Cover
Title Page
Author biographies
Preface
About the companion website
1 Introduction
1. 1.1 Introduction
2. 1.2 Terminology; nomenclature
3. 1.3 Document structure
4. 1.4 Possible subject areas for your research
5. 1.5 Professional bodies and the non-technical or technical dissertation or project
6. 1.6 Qualitative or quantitative analysis?
7. 1.7 The student/supervisor relationship and time management
8. 1.8 Ethical compliance and risk assessments
9. 1.9 House style or style guide
10. 1.10 Writing style
11. 1.11 Proofreading
12. 1.12 Extra support?
13. 1.13 A research proposal
14. 1.14 A viva or viva voce
15. Summary
16. References
2 The introduction chapter to the dissertation or project
1. 2.1 Introduction contents
2. 2.2 Articulation or description of the problem and provisional objectives
3. References
3 Review of theory and the literature
1. 3.1 Introduction
2. 3.2 Style and contents of a literature review
3. 3.3 Judgements or opinions?
4. 3.4 Sources of data
5. 3.5 Methods of finding the literature
6. 3.6 Embedding theory in dissertations and projects
7. 3.7 Referencing as evidence of reading
8. 3.8 Citing literature sources in the narrative of your work
9. 3.9 References or bibliography or both?
10. 3.10 Common mistakes by students
11. 3.11 Using software to help with references
12. 3.12 Avoiding the charge of plagiarism
13. Summary of this chapter
14. References
4 Research goals and their measurement
1. 4.1 Introduction
2. 4.2 Aim
3. 4.3 Research questions
4. 4.4 Objectives
5. 4.5 Variables
6. 4.6 A hypothesis with one variable
7. 4.7 A hypothesis with two variables: independent and dependent
8. 4.8 Writing the hypothesis: nulls and tails – a matter of semantics
9. 4.9 ‘Lots’ of variables at large, intervening variables
10. 4.10 Ancillary or subject variables
11. 4.11 No relationship between the IV and the DV
12. 4.12 Designing measurement instruments; use authoritative tools and adapt the work of others
13. 4.13 Levels of measurement
14. 4.14 Examples of categorical or nominal data in construction
15. 4.15 Examples of ordinal data in construction
16. 4.16 Examples of interval and ratio data in construction
17. 4.17 Types of data
18. 4.18 Money and CO₂ as variables
19. 4.19 Three objectives, each with an IV and DV: four variables to measure
20. 4.20 Summarising research goals; variables and their definition
21. Summary of this chapter
22. References
5 The Methodology chapter; analysis, results and findings
1. 5.1 Introduction
2. 5.2 Approaches to collecting data
3. 5.3 Data measuring and collection
4. 5.4 Issues mostly relevant to just questionnaires
5. 5.5 Ranking studies
6. 5.6 Other analytical tools
7. 5.7 Incorporating reliability and validity
8. 5.8 Analysis, results and findings
9. References
6 Laboratory experiments
1. 6.1 Introduction
2. 6.2 Test methodology
3. 6.3 Sourcing test materials
4. 6.4 Reliability and validity of findings
5. 6.5 Sample size
6. 6.6 Laboratory recording procedures
7. 6.7 Dissertation/project writing (introduction, methodology and results)
8. 6.8 Health and safety in the laboratory; COSHH and risk assessments
9. 6.9 Role of the supervisor
10. 6.10 Possible research topics for technical dissertations or projects, construction and civil engineering
11. 6.11 Examples of research proposals
12. 6.12 Research objectives and sample findings by the author
13. Bibliography
7 Qualitative data analysis
1. 7.1 Introduction
2. 7.2 The process of qualitative data collection
3. 7.3 Steps in the analytical process
4. References
8 Quantitative data analysis; descriptive statistics
1. 8.1 Introduction
2. 8.2 Examples of the use of descriptive statistical tools
3. 8.3 Ancillary variables
4. 8.4 Illustration of relevant descriptive statistics in charts
5. 8.5 Normal distributions; Z scores
6. 8.6 A second variable for descriptive analysis; an IV and a DV
7. References
9 Quantitative data analysis; inferential statistics
1. 9.1 Introduction
2. 9.2 Probability values and three key tests: chi-square, difference in means and correlation
3. 9.3 The chi-square test
4. 9.4 Determining whether the dataset is parametric or non-parametric
5. 9.5 Difference in mean tests; the t-test
6. 9.6 Difference in means; the unrelated Mann–Whitney test
7. 9.7 Difference in means; the related Wilcoxon t-test
8. 9.8 Difference in means; the parametric related t-test
9. 9.9 Correlations
10. 9.10 Using correlation coefficients to measure internal reliability and validity in questionnaires
11. 9.11 Which test?
12. 9.12 Confidence intervals
13. 9.13 Summarising results
14. Summary of this chapter
15. References
10 Discussion, conclusions, recommendations and appendices
1. 10.1 Introduction
2. 10.2 Discussion
3. 10.3 Conclusions and recommendations
4. 10.4 Appendices
5. 10.5 The examiner’s perspective
6. 10.6 Summary of the dissertation or project process
7. Summary of this chapter
8. References
List of appendices
1. Appendix A: Glossary to demystify research terms
2. References
3. Appendix B: Research ethics and health and safety examples
4. Appendix C: An abstract, problem description and literature review
5. References
6. Appendix D: Eight research proposals
7. References
8. References
9. References and bibliography
10. Bibliography
11. References
12. References
13. References
14. Appendix E: Raw data for a qualitative study
15. Appendix F: Statistical tables
Index
End User License Agreement

List of Tables

Chapter 03
1. Table 3.1 Journals in the field of civil engineering.
2. Table 3.2 Journals in the field of Construction Management.
Chapter 04
1. Table 4.1 Performance variables measured in construction.
2. Table 4.2 Environment variables measured in construction.
3. Table 4.3 Examples of Intervening variables.
4. Table 4.4 The impact of the ancillary variable ‘age of site managers’ on the DV profitability: the IV does influence the DV, but ancillary variables do not influence the DV.
5. Table 4.5 The impact of the ancillary variable ‘age of site managers’ on the DV profitability; the IV does not influence the DV, but ancillary variables do influence the DV.
6. Table 4.6 Scoring judgements for ten bricklayers based on eight criteria; scoring range 1 to 5 with 1 as the best score.
7. Table 4.7 Scoring judgements for ten bricklayers based on eight criteria; scoring range 0 to 4 with 4 as the best score.
8. Table 4.8 Exemplar of categorical, ordinal and interval data based on student assessment scores.
9. Table 4.9 Variables to measure in construction at the categorical/nominal, ordinal and interval level.
Chapter 05
1. Table 5.1 Eleven point (0–10) points for the rating of disabled access provision; ten criteria weighted.
2. Table 5.2 Ranking of data; factors that keep sites safe.
Chapter 06
1. Table 6.1 Toughness index tables for steel and FRP rebar.
Chapter 07
1. Table 7.1 Example of using a table for analysis: the private housing sector (PHS).
Chapter 08
1. Table 8.1 Raw dataset arising from ten participants; the measurement of leadership style of their bosses based on a multiple-item scale of six questions.
2. Table 8.2 Expansion of table 8.1 to include missing data (shaded in grey) and percentage (%), median (Md), mode (Mo), minimum, maximum, and standard deviation (SD) for each question in columns.
3. Table 8.3 Table 8.3 Expansion of table 8.1 to include missing data (shaded in grey) and percentage (%), median (Md), mode (Mo), minimum, maximum and standard deviation (SD) for each participant in rows.
4. Table 8.4 Calculation of the standard deviation of a sample in six steps from a column of ten raw scores.
5. Table 8.5 Ancillary variables, gender, age and qualifications of participants.
6. Table 8.6 Z scores and predicted or inferred n values for job satisfaction in UK construction workers; n = 1.15 million.
7. Table 8.7 Data added to table 8.3 to show a dependent variable (DV) ‘motivation of workers’.
Chapter 09
1. Table 9.1 Frequency counts in a 2 × 2 contingency table; n = 30.
2. Table 9.2 Raw data for 30 completed projects. Type of procurement method and whether completed to budget; allocated to one of for cells.
3. Table 9.3 Codes for allocating raw data to the contingency table.
4. Table 9.4 Expected frequency counts if the IV did not influence the DV.
5. Table 9.5 Four steps to calculate the chi-square value χ².
6. Table 9.6 Degrees of freedom; only one piece of data is needed (plus overall totals), to be able to determine the values of the missing data.
7. Table 9.7 A 2 × 3 contingency table; the DV in three groups.
8. Table 9.8 The chi-square calculation in Excel.
9. Table 9.9 A contingency table with smaller differences between frequency counts than table 9.1; p = 0.13.
10. Table 9.10 A contingency table with frequency counts increased tenfold over table 9.8; p = 0.00.
11. Table 9.11 The one row chi-square. Completion of projects to time.
12. Table 9.12 The one row chi-square. Motorcyclist accidents.
13. Table 9.13 Frequency counts for job satisfaction of construction industry workers in five-point class intervals.
14. Table 9.14 Raw data for the Mann–Whitney test. Method of procurement with two values or in two groups.
15. Table 9.15 Raw data rearranged to facilitate the ranking process for the Mann–Whitney test.
16. Table 9.16 Using Excel to calculate the p value in the Mann–Whitney t-test.
17. Table 9.17 Raw data for the Wilcoxon test and the calculation procedure to determine t.
18. Table 9.18 Using Excel to calculate the p value in the Wilcoxon t-test.
19. Table 9.19 Raw data for the parametric related t-test and the calculation procedure to determine t.
20. Table 9.20 Raw data for Spearman’s rho and calculating ∑D² in a table. Raw data in columns B and E; ∑D² in cell 32I = 2643.
21. Table 9.21 Using Excel to calculate Pearson’s correlation coefficient.
22. Table 9.22 Correlations to calculate internal reliability of a multiple-item scale.
23. Table 9.23 Which test; chi-square, difference-in-means or correlation?
24. Table 9.24 Which difference-in-means test?
25. Table 9.25 Summary of statistical test results in this chapter.
bapp
1. Table D.1 Proposed company compliance with health and safety report against internal management account profitability figure.
2. Table D.2 Hypothetical dataset for 50 Projects. Data for the IV, DV and MVs 1 to 6 inclusive.

List of Illustrations

Chapter 01
1. Figure 1.1 Using the ‘snipping tool’ to cut and paste figures, tables or images in Word.
2. Figure 1.2 Research paradigms.
3. Figure 1.3 The supervisory grid and the proactive or laissez-faire relationship. Which cell do you choose?
4. Figure 1.4 The relationship between the quality of the dissertation or project process (IV) and student percentage marks (DV); n = 16 (sample size).
5. Figure 1.5 Typical programme. Assumed: one-year study period, and the university submission date is before the Easter break in April.
6. Figure 1.6 The spelling and grammar check facility in Word used to locate typographical errors.
7. Figure 1.7 The find and replace facility in Word used to locate repeated errors.
Chapter 02
1. Figure 2.1 Causes, evidence and effects of a problem.
2. Figure 2.2 A crack in a research document founded on a poorly defined problem.
Chapter 03
1. Figure 3.1 The literature review constructed as though through a funnel (adapted from Holt, 1998, p. 67).
2. Figure 3.2 Weighing the evidence before coming to judgements; which is best, competition or partnering?
3. Figure 3.3 The analogy of the spider’s web constructing new theories.
4. Figure 3.4 Fourteen ‘styles’ on Word to set out references.
5. Figure 3.5 Six of the source options in Word.
6. Figure 3.6 Example Turnitin submission; 61% similarity, 23% plagiarism.
Chapter 04
1. Figure 4.1 The Castle Museum, York. Dense sand and stone plinths represent the description of the problem and literature review. Four columns represent research questions (RQ), objectives (OB) and hypotheses (H), all supporting the aim represented by the roof parapet. In between the objectives are variables (VARS).
2. Figure 4.2 A time frame: the relationship between the independent variable (IV) and the dependent variable (DV).
3. Figure 4.3 Variables ‘melting’ into one another.
4. Figure 4.4 Intervening variables.
5. Figure 4.5 Ancillary or subject variables also simultaneously acting on a DV.
6. Figure 4.6 Why we need journals with negative results. Source: Science for all.
7. Figure 4.7 Permutations for types of data; given the eight possible permutations, what kind of data do you have?
Chapter 05
1. Figure 5.1 Are you clear about your objectives, your variables and the potential values of each variable? If not, stop and seek advice.
2. Figure 5.2 Development from the aim to an objective with a variable; subsequently definition of the variable and a measurement tool that focuses upon the definition .
3. Figure 5.3 Questions constructed in Google Drive questionnaire software.
4. Figure 5.4 Do you have a weak link in your research? Validity of the whole study broken?
5. Figure 5.5 How reliable and valid is your work on a 0–10 scale?
Chapter 06
1. Figure 6.1 Test programme.
2. Figure 6.2 Mean density of concrete cubes (kg/m³) per batch type, showing standard error.
3. Figure 6.3 The risk assessment grid and template.
4. Figure 6.4 Detailed analysis of laboratory procedures.
5. Figure 6.5 Typical steel load/extension curve.
6. Figure 6.6 Typical FRP load/extension curve.
7. Figure 6.7 Synthetic CMOD – Comparison of individual beam performance, showing error bars.
8. Figure 6.8 Steel CMOD Comparison of individual beam performance.
9. Figure 6.9 Concrete strength development.
10. Figure 6.10 Comparison of calcite deposition.
11. Figure 6.11 Calcite formation on the surface of sandstone.
12. Figure 6.12 Calcite formation bonding sand grains 1000× magnification.
13. Figure 6.13 Section through anchor bolt under pull-out load.
14. Figure 6.14 Stress diagram surrounding the bolt under load in a concrete slab.
15. Figure 6.15 Plan view of pull-out test slab (dimensions in mm).
16. Figure 6.16 Mean pull-out cone diameters.
Chapter 07
1. Figure 7.1 Steps in the qualitative data analysis process.
Chapter 08
1. Figure 8.1 Using Excel to calculate descriptive statistics: ‘Formulas’, ‘More Functions’ and then ‘Statistical’.
2. Figure 8.2 Using Excel to calculate the mean of a column of numbers.
3. Figure 8.3 Using charts in Excel to communicate your data.
4. Figure 8.4 Using Excel to illustrate a frequency histogram.
5. Figure 8.5 Data taken from a table in Excel to produce a line diagram.
6. Figure 8.6 The bellshape and numerical features of the normal distribution.
7. Figure 8.7 Predicted normal distribution for job satisfaction of workers in the UK construction industry.
8. Figure 8.8 Six hypothetical datasets plotted in a frequency histogram with n of 50: normal distribution, skewed distribution, flat distribution, stepped, U-shaped and irregular with no pattern.
9. Figure 8.9 Use of a scatter diagram to illustrate a relationship between an IV (leadership style) and a DV (motivation).
Chapter 09
1. Figure 9.1 Potential for significant differences in prices; cost predictability.
2. Figure 9.2 Frequency counts: job satisfaction of people working in the UK construction industry where 0 = extremely unsatisfied and 100 = extremely satisfied; n = 50.
3. Figure 9.3 Comparison of two groups of data using frequency histograms.
4. Figure 9.4 Potential shapes deriving from scatter plots.
5. Figure 9.5 Scatter diagram to illustrate the relationship between the IV time predictability, and the DV cost predictability.
6. Figure 9.6 Scatter diagram showing the relationship between question 1 and the other five questions; correlation coefficient = 0.78.
7. Figure 9.7 Scatter diagram showing the relationship between question 5 and the other five questions; correlation coefficient = −0.36.
bapp
1. Figure c.1 Benefits of early integration of contractors
2. Figure c.2 Potential barriers to collaboration,
3. Figure D.1 Proposed correlation between average company’s compliance with health and safety and profitability.

Preface

There are many changes between the first and second editions. Most important, are welcome contributions from Dr Fred Sherratt and Dr Alan Richardson. Fred has strengthened sections of the text related to qualitative research and methodology, and has also added a glossary of research terms in Appendix A. Alan has added chapter 6, which examines in greater detail technical civil engineering projects. There are eight exemplar research proposals included in Appendix D that cover the fields of building and civil engineering. The authors of these proposals are acknowledged.

The word ‘projects’ has been added to the title, such that it now reads ‘Writing Built Environment Dissertations and Projects’. Most universities use the term ‘dissertation’ for building degrees and ‘projects’ for civil engineering. The content of the book has been updated to ensure that it does indeed embrace the needs of civil engineers.

There is emphasis on the difference between ‘non-technical’ work mostly found on building programmes and ‘technical’ civil engineering projects. Some examples from the first edition are retained, but many are updated and changed. Exemplar datasets in tables are produced in Excel, since spreadsheets are useful for collating and sorting raw data; also for performing analysis. Some examples are screenshots from Word. It is acknowledged that you may use spreadsheets and word processors other than Microsoft. Many new figures and tables are introduced to help support explanations.

The aim of the text is to provide practical guidance on the preparation of undergraduate dissertations and projects in the built environment. Students doing research at masters and PhD level, may also find the text useful. It is hoped that it will give students the platform to attain the maximum possible mark. Some sections of the book may contribute towards enhanced performance in other modules. For example, suggestions about how to develop theory and use literature as part of a critical appraisal are common to many subjects in the built environment and indeed other disciplines. The book is ordered around a structure that may be useful for a research document; that is, it starts with material that should be contained in an introduction chapter and finishes with material that should be in the conclusion. Embedded throughout the book are issues around study skills and ethics. There are many examples included, using a variety of methodological designs in which students are encouraged to consider the concepts of reliability and validity. A key difference between dissertations/projects and other courseworks is that the middle of the document should include a data collection process and some analysis. Suggestions are made about how to collect data and how to do analysis. The analytical chapters cover qualitative and quantitative approaches. The qualitative chapter demonstrates how to include some rigour in the analytical process, rather

Acknowledgement: the authors are grateful to those referees who gave valuable feedback on the first edition; thank you.

Writing Built Environment Dissertations and Projects

Practical Guidance and Examples

Author biographies

Peter Farrell

Fred Sherratt

Alan Richardson

Preface