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Research Methods and Statistics in Psychology

Student resources, chapter 4: experimental design.

Learning Objectives:

• Identify the key components of an experiment, and go through the decisions involved in putting them together in order to design a good experiment.

With this objective in mind, watch the following video

Dr. Simine Vazire briefly describes experimental research.

What is experimental research?

• When you manipulate the both variables
• When you manipulate the dependent variable, to see if it has an effect on the independent variable
• When you manipulate the independent variable, to see if it has an effect on the dependent variable

Click for the correct answer.

Experimental Design

• First Online: 12 October 2022

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• Edward B. Magrab 2  

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In this chapter, the terms used in experimental design are introduced: response variable, factor, extraneous variable, level, treatment, blocking variable, replication, contrasts, and effects. The relations needed to analyze a one-factor experiment, a randomized complete block design, a two-factor experiment, and a 2 k -factorial experiment are derived. For these experiments, an analysis of variance is used to determine the factors that are most influential in determining its output and, where appropriate, whether the factors interact.

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When μ i = μ j , i  ≠  j , we see from Eq. ( 4.10 ) that μ  +  τ i  =  μ  +  τ j  →  τ i  =  τ j . Then, Eq. ( 4.11 ) becomes \( \sum \limits_{i=1}^a{n}_i{\tau}_i={n}_T{\tau}_i=0; \) therefore, τ i must equal zero for all i .

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Magrab, E.B. (2022). Experimental Design. In: Engineering Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-05010-7_4

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FAQs About Experimental Research Papers (APA)

What is a research paper? 

A researcher uses a research paper to explain how they conducted a research study to answer a question or test a hypothesis. They explain why they conducted the study, the research question or hypothesis they tested, how they conducted the study, the results of their study, and the implications of these results. 

What is the purpose of an experimental research paper? 

A research paper is intended to inform others about advancement in a particular field of study. The researcher who wrote the paper identified a gap in the research in a field of study and used their research to help fill this gap. The researcher uses their paper to inform others about the knowledge that the results of their study contribute. 

What sections are included in an experimental research paper?

A typical research paper contains a Title Page, Abstract, Introduction, Methods, Results, Discussion, and References section. Some also contain a Table and Figures section and Appendix section. 

What citation style is used for experimental research papers? 

APA (American Psychological Association) style is most commonly used for research papers. 

Structure Of Experimental Research Papers (APA)

• Answers the question of “What is this paper about and who wrote it?”
• Located on the first page of the paper 
• The author’s note acknowledges any support that the authors received from others
• A student paper also includes the course number and name, instructor’s name, and assignment due date
• Contains a title that summarizes the purpose and content of the research study and engages the audience 
• No longer than 250 words
• Summarizes important background information, the research questions and/or hypothesis, methods, key findings, and implications of the findings
• Explains what the topic of the research is and why the topic is worth studying
• Summarizes and discusses prior research conducted on the topic 
• Identifies unresolved issues and gaps in past research that the current research will address
• Ends with an overview of the current research study, including how the independent and dependent variables, the research questions or hypotheses, and the objective of the research 
• Explains how the research study was conducted 
• Typically includes 3 sections: Participants, Materials, and Procedure
• Includes characteristics of the subjects, how the subjects were selected and recruited, how their anonymity was protected, and what feedback was provided to the participants
• Describes any equipment, surveys, tests, questionnaires, informed consent forms, and observational techniques 
• Describes the independent and dependent variables, the type of research design, and how the data was collected
• Explains what results were found in the research study 
• Describes the data that was collected and the results of statistical tests 
• Explains the significance of the results 
• Accepts or denies the hypotheses 
• Details the implications of these findings 
• Addresses the limitations of the study and areas for future research 
• Includes all sources that were mentioned in the research study 
• Adheres to APA citation styles
• Includes all tables and/or figures that were used in the research study 
• Each table and figure is placed on a separate page 
• Tables are included before figures
• Begins with a bolded, centered header such as “ Table 1 ”
• Appends all forms, surveys, tests, etc. that were used in the study 
• Only includes documents that were referenced in the Methods section 
• Each entry is placed on a separate page 
• Begins with a bolded, centered header such as “ Appendix A ”

Tips For Experimental Research Papers (APA)

• Initial interest will motivate you to complete your study 
• Your entire study will be centered around this question or statement 
• Use only verifiable sources that provide accurate information about your topic 
• You need to thoroughly understand the field of study your topic is on to help you recognize the gap your research will fill and the significance of your results
• This will help you identify what you should study and what the significance of your study will be 
• Create an outline before you begin writing to help organize your thoughts and direct you in your writing 
• This will prevent you from losing the source or forgetting to cite the source 
• Work on one section at a time, rather than trying to complete multiple sections at once
• This information can be easily referred to as your write your various sections 
• When conducting your research, working general to specific will help you narrow your topic and fully understand the field your topic is in 
• When writing your literature review, writing from general to specific will help the audience understand your overall topic and the narrow focus of your research 
• This will prevent you from losing sources you may need later 
• Incorporate correct APA formatting as you write, rather than changing the formatting at the end of the writing process 

Checklist For Experimental Research Papers (APA)

• If the paper is a student paper, it contains the title of the project, the author’s name(s), the instructor's name, course number and name, and assignment due date
• If the paper is a professional paper, it includes the title of the paper, the author’s name(s), the institutional affiliation, and the author note
• Begins on the first page of the paper
• The title is typed in upper and lowercase letters, four spaces below the top of the paper, and written in boldface 
• Other information is separated by a space from the title

Title (found on title page)

• Informs the audience about the purpose of the paper 
• Captures the attention of the audience 
• Accurately reflects the purpose and content of the research paper 

Abstract 

• Labeled as “ Abstract ”
• Begins on the second page 
• Provides a short, concise summary of the content of the research paper 
• Includes background information necessary to understand the topic 
• Background information demonstrates the purpose of the paper
• Contains the hypothesis and/or research questions addressed in the paper
• Has a brief description of the methods used 
• Details the key findings and significance of the results
• Illustrates the implications of the research study 
• Contains less than 250 words

Introduction 

• Starts on the third page 
• Includes the title of the paper in bold at the top of the page
• Contains a clear statement of the problem that the paper sets out to address 
• Places the research paper within the context of previous research on the topic 
• Explains the purpose of the research study and what you hope to find
• Describes the significance of the study 
• Details what new insights the research will contribute
• Concludes with a brief description of what information will be mentioned in the literature review

Literature Review

• Labeled as “ Literature Review”
• Presents a general description of the problem area 
• Defines any necessary terms 
• Discusses and summarizes prior research on the selected topic 
• Identifies any unresolved issues or gaps in research that the current research plans to address
• Concludes with a summary of the current research study, including the independent and dependent variables, the research questions or hypotheses, and the objective of the research  
• Labeled as “ Methods ”
• Efficiently explains how the research study was conducted 
• Appropriately divided into sections
• Describes the characteristics of the participants 
• Explains how the participants were selected 
• Details how the anonymity of the participants was protected 
• Notes what feedback the participants will be provided 
• Describes all materials and instruments that were used 
• Mentions how the procedure was conducted and data collected
• Notes the independent and dependent variables 
• Includes enough information that another researcher could duplicate the research 

Results 

• Labeled as “ Results ”
• Describes the data was collected
• Explains the results of statistical tests that were performed
• Omits any analysis or discussion of the implications of the study 

Discussion 

• Labeled as “ Discussion ”
• Describes the significance of the results 
• Relates the results to the research questions and/or hypotheses
• States whether the hypotheses should be rejected or accepted 
• Addresses limitations of the study, including potential bias, confounds, imprecision of measures, and limits to generalizability
• Explains how the study adds to the knowledge base and expands upon past research
• Labeled as “ References ”
• Correctly cites sources according to APA formatting 
• Orders sources alphabetically
• All sources included in the study are cited in the reference section 

Table and Figures (optional)

•  Each table and each figure is placed on a separate page 
• Tables and figures are included after the reference page
• Tables and figures are correctly labeled
• Each table and figure begins with a bolded, centered header such as “ Table 1 ,” “ Table 2 ,”

Appendix (optional) 

• Any forms, surveys, tests, etc. are placed in the Appendix
• All appendix entries are mentioned in the Methods section 
• Each appendix begins on a new page
• Each appendix begins with a bolded, centered header such as “ Appendix A, ” “ Appendix B ”

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Improved Surface Drainage of Pavements: Final Report (1998)

Chapter: chapter 4 experimental studies.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

CHAPTER 4 EXPERIMENTAL STUDIES A number of experimental field and laboratory studies were necessary In order to provide the data needed to develop the models used In the PAVDRN software. Permeability measurements were obtained in the laboratory for open-graded laboratory and field asphalt mixtures in order to obtain their coefficients of permeability. Mean texture depth measurements were obtained for all of the pavement surfaces tested In the laboratory and field using either the sand patch or a profiling method. Water film thickness measurements were obtained In the laboratory with a color-~ndicat~ng gauge and a point gauge. The color ndicat~g gauge was used exclusively In the field for water film thickness measurements. The Indoor artificial rainfall simulator at Penn State was used in the laboratory to determine ManIiing's n for porous asphalt surfaces and to extend the existing data on Portland cement concrete surfaces to longer flow paths as required for PAVDRN. In the field, full-scale skid testing measurements were needed to extend the hydroplaning model to porous pavement surfaces and to verify the effect of Portland cement concrete grooving on hydroplane g speed. These data were obtained by conducting filll-scale skid test measurements on porous asphalt surfaces installed at the Penn State Pavement Durability Research Facility. Full-scale skid testing was also performed on grooved PCC surfaces at the Wallops Flight Facility. The fulI-scale held skid testing required measurements 69

at different speeds on the surfaces flooded with water at different fihn thicknesses. The test facilities, test melons, and test results are discussed In this chapter. TEST FACILITIES Indoor Artificial Rain Facility The pavement test surfaces were formed ~ a rectangular channel that was 0.30 m wide and 7~3 m long. The sides of the channel were formed by two BO-mm by 160-mm steel angles that were mounted 0.30 m apart, as shown ~ figure 15. To complete the channel, the steel angles and 20-mm thick sheets of plywood were bolted to the top flange of a 7.3-m wide flange WI2x53 steed beam as shown In figure 16. A jacking system allowed the longitudinal slope of the beam to be adjusted to provide a range of slopes. The porous asphalt concrete and Portland cement concrete were placed In the channel, providing the test surfaces for measuring Mami'ng's n. Artificial rainfall was generated with a series of nozzles placed above the test surface, as shown In figure 17. Extensive evaluations were performed previously to calibrate the rainfall rate and to select appropriate nozzles, spray angles, nozzle distances from the channel pressure settings, etc., to ensure that the rainfall rate was uniform over the entire surface (35). Consequently, the procedures and testing equipment developed previously were used for this study (299. 70

Plywood base Porous asphalt mixture Steel angle to form sides . Ad. ~ '~-~-~'J-~-~-~-~'~'~-~- - -~'~-~-~'~-~-~'~-~ ke%-%'~'~'~'~'~'%-~-~-~-~-~-~-~-~-~-%'%- - -~-~-~-~-~. '~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~d i..~.~-.~-.~.~-.~.~.~-.~.~-.~-.~-~-.~-.~.~.~-.~-.~-.~-.~-.~-.~-.~-~-~1 Steel"~" section to support base and mixture a, , Note: Elevation of one end of steel beam can be adjusted to change longitudinal slope of drainage surface.  Figure 15. Cross-section of pavement used in laboratory rainfall simulator. 71

Figure 16. Overall view of test channel used with laboratory rainfall simulator. 72

~ : ~.,~ ~ ~ ~ ~ ~:::::: :~: ~i: :~: :::::::::: ::::::::::::::::::::::::::::::::::::::::::::::::::: ::: ~:~::~: ~:~::~:::~:::::: If: Figure 17. Laboratory rainfall simulator. 73 _ ~

The channel was limited In length to 7.3 m. With this length and the maximum rainfall rate, the largest Reynold's number that could be generated was approximately 130. However, in this study it was necessary to measure WET values in flow regimes with Reynold's numbers greater than 140. Since the maximum rainfall intensity was 75 mm/in, it was necessary to effectively increase the drainage path lengths to achieve higher Reynold's numbers. This was done by introducing a flow at the top of the channel so that the channel represented the last 7.3-m segment of a longer flow path. For example, to create a 14.6-m long flow path, the flow that would be accumulated over the first 7.3-m segment was introduced at the top of the channel, effectively making the channel act as the last 7.3-m segment of a 14.6-m long flow path. The flow introduced at the top of the channel was commensurate with the rainfalls rate on the channel, adjusted for non-turbulent conditions in the first 0.5 m of flow. A small adjustment in the introduced flow rate, as calculated on the basis of the rainfall rate, was necessary because the turbulence caused by pelting raindrops impede flow. Approximately 0.5 m was required to develop fully turbulent flow, causing the actual flow to be greater than under conditions where the flow on the entire 7.3-m channel length was filly turbulent. This phenomenon has been observed by others when analyzing the short, sudden rise in flow at the end of rainfall-runoff hydrographs (39J . The adjustment was determined experimentally by measuring the flow at the end of the channel for different rainfall rates. The flow was introduced at the top of the channel in a gentle spray applied directly onto the concrete surface in the channel. 74

For the porous asphalt mixtures, the How through the mixture had to be determined and evaluated e A distribution box with a baffle was placed at the top of the channel to provide a base flow through the porous mixes. The bottom of the channel was sealed to a depth of 12 mm below the top of the surface, effectively forming a dam to prevent ~awdown effects of the flow through the porous asphalt. If the bottom was left completely open, the water surface profile would draw down dramatically at the end of the channel, which would lessen the length of the channel that could be used for experimentation. This arrangement is shown in figure 18. Production and Placement of Porous Mixes Three porous mixes were tested In ache laboratory under artificial rainfall. Each mixture was designed to yield a different mean texture depth and air-void content. Attempts to place hot-mixed asphalt in the channel were not successful, and instead, a slow-setting epoxy was used as the binder for these mixtures by replacing the asphalt binder on a vol~netric basis. The epoxy had a curing tune of six hours, which allowed for an adequate time to place and compact the mixes. A number of trial mixtures were prepared to obtain a range In air void content and MTD. The composition of the resulting three porous asphalt mixtures placed in the laboratory is shown in table 8, and the gradations are presented in figure 19. The mixes were prepared from a blend of two coarse aggregates (PennnOT gradation IB and 2B, bow Innestone, retained on No. 4 sieve) and washed glacial sand (siliceous, passing No. 4 sieve). Norrunal 75

·1 a) ~ - ~ - ~ a) ~ ~ - co o - an al := ce m cats .E ~ 3 ~ O O ~ m .= it' \ \ \ ce a, ma,, oEl ! I; ,1~ x . - Q In In 3 2 o ID 3 Cal o (D- In ~ ~ o CO ° Figure 18. Cross-section of flow for porous asphalt sections In laboratory 76 .

1 .~ i 1 1 ; 1 i I , Ad ~ ~ 2 X ~ an Y CD CO ~_ oh an oh : I l : 1 1 ~ m {D 0 ye Cal Q 1 1 Con ~2 o o Q  J 1 1 _ Al ~ cry E - _ ~ ~ cad E E _ _ ., E 1~''-~'- 1-..t -ant. ~-.~ -A 1 1 1 1 1 - ~ 4g, f 1 l l =~- 5= 1 1 , .V ~ ~-~ ~. o o o o ~ CO C ~ o o o o 0 ~ ac ~ ~ E 0 0 0 ~ u, 6u!ssed o/O ~ E 1 _ _ _ %, `. . -\, ~: \\ (' \. \\~.t Figure 19. Gradations of laboratory and field porous asphalt mr~tures. 77

Table 8. Mixture designs for porous asphalt laboratory mixes. Component Mixture (% by total weight of aggregate) A B C 2B Aggregate ~44 IB Aggregate 75 75 34 Washed Sand 19 19 20 Hydrated Lune 6 6 2 Epoxy (%wt. of 7 7 5.5 total mix) maximum size is 9 and IS mm for IB and 2B aggregates, respectively. Hydrated lime was added to thicken the epoxy and prevent drainage of the epoxy from the mixture. Mixture A was designed using the guidelines and design process as outlined for open-graded friction courses as published by NCHRP (109. This mixture was placed by hand, resulting in a very high a~-void content, as illustrated In table 8. Mixtures B and C were placed with a vibratory compactor; the gradations and max~m~,rn aggregate size were selected to account for the increased compaction and to give a range in air voids and MTD. The compactor, developed as part of this study, consisted of a 0.30-m square by 25.4-mm thick steel plate with an air vibrator mounted on top of the plate. The vibrator is used commercially for applications such as vibrating granular materials from storage bins. It is rated at 2,400 cycles per minute with 7.2 kN of applied force per cycle. The entire assembly weighed 580 N. A photograph of the assembly is shown In figure 20. 78

Figure 20. Photograph of vibratory compactor. 79

Cores were taken from the mixtures placed ~ the channel, and sections were removed for sand catch and profile testing. Air void content was determined for each mix ~ accordance with ASTM D 3203-88, "Standard Test Method for Percent Air Voids In Compacted Dense and Open Bituminous Paving Mixtures, and the results are shown In table 9 Table 9. Air voids In laboratory porous asphalt mixes. . Distance along Channel (m) Mix A Mix B % Air voids Mix C . 0.9 32.1 23.0 19.5 1.8 33.7 -- 20.2 2.7 34.6 23.7 23.4 3.7 29.0 22.9 19.8 4.6 32.5 22.9 21.8 5.5 32.5 22.5 20.0 6.4 7.3 Avg. 33.1 33.0 32.6 25.5 23.4 20.7 -20.8 Maxi Theoretical 2.460 2.467 2.504 Specific Gravity Outdoor Test Facilities Full-scale field skid testing was needed to verify the hydroplaning potential of the open graded asphalt concrete and grooved Portland cement concrete surfaces. Initially' this testing 80

was scheduled for the Wallops Flight Facility, and full-scale porous pavement sections were to be Installed at the facility. The facility offered a large flat area for testing at high speed with excellent support services. Unfortunately, after considerable planning, it became logistically impossible to place the test sections at the Wallops Flight Facility. After careful consideration of the alternatives, a decision was made to install four porous asphalt sections at the Penn State Pavement Durability Research Facility. However, it was decided that the research would continue to include the testing of the grooved and un-grooved PCC pavement at the Wallops Flight Facility, given that this pavement was in place, and no new construction would be required. Thus, the field skid testing was conducted on two PCC sections (broomed and broomed with groovings at the Wallops Flight Facility and on four open-graded asphalt concrete sections at the Penn State Pavement Durability Research Facility. Penn State Pavement Durability Research Facility The existing surface on which the mixes were to be placed required leveling because the surface was rutted and had a large cross-slope. The testing area was a tangent section at the facility with an average longitudinal slope of one percent, approximately 3.65 m (12 fit) wide and 200 m (600 It) long. First, the surface was milled to eliminate the existing cross- slope. Next, a typical PennDOT dense-graded ID-2 surface overlay (dense-graded with 9.0- mm top size) was placed over the test area to further eliminate any cross-slope and to provide a smooth testing area. The 2-m (6-ft) wide middle portion of the new overlay was then milled to a depth of approximately 40 mm (~.5 Ins. This, ~ essence, created a 2-m wide and 200-m long "bath tub" In which the porous asphalt mixes were placed, as presented In figure 21. 81

Width of flooded section Tubing fastened to / pavement surface \ / with silicone sealant V ~ /:...,-. Porous asphalt mixture / ~/~' / . IJi hi/ :, ~ // /,// '\\\ ~ Milled base '\,~ / /~ / vat ~ .. , . ~ , , ~ , ~ ~, i// ~ _ , ,,,~v-/ , // // / / / Dense-graded // / wearing course / / ~ Surface / seal New wearing course mix ` Vertical and horizontal surfaces sealed with asphalt cement to prevent leakage Figure 21. Schematic of test sections at the Penn State Pavement Durability Research Facility. 82

Four porous asphalt mixes were designed and placed at the test track facility with the cooperation of a local hot-mix contractor. A 40-m transition zone was established between each test section to allow the mixes to "run out" of the paver as the mix design was changed during the paving operation. This procedure was to ensure that the material In each test section was representative of the desired mix and not con~ninated with material from an adjacent section. Wooden 2-ft-by-4-ft boards were placed across the 4-m (12-ft) lane width at the end of each test section to separate the test section from the transition sections. These boards were later removed, leaving a small trench across the pavement at the ends of each test section. The gradation used for each of the mixtures is presented In table 10. The coarse aggregate was a local Innestone, and the sand was from a siliceous glacial river deposit (same material as used in the laboratory mixtures). The mixtures were designed to yield a range of air void contents and maximum aggregate size. Mixture ~ is based on the FHWA mixture design procedure for open-graded asphalt maces as outlined ~ NCHRP Synthesis 49 (109. Mixture 2 was based on a gradation reported by Hud~eston et al. (9J. Mixtures 3 and 4 were designed to represent Apical gradations as being performed by transportation agencies in France (161. Polyester fibers were added to each niLxture to men mire any tendency for drainage of the asphalt binder during construction. The binder content was selected in accordance with the standard design procedures detailed elsewhere, resulting In the binder contents shown In table 10 (101. 83

Table 10. Porous asphalt mix designs at the Penn State Pavement Durability Research Facility. Sieve Percent Passing Size Mix 1 Mix2 Mix3 Mix4 38 mm 100 100 100 100 25 mm 100 100 100 100 l9mm 100 99 100 100 13 mm 100 62 100 100 9mm 97 27 97 100 No. 4 28 6.9 29 76 No. 8 13 4.9 7.1 16 No.16 7.0 3.2 3.3 8.3 No. 30 4.4 2.2 2.4 5.5 No. 50 3.0 1.7 1.9 4.0 No. 100 2.0 1.4 1.5 2.9 No. 200 1.0 0.6 0.8 1.2 Asphalt Cement(%)l 6.5 5.0 6.0 6.5 Polyester Fibers(%)2 0.5 0.4 0.4 0.4 1Based on total weight of aggregate. 2Based on total weight of mixture. The objective of the testing at Penn State was to determine the effect of the water fihn thickness on the hydroplaning potential, which required that the test sections be flooded during the testing. Applying water in the conventional manger with the standard ASTM E 274-90 ("Standard Test Method for Skid Resistance of Paved Surfaces Using a Full-Scale Tire") skid Mailer would not give controlled or measurable water film thicknesses, and therefore, it was necessary to flood the test sections. Water was introduced ~ the trough formed by the four 84

wooden boards at the head of each section. The water was then allowed to flow over the entire length of the section, as depicted in figure 22. The depth of flow was controlled by adjusting the rate at which water was added to the trough. The longitudinal slope, approximately one percent, provided a reasonably uniform flow over the length of the section except at the beginning and end of the sections. The test sections were designed so that the flow of water through the pavement could be measured, thereby obtaining ~n-situ permeability measurements. This proved impractical because, in spite of being sealed with hot asphalt cement, leaks occurred in the depressed section. Water film thickness measurements were obtained just prior to each skid test using a color-indicat~ng gauge as described later In this chapter. Sand patch and profile measurements were also acquired for each section, and cores were obtained for laboratory permeability testing. The skid tester used for this project is a Penn State design, In which a s~ngle-whee! trailer is affixed to the rear of a modified heavy-duty pickup truck. The tester, commonly referred to as the s~gle-whee} skid tester, incorporates a s~x-force transducer into its design. This enables horizontal, vertical, and side force measurements. For this project, the s~ngle whee} skid tester was mounted in the center of the pickup to eliminate the effects of the truck tires on the waterfihn thickness. The testing was performed in accordance with ASTM Standard E 274. A photograph of the tester can be found in figure 23. 85

Figure 22. Introduction of water onto test section at the Penn State Pavement Durability Research Facility. 86

I: ::: :-:: ::: :: ::: :-:::: :::.::: :: :: :. ~ : : :-: .: ::: ::: ::::: I: ~ ::::: ::: :::: :: :::: :~: :: :: E .. .% A... ... ~ . ~ hi,. . ~ > . .. . . ·. ~,. ~. .. ~ .. ~ .~. ~ ..~ . Figure 23. Skid test in progress at the Penn State Pavement Durability Research Facility 87

Wallops Flight Facility The testing at the Wallops Flight Facility was performed in much the same mater as at Penn State. The sections were dammed, and water was flooded over the sections. Unforhmately, the water fiLn thickness was not as controlled as at the Penn State site, and only one water film thickness was reliably obtained. A photograph of a test in progress is shown in figure 24, and a photograph of the Portland cement concrete surface is presented in figure 25. MEASUREMENT TECHNIQUES Measurement of Water Film Thickness A point gauge was used to measure water film thicknesses in the laboratory (299. A point gauge is a pouted probe that is lowered from a stand until it comes in contact with the water surface. WET measurements were made at 0.3-m increments along the length of the channel. Three measurements, located at the m~-width and at the quarter-widths of the surface, were obtained at 0.3-m increments along the length of the surface. The three measurements were then averaged to obtain one WFT measurement for each 0.30-m increment along the length of the surface. 88

Figure 24. Test in progress at the Wallops Flight Facility. 89

Figure 25. Grooved concrete surface at the Wallops Flight Facility. 90

below the top of the asperities of the aggregates. The use of a flow date is discussed by Reed et al. (29) . To obtain the data, a 25-mm~iameter metal disc was first placed on the pavement surface at the measurement location, and a point gauge reading was obtained on the top of the disc. Next, a reading was taken on the surface of the flowing water, and the thickness of the washer plus one MID were subtracted from the point gauge reading on the water surface to obtain the flow depth. The method is illustrated In figure 26. In order to relate the hydroplaning speed to the water fihn thickness and to validate the water film thickness model, the water film thicknesses had to be measured In the field and In the laboratory during rainfall. The point gauge and other devices available for making these measurements were judged unacceptable for field use because the measurements are slow and tedious to perform and cannot be obtained during rainfall. Therefore, alternate procedures for measuring the WET were considered. The gauge that was ultimately adopted consists of a sheet-metal fixture bent In the form ot an Inverted "U." as shown In figure 27. The legs of the U are approximately SO mm high, and spaced 30 mm apart. The fixture is approximately 150 mm In length. To make a water film thickness measurement, the "legs" of the fixture were coated with a paint-like coating that changes color when wet. To obtain the data, the fixture was placed on the pavement with its "legs" immersed In the water fiIrn. The water film thickness is then determined as the dunension over which the coating changes color. 91

Note: Measured water film thickness is the difference betvveen point gauge reading on water film and washer plus thickness of washer as follows: WFT = (RPG' - RPGw3 + tw where WFT = Water film thickness RAG, = Reading of point gauge in contact with surface of water film RPGW = Reading of point gauge in contact with top of metal disk t`N = Thickness of metal disk Total flow depth, y RPG' Water film thickness, / WFT / ~ Ll t' ;; ~iVi . ~ MTD / ~ P_ , ~ ~ W Pavement surface ~ Figure 26. Measurement of water film thickness with point gauge on a porous asphalt surface In laboratory. 92

Sheet metal frame on pavement surface Top of water film Water-sensitive coating Height of coating with color change ~ Pavement surface 1. Figure 27. Schematic of the color-indicatillg water film thickness gauge. 93

The coating is initially yellow but turns bright red when it comes in contact with water. The device was calibrated in the laboratory by comparing water depths from the Portland cement concrete surface measured with the point gauge and the color-~ndicating gauge. The water film thickness measurements were obtained with a point gauge and with the color indicatiIlg gauge. The water film thickness values measured with the color-indicat~ng gauge were larger than the water fiDn thickness values measured with the point gauge because water "wicks" up the coating when the coating is wet. Sixty pairs of data points were obtained In the laboratory, with the color-~ndicating gauge and the pout gauge. A regression of the data points resulted In the relationship: KK = 0.907 WFT + 3.~1 where KK WET Color-indicating gauge reading (mm) Actual water fiDn thickness value (mm) (26) with a correlation coefficient (R2) of O.85. This relationship is displayed graphically ~ figure 28. The color-~ndicat~g gauge was used for all of the held testing conducted at the Penn State Pavement Durability Research Facility and the Wallops Flight Facility. 94

10 ~9 ·3 J _ In ~ ~ _ ~ a) E ~ V CD by oh _ 5 a) ~ ._ Y ~ e_ _ ._ O o ._ Cal a) - 8 7 t! 4 Cow o 3 2 1 o lo -A °~a9' KK= O.907W~+3.81 R2 = O.851 . . . . . . . . . . . . . . . . . j , _ o 1 2 3 4 5 Water film thickness, WET, measured with point gauge, mm Figure 28. Correlation of water film thickness measurements obtained with the color indicating gauge and point gauge. 95

Measurement of Surface Tenure A portable texture measuring device was used to perform surface texture profiles for the laboratory and field mixtures. The device produces an analog profile of the surface that can be digitized and analyzed statistically. The device is essentially a probe that is moved along Me surface of the pavement and is described in detail elsewhere (409. A motor and appropriate electronic circuitry cause the probe to follow the pavement surface as the probe is moved horizontally over the surface. The procedure for using this device has been standardized by ASTM as Designation E IS45-96, "Standard Practice for Calculating Mean Profile Depth." The procedure requires that two profile segments, each 100 mm in length, be obtained. The mean profile depth (MPD) for each of these profile segments is calculated by regressing the profile depth versus profile length, and the two values are then averaged to obtain the mean profile depth. The process is s~nnTnar~zed In figure 29. The mean profile depth can be used to estimate the mean texture depth (ETD in equation 27) by a linear transformation (411: MTD=ETD = 0.2 ~ 0.8 MPD (mm) The mean texture depth is by definition obtained with the "sand patch" (volumetric) (27) method, ASTM E 965, "Standard Method for Measuring Surface Macrotexture Depth Using a Volumetric Technique." The ETD is an estimate of the MTD. Mean texture depths were also obtained from sand patch testing (ASTM E 965) performed in the field and on laboratory 96

I . Profile measurements Calibrate the measuring system (when appropriate) and measure the profile of the surface. 2. Handling of invalid readings Readings of this profile that are invalid (drop-outs) shall be eliminated or corrected. 3. High-pass fiZtenug Unless slope suppression according to point 6 in the following is used, high-pass filtering should be performed. It consists of removing spatial frequency component that are below the specified passband. 4. Low-pass fiItenng Remove frequency components that are above the specified passband. This can be accomplished either by analog filtering or averaging of adjacent samples, or automatically met through the performance of the sensor. 5. Baseline limiting Pick out a part of the profile that has a satisfactory baseline. 6. Slope suppression The slope will be suppressed by the calculation of the regression Ime and subsequent subtraction of this line. An alternative is to apply appropriate high-pass filtering (see point 3 above). 7. Peal determination The peak value of the profile over the baseline length is detected. 8. MPD determination The mean profile depth (MPD) is calculated as the peak according to pout 7 above minus the profile average, which will be O according to points 3 or 6 above. 9. ETD calculation The MPD value is transformed to an estimated texture depth (ETD) by applying a transformation equation, ETD = 0.2 ~ 0.8 MPD. 10. Averaging of MPD and ETD values Individual values measured on a site or a number of laboratory samples are averaged. This includes the calculation of the standard deviation. Figure 29. Steps in determining texture depth using the profiling method (42~. 97

samples. In this test, a known weight of glass beads is placed on the pavement surface and spread by hand with a rigid scrapper until the surface voids are filled. The area of the resulting "patch" of glass beads is related to the MID. Measurement of Mann~ng's n Mann~ng's n for the Portland cement concrete surface and for the porous asphalt surface was calculated by measuring the water film thickness on these surfaces with varying rainfall rates arid surface slope. The theoretical base for the calculation is given in Appendix C, and the results of the calculations are presented in Chapter 3. Measurement of Permeability The static coefficient of permeability of porous asphalt concrete mixtures is a necessary input for the PAVDRN model. The customary procedure for measuring the In situ permeability of porous asphalt mixtures is to use an outflow meter. The outflow meter does not give permeability values ~ ndamental units, but instead provides an empirical measurement of the permeabili~ n terms of the quantity of flow per unit of time. Because the flow is unconfined In a radial direction, it is not possible to calculate a coefficient of permeability from the coIlventional outflow meter. Further, for OGAC, the flow is partially in the macrotexture and partially within the mix. Therefore, a direct measurement of static permeability was used for the mixtures that were tested as part of this project. Because of the 98

large coefficient of permeability of porous asphalt mixtures, a standard falling head parameter cannot be used. In order to obtain a measurable flow, a large quantity of water would be required and the rate of flow would be excessive, certainly in the turbulent region. Consequently, a drainage lag permeameter, originally described by Barker et al. (43) was used for the permeability measurements. The device, as shown in figure 30, consists of a tank, a sample container that confines the flow to the vertical direction, and a quick-release valve. Full thickness samples from the artificial rain facility were cut into squares approximately 80 mm by 80 mm in length and width, and sheet metal was expoxied to the sides of the samples to constrain the flow in the vertical direction. The permeability of the samples was measured in both the vertical and horizontal direction by testing samples oriented in both directions. Separate samples were used for each direction. This procedure ensured that there was no leakage around the periphery of the samples and that the flow occurred In the vertical direction. S~x-~nch cores were obtained from the test track facility, and the lower layer of hot mix was trimmed from the cores, yielding a section that consisted of only the permeable asphalt mixture. These cores were sealed around their circumference to confine the How to the vertical direction. The cores were then inserted ~ a 6-'n diameter sheet metal tube and sealed around their circumference using silicone sealant, ~ the same manner as the rectangular samples from the indoor rain facility. Once the cores were tested in the vertical direction, they were removed from the container, and rectangular-shaped sections for testing in the horizontal direction were sawn from the cores. These cores were tested in the same manner as the 99

Water height after time t' 1 - - Initial water height 7 r ~l / / \ . Sample Container Test specimen Perforated shelf \ - 1 1 Figure 30. Schematic of drainage lag permemneter. 100 Tank Quick opening valve 7

rectangular-shaped cores from the artificial rain facility. This procedure provided a vertical and horizontal coefficient of permeability for the field cores. All of the samples were vacuum-saturated prior to testing. The samples were immersed In a flooded transfer vessel to a level above the sheet metal containers and placed In a vacuum chamber. A vacuum was applied to die samples until they ceased bubbling, using techniques similar to those used In measuring the maximum specific gravity for asphalt concrete, as specified ASTM D 3203-94, "Standard Test Method for Percent Air Voids In Compacted Dense and Open Bituminous Paving Mixtures." Once the samples were saturated, they were placed In the tank, the quick opening value was opened, and the water draining from the tank was collected in a container during the tune interval when the water level in the tank Intersected successive points on the hook gauge. This provided sufficient data to calculate the coefficient of permeability in accordance with the equation reported by Barker (43), where: 276 ad h, k= A log h Q = k A h d 101 (28) (29)

where Q = Rate of flow (ft3/s) (1 ft3/s-0.028 m3/s) k = Coefficient of permeability A = Gross area of sample perpendicular to direction of flow (ft2) (1 ft2 = 0.093 m2) h = highs, Head loss at distance d In sample In direction of flow (ft) (1 It = 0.305m) Three tests were performed on each core In both the vertical and horizontal flow direction. Measured permeability values for the porous asphalt mixes from the field (mixtures 1 through 4) and the laboratory mixtures (mixtures A through C) ranged from 20 to 40 mm/s. Given the narrow range of ache measured values and the likelihood of reduced permeability resulting from plugging due to road detritus, the use of the drainage lag parameter is not recommended for routme testing or as a design procedure. TEST RESULTS Flow on Porous Asphall Sections In order to determine the surface flow rate for the porous mixtures it was necessary to determine the flow rate through each mixture that would saturate the mixture to a height of one MTD. This "base flow" was subtracted from the total flow to yield only the surface flow as illustrated ~ figure 3 I. In order to determine the base flow, a plot of flow depth versus total flow was constructed as shown in figure 32. These plots were prepared for each surface and 102

Rainfall Intensity, I . I l l' ~I I I 1. 1 1 1 1 _ Total AL surface _ . tl~lVV~-$ flow A A MA ]~--' ~ Base flow J" J Figure 3 1. Definition of base and surface flow in porous asphalt sections. 103

160 140 120 100 80 3 LL 60 40 20 o : _ 1 1 R2 = 0,99 F , . 1 . 0 5 10 15 20 25 Distance along channel, m Figure 32. Plot of total flow versus flow path to determine flow depth. 104

for each rainfall rate and slope. The base flow rate depended on the mix, the slope of the channel, and the rainfall intensity and varied from 1.5 ml/s for mixture B (25 mTn/h) to 53 ml/s for mixture A (75 mm/h). The use of the water film thickness values corrected for the base flow is discussed in detail in Appendix C where the development surface-specific equations for Manning's n is presented. Texture Measurements Texture measurements were made on the surfaces tested In the laboratory and field. The conventional sand patch technique causes problems with highly open mixtures because the glass beads flow into the internal voids in the mixture, giving a false value of texture depth. To overcome this problem, texture measurements were made on the laboratory porous mixtures using the conventional sand patch procedure on a cast of the surface. Texture depths were also estimated from profile measurements made on the original surfaces as presented in table 1 1. The casts, or replicates, were made by first placing silicone rubber on the original surface over an area of approximately 0.30 m by 0.30 m (1 ft by 1 ft). A plate was placed over the silicone rubber in order to force the rubber into the surface texture. Once the silicone had cured, it was removed and placed into a second form. A polyester casting resin was then poured over the surface of the silicone rubber and, on curing, separated from the rubber. The casting resin gave a positive replicate of the original surface. Casts were obtained from each porous asphalt surface, spaced at equal intervals down the length of the test surface that was 7.3 m long by 0.3 m wide, and sand patch measurements were made on the casts. The results of this procedure are shown In table ~ ~ . The mean 105

texture depths of mixtures A, B. and C are visibly different and fall within the expected range of ~ mm (0.04 in) to 3 mm (0. 12 Ins. Profile traces were used to calculate estimated texture depth (ETD) according to ASTM E IS45-96, "Standard Method for Measuring Surface Macrotexture Depth Using a Volumetric Technique," ISO standard as described In figure 29 (421. The results are presented in table ~ ~ . The sand patch measurements on the original surface are suspect, especially for mixture A. The profile measurements were difficult to obtain because the probe constantly stalled In the deep voids. Based on these facts, sand patch measurements on replicates of the surface are the recommended technique for making texture measurements even though it may not be convenient for field testing, particularly on highly trafficked pavements. Texture measurements made at the Penn State Pavement Durability Research Facility are found in table 12. Table 11. Texture Kept measurements on laboratory porous asphalt sections. Distance MTD Values (mm) along channel (m) Mix A Mix B MLX C 0.3 1.45 1.04 2.34 1.5 1.60 -- - 3.2 2.13 1.07 2.24 3.6 1.57 1.45 - 4.8 - 1.24 1.98 6.3 1.47 1.93 Average 1.70 1.24 2.13 Sand patch directly on 5.1 1.9 2.3 surface, Average (mm) MTD estimated from profile measurements 2.54 2.26 2.92 directly on surface (see figure 29) 106

Table 12. Sand patch data obtained at the Penn State Pavement Durability Research Facility. _ Mean Texture Depth Sand Patch Diameter (rnm) (in) MixStation ~2 3 4 Average Station Section Average Average 1105 149.2 136.5 139.7 139.7 1411.55 75 139.7 139.7 146.1 136.5 1401.60 45 146.1 146.1 146.1 139.7 1441.55 15 152.4 158.8 158.8 158.8 157 145 1.27 1.5 2 105 88.9 95.3 88.9 88.9 90 3.66 75 95.3 95.3 88.9 88.9 92 3.66 101.6 101.6 101.6 88.9 98 3.12 15 88.9 88.9 82.6 82.6 85 91 4.11 3 105 133.4 133.4 133.4 136.5 134 1.73 75 136.5 139.7 133.4 139.7 137 1.65 45 146.1 146.1 139.7 139.7 142 1.55 15 146.1 127.0 139.7 146.1 139 138 1.60 1.6 4 105 165.1 165.1 158.8 168.3 164 1.14 75 177.8 165.1 171.5 177.8 173 1.04 177.8 165.1 177.8 177.8 174 1.02 15 171.5 158.8 165 166 169 1.12 1.1 Full-Scale Skid Testing Full-scale skid testing was done at the Penn State Pavement Durability Research Facility and at the Wallops Flight Facility. The results of the testing performed at the Penn 107

State Durability Research Facility are presented In figures 33 through 36 for the four test sections. A great effort was required to obtain these results. The sections were dammed along their side and flooded (one section at a tune) as described previously In this chapter. The skid trailer was driven at different speeds down the track, and the tire, a bald ASTM E 524-88 ("Standard Specification for Standard Smooth Tire for pavement Skid-Resistance Tests") tire, was locked over the flooded middle portion of the section. Water film Sickness measurements were taken with the color-~ndicat~ng gauge at intervals along the section immediately before each test as described previously. This resulted in nearly 50 sets of skid resistance-water film thickness data. In general, relatively uniform water film measurements were obtained, and only a few of the data sets were discarded. Analog traces of wheel friction recorded by the tester were examined for anomalous data. In order to obtain a zero thickness value of skid resistance, the wheel of the trailer was locked on each section with no flooding but with a damp surface. In general, replicate runs were made at each water film thickness and speed. Although there is considerable variability In the data, several conclusions can be drawn from the test results. For the water film thicknesses that were tested, the skid resistance values were less than the "zero thickness" values. For each section, the skid resistance decreased as the water film thickness Increased. However, the skid resistance typically reached a minimum and then unexpectedly increased with increasing water film thickness. After some thought, this was considered reasonable, explained by the "plough~ng" effect of the wave of water pushed by the locked tire. Minim~:nn skid resistance values were in the range of four to ten depending on the test section. Hydroplaning occurred on all of the test sections at 60 and 90 Inch when the water film thickness became high. 108

1 · Flooded pavement, 30 km/in · Flooded pavement, 60 km/in · Flooded pavement, 90 km/in 0 Wet pavement, 60 km/in 1l 0 Flooded pavement, ribbed tire, 60 km/in I 60 50 Z~n a) Q ~ 30 ~5 z ~ 20 In 40 10 l 3° km/in | \ ~ ~60 km/in l \ 4~ |9o km/in | ~ - . . i 0.0 5.0 1 0.0 1 5.0 Water film thickness, mm Figure 33. Skid resistance measurements at ache Penn State Pavement Durability Research Facility, mixture I. 109

Flooded pavement, 30 km/in · Flooded pavement, 60 km/in · Flooded pavement, 90 km/in 0 Wet pavement, 60 km/in ll l 50 45 40 z 35 ; 30 Q z co 1 5 25 20 10 5 O \ \/|30km/h| / .~ ~ 1 ~ \ / L \ ~. \ ~160 km/in| '~ . ~ ~ 0.0 5.0 1 0.0 1 5.0 20.0 Water film thickness, mm ,~ Figure 34. Skid resistance measurements at the Penn State Pavement Durability Research Facility, mixture 2. ~0

60 50 Z 40 Q 30 ~ 20 cn 10 O 0.0 l Flooded pavement, 30 km/in · Floodecl pavement, 60 km/in · Flooded pavement, 90 km/in o Wet pavement, 60 km/in \~ / - ~ 130 km/in 1 60 km/in I 1 / 90 km/in ~ - - . ! l l - 5.0 1 0.0 1 5.0 20.0 Water film thickness, mm Figure 35. Skid resistance measurements at the Penn State Pavement Durability Research Facility, mixture 3 .

· Flooded pavement, 30 km/in · Flooded pavement, 60 km/in · Flooded pavement, 90 km/in o Wet pavement, 60 km/in 70 60 z 50 ~n - ~ 40 ~ Q z 30 ~5 ._ C'' 20 10 o /13° km/in| ~ ~ my, ,/~k ~ ~ : ! . . ~I 1 0-0 5.0 1 0.0 1 5.0 20.0 Water film thickness, mm Figure 36. Skid resistance measurements at the Penn State Pavement Durability Research Facility, mixture 4. ~2

The results from the testing In the grooved and plain Portland cement concrete at the Wallops Flight Facility are shown in table 13 arid figures 37 and 38. Quite surprisingly, the skid resistance versus water film thickness relationship for the grooved versus the plain Portland cement concrete surface was very similar when the mew texture depth is calculated using the surface at the top of the grooves as the Datsun. Thus, although the grooves are a definite aid ~ removing water from the pavement surface, they do little to relieve the water film from beneath the tire. This effect is not apparent ~ the standard ASTM E 274 test as illustrated In figures 34 and 35. ~ the opinion of the researchers, this is also the case with porous asphalt surfaces. In other words, We main contribution offered by porous asphalt pavement surfaces to the lowering of hydroplaning speed, even though it is a very significant contribution, is ache Increase In the mean texture depth that these surfaces offer. These findings do not agree with maIly practitioners who fee! that the grooving and large texture ~ porous mixtures allows the water to drain from beneath the tire. Of course, Me findings here are for the locked bald tire according to ASTM E 274, and the findings may be different for more heavily loaded truck tires or grooved passenger tires. ~3

Table 13. Skid resistance test data obtained at the Wallops Flight Facility . Pavement Water Film (skp~ ehd) Skid Number Average Brushed Concrete 12.5~' 60 14.8 14.8 12.5 75 9.6 9.6 12.5 90 6.1 6.1 12.5 82 7.1 7.1 12.5 100 4.6 4.6 Grooved Concrete 12.5 60 17.3 17.3 12.5 80 12.7 12.7 12.5 90 6.0 6.0 Brushed Concrete ASTM`2' 30 26.9 ASTM 30 31.5 ASTM 30 31.8 30.1 ASTM 60 18.6 ASTM 60 20.3 ASTM 60 24.2 23.2 ASTM 90 13.8 ASTM 90 15.3 ASTM 90 17.0 15.4 Grooved Concrete ASTM 30 30.9 ASTM 30 32.9 31.9 ASTM 60 22.4 ASTM 60 22.6 ASTM 60 46.2 30.4 ASTM 90 30.1 30.1 (')Flooded with water prior to testing. (~'Water applied in front of tire in accordance with ASTM E 274. ~4

II A S Tag Standennj T est L B' Flood ed to ~ 2 m m 3 5 3 0 2 5 e 2 0 , 1 5 ._ 1 0 5 o n 3 0 6 0 7 5 82 90 1 00 S peed k m/h Ft ore 37. lest res ^ far pi ^ ccdlcretc sections at the TVillcqps Il1~1~ Facility 115

MASTS Standard Test ~ Flooded to 12 mm 35 30 25 20 - z ~ 15 ._ oh 10 5 a KEgg 1 ~ 1 9 30 1 60 80 Speed, km/in 90 Figure 38. Test results for grooved concrete sections at the Wallops Flight Facility 116 .

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Chapter 4. Experimental Study Designs

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• Experimental Study Designs: Introduction
• Experimental Design
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Experimental study designs are the primary method for testing the effectiveness of new therapies and other interventions, including innovative drugs. By the 1930s, the pharmaceutical industry had adopted experimental methods and other research designs to develop and screen new compounds, improve production outputs, and test drugs for therapeutic benefits. The full potential of experimental methods in drug research was realized in the 1940s and 1950s with the growth in scientific knowledge and industrial technology. 1

In the 1960s, the controlled clinical trial, in which a group of patients receiving an experimental drug is compared with another group receiving a control drug or no treatment, became the standard for doing pharmaceutical research and measuring the therapeutic benefits of new drugs. 1 By the same time, the double-blind strategy of drug testing, in which both the patients and the researcher are unaware of which treatment is being taken by whom, had been adopted to limit the effect of external influences on the true pharmacological action of the drug. The drug regulations of the 1960s also reinforced the importance of controlled clinical trials by requiring that proof of effectiveness for new drugs be made through use of these research methods. 2,3

In pharmacoepidemiology, the primary use of experimental design is in performing clinical trials, most notably randomized, controlled clinical trials. 4 These studies involve people as the units of analysis. A variation on this experimental design is the community intervention study, in which groups of people, such as whole communities, are the unit of analysis. Key aspects of the clinical and community intervention trial designs are randomization, blinding, intention-to-treat analysis, and sample size determination.

An experiment is a study designed to compare benefits of an intervention with standard treatments, or no treatment, such as a new drug therapy or prevention program, or to show cause and effect (see Figure 3-2 ). This type of study is performed prospectively. Subjects are selected from a study population, assigned to the various study groups, and monitored over time to determine the outcomes that occur and are produced by the new drug therapy, treatment, or intervention.

Experimental designs have numerous advantages compared with other epidemiological methods. Randomization, when used, tends to balance confounding variables across the various study groups, especially variables that might be associated with changes in the disease state or the outcome of the intervention under study. Detailed information and data are collected at the beginning of an experimental study to develop a baseline; this same type of information also is collected at specified follow-up periods throughout the study. The investigators have control over variables such as the dose or degree of intervention. The blinding process reduces distortion in assessment. And, of great value, and not possible with other methods, is the testing of hypotheses. Most important, this design is the only real test of cause–effect relationships.

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A Complete Guide to Experimental Research

Published by Carmen Troy at August 14th, 2021 , Revised On August 25, 2023

A Quick Guide to Experimental Research

Experimental research refers to the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to find out the cause-and-effect relationship between two or more variables. 

The subjects/participants in the experiment are selected and observed. They receive treatments such as changes in room temperature, diet, atmosphere, or given a new drug to observe the changes. Experiments can vary from personal and informal natural comparisons. It includes three  types of variables ;

• Independent variable
• Dependent variable
• Controlled variable

Before conducting experimental research, you need to have a clear understanding of the experimental design. A true experimental design includes  identifying a problem , formulating a  hypothesis , determining the number of variables, selecting and assigning the participants,  types of research designs , meeting ethical values, etc.

There are many  types of research  methods that can be classified based on:

• The nature of the problem to be studied
• Number of participants (individual or groups)
• Number of groups involved (Single group or multiple groups)
• Types of data collection methods (Qualitative/Quantitative/Mixed methods)
• Number of variables (single independent variable/ factorial two independent variables)
• The experimental design

Types of Experimental Research

Laboratory Experiment  

It is also called experimental research. This type of research is conducted in the laboratory. A researcher can manipulate and control the variables of the experiment.

Example: Milgram’s experiment on obedience.

Field Experiment

Field experiments are conducted in the participants’ open field and the environment by incorporating a few artificial changes. Researchers do not have control over variables under measurement. Participants know that they are taking part in the experiment.

Natural Experiments

The experiment is conducted in the natural environment of the participants. The participants are generally not informed about the experiment being conducted on them.

Examples: Estimating the health condition of the population. Did the increase in tobacco prices decrease the sale of tobacco? Did the usage of helmets decrease the number of head injuries of the bikers?

Quasi-Experiments

A quasi-experiment is an experiment that takes advantage of natural occurrences. Researchers cannot assign random participants to groups.

Example: Comparing the academic performance of the two schools.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Conduct Experimental Research?

Step 1. identify and define the problem.

You need to identify a problem as per your field of study and describe your  research question .

Example: You want to know about the effects of social media on the behavior of youngsters. It would help if you found out how much time students spend on the internet daily.

Example: You want to find out the adverse effects of junk food on human health. It would help if you found out how junk food frequent consumption can affect an individual’s health.

Step 2. Determine the Number of Levels of Variables

You need to determine the number of  variables . The independent variable is the predictor and manipulated by the researcher. At the same time, the dependent variable is the result of the independent variable.

In the first example, we predicted that increased social media usage negatively correlates with youngsters’ negative behaviour.

In the second example, we predicted the positive correlation between a balanced diet and a good healthy and negative relationship between junk food consumption and multiple health issues.

Step 3. Formulate the Hypothesis

One of the essential aspects of experimental research is formulating a hypothesis . A researcher studies the cause and effect between the independent and dependent variables and eliminates the confounding variables. A  null hypothesis is when there is no significant relationship between the dependent variable and the participants’ independent variables. A researcher aims to disprove the theory. H0 denotes it.  The  Alternative hypothesis  is the theory that a researcher seeks to prove.  H1or HA denotes it. 

Why should you use a Plagiarism Detector for your Paper?

It ensures:

• Original work
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Step 4. Selection and Assignment of the Subjects

It’s an essential feature that differentiates the experimental design from other research designs . You need to select the number of participants based on the requirements of your experiment. Then the participants are assigned to the treatment group. There should be a control group without any treatment to study the outcomes without applying any changes compared to the experimental group.

Randomisation:  The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling.

Stratified sampling : It’s a type of random selection of the participants by dividing them into strata and randomly selecting them from each level. 

Matching:   Even though participants are selected randomly, they can be assigned to the various comparison groups. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.  

What is Replicability?

When a researcher uses the same methodology  and subject groups to carry out the experiments, it’s called ‘replicability.’ The  results will be similar each time. Researchers usually replicate their own work to strengthen external validity.

Step 5. Select a Research Design

You need to select a  research design  according to the requirements of your experiment. There are many types of experimental designs as follows.

Step 6. Meet Ethical and Legal Requirements

• Participants of the research should not be harmed.
• The dignity and confidentiality of the research should be maintained.
• The consent of the participants should be taken before experimenting.
• The privacy of the participants should be ensured.
• Research data should remain confidential.
• The anonymity of the participants should be ensured.
• The rules and objectives of the experiments should be followed strictly.
• Any wrong information or data should be avoided.

Tips for Meeting the Ethical Considerations

To meet the ethical considerations, you need to ensure that.

• Participants have the right to withdraw from the experiment.
• They should be aware of the required information about the experiment.
• It would help if you avoided offensive or unacceptable language while framing the questions of interviews, questionnaires, or Focus groups.
• You should ensure the privacy and anonymity of the participants.
• You should acknowledge the sources and authors in your dissertation using any referencing styles such as APA/MLA/Harvard referencing style.

Step 7. Collect and Analyse Data.

Collect the data  by using suitable data collection according to your experiment’s requirement, such as observations,  case studies ,  surveys ,  interviews , questionnaires, etc. Analyse the obtained information.

Step 8. Present and Conclude the Findings of the Study.

Write the report of your research. Present, conclude, and explain the outcomes of your study .  

Frequently Asked Questions

What is the first step in conducting an experimental research.

The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.

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1.4: Chapter 4 Theories in Scientific Research

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• Page ID 84799

• William Pelz
• Herkimer College via Lumen Learning

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As we know from previous chapters, science is knowledge represented as a collection of “theories” derived using the scientific method. In this chapter, we will examine what is a theory, why do we need theories in research, what are the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also presents illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behavior, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviors using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: (1) correlations between two constructs, (2) temporal precedence (the cause must precede the effect in time), and (3) rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: (1) you forgot that you had an exam on that day, (2) you arrived late to the exam due to a traffic jam, (3) you panicked midway through the exam, (4) you had to work late the previous evening and could not study for the exam, or even (5) your dog ate your text book. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalizable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or that they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalizable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalized explanations for patterns of events, behaviors, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theory is not. Theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic of the occurrence of natural or social phenomenon by explaining what are the key drivers and key outcomes of the target phenomenon and why, and what underlying processes are responsible driving that phenomenon. Second, they aid in sense-making by helping us synthesize prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be reevaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ “range of vision,” causing them to miss out on important concepts that are not defined by the theory.

Building Blocks of a Theory

David Whetten (1989) suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the “what” of theories (i.e., what concepts are important for explaining a phenomenon), propositions capture the “how” (i.e., how are these concepts related to each other), logic represents the “why” (i.e., why are these concepts related), and boundary conditions/assumptions examines the “who, when, and where” (i.e., under what circumstances will these concepts and relationships work). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organizational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning, may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definition that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organizational, etc.). Measurable representations of abstract constructs are called variables . For instance, intelligence quotient (IQ score) is a variable that is purported to measure an abstract construct called intelligence. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical plane, while variables are operationalized and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualized at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but MUST be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, is called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a “glue” that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the “explanation” that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into a cohesive “system of propositions” that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximization based on cost and benefit expectations as a way of understand human behavior. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximizes their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in a organization. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behavior), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of its implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a Good Theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the “goodness” of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

• Logical consistency : Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these “building blocks” of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.
• Explanatory power : How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-square) value in regression equations.
• Falsifiability : British philosopher Karl Popper stated in the 1940’s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as “a day with high temperatures is a hot day” are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires presence of rival explanations it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with!
• Parsimony : Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to 14 th century English logician Father William of Ockham (and hence called “Ockham’s razor” or “Occam’s razor), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such approach defeats the purpose of having a theory, which are intended to be “simplified” and generalizable explanations of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalized to other contexts, settings, and populations.

Approaches to Theorizing

How do researchers build theories? Steinfeld and Fulk (1990) [2] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviors. Such approach is often called “grounded theory building”, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non -confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organizational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorizing is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organizational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by building on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorizing using a deductive approach. For instance, Markus (1987) [3] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of Social Science Theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories; readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency Theory. Agency theory (also called principal-agent theory), a classic theory in the organizational economics literature, was originally proposed by Ross (1973) [4] to explain two-party relationships (such as those between an employer and its employees, between organizational executives and shareholders, and between buyers and sellers) whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimize the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organizational level.

The two parties in this theory are the principal and the agent; the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest (such as personal pay) over corporate interests. Hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behavior or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behavior-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as a commissions or a fee payable upon task completion, or mixed contracts that combine behavior-based and outcome-based incentives. An employee stock option plans are is an example of an outcome-based contract while employee pay is a behavior-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behavior-based contracts, such as investing in monitoring mechanisms (such as hiring supervisors) to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of Planned Behavior. Postulated by Azjen (1991) [5] , the theory of planned behavior (TPB) is a generalized theory of human behavior in the social psychology literature that can be used to study a wide range of individual behaviors. It presumes that individual behavior represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviors are based on one’s intention regarding that behavior, which in turn is a function of the person’s attitude toward the behavior, subjective norm regarding that behavior, and perception of control over that behavior (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behavior in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behavior, weighted by the desirability of those consequences.

Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behavior, and represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioral control is one’s perception of internal or external controls constraining the behavior in question. Internal controls may include the person’s ability to perform the intended behavior (self-efficacy), while external control refers to the availability of external resources needed to perform that behavior (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behavior but lack the resources needed to do so, and therefore suggests that posits that behavioral control can have a direct effect on behavior, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioral control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviors (such as not having high-speed Internet access for web surfing).

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organizations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels (such as mass media or prior adopters) and are persuaded to adopt it. Diffusion is a temporal process; the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern therefore an S-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on their time of their adoption. The rate of diffusion a lso depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviors).

At the micro (adopter) level, Rogers (1995) [6] suggests that innovation adoption is a process consisting of five stages: (1) knowledge: when adopters first learn about an innovation from mass-media or interpersonal channels, (2) persuasion: when they are persuaded by prior adopters to try the innovation, (3) decision: their decision to accept or reject the innovation, (4) implementation: their initial utilization of the innovation, and (5) confirmation: their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: (1) relative advantage: the expected benefits of an innovation relative to prior innovations, (2) compatibility: the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, (3) complexity: the extent to which the innovation is difficult to learn and use, (4) trialability: the extent to which the innovation can be tested on a trial basis, and (5) observability: the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk- taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources (such as friends and family) as their primary source of information. IDT has been criticized for having a “pro-innovation bias,” that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

Elaboration Likelihood Model . Developed by Petty and Cacioppo (1986) [7] , the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in the psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, events, or behavior and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two “routes” of influence, the central route and the peripheral route, which differ in the amount of thoughtful information processing or “elaboration” required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinize the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external “cues” such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalized in the ELM using the argument quality and peripheral cues constructs respectively.

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General Deterrence Theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated General Deterrence Theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviors. This theory holds that people are fundamentally rational (for both conforming and deviant behaviors), and that they freely choose deviant behaviors based on a rational cost-benefit calculation. Because people naturally choose utility-maximizing behaviors, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviors in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalized causes of criminal behaviors, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision making process and situational factors that influence that process. Hence, a criminal’s personal situation (such as his personal values, his affluence, and his need for money) and the environmental context (such as how protected is the target, how efficient is the local police, how likely are criminals to be apprehended) play key roles in this decision making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviors, but how to make criminal activities less attractive and therefore prevent crimes. To that end, “target hardening” such as installing deadbolts and building self-defense skills, legal deterrents such as eliminating parole for certain crimes, “three strikes law” (mandatory incarceration for three offenses, even if the offenses are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighborhood watch programs, special task forces on drugs or gang -related crimes, and increased police patrols, and educational programs such as highly visible notices such as “Trespassers will be prosecuted” are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

[1] Bacharach, S. B. (1989). “Organizational Theories: Some Criteria for Evaluation,” Academy of Management Review (14:4), 496-515.

[2] Steinfield, C.W. and Fulk, J. (1990). “The Theory Imperative,” in Organizations and Communications Technology , J. Fulk and C. W. Steinfield (eds.), Newbury Park, CA: Sage Publications.

[3] Markus, M. L. (1987). “Toward a ‘Critical Mass’ Theory of Interactive Media: Universal Access, Interdependence, and Diffusion,” Communication Research (14:5), 491-511.

[4] Ross, S. A. (1973). “The Economic Theory of Agency: The Principal’s Problem,” American Economic Review (63:2), 134-139.

[5] Ajzen, I. (1991). “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes (50), 179-211.

[6] Rogers, E. (1962). Diffusion of Innovations . New York: The Free Press. Other editions 1983, 1996, 2005.

[7] Petty, R. E., and Cacioppo, J. T. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change . New York: Springer-Verlag.

Contributors and Attributions

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike