the null hypothe

is true. If the level of significance is near zero, then it is more probable that the observed differences y the result of the influence of the independent variable being considered.

PPENDIX II. REFERENCES

1. Adrian, J. J. (1988). Construction claims, a quantitative approach. Prentice-Hall, Inc., Englewood

Cliffs, N.J.

2. Business Roundtable (BRT). (1980). "Scheduled overtime effect on construction projects." Rep. C

New York, N.Y., 12-13.


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3. Construction Industry institute (Cil). (1988). "The effects of scheduled overtime and shift schedule o

construction craft productivity." Rep. of the Productivity Measurements Task Force, Source Docu

43,Austin, Tex.4. Construction Industry Institute (CII). (1994). "Effects of scheduled overtime on labor productivity: a

quantitative analysis." Rep. of the Overtime Task Force,Austin, Tex.

5. Devote, J. L. (1991). Probabili ty and statistics for engineering and the sciences. Brooks/ColePublishing Co., Pacific Grove, Calif.

6. Haneiko, J. B., and Henry, W. C. (1991). "Impacts to construction productivity." Proc., Am. Power C

Illinois Inst. of Technol., Chicago, Ill., Vol. 53-II, 897-9007. Overtime and productivity in electrical construction. (1989). National Electrical ContractorsAssociation, Bethesda, Md.

8. Sanders, S. R., and Thomas, H. R. (1990). "Masonry conversion factors." Masonry Soc. J., 9(i), 95104.

9. Thomas, H. R. (1992). "Effects of scheduled overtime on labor productivity." J. Constr. Engrg. and

Mgmt.,ASCE, 118(l), 60-76.0. Thomas, H. R., Arnold, T. M., and Oloufa, A. A. (1995). "Quantification of labor inefficiencies result

from schedule compression and acceleration." Final Rep. to the Electrical Contracting Found., InPennsylvania Transp. Inst., Pennsylvania State Univ., University Park, Pa. Thomas,

1. H. R., and Kramer, D. F. (1987). The manual of construction productivity measurement andperformance evaluation. Construction Industry Institute, Austin, Tex.

2. Thomas, H. R., and Napolitan, C. L. (1995). "Quantitative effects of construction changes on labor

productivity." J. Constr. Engrg. and Mgmt.,ASCE, 121(3), 290-296.

3. Thomas, H. R., and Rounds, J. (1991). Procedures manual for collecting productivity and related

on overtime activities on industrial construction projects: electrical and piping. Construction InduInstitute, Austin, Tex.

4. Thomas, H. R., Smith, G. R., Sanders, S. R., and Mannering, F. L. (1989). "An exploratory study of

productivity forecasting using the factor model for masonry." Rep. to the Nat. Sci. Found. Grant N

MSM861160, Pennsylvania Transp. Inst., Pennsylvania State Univ., University Park, Pa.

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Scheduled Overtime and Labor Productivity_ Quantitative Analysis