Statistical characteristics of formation and evolution of structure in the universe

Chapter 1Statistics（统计学）：研究数据资料的收集、整理、分析和解释（interpretation）的科学。

Biostatistics（生物统计学）：统计学应用于生物科学Variable（变量）：指某种特征，它的表现在不同个体间或不同组间存在变异性。

Observation（观测值）：指对变量的表现进行观察或测量所获得的数值，有时也被称为变数（variate）Population（总体）：又叫“统计总体”，是指一个统计问题研究对象的全体，它是具有某种（或某些）共同特征的元素的集合。

Individual（个体）：总体中每一个研究对象称作个体。

Sample（样本）：从总体中按一定方法抽取部分具有代表性的个体，这部分个体称为样本。

Parameter（参数）：描述总体特征的数，如总体平均数、总体方差等。

Statistic（统计量）：描述样本特征的量，如样本平均数、样本方差、样本相关系数等。

Accuracy（准确性）：指观测值或估计值与真值的接近程度。

Precision（精确性）：对同一物体的重复观察值或估计值彼此之间的接近程度。

Chapter 2Raw data（直接数据）:数据调查与实验未经处理的数据；Continuous data（连续性数据）：指在一定范围内可取任何实数值的数据。

Discrete data（离散性数据）：在一定范围内只能取有限种可能值的数据。

Count data（计数数据）：用计数的方式得到的数据资料，必须用整数来表示。

Classification data（分类资料）：可自然的或人为的分为2个或多个不同类别的资料。

例如：男生记做1 女生记做2频数（率）分布（frequency distribution）；；下四分位数（lower quartile）；中位数（median）；上四分位数（upper quartile）；条形图（bar chart）；直方图（histogram）；饼图（pie chart）；散点图（scatter plot），组间距（interval）Percentile（百分位数）：一组n个观测值按数值大小排列，小于某数值的数据个数占全体个数的x%,则为x%分位数。

再论中介模型滥⽤：如何规范地实施因果中介效应分析因果中介效应估计、敏感性分析、⼯具变量模型。

近年来，⼤量的经济学论⽂滥⽤中介效应模型，参考⽂献是⼀遍中⽂⼼理学论⽂，特别以硕⼠论⽂居多，引起严肃经济学者的警觉和批评。

在这个⽅程组中有很多的问题存在：y=a+bx+u (1)m=a1x+u1 (2)y=a2x+b2m+u2 (3）很显然（1）式中⾄少遗漏了中介变量m，则导致严重内⽣性问题，内⽣性导致b的估计是有偏的，b都估计不对，何谈后⾯的因果效应和机制分析的识别？且不说有没有考虑三个⼦⽅程的内⽣性问题了！令⼈悲哀和⽆免，其实只需要基本的初等计量经济学知识！本推⽂将介绍在因果分析框架下中介分析模型。

此外，管理学的调节效应其实就是规范实证经济学⾥⾯的交互项模型，即相关异质性因果效应分析：即将开幕的STATA前沿培训精讲：带异质性处理效应的双向固定效应估计|从精确断点、模糊断点估计的实际操作|弱⼯具变量稳健推断异质性分析、机制分析的内容可选择学习：即将开班 | 结构模型、Stata实证前沿、Python数据挖掘暑假⼯作坊当然，⽐较合理地机制分析是基于理论框架的科学分析，这也可以在以上暑假⼯作坊课程中的结构估计部分学习之，其也提供⽂本分析的内容。

欢迎咨询！Causal mediation analysisRaymond Hicks,Niehaus Center for Globalization and GovernancePrinceton University,Princeton, NJ,rhicks@Dustin Tingley,Department of Government,Harvard UniversityCambridge, MA,dtingley@Abstract. Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social-science fields, especially psychology, and incre epidemiology.Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the sta analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the required for interpreting mediation results causally.摘要：估计解释变量与被解释变量之间的联系机制，也被称为“中介分析”，是各种社会科学领域的核⼼，尤其是⼼理学，并逐渐成为流⾏病学等领域的核⼼。

1Introduction to strategic management accounting1.1I ntroduction to planning, control and decision making☞Strategic planning is the process of deciding on objectives of the organization, on changes in these objectives, on the resource to attain these objectives, and on the policies that are to govern the acquisition, use and disposition of these resources.☞Characteristics of strategic information⏹Long term and wide scope⏹Generally formulated in writing⏹Widely circulated广泛流传⏹Doesn’t trigger direct action, but series of lesser plans⏹Includes selection of products, purchase of non-current assets, required levels ofcompany profit☞Management control: the process by which management ensure that resources are obtained and used effectively and efficiently in the accomplishment of the organisation’s objectives. It is sometimes called tactics ad tactical planning.☞Characteristics of management accounting information⏹Short-term and non-strategic⏹Management control planning activities include preparing annual sales budget⏹Management control activities include ensuring budget targets are reached⏹Carried out in a series of routine and regular planning and comparison procedures⏹Management control information covers the whole organisation, is routinely collected,is often quantitative and commonly expressed in money terms (cash flow forecasts, variance analysis reports, staffing levels⏹Source of information likely to be endogenous内生的☞Characteristics of operational control⏹Short-term and non-strategic⏹Occurs in all aspects of an organisations activities and need for day to dayimplementation of plans⏹Often carried out at short notice⏹Information likely to have an endogenous source, to be detailed transaction data,quantitative and expressed in terms of units/hours⏹Includes customer orders and cash receipts.1.2Management accounting information for strategic planning and control☞Strategic management accounting is a form of management accounting in which emphasis is placed on information about factors which are external to the organisation, as well as non-financial and internally-generated information.⏹External orientation: competitive advantage is relative; customer determination⏹Future orientation: forward- and outward looking; concern with values.⏹Goal congruence: translates the consequences of different strategies into a commonaccounting language for comparison; relates business operations to financial performance.1.3Planning and control at strategic and operational levels☞Linking strategy and operations, if not: unrealistic plans, inconsistent goals, poor communication, inadequate performance measurement.1.3.1Strategic control systems☞Formal systems of strategic control:⏹strategy review;⏹identify milestones of performance( outline critical success factors, short-term stepstowards long-term goals, enables managers to monitor actions)⏹Set target achievement levels (targets must be reasonably precise, suggest strategiesand tactics, relative to competition)⏹Formal monitoring of the strategic process⏹Reward.☞Desired features of strategic performance measures⏹Focus on what matters in the long term⏹Identify and communicate drivers of success⏹Support organisational learning⏹Provide a basis for reward⏹Measurable; meaningful; acceptable;⏹Described by strategy and relevant to it⏹Consistently measured⏹Re-evaluated regularly1.4Benchmarking1.4.1Types of benchmarking☞Internal benchmarking: easy; no innovative or best-practice.☞Industry benchmarking:⏹Competitor benchmarking: difficult to obtain information⏹Non-competitor benchmarking: motivate☞Functional benchmarking: find new, innovative ways to create competitive advantage1.4.2Stages of benchmarking☞Set objectives and determine the area to benchmark☞Establish key performance measures.☞Select organizations to study☞Measure own and others performance☞Compare performance☞Design and implement improvement prgoramme☞Monitor improvements1.4.3Reasons for benchmarking☞Assess current strategic position☞Assess generic competitive strategy☞Spur to innovation☞Setting objectives and targets☞Cross comparisons☞Implementing change☞Identifies the process to improve☞Helps with cost reduction, or identifying areas where improvement is required☞Improves the effectiveness of operations☞Delivers services to a defined standard☞Provide early warning of competitive disadvantage1.4.4Disadvantages of benchmarking☞Implies there is one best way of doing business☞Yesterday’s solution to tomorrow’s problem☞Catching-up exercise rather than the development of anything distinctive☞Depends on accurate information about comparator companies☞Potential negative side effects of ‘what gets measured gets done’.2Performance management and control of the organization2.1Strengths and weaknesses of alternative budget models2.1.1Incremental budgeting☞Is the traditional approach to setting a budget and involves basing next year’s budget on the current year’s results plus an extra amount for estimated growth of inflation next year. ☞Strengths: easy to prepare; can be flexed to actual levels to provide more meaningful control information☞Weaknesses: does not take account of alternative options; does not look for ways of improving performance; only works if current operations are as effective, efficient and economical as they can be; encourage slack in the budget setting process.2.1.2Zero based budgeting☞Preparing a budget for each cost centre from scratch.☞Strengths:⏹Provides a budgeting and planning tool for management that responds to changes inthe business environment.⏹Requires the organization to look very closely at its cost behavior patterns, andimproves understanding of cost-behaviour patterns.⏹Should help identify inefficient or obsolete processes, and thereby also help reducecosts.⏹Results in a more efficient allocation of resources⏹Be particularly useful in not-for-profit organizations which have a focus on achievingvalue for money.☞Weaknesses:⏹Requires a lot of management time and effort⏹Requires training in the use of ZBB techniques so that these are applied properly⏹Questioning current practices and processes can be seen as threatening2.1.3Rolling budgets☞Continuously updated by adding a further period when the earliest period has expired.☞Strengths:⏹Reduce the uncertainty of budgeting for business operating in an unstableenvironment. It is easier to predict what will happen in the short-term.⏹Most suitable form of budgeting for organizations in uncertain environments, wherefuture activity levels, costs or revenues cannot be accurately foreseen.⏹Planning and control is based on a more recent plan which is likely to be morerealistic an more relevant than a fixed annual budget drawn up several months ago.⏹The process of updating the budget means that managers identify current changes( and so can respond to these changes more quickly)⏹More realistic targets provide a better basis on which to appraise managers’performance⏹Realistic budgets are likely to have a better motivational effect on managers.☞Weaknesses:⏹Require time, effort and money to prepare and keep updating. If managers spend toolong preparing/revising budgets, they will have less time to control and manage actual results⏹Managers may not see the value in the continuous updating of budgets⏹May be demotivating if targets are constantly changing⏹It may not be necessary to update budgets so regularly in a stable operatingenvironment.2.1.4Flexible budgets☞Recognizing the potential uncertainty, budgets designed to adjust costs levels according to changes in the actual levels of activity and output.☞Strengths:⏹Finding out well in advance the costs of idle time and so on if the output falls belowbudget.⏹Being able to plan for the alternative use of spare capacity if output falls short ofbudget☞Weaknesses:⏹As many errors in modern industry are fixed costs, the value of flexible budgets as aplanning tool are limited.⏹Where there is a high degree of stability, the administrative effort in flexiblebudgeting produces little extra benefit. Fixed budgets can be perfectly adequate in these circumstances.2.1.5Activity based budgeting☞Involves defining the activities that underlie the financial figures in each function and usingthe level of activity to decide how much resources should be allocated, how well it is being managed and to explain variance from budget.☞Strengths:⏹Ensures that the organisation’s overall strategy and any changes to that strategy willbe taken into account.⏹Identifies critical success factors which are activities that a business must perform wellif it is to succeed⏹Recognizes that activities drive costs; so encourages a focus on controlling andmanaging cost drivers rather than just the costs⏹Concentrate on the whole activities so that there is more likelihood of getting it rightfirst time.☞Weaknesses:⏹Requires time and effort to prepare so suited to a more complex organization withmultiple cost drivers.⏹May be difficult to identify clear individual responsibilities for activities⏹Only suitable for organization which have adopted an activity-based costing system⏹ABBs are not suitable for all organization, especially with significant proportions offixed overheads.2.1.6The future of budgeting☞Criticisms of traditional budgeting⏹Time consuming and costly⏹Major barrier to responsiveness, flexibility and change⏹Adds little value given the amount of management time required⏹Rarely strategically focused⏹Makes people feel undervalued⏹Reinforces department barriers rather than encouraging knowledge sharing⏹Based on unsupported assumptions and guesswork as opposed to sound,well-constructed performance data⏹Development and updated infrequently2.2Budgeting in not-for-profit organizations☞Special issues: the budget process inevitably has considerable influence on organizational processes, and represents the financial expression of policies resulting from politically motivated goals and objectives. The reality of life for many public sector managers is an subjected to(受---支配) growing competition.⏹Be prevented from borrowing funds⏹Prevent the transfer of funds from one budget head to another without compliancewith various rules and regulations⏹Plan one financial year.⏹Incremental budgeting and the bid system are widely used.2.3Evaluating the organisation’s move beyond budgeting2.3.1Conventional budgeting in a changing environment☞Weaknesses of traditional budgets:⏹Adds little value, requires far too much valuable management time⏹Too heavy a reliance on the ‘agreed’ budget has an adverse impact on managementbehavior, which can become dysfunctional(功能失调的) with regard to(关于) the objectives of the organization as a whole⏹The use of budgeting as a base for communicating corporate goals, is contrary to theoriginal purpose of budgeting as a financial control mechanism⏹Most budgets are not based on a rational, causal(因果关系的) model of resourceconsumption, but are often the result of protracted internal bargaining processes.⏹Conformance to budget is not seen as compatible with a drive towards continuousimprovement⏹Traditional budgeting processes have insufficient external focus.2.3.2The beyond budgeting model☞Rolling budgets focus management attention on current and likely future realities within the organizational context, it is seen as an attempt to keep ahead of change, or strictly speaking to be more in control of the response to the challenges facing the organization. ☞Benefits:⏹Creates and fosters a performance climate based on competitive success. Managerialfocus shifts from beating other managers for a slice(部分) of resources to beating the competition.⏹It motivates properly by giving them challenges, responsibilities and clear values asguidelines. Rewards are team-based⏹It empowers operational managers to act by removing resource constraints. Speedingup the response to environmental threats and enabling quick exploitation of new opportunities.⏹It devolves performance responsibilities to operational management who are closer tothe action.⏹It establishes customer-orientated teams that are accountable for profitable customeroutcomes.⏹Creates transparent and open information systems throughout the organization,provides fast, open and distributed information to facilitate control at all levels.3Business structure, IT development and other environmental and ethical issues3.1Business structure and information needs3.1.1Functional departmentation☞Information characteristics and needs: information flows vertically; functions tend to be isolated☞Implications for performance management⏹Structure is based on work specialism⏹Economies of scale⏹Does not reflect the actual business processes by which values is created⏹Hard to identify where profits and losses are made on individual products or inindividual markets⏹People do not have an understanding of how the whole business works⏹Problems of co-ordinating the work of different specialisms.3.1.2The divisional form☞Information characteristics and needs⏹Divisionalisation is the division of a business into autonomous regions⏹Communication between divisions and head office is restricted, formal and related toperformance standards⏹Headquarters management influence prices and therefore profitability when it setstransfer prices between divisions.⏹Divisionalisation is a function of organisation size, in numbers and in product-marketactivities.☞Implications for performance management⏹Divisional management should be free to use their authority to do what they think isright, but must be held accountable to head office⏹ A division must be large enough to support the quantity and quality of managementit needs⏹Each division must have a potential for growth in its own area of operations⏹There should be scope and challenge in the job for the management of the division☞Advantages:⏹Focuses the attention of subordinate(下级) management on business performanceand results⏹Management by objectives can be applied more easily⏹Gives more authority to junior managers, more senior positions⏹Tests junior managers in independent command early in their careers and at areasonably low level in the management hierarchy.⏹Provides an organisation structure which reduces the number of levels ofmanagement.☞Problems:⏹Partly insulated from shareholders and capital markets⏹The economic advantages it offers over independent organisations ‘reflectfundamental inefficiencies in capital markets’⏹The divisions are more bureaucratic than they would be as independent corporation⏹Headquarters management usurp divisional profits by management charges,cross-subsidies, unfair transfer pricing systems.⏹Sometime, it is impossible to identify completely independent products or markets⏹Divisionalisation is only possible at a fairly senior management level⏹Halfway house(中途地点)⏹Divisional performance is not directly assessed by the market⏹Conglomerate diversification3.1.3Network organisations☞Information characteristics and needs: achieve innovative response in a changingcircumstances; communication tends to be lateral(侧面的), information and advice are given rather than instructions(指令) and decisions.☞Virtual teams: share information and tasks; make joint decision; fulfil the collaborative function of a team)☞Implications for performance management⏹Staffing: shamrock organisation⏹Leasing of facilities such as IT, machinery and accommodation(住房)⏹Production itself might be outsourced⏹Interdependence of organisations☞Benefits: cost reduction; increased market penetration; experience curve effects.3.2Business process re-engineering3.2.1Business processes and the technological interdependence betweendepartments☞Pooled interdependence(联营式相互依赖): each department works independently to the others, subjects to achieve the overall goals☞Sequential interdependence(序列式相互依存): a sequence with a start and end point.Management effort is required to ensure than the transfer of resources between departments is smooth.☞Reciprocal interdependence(互惠式相互依存): a number of departments acquire inputs from and offer outputs to each other.3.2.2Key characteristics of organisations which have adopted BPR☞Work units change from functional departments to process teams, which replace the old functional structure☞Jobs change. Job enlargement and job enrichment☞People’s roles change. Make decisions relevant to the process☞Performance measures concentrate on results rather than activities.☞Organisation structures change from hierarchical to flat3.3Business integration3.3.1Mckinsey 7S model☞Hard elements of business behaviour⏹Structure: formal division of tasks; hierarchy of authority⏹Strategy: plans to outperform胜过its competitors.⏹Systems: technical systems of accounting, personnel, management information☞‘soft’ elements⏹Style: shared assumptions, ways of working, attitudes and beliefs⏹Shared values: guiding beliefs of people in the organisation as to why it exists⏹Staff: people⏹Skills: those things the organisation does well3.3.2Teamwork and empowerment☞Aspects of teams:⏹Work organisation: combine the skills of different individuals and avoid complexcommunication⏹Control: control the behaviour and performance of individuals, resolve conflict⏹Knowledge generation: generate ideas⏹Decision making: investigate new developments, evaluate new decisions☞Multi-disciplinary teams:⏹Increases workers‘ awareness of their overall objectives and targets⏹Aids co-ordination⏹Helps to generate solutions to problems, suggestions for improvements☞Changes to management accounting systems⏹Source of input information: sources of data, methods used to record data⏹Processing involved: cost/benefit calculation⏹Output required: level of detail and accuracy of output, timescales involved⏹Response required:⏹When the output is required:3.4Information needs of manufacturing and service businesses3.4.1Information needs of manufacturing businesses☞Cost behaviour:⏹Planning: standard costs, actual costs compared with⏹Decision making: estimates of future costs to assess the likely profitability of a product⏹Control: monitor total cost information☞Quality: the customer satisfaction is built into the manufacturing system and its outputs☞Time: production bottlenecks, delivery times, deadlines, machine speed☞Innovation: product development, speed to market, new process. Experience curve, economies of scale, technological improvements.☞Valuation:☞Strategic, tactical and operational information⏹Strategic: future demand estimates, new product development plans, competitoranalysis⏹Tactical: variance analysis, departmental accounts, inventory turnover⏹Operational: production reject rates, materials and labour used, inventory levels3.4.2Service businesses☞Characteristics distinguish from manufacturing:⏹Intangibility: no substance⏹Inseparability/simultaneity: created at the same time as they are consumed⏹Variability/heterogeneity异质性: problem of maintaining consistency in the standardof output⏹Perishability非持久性:⏹No transfer of ownership:☞Strategic, tactical and operational information⏹Strategic: forecast sales growth and market share, profitability, capital structure⏹Tactical: resource utilisation, customer satisfaction rating⏹Operational: staff timesheets, customer waiting time, individual customer feedback3.5Developing management accounting systems3.5.1Setting up a management accounting system☞The output required: identify the information needs of managers☞When the output is required:☞The sources of input information: the output required dictate the input made3.6Stakeholders’ goals and objectives3.6.1The stakeholder view☞Organisations are rarely controlled effectively by shareholders☞Large corporations can manipulate markets. Social responsibility☞Business receive a lot of government support☞Strategic decisions by businesses always have wider social consequences.3.6.2Stakeholder theory☞Strong stakeholder view: each stakeholder in the business has a legitimate claim on management attention. Management’s job is to balance stakeholder demands:⏹Managers who are accountable to everyone are accountable to none⏹Danger of the managers favour their own interests⏹Confuses a stakeholder’s interest in a firm with a person citizenship of a state⏹People have interest, but this does not give them rights.3.7Ethics and organisation3.7.1Short-term shareholder interest(laissez-faire自由主义stance)☞Accept a duty of obedience to the demands of the law, but would not undertake to comply with any less substantial rules of conduct.3.7.2Long-term shareholder interest (enlightened self-interest开明自利)☞The organisation’s corporate image may be enhanced by an assumption of wider responsibilities.☞The responsible exercise of corporate power may prevent a built-up of social and political pressure for legal regulation.3.7.3Multiple stakeholder obligations☞Accept the legitimacy of the expectations of stakeholders other than shareholders. It is important to take account of the views of stakeholders with interests relating to social and environmental matters.☞Shape of society: society is more important than financial and other stakeholder interests.3.7.4Ethical dilemmas☞Extortion: foreign officials have been known to threaten companies with the complete closure of their local operations unless suitable payments are made☞Bribery: payments for service to which a company is not legally entitled☞Grease money: cash payments to the right people to oil the machinery of bureaucracy.☞Gifts: are regard as an essential part of civilised negotiation.4Changing business environment and external factors4.1The changing business environment4.1.1The changing competitive environment☞Manufacturing organisations:⏹Before 1970s, domestic markets because of barriers of communication andgeographical distance, few efforts to maximise efficiency and improve management practices.⏹After 1970s, overseas competitors, global networks for acquiring raw materials anddistributing high-quality, low-priced goods.☞Service organisations:⏹Prior to the 1980s: service organisations were government-owned monopolies, wereprotected by a highly-regulated, non-competitive environment.⏹After 1980s: privatisation of government-owned monopolies and deregulation, intensecompetition, led to the requirement of cost management and management accounting information systems.☞Changing product life cycles: competitive environment, technological innovation, increasingly discriminating and sophisticated customer demands.☞Changing customer requirements: Cost efficiency, quality (TQM), time (speedier response to customer requests), innovation☞New management approaches: continuous improvement, employee empowerment; total value-chain analysis☞Advanced manufacturing technology(AMT): encompasses automatic production technology, computer-aided design and manufacturing, flexible manufacturing systems and a wide array of innovative computer equipment.4.1.2The limitation of traditional management accounting techniques in achanging environment☞Cost reporting: costs are generally on a functional basis, the things that businesses do are “process es’ that cut across functional boundaries☞Absorption costing(归纳成本计算法)☞Standard costing: ignores the impact of changing cost structures; doesn’t provide any incentive to try to reduce costs further, is inconsistent with the philosophy of continuous improvement.☞Short-term financial measures: narrowly focused☞Cost accounting methods: trace raw materials to various production stages via WIP. With JIT systems, near-zero inventories, very low batch sizes, cost accounting and recording systems are greatly simplified.☞Performance measures: product the wrong type of response☞Timing: cost of a product is substantially determined when it is being designed, however, management accountants continue to direct their efforts to the production stage.☞Controllability: only a small proportion of ‘direct costs’are genuinely controllable in the short term.☞Customers: many costs are driven by customers, but conventional cost accounting does not recognise this.☞The solution: changes are taking place in management accounting in order to meet the challenge of modern developments.4.2Risk and uncertainty4.2.1Types of risk and uncertainty☞Physical: earthquake, fire, blooding, and equipment breakdown. Climatic changes: global warming, drought;☞Economic: economic environment turn out to be wrong☞Business: lowering of entry barriers; changes in customer/supplier industries; new competitors and factors internal to the firm; management misunderstanding of core competences; volatile cash flows; uncertain returns☞Product life cycle:☞Political: nationalisation, sanctions, civil war, political instability☞Financial:4.2.2Accounting for risk☞Quantify the risk:⏹Rule of thumb methods: express a range of values from worst possible result to bestpossible result with a best estimate lying between these two extremes.⏹Basic probability theory: expresses the likelihood of a forecast result occurring⏹Dispersion or spread values with different possible outcomes: standard deviation.4.2.3Basic probability theory and expected valuesEV=ΣpxP=the probability of an outcome occurringX=the value(profit or loss) of that outcome4.2.4Risk preference☞Risk seeker: is a decision maker who is interested trying to secure the best outcomes no matter how small the chance they may occur☞Risk neutral: a decision maker is concerned with what will be the most likely outcome☞Risk averse: a decision maker acts on the assumption that the worst outcome might occur ☞Risk appetite is the amount of risk an organisation is willing to take on or is prepared to accept in pursuing its strategic objectives.4.2.5Decision rules☞Maximin decision rule: select the alternative that offers the least unattractive worst outcome. Maximise the minimum achievable profit.⏹Problems: risk-averse approach, lead to defensive and conservative, without takinginto account opportunities for maximising profits⏹Ignores the probability of each different outcome taking place☞Maximax: looking for the best outcome. Maximise the maximum achievable profit⏹It ignores probabilities;⏹It is over-optimistic☞Minimax regret rule: minimise the regret from making the wrong decision. Regret is the opportunity lost through making the wrong decision⏹Regret for any combination of action and circumstances=profit for best action in shoescircumstances – profit for the action actually chosen in those circumstances4.3Factors to consider when assessing performance4.3.1Political factors☞Government policy; government plans for divestment(剥夺)/rationalisation; quotas, tariffs, restricting investment or competition; regulate on new products.☞Government policy affecting competition: purchasing decisions; regulations and control;policies to prevent the concentration of too much market share in the hands of one or two producers4.3.2Economic environment☞Gross domestic product: grown or fallen? Affection on the demand of goods/services☞Local economic trends: businesses rationalising or expanding? Rents increasing/falling?The direction of house prices moving? Labour rates☞Inflation: too high to making a plan, uncertain of future financial returns; too low to depressing consumer demand; encouraging investment in domestic industries; high rate leading employees to demand higher money wages to compensate for a fall in the value of their wages☞Interest rates: affect consumer confidence and liquidity, demand; cost of borrowing increasing, reducing profitability;☞Exchange rates: impact on the cost of overseas imports; prices affect overseas customers ☞Government fiscal policy: increasing/decreasing demands; corporate tax policy affecting on the organisation; sales tax(VAT) affecting demand.☞Government spending:☞Business cycle: economic booming or in recession; counter-cyclical industry; the forecast state of the economic4.3.3Funding☞Reasons for being reluctant to obtain further debt finance:⏹Fear the company can’t service the debt, make the required capital and interestpayments on time⏹Can’t use the tax shield, to obtain any tax benefit from interest payments⏹Lacks the asset base to generate additional cash if needed or provide sufficientsecurity⏹Maintain access to the capital markets on good terms.4.3.4Socio-cultural factors☞Class: different social classes have different values。

交通行业术语（中英文对照）Stop-line ----- 停车线A con gested link ---- 阻塞路段Weighti ng factor ----- 权重因子Con troller --- 控制器Emissio ns Model ---- 排气仿真the traffic pattern ----- 交通方式Con troller --- 信号机Amber ----- 黄灯Start-up delay ---- 启动延误Lost time ----- 损失时间Off-peak——非高峰期The morni ng peak -- 早高峰Pedestria n crossin ——人行横道Coord in ated con trol systems——协调控制系统On-I ine ---- 实时Two-way ----- 双向交通Absolute Offset ----- 绝对相位差Overlapp ing Phase——搭接相位Critical Phase ----- 关键相位Cha nge Interval --- 绿灯间隔时间Arterial In tersection Con trol 干线信号协调控制Fixed-time Con trol ----- 固定式信号控制Real-time Adaptive Traffic Con trol ---- 实时自适应信号控制Green Ratio ---- 绿信比Through movemen ----- 直行车流Congestion ----- 阻塞，拥挤The perce ntage congestion——阻塞率The degree of saturation——饱和度The effective gree n time --- 有效绿灯时间The maximum queue value——最大排队长度Flow Profiles ------ 车流图示Double cycli ng ----- 双周期Si ngle cycli ng --- 单周期Peak高峰期The eve ning peak periods——晚高峰Siemens --- 西门子Pelican ---- 人行横道Fixed time plans ---- 固定配时方案On e-way traffic ---- 单向交通Green Ratio ---- 绿信比Relative Offset ----- 相对相位差Non-o verlapp ing Phase——非搭接相位Saturatio n Flow Rate -- 饱和流率Isolated In tersecti on Contro -- 单点信号控制（点控）Area-wide Con trol ---- 区域信号协调控制Vehicle Actuated （\A）-- 感应式信号控制The Mi nimum Green Time ---- 最小绿灯时间Unit Exte nsion Time --- 单位绿灯延长时间The Maximum Green Time ---- 最大绿灯时间Oppos ing traffic --- 对向交通（车流）Actuati on ---- Con trol ----- 感应控制方式Pre-timed Control ------ 定周期控制方式Remote Contro ----- 有缆线控方式Self —I nductfa ns --- 环形线圈检测器Signal ----- s pacin -------- 信号间距Though-traffic lane ----- 直行车道Inbound ---- 正向Outbound ---- 反向第一章交通工程--- Traffic Engin eeri ng运输工程--- Tran sportati on Engin eeri ng航空交通--- Air Tran sportati on水上交通--- Water Tran sportati on管道交通--- Pipeli ne Tran sportati on交通系统--- Traffic System交通特性--- Traffic Characteristics人的特性--- Huma n Characteristics车辆特性--- Vehicular Characteristics交通流特性--- Traffic Flow Characteristics道路特性--- Roadway Characteristics交通调查--- Traffic Survey交通流理论--- Traffic Flow Theory交通管理--- Traffic Man ageme nt交通环境保护---- T raffic En vir onment Protecti on 交通设计--- Traffic Desig n交通统计学--- Traffic Statistics交通心理学--- Traffic Psychology汽车力学--- Automobile Mecha nics交通经济学--- Traffic Econo mics汽车工程--- Automobile Engin eeri ng人类工程--- Huma n Engin eeri ng环境工程--- En vir onment Engin eeri ng自动控制--- Automatic Con trol电子计算机Electric Computer第一章公共汽车一Bus无轨电车Trolley Bus有轨电车Tram Car大客车Coach小轿车Seda n载货卡车Truck拖挂车Trailer平板车Flat-bed Truck动力特性一Drivi ng Force Characteristics 牵引力Tractive Force空气阻力Air Resista nee滚动阻力Rolli ng Resista nee坡度阻力Grade Resista nee加速阻力Accelerati on Resista nee附着力一一 Adhesive Force汽车的制动力Braki ng of Motor Vehicle 自行车流特性Bicycle flow Characteristics驾驶员特性Driver Characteristics刺激Stimulation感觉--- Sense判断--- Judgme nt行动--- Action视觉--- Visual Sense听觉--- Heari ng Sense嗅觉--- Se nse of Smell味觉--- Sense of Touch视觉特性--- Visual Characteristics视力--- Visi on视野--- Field of Visio n色彩感觉--- Color Sense眩目时的视力--- Glare Visio n视力恢复--- Retur n Time of Visio n动视力--- Visual in Motion亮度--- Luminance照度--- Luminance反应特性--- Reactive Characteristics刺激信息--- Stimula nt In formati on驾驶员疲劳与兴奋---- Drivi ng Fati ng and Excitability交通量--- Traffic Volume地点车速Spot Speed瞬时车速In sta ntan eous Speed时间平均车速Time mean Speed空间平均车速Space mean speed车头时距Time headway车头间距一一Space headway。

统计学方法英语As an essential tool in data analysis, statistical methods play a crucial role in various fields such as economics, psychology, biology, and social sciences. 统计学方法作为数据分析中的重要工具，在经济学、心理学、生物学和社会科学等领域起着至关重要的作用。

By utilizing statistical techniques, researchers are able to draw meaningful conclusions from data, identify trends and patterns, and make informed decisions. 通过利用统计技术，研究人员能够从数据中得出有意义的结论，识别趋势和模式，并做出明智的决策。

Statistical methods provide a framework for organizing, analyzing, and interpreting data to extract valuable insights that can inform decision-making processes. 统计方法提供了一个框架，用于组织、分析和解释数据，从而提取有价值的洞察，可以指导决策过程。

One of the key advantages of statistical methods is their ability to quantify uncertainty and variability in data. 统计方法的一个关键优势是其能力量化数据中的不确定性和变异性。

By using probability theory and hypothesis testing, statisticians can assess the reliability of their findings and make valid inferences about populations based on sample data. 通过使用概率论和假设检验，统计学家可以评估其发现的可靠性，并根据样本数据对总体进行有效推断。

质量工程师英语词汇汇总1. PDCA : : Plan、Do、Check、Action 策划、实施、检查、处置2. PPAP : Production PartApproval Process 生产件批准程序3. APQP : Advaneed ProductQuality Planning 产品质量先期策划4. FMEA : Potential FailureMode and Effects Analysis 潜在失效模式及后果分析5.SPC : Statistical ProcessControl 统计过程控制6. MSA : Measurement SystemAnalysis 测量系统控制7. CP : Control Plan 控制计划8. QSA : Quality SystemAssessment 质量体系评定9. PPM : Parts Per Million 每百万零件不合格数10. QM : : Quality Manua 质量手册11. QP : : Quality Procedure 质量程序文件/Quality Planning 质量策划/QualityPlan质量计划12. CMK :机器能力指数13. CPK :过程能力指数14. CAD : : Computer-AidedDesign 计算机辅助能力设计15.OEE : Overall Equipment Effectiveness 设备总效率精选资料16.QFD : Quality FunctionDeployment 质量功能展开功能展开17. FIF0: : First in, First out 先进先出先进先出18. COPS : : Customer OrientedProcesses 顾客导向过程顾客导向过程19. TCQ: : Time 、Cost 、Quality 时间、成本、质量时间、成本、质量 20. MPS : Management Processes 管理性过程管理性过程21.SPS : Support Processes 支持性过程支持性过程22. TQM : Total QualityManagement 全面质量管理全面质量管理23. PQA : Product QualityAssuranee产品质量保证（免检）产品质量保证（免检）24. QP-QC-QI :质量改进质量三步曲，质量计划—质量控制—质量改进25. QAF : Quality AssuranceFile 质量保证文件质量保证文件26. QAP : Quality AssurancePlan 质量保证计划质量保证计划27. PFC : Process Flow Chart 过程流程图过程流程图 28. QMS : Quality ManagementSystems 质量管理体系质量管理体系29. JIT : Just In Time准时（交货）准时（交货） 30. ERP : EnterpriseRequirement Planning企业需求计划企业需求计划 31. QC : Quality Control 质量控制质量控制32. QA : Quality Audit质量审核质量审核质量审核 /QalityAssuranee 质量保证质量保证33.IQC : In Come QualityControl进货质量控制进货质量控制过程质量控制34.IPQC : In Process QualityControl 过程质量控制成品质量控制35.FQC : Final QualityControl 成品质量控制出货质量控制36.OQC : Out Quality Control 出货质量控制人、机、料、 37.4M1E : Man、Machine、Material、Method、Environment 人、机、料、法、环法、环做什么 / 谁做/ 时38.5W1H : Why、What、Who、When、Where、How 为何/ 做什么如何做间/地点/如何做整理、整顿、清39.6S : Seiri、Seiton、Seiso、Seiketsu、Shitsuke 、Safety 整理、整顿、清扫、清洁、素养、安全扫、清洁、素养、安全（三种）可记录工伤值40. TRI值：Total Record Injury （三种）可记录工伤值41.SMART :精明原则，SpecificMeasurable Achievable Result OrientedTimed （具体的描述、可以测量的、可以通过努力实现的、有结果导向性的、有时间性的）间性的）----------- 企业常用英文缩写----------- 管理1.5S : 5S 管理2. ABC :作业制成本制度(Activity-BasedCosting )3. ABB :实施作业制预算制度(Activity-BasedBudgeting )4. ABM :作业制成本管理(Activity-BaseManagement )5. APS :先进规画与排程系统(AdvancedPlanning and Scheduling )6. ASP :应用程序服务供货商(ApplicationService Provider )7. ATP :可承诺量(Available ToPromise )8. AVL :认可的供货商清单(ApprovedVendor List )9. BOM :物料清单(Bill OfMaterial )10. BPR :企业流程再造(Bus in essProcess Ree ngi neering )11. BSC :平衡记分卡(BalancedScoreCard )12. BTF :计划生产(Build ToForecast )13. BTO :订单生产(Build To Order )14. CPM :要径法(Critical PathMethod )15. CPM :每一百万个使用者会有几次抱怨(Complaintper Million )16. CRM :客户关系管理(CustomerRelationship Management )17. CRP :产能需求规划(CapacityRequirements Planning )18. CTO :客制化生产(Con figurati on To Order )19. DBR :限制驱导式排程法(Drum-Buffer-Rope )20. DMT :成熟度验证(Desig nM aturi ng Testi ng )21. DVT :设计验证(Desig nV erificati on Testi ng )22. DRP :运销资源计划(DistributionResource Planning )23. DSS :决策支持系统(DecisionSupport System )24. EC :设计变更/工程变更(EngineerChange )25. EC :电子商务(ElectronicCommerce )26. ECRN :原件规格更改通知(EngineerChange Request Notice )27. EDI :电子数据交换(ElectronieData Interchange )28. EIS :主管决策系统(Executivelnformation System )29. EMC :电磁相容(ElectricMagnetic Capability )30. EOQ :基本经济订购量(EconomicOrder Quantity )31. ERP :企业资源规划(EnterpriseResource Planning )32. FAE :应用工程师(FieldApplication Engineer )33. FCST :预估(Forecast)34. FMS :弹性制造系统(FlexibleManufacture System )35. FQC :成品质量管理(Finish orFinal Quality Control )36.IPQC:制程质量管理(In-ProcessQuality Control )37.IQC :进料质量管理(In comi ngQuality Co ntrol )38.ISO :国际标准组织(InternationalOrganization for Standardization )39.ISAR :首批样品认可(InitialSample Approval Request )40. JIT :实时管理(Just In Time )41. KM :知识管理(KnowledgeManagement )42. L4L :逐批订购法(Lot-for-Lot )43. LTC :最小总成本法(Least TotalCost )44. LUC :最小单位成本(Least UnitCost )45. MES :制造执行系统(ManufacturingExecution System )46. MO :制令(Manufacture Order )47. MPS :主生产排程(MasterProduction Schedule )48. MRO :请修(购)单(MaintenanceRepair Operation )49. MRP :物料需求规划(MaterialRequirement Planning )50. MRPII :制造资源计划(ManufacturingResource Planning )更改预估量的通知 Notice forChanging Forecast51. NFCF :更改预估量的通知52.OEM :委托代工(OriginalEquipment Manufacture )53.ODM :委托设计与制造(OriginalDesign & Manufacture )54.OLAP :在线分析处理(On-LineAnalytical Processing )55.OLTP :在线交易处理(On-LineTransaction Processing )56.OPT :最佳生产技术(OptimizedProduction Techno logy )57.OQC :出货质量管理(Out-goingQuality Control )58. PDCA : PDCA 管理循环(Plan-Do-Check-Action )59. PDM :产品数据管理系统(ProductData Management )60. PERT :计划评核术(ProgramEvaluation and Review Technique )61. P0 :订单(Purchase Order )62. POH :预估在手量(Product。

「中國人個性測量表」在西方樣本的初步研究香港中文大學心理學系張樹輝發表於:「泛華心理學研究的切磋與交流」第四屆華人心理學家學術研討會暨第六屆華人心理與行為科際學術研討會2002年11月9日至11日台灣台北中央研究所本研究得到香港特區政府研究資助委員會(Earmarked Grant Project #2120149) 及香港中文大學 (Direct Grant #2020662) 資助。

本文所採用的中國人個性測量表(CPAI)是香港中文大學心理學系及中國科學院心理研究所之合作成果，作者包括張妙清教授、梁覺教授、張建新教授、宋維真教授、及張建平教授。

本文部份結果取材自另一篇論文，由張妙清、張樹輝、梁覺、Colleen Ward、及梁天樂合撰。

該論文將對本研究有更詳細的討論。

特此鳴謝Prof. Colleen Ward在新加坡收集華人樣本，及梁天樂教授在美國收集的白人樣本。

本論文主要對「中國人個性測量表」（The Chinese Personality Assessment Inventory，簡稱CPAI）在西方樣本的因素結構（Factor Structure）作初步探討。

CPAI是張妙清等（1996）根據經驗歸納法，從華人文化的角度出發而編訂的一套具中華文化相關性的本土化人格量表。

在2001年張妙清等發現，把CPAI與西方著名的人格量表NEO-PI-R進行聯合因素分析（Joint Factor Analysis），人際關係向度是獨立於NEO-PI-R的五個向度。

其他的研究亦顯示人際關係向度在華人社會中起一定的作用。

Ho（2001）認為，人在社會中的行為並不獨立於其他人的影響，而早期的西方心理學亦有關於人際關係的理論（例如Sullivan，1953、Wiggins，1979）。

因此作為中華文化的其中一個人格心理學角度，值得研究本土化華人量表CPAI在西方文化的樣本的因素結構。

本論文包括兩個研究。

第一個研究主要是有關英語版的CPAI的編訂及初步驗證。

Network impacts of a road capacity reduction:Empirical analysisand model predictionsDavid Watling a ,⇑,David Milne a ,Stephen Clark baInstitute for Transport Studies,University of Leeds,Woodhouse Lane,Leeds LS29JT,UK b Leeds City Council,Leonardo Building,2Rossington Street,Leeds LS28HD,UKa r t i c l e i n f o Article history:Received 24May 2010Received in revised form 15July 2011Accepted 7September 2011Keywords:Traffic assignment Network models Equilibrium Route choice Day-to-day variabilitya b s t r a c tIn spite of their widespread use in policy design and evaluation,relatively little evidencehas been reported on how well traffic equilibrium models predict real network impacts.Here we present what we believe to be the first paper that together analyses the explicitimpacts on observed route choice of an actual network intervention and compares thiswith the before-and-after predictions of a network equilibrium model.The analysis isbased on the findings of an empirical study of the travel time and route choice impactsof a road capacity reduction.Time-stamped,partial licence plates were recorded across aseries of locations,over a period of days both with and without the capacity reduction,and the data were ‘matched’between locations using special-purpose statistical methods.Hypothesis tests were used to identify statistically significant changes in travel times androute choice,between the periods of days with and without the capacity reduction.A trafficnetwork equilibrium model was then independently applied to the same scenarios,and itspredictions compared with the empirical findings.From a comparison of route choice pat-terns,a particularly influential spatial effect was revealed of the parameter specifying therelative values of distance and travel time assumed in the generalised cost equations.When this parameter was ‘fitted’to the data without the capacity reduction,the networkmodel broadly predicted the route choice impacts of the capacity reduction,but with othervalues it was seen to perform poorly.The paper concludes by discussing the wider practicaland research implications of the study’s findings.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionIt is well known that altering the localised characteristics of a road network,such as a planned change in road capacity,will tend to have both direct and indirect effects.The direct effects are imparted on the road itself,in terms of how it can deal with a given demand flow entering the link,with an impact on travel times to traverse the link at a given demand flow level.The indirect effects arise due to drivers changing their travel decisions,such as choice of route,in response to the altered travel times.There are many practical circumstances in which it is desirable to forecast these direct and indirect impacts in the context of a systematic change in road capacity.For example,in the case of proposed road widening or junction improvements,there is typically a need to justify econom-ically the required investment in terms of the benefits that will likely accrue.There are also several examples in which it is relevant to examine the impacts of road capacity reduction .For example,if one proposes to reallocate road space between alternative modes,such as increased bus and cycle lane provision or a pedestrianisation scheme,then typically a range of alternative designs exist which may differ in their ability to accommodate efficiently the new traffic and routing patterns.0965-8564/$-see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.tra.2011.09.010⇑Corresponding author.Tel.:+441133436612;fax:+441133435334.E-mail address:d.p.watling@ (D.Watling).168 D.Watling et al./Transportation Research Part A46(2012)167–189Through mathematical modelling,the alternative designs may be tested in a simulated environment and the most efficient selected for implementation.Even after a particular design is selected,mathematical models may be used to adjust signal timings to optimise the use of the transport system.Road capacity may also be affected periodically by maintenance to essential services(e.g.water,electricity)or to the road itself,and often this can lead to restricted access over a period of days and weeks.In such cases,planning authorities may use modelling to devise suitable diversionary advice for drivers,and to plan any temporary changes to traffic signals or priorities.Berdica(2002)and Taylor et al.(2006)suggest more of a pro-ac-tive approach,proposing that models should be used to test networks for potential vulnerability,before any reduction mate-rialises,identifying links which if reduced in capacity over an extended period1would have a substantial impact on system performance.There are therefore practical requirements for a suitable network model of travel time and route choice impacts of capac-ity changes.The dominant method that has emerged for this purpose over the last decades is clearly the network equilibrium approach,as proposed by Beckmann et al.(1956)and developed in several directions since.The basis of using this approach is the proposition of what are believed to be‘rational’models of behaviour and other system components(e.g.link perfor-mance functions),with site-specific data used to tailor such models to particular case studies.Cross-sectional forecasts of network performance at specific road capacity states may then be made,such that at the time of any‘snapshot’forecast, drivers’route choices are in some kind of individually-optimum state.In this state,drivers cannot improve their route selec-tion by a unilateral change of route,at the snapshot travel time levels.The accepted practice is to‘validate’such models on a case-by-case basis,by ensuring that the model—when supplied with a particular set of parameters,input network data and input origin–destination demand data—reproduces current mea-sured mean link trafficflows and mean journey times,on a sample of links,to some degree of accuracy(see for example,the practical guidelines in TMIP(1997)and Highways Agency(2002)).This kind of aggregate level,cross-sectional validation to existing conditions persists across a range of network modelling paradigms,ranging from static and dynamic equilibrium (Florian and Nguyen,1976;Leonard and Tough,1979;Stephenson and Teply,1984;Matzoros et al.,1987;Janson et al., 1986;Janson,1991)to micro-simulation approaches(Laird et al.,1999;Ben-Akiva et al.,2000;Keenan,2005).While such an approach is plausible,it leaves many questions unanswered,and we would particularly highlight two: 1.The process of calibration and validation of a network equilibrium model may typically occur in a cycle.That is to say,having initially calibrated a model using the base data sources,if the subsequent validation reveals substantial discrep-ancies in some part of the network,it is then natural to adjust the model parameters(including perhaps even the OD matrix elements)until the model outputs better reflect the validation data.2In this process,then,we allow the adjustment of potentially a large number of network parameters and input data in order to replicate the validation data,yet these data themselves are highly aggregate,existing only at the link level.To be clear here,we are talking about a level of coarseness even greater than that in aggregate choice models,since we cannot even infer from link-level data the aggregate shares on alternative routes or OD movements.The question that arises is then:how many different combinations of parameters and input data values might lead to a similar link-level validation,and even if we knew the answer to this question,how might we choose between these alternative combinations?In practice,this issue is typically neglected,meaning that the‘valida-tion’is a rather weak test of the model.2.Since the data are cross-sectional in time(i.e.the aim is to reproduce current base conditions in equilibrium),then in spiteof the large efforts required in data collection,no empirical evidence is routinely collected regarding the model’s main purpose,namely its ability to predict changes in behaviour and network performance under changes to the network/ demand.This issue is exacerbated by the aggregation concerns in point1:the‘ambiguity’in choosing appropriate param-eter values to satisfy the aggregate,link-level,base validation strengthens the need to independently verify that,with the selected parameter values,the model responds reliably to changes.Although such problems–offitting equilibrium models to cross-sectional data–have long been recognised by practitioners and academics(see,e.g.,Goodwin,1998), the approach described above remains the state-of-practice.Having identified these two problems,how might we go about addressing them?One approach to thefirst problem would be to return to the underlying formulation of the network model,and instead require a model definition that permits analysis by statistical inference techniques(see for example,Nakayama et al.,2009).In this way,we may potentially exploit more information in the variability of the link-level data,with well-defined notions(such as maximum likelihood)allowing a systematic basis for selection between alternative parameter value combinations.However,this approach is still using rather limited data and it is natural not just to question the model but also the data that we use to calibrate and validate it.Yet this is not altogether straightforward to resolve.As Mahmassani and Jou(2000) remarked:‘A major difficulty...is obtaining observations of actual trip-maker behaviour,at the desired level of richness, simultaneously with measurements of prevailing conditions’.For this reason,several authors have turned to simulated gaming environments and/or stated preference techniques to elicit information on drivers’route choice behaviour(e.g. 1Clearly,more sporadic and less predictable reductions in capacity may also occur,such as in the case of breakdowns and accidents,and environmental factors such as severe weather,floods or landslides(see for example,Iida,1999),but the responses to such cases are outside the scope of the present paper. 2Some authors have suggested more systematic,bi-level type optimization processes for thisfitting process(e.g.Xu et al.,2004),but this has no material effect on the essential points above.D.Watling et al./Transportation Research Part A46(2012)167–189169 Mahmassani and Herman,1990;Iida et al.,1992;Khattak et al.,1993;Vaughn et al.,1995;Wardman et al.,1997;Jou,2001; Chen et al.,2001).This provides potentially rich information for calibrating complex behavioural models,but has the obvious limitation that it is based on imagined rather than real route choice situations.Aside from its common focus on hypothetical decision situations,this latter body of work also signifies a subtle change of emphasis in the treatment of the overall network calibration problem.Rather than viewing the network equilibrium calibra-tion process as a whole,the focus is on particular components of the model;in the cases above,the focus is on that compo-nent concerned with how drivers make route decisions.If we are prepared to make such a component-wise analysis,then certainly there exists abundant empirical evidence in the literature,with a history across a number of decades of research into issues such as the factors affecting drivers’route choice(e.g.Wachs,1967;Huchingson et al.,1977;Abu-Eisheh and Mannering,1987;Duffell and Kalombaris,1988;Antonisse et al.,1989;Bekhor et al.,2002;Liu et al.,2004),the nature of travel time variability(e.g.Smeed and Jeffcoate,1971;Montgomery and May,1987;May et al.,1989;McLeod et al., 1993),and the factors affecting trafficflow variability(Bonsall et al.,1984;Huff and Hanson,1986;Ribeiro,1994;Rakha and Van Aerde,1995;Fox et al.,1998).While these works provide useful evidence for the network equilibrium calibration problem,they do not provide a frame-work in which we can judge the overall‘fit’of a particular network model in the light of uncertainty,ambient variation and systematic changes in network attributes,be they related to the OD demand,the route choice process,travel times or the network data.Moreover,such data does nothing to address the second point made above,namely the question of how to validate the model forecasts under systematic changes to its inputs.The studies of Mannering et al.(1994)and Emmerink et al.(1996)are distinctive in this context in that they address some of the empirical concerns expressed in the context of travel information impacts,but their work stops at the stage of the empirical analysis,without a link being made to net-work prediction models.The focus of the present paper therefore is both to present thefindings of an empirical study and to link this empirical evidence to network forecasting models.More recently,Zhu et al.(2010)analysed several sources of data for evidence of the traffic and behavioural impacts of the I-35W bridge collapse in Minneapolis.Most pertinent to the present paper is their location-specific analysis of linkflows at 24locations;by computing the root mean square difference inflows between successive weeks,and comparing the trend for 2006with that for2007(the latter with the bridge collapse),they observed an apparent transient impact of the bridge col-lapse.They also showed there was no statistically-significant evidence of a difference in the pattern offlows in the period September–November2007(a period starting6weeks after the bridge collapse),when compared with the corresponding period in2006.They suggested that this was indicative of the length of a‘re-equilibration process’in a conceptual sense, though did not explicitly compare their empiricalfindings with those of a network equilibrium model.The structure of the remainder of the paper is as follows.In Section2we describe the process of selecting the real-life problem to analyse,together with the details and rationale behind the survey design.Following this,Section3describes the statistical techniques used to extract information on travel times and routing patterns from the survey data.Statistical inference is then considered in Section4,with the aim of detecting statistically significant explanatory factors.In Section5 comparisons are made between the observed network data and those predicted by a network equilibrium model.Finally,in Section6the conclusions of the study are highlighted,and recommendations made for both practice and future research.2.Experimental designThe ultimate objective of the study was to compare actual data with the output of a traffic network equilibrium model, specifically in terms of how well the equilibrium model was able to correctly forecast the impact of a systematic change ap-plied to the network.While a wealth of surveillance data on linkflows and travel times is routinely collected by many local and national agencies,we did not believe that such data would be sufficiently informative for our purposes.The reason is that while such data can often be disaggregated down to small time step resolutions,the data remains aggregate in terms of what it informs about driver response,since it does not provide the opportunity to explicitly trace vehicles(even in aggre-gate form)across more than one location.This has the effect that observed differences in linkflows might be attributed to many potential causes:it is especially difficult to separate out,say,ambient daily variation in the trip demand matrix from systematic changes in route choice,since both may give rise to similar impacts on observed linkflow patterns across re-corded sites.While methods do exist for reconstructing OD and network route patterns from observed link data(e.g.Yang et al.,1994),these are typically based on the premise of a valid network equilibrium model:in this case then,the data would not be able to give independent information on the validity of the network equilibrium approach.For these reasons it was decided to design and implement a purpose-built survey.However,it would not be efficient to extensively monitor a network in order to wait for something to happen,and therefore we required advance notification of some planned intervention.For this reason we chose to study the impact of urban maintenance work affecting the roads,which UK local government authorities organise on an annual basis as part of their‘Local Transport Plan’.The city council of York,a historic city in the north of England,agreed to inform us of their plans and to assist in the subsequent data collection exercise.Based on the interventions planned by York CC,the list of candidate studies was narrowed by considering factors such as its propensity to induce significant re-routing and its impact on the peak periods.Effectively the motivation here was to identify interventions that were likely to have a large impact on delays,since route choice impacts would then likely be more significant and more easily distinguished from ambient variability.This was notably at odds with the objectives of York CC,170 D.Watling et al./Transportation Research Part A46(2012)167–189in that they wished to minimise disruption,and so where possible York CC planned interventions to take place at times of day and of the year where impacts were minimised;therefore our own requirement greatly reduced the candidate set of studies to monitor.A further consideration in study selection was its timing in the year for scheduling before/after surveys so to avoid confounding effects of known significant‘seasonal’demand changes,e.g.the impact of the change between school semesters and holidays.A further consideration was York’s role as a major tourist attraction,which is also known to have a seasonal trend.However,the impact on car traffic is relatively small due to the strong promotion of public trans-port and restrictions on car travel and parking in the historic centre.We felt that we further mitigated such impacts by sub-sequently choosing to survey in the morning peak,at a time before most tourist attractions are open.Aside from the question of which intervention to survey was the issue of what data to collect.Within the resources of the project,we considered several options.We rejected stated preference survey methods as,although they provide a link to personal/socio-economic drivers,we wanted to compare actual behaviour with a network model;if the stated preference data conflicted with the network model,it would not be clear which we should question most.For revealed preference data, options considered included(i)self-completion diaries(Mahmassani and Jou,2000),(ii)automatic tracking through GPS(Jan et al.,2000;Quiroga et al.,2000;Taylor et al.,2000),and(iii)licence plate surveys(Schaefer,1988).Regarding self-comple-tion surveys,from our own interview experiments with self-completion questionnaires it was evident that travellersfind it relatively difficult to recall and describe complex choice options such as a route through an urban network,giving the po-tential for significant errors to be introduced.The automatic tracking option was believed to be the most attractive in this respect,in its potential to accurately map a given individual’s journey,but the negative side would be the potential sample size,as we would need to purchase/hire and distribute the devices;even with a large budget,it is not straightforward to identify in advance the target users,nor to guarantee their cooperation.Licence plate surveys,it was believed,offered the potential for compromise between sample size and data resolution: while we could not track routes to the same resolution as GPS,by judicious location of surveyors we had the opportunity to track vehicles across more than one location,thus providing route-like information.With time-stamped licence plates, the matched data would also provide journey time information.The negative side of this approach is the well-known poten-tial for significant recording errors if large sample rates are required.Our aim was to avoid this by recording only partial licence plates,and employing statistical methods to remove the impact of‘spurious matches’,i.e.where two different vehi-cles with the same partial licence plate occur at different locations.Moreover,extensive simulation experiments(Watling,1994)had previously shown that these latter statistical methods were effective in recovering the underlying movements and travel times,even if only a relatively small part of the licence plate were recorded,in spite of giving a large potential for spurious matching.We believed that such an approach reduced the opportunity for recorder error to such a level to suggest that a100%sample rate of vehicles passing may be feasible.This was tested in a pilot study conducted by the project team,with dictaphones used to record a100%sample of time-stamped, partial licence plates.Independent,duplicate observers were employed at the same location to compare error rates;the same study was also conducted with full licence plates.The study indicated that100%surveys with dictaphones would be feasible in moderate trafficflow,but only if partial licence plate data were used in order to control observation errors; for higherflow rates or to obtain full number plate data,video surveys should be considered.Other important practical les-sons learned from the pilot included the need for clarity in terms of vehicle types to survey(e.g.whether to include motor-cycles and taxis),and of the phonetic alphabet used by surveyors to avoid transcription ambiguities.Based on the twin considerations above of planned interventions and survey approach,several candidate studies were identified.For a candidate study,detailed design issues involved identifying:likely affected movements and alternative routes(using local knowledge of York CC,together with an existing network model of the city),in order to determine the number and location of survey sites;feasible viewpoints,based on site visits;the timing of surveys,e.g.visibility issues in the dark,winter evening peak period;the peak duration from automatic trafficflow data;and specific survey days,in view of public/school holidays.Our budget led us to survey the majority of licence plate sites manually(partial plates by audio-tape or,in lowflows,pen and paper),with video surveys limited to a small number of high-flow sites.From this combination of techniques,100%sampling rate was feasible at each site.Surveys took place in the morning peak due both to visibility considerations and to minimise conflicts with tourist/special event traffic.From automatic traffic count data it was decided to survey the period7:45–9:15as the main morning peak period.This design process led to the identification of two studies:2.1.Lendal Bridge study(Fig.1)Lendal Bridge,a critical part of York’s inner ring road,was scheduled to be closed for maintenance from September2000 for a duration of several weeks.To avoid school holidays,the‘before’surveys were scheduled for June and early September.It was decided to focus on investigating a significant southwest-to-northeast movement of traffic,the river providing a natural barrier which suggested surveying the six river crossing points(C,J,H,K,L,M in Fig.1).In total,13locations were identified for survey,in an attempt to capture traffic on both sides of the river as well as a crossing.2.2.Fishergate study(Fig.2)The partial closure(capacity reduction)of the street known as Fishergate,again part of York’s inner ring road,was scheduled for July2001to allow repairs to a collapsed sewer.Survey locations were chosen in order to intercept clockwiseFig.1.Intervention and survey locations for Lendal Bridge study.around the inner ring road,this being the direction of the partial closure.A particular aim wasFulford Road(site E in Fig.2),the main radial affected,with F and K monitoring local diversion I,J to capture wider-area diversion.studies,the plan was to survey the selected locations in the morning peak over a period of approximately covering the three periods before,during and after the intervention,with the days selected so holidays or special events.Fig.2.Intervention and survey locations for Fishergate study.In the Lendal Bridge study,while the‘before’surveys proceeded as planned,the bridge’s actualfirst day of closure on Sep-tember11th2000also marked the beginning of the UK fuel protests(BBC,2000a;Lyons and Chaterjee,2002).Trafficflows were considerably affected by the scarcity of fuel,with congestion extremely low in thefirst week of closure,to the extent that any changes could not be attributed to the bridge closure;neither had our design anticipated how to survey the impacts of the fuel shortages.We thus re-arranged our surveys to monitor more closely the planned re-opening of the bridge.Unfor-tunately these surveys were hampered by a second unanticipated event,namely the wettest autumn in the UK for270years and the highest level offlooding in York since records began(BBC,2000b).Theflooding closed much of the centre of York to road traffic,including our study area,as the roads were impassable,and therefore we abandoned the planned‘after’surveys. As a result of these events,the useable data we had(not affected by the fuel protests orflooding)consisted offive‘before’days and one‘during’day.In the Fishergate study,fortunately no extreme events occurred,allowing six‘before’and seven‘during’days to be sur-veyed,together with one additional day in the‘during’period when the works were temporarily removed.However,the works over-ran into the long summer school holidays,when it is well-known that there is a substantial seasonal effect of much lowerflows and congestion levels.We did not believe it possible to meaningfully isolate the impact of the link fully re-opening while controlling for such an effect,and so our plans for‘after re-opening’surveys were abandoned.3.Estimation of vehicle movements and travel timesThe data resulting from the surveys described in Section2is in the form of(for each day and each study)a set of time-stamped,partial licence plates,observed at a number of locations across the network.Since the data include only partial plates,they cannot simply be matched across observation points to yield reliable estimates of vehicle movements,since there is ambiguity in whether the same partial plate observed at different locations was truly caused by the same vehicle. Indeed,since the observed system is‘open’—in the sense that not all points of entry,exit,generation and attraction are mon-itored—the question is not just which of several potential matches to accept,but also whether there is any match at all.That is to say,an apparent match between data at two observation points could be caused by two separate vehicles that passed no other observation point.Thefirst stage of analysis therefore applied a series of specially-designed statistical techniques to reconstruct the vehicle movements and point-to-point travel time distributions from the observed data,allowing for all such ambiguities in the data.Although the detailed derivations of each method are not given here,since they may be found in the references provided,it is necessary to understand some of the characteristics of each method in order to interpret the results subsequently provided.Furthermore,since some of the basic techniques required modification relative to the published descriptions,then in order to explain these adaptations it is necessary to understand some of the theoretical basis.3.1.Graphical method for estimating point-to-point travel time distributionsThe preliminary technique applied to each data set was the graphical method described in Watling and Maher(1988).This method is derived for analysing partial registration plate data for unidirectional movement between a pair of observation stations(referred to as an‘origin’and a‘destination’).Thus in the data study here,it must be independently applied to given pairs of observation stations,without regard for the interdependencies between observation station pairs.On the other hand, it makes no assumption that the system is‘closed’;there may be vehicles that pass the origin that do not pass the destina-tion,and vice versa.While limited in considering only two-point surveys,the attraction of the graphical technique is that it is a non-parametric method,with no assumptions made about the arrival time distributions at the observation points(they may be non-uniform in particular),and no assumptions made about the journey time probability density.It is therefore very suitable as afirst means of investigative analysis for such data.The method begins by forming all pairs of possible matches in the data,of which some will be genuine matches(the pair of observations were due to a single vehicle)and the remainder spurious matches.Thus, for example,if there are three origin observations and two destination observations of a particular partial registration num-ber,then six possible matches may be formed,of which clearly no more than two can be genuine(and possibly only one or zero are genuine).A scatter plot may then be drawn for each possible match of the observation time at the origin versus that at the destination.The characteristic pattern of such a plot is as that shown in Fig.4a,with a dense‘line’of points(which will primarily be the genuine matches)superimposed upon a scatter of points over the whole region(which will primarily be the spurious matches).If we were to assume uniform arrival rates at the observation stations,then the spurious matches would be uniformly distributed over this plot;however,we shall avoid making such a restrictive assumption.The method begins by making a coarse estimate of the total number of genuine matches across the whole of this plot.As part of this analysis we then assume knowledge of,for any randomly selected vehicle,the probabilities:h k¼Prðvehicle is of the k th type of partial registration plateÞðk¼1;2;...;mÞwhereX m k¼1h k¼1172 D.Watling et al./Transportation Research Part A46(2012)167–189。

LOOKING BEYOND THE FIVE-FACTOR MODEL: COLLEGE SELF-EFFICACY AS A MODERATOR OF THE RELATIONSHIPBETWEEN TELLEGEN’S BIG THREE MODEL OF PERSONALITY AND HOLLAND’S MODEL OF VOCATIONAL INTEREST TYPESBy Elizabeth A BarrettThe Five-Factor Model (FFM) of personality and Tellegen’s Big Three Model of personality were compared to determine their ability to predict Holland’s RIASEC interest types. College self-efficacy was examined as a moderator of the relationship between Tellegen’s Big Three model and the RIASEC interest types. A sample of 194 college freshmen (i.e., less than 30 credits completed) was drawn from the psychology participant pool of a mid-sized Midwestern university. Instruments included the International Personality Item Pool (IPIP) to measure the FFM; the Multidimensional Personality Questionnaire Brief Form (MPQ-BF) to measure Tellegen’s Big Three model of personality; the College Self-Efficacy Inventory (CSEI) to measure college self-efficacy; and the Self Directed Search (SDS) to measure Holland’s RIASEC model of vocational interests. Findings from correlational analyses supported previous research regarding relationships among the FFM and the RIASEC interest types, and relationships among Tellegen’s Big Three and the RIASEC interest types. As hypothesized and tested via regressions for each of the six interest types, Tellegen’s Big Three model predicted all six vocational interests types (p < .001 for all), while the FFM only predicted two types at p < .05. College self-efficacy did not moderate the relationship between Tellegen’s Big Three and the RIASEC interest types. Implications and future research are discussed.ACKNOWLEDGEMENTSSeveral people have assisted me with the completion of this thesis, and I wish to thank the following people for their help and guidance:In acknowledgement of excellent guidance of this project, Dr. McFadden chairperson, whose knowledge and encouragement pushed the progress of this thesis and my development as a writer and researcher.Dr. Adams and Dr. Miron, committee members, who graciously gave their time and shared their expertise in the completion of this project, specifically, Dr. Adams who aided with the data analysis of this project and worked through multiple analysis problems with me.iiTABLE OF CONTENTSPage INTRODUCTION (1)THEORY AND LITERATURE REVIEW (4)Personality Traits and Vocational Interests Defined (4)The Five-Factor Model (FFM) of Personality (4)Holland’s Theory of Vocational Interest Types (5)Overlap between the FFM and RIASEC (7)Criticisms and Limitations of the FFM (9)Looking Beyond the FFM: Tellegen’s Big Three Model of Personality (11)Comparing Tellegen’s Big Three and the FFM (14)College Self-Efficacy: Moderating Role (16)Conclusion (20)METHOD (22)Participants (22)Procedure (23)Measures (23)Methods of Data Analysis and Missing Data (27)RESULTS (29)Descriptive Statistics (29)Relationship between the FFM and the RIASEC Interest Types:Hypothesis 1a-1e (29)Relationship between Tellegen’s Big Three and the RIASEC InterestTypes: Hypothesis 2a-2c (30)Comparing the FFM and Tellegen’s Big Three: Hypothesis 3 (31)College Self-Efficacy as Moderator: Hypothesis 4 (32)iiiTABLE OF CONTENTS (continued) DISCUSSION (34)The Relationship between the FFM and the RIASEC Interest Types (34)The Relationship between Tellegen’s Big Three and the RIASECInterest Types (35)Comparing Tellegen’s Big Three and the FFM (36)College Self-Efficacy as a Moderator (37)Limitations (38)Implications and Future Research (39)Conclusions (41)APPENDIXES (42)Appendix A: Tables (42)Table A-1. Holland’s Vocational Personality Types Described: RIASEC.. 43 Table A-2. Overlap Between the FFM and RIASEC (44)Table A-3. The Big Three of Tellegen measured by the MPQ: HigherOrder and Primary Trait Scales (45)Table A-4. Means, Standard Deviations, and Intercorrelations (47)Table A-5. Regression Analysis: Realistic Interest Type as DependentVariable (48)Table A-6. Regression Analysis: Investigative Interest Type as DependentVariable (48)Table A-7. Regression Analysis: Artistic Interest Type as DependentVariable (49)Table A-8. Regression Analysis: Social Interest Type as DependentVariable (49)Table A-9. Regression Analysis: Enterprising Interest Type as DependentVariable (50)Table A-10. Regression Analysis: Conventional Interest Type asDependent Variable (50)Table A-11. Hierarchical Multiple Moderated Regression Analysis:Realistic Interest Type as Dependent Variable (51)Table A-12. Hierarchical Multiple Moderated Regression Analysis:Investigative Interest Type as Dependent Variable (51)Table A-13. Hierarchical Multiple Moderated Regression Analysis:Artistic Interest Type as Dependent Variable (52)Table A-14. Hierarchical Multiple Moderated Regression Analysis: SocialInterest Type as Dependent Variable (52)Table A-15. Hierarchical Multiple Moderated Regression Analysis:Enterprising Interest Type as Dependent Variable (53)ivTABLE OF CONTENTS (continued)Table A-16. Hierarchical Multiple Moderated Regression Analysis:Conventional Interest Type as Dependent Variable (53)Appendix B: Figure (54)Figure B-1. Holland’s Hexagonal Model (55)Appendix C: Information Sheet and Surveys (56)REFERENCES (73)vINTRODUCTIONPersonality traits and vocational interests are two major individual difference domains that influence numerous outcomes associated with work and life success. For example, research has shown that congruence between personality traits and one’s vocation is related to greater job performance and job satisfaction (Barrick, Mount, & Judge, 2001; Hogan & Blake, 1999; Zak, Meir, & Kraemer, 1979). Additionally, specific personality traits are hypothesized to play a role in determining job success within related career domains (Sullivan & Hansen, 2004). Personality is a relatively enduring characteristic of an individual, and therefore could serve as a stable predictor of why people choose particular jobs and careers.Personality traits and vocational interests are linked by affecting behavior through motivational processes (Holland, 1973, 1985). Personality traits and vocational interests influence choices individuals make about which tasks and activities to engage in, how much effort to exert on those tasks, and how long to persist with those tasks (Holland, 1973, 1985; Mount, Barrick, Scullen, & Rounds, 2005). Research has shown that when individuals are in environments congruent with their interests, they are more likely to be happy because their beliefs, values, interests, and attitudes are supported and reinforced by people who are similar to them (Mount, Barrick, Scullen, & Rounds, 2005). Furthermore, research has demonstrated that personality and interests may shape career decision making and behavior; personality and interests guide the development ofknowledge and skills by providing the motivation to engage in particular types of activities (Sullivan & Hansen, 2004).As relatively stable dispositions, personality traits influence an individual’s behavior in a variety of life settings, including work (Dilchert, 2007). Individuals often prefer jobs requiring them to display behaviors that match their stable tendencies. Thus individuals will indicate a liking for occupations for which job duties and job environments correspond to their personality traits. Such a match between personal tendencies and job requirements can support adjustment and eventually occupational success, making the choice of a given job personally rewarding on multiple levels (Dilchert, 2007).People applying for jobs need to try to understand themselves more fully in order to determine if they will be satisfied with their career choices based on their personality traits. This process can be aided by vocational counselors who conduct vocational assessments. The purpose of vocational assessment is to enhance client self-understanding, promote self-exploration, and assist in realistic decision making (Carless, 1999). According to a model proposed by Carless (1999), career assessment is based on the assumption that comprehensive information about the self (e.g., knowledge of one’s personality) in relation to the world of work is a necessary prerequisite for wise career decision making.Self-efficacy beliefs--personal expectations about the ability to succeed at tasks (Bandura, 1986)--are often assessed by vocational counselors. This study examines college self-efficacy--belief in one’s ability to perform tasks necessary for success incollege (Wang & Castaneda-Sound, 2008). Self-efficacy determines the degree to which individuals initiate and persist with tasks (Bandura, 1986), and research has found that personality may influence exploration of vocational interests, through high levels of self-efficacy (Nauta, 2007).There is an abundance of literature supporting that the Five-Factor Model (FFM) (discussed in depth in following sections) of personality predicts Holland’s theory of vocational interest types; however, there is little literature that extends beyond use of the Five-Factor Model. This is due to the adoption of the FFM as an overriding model of personality over the past fifteen years. However, as will be demonstrated later, several criticisms of the model have surfaced. In light of these criticisms the purpose of this study is to extend the existing literature that has established links between the FFM and Holland’s vocational interest types, while examining the relationship between an alternate personality model, Tellegen’s Big Three (as measured by the Multidimensional Personality Questionnaire (MPQ)) and Holland’s types. Furthermore, this study will examine the moderating role that college self-efficacy plays on the relationship between Tellegen’s Big Three and Holland’s interest types.THEORY AND LITERATURE REVIEWPersonality Traits and Vocational Interests DefinedPersonality traits refer to characteristics that are stable over time and are psychological in nature; they reflect who we are and in aggregate determine our affective, behavioral, and cognitive styles (Mount, Barrick, Scullen, & Rounds, 2005). Vocational interests reflect long-term dispositional traits that influence vocational behavior primarily through one’s preferences for certain environments, activities, and types of people (Mount, Barrick, Scullen, & Rounds, 2005).The Five-Factor Model (FFM) of PersonalityThe FFM, often referred to as the Big Five personality dimensions, is a major model that claims personality consists of five dimensions: Openness to Experience (i.e., imaginative, intellectual, and artistically sensitive), Conscientiousness (i.e., dependable, organized, and persistent), Extraversion (i.e., sociable, active, and energetic), Agreeableness (i.e., cooperative, considerate, and trusting), and Neuroticism, sometimes referred to positively as emotional stability (i.e., calm, secure, and unemotional) (Harris, Vernon, Johnson, & Jang, 2006; McCrae & Costa, 1986; McCrae & Costa, 1987; Mount, Barrick, Scullen, & Rounds, 2005; Nauta, 2004; Sullivan & Hansen, 2004). The FFM provides the foundation for several personality measures (e.g., NEO-PI, NEO-PI-R, NEO-FFI) that have proved to be valid and reliable and are widely utilized in research today (Costa & McCrae, 1992). There appears to be a large degree of consensusregarding the FFM of personality and the instruments used to measure the model. For instance, the FFM has been shown to have a large degree of universality (McCrae, 2001), specifically in terms of stability across adulthood (McCrae & Costa, 2003) and cultures (DeFruyt & Mervielde, 1997; Hofstede & McCrae, 2004, McCrae, 2001).Holland’s Theory of Vocational Interest TypesHolland’s theory of vocational interests has played a key role in efforts to understand vocational interests, choice, and satisfaction.Holland was very clear that he believed personality and vocational interests are related:If vocational interests are construed as an expression of personality, then theyrepresent the expression of personality in work, school subjects, hobbies,recreational activities, and preferences. In short, what we have called ‘vocational interests’ are simply another aspect of personality…If vocational interests are anexpression of personality, then it follows that interest inventories are personalityinventories. (Holland, 1973, p.7)Vocational interest types, as classified by Holland, are six broad categories (discussed later in the section) that can be used to group occupations or the people who work in them. Holland’s theory of vocational interest types and work environments states that employees’ satisfaction with a job as well as propensity to leave that job depends on the degree to which their personalities match their occupational environments (Holland, 1973, 1985). Furthermore, people are assumed to be most satisfied, successful, and stable in a work environment that is congruent with their vocational interest type. Two ofHolland’s basic assumptions are: (a) individuals in a particular vocation have similar personalities, and (b) individuals tend to choose occupational environments consistent with their personality (Holland, 1997).A fundamental proposition of Holland’s theory is that, when differentiated by their vocational interests, people can be categorized according to a taxonomy of six types, hereinafter collectively referred to as RIASEC (Holland, 1973, 1985). Holland’s theory states that six vocational interest types--Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (RIASEC)--influence people to seek environments which are congruent with their characteristics (Harris, Vernon, Johnson, & Jang, 2006; Holland, 1973, 1985; Nauta, 2004; Roberti, Fox, & Tunick, 2003; Sullivan & Hansen, 2004; Zak, Meir, & Kraemer, 1979). Holland used adjective descriptors to capture the distinctive characteristics of each interest type (Hogan & Blake, 1999). These are summarized in Table A-1. Holland’s approach to the assessment of vocational interest types was based on the assumption that members of an occupational group have similar work-related preferences and respond to problems and situations in similar ways (Carless, 1999).Realistic types like the systematic manipulation of machinery, tools, or animals. Investigative types have interests that involve analytical, curious, methodical, and precise activities. The interests of Artistic types are expressive, nonconforming, original, and introspective. Social types want to work with and help others. Enterprising types seek to influence others to attain organizational goals or economic gain. Finally, Conventional types are interested in systematic manipulation of data, filing records, or reproducing materials (Tokar, Vaux, & Swanson, 1995).According to Holland’s theory, these interest types differ in their relative similarity to one another, in ways that can be represented by a hexagonal figure with the types positioned at the six points (see Figure B-1). Adjacent types (e.g., Realistic and Investigative) are most similar; opposite types (e.g., Realistic and Social) are least similar, and alternating types (e.g., Realistic and Artistic) are assumed to have an intermediate level of relationship (Holland, 1973, 1985; Tokar, Vaux, & Swanson, 1995).Overlap between the FFM and RIASECMany studies provide evidence of the links between the FFM of personality and the RIASEC interest types. An extensive review of the research investigating the links between the FFM and the RIASEC types identified ten studies that found Extraversion predicts interest in jobs that focus on Social and Enterprising interests. Ten studies showed Openness to Experience predicts interest in jobs that focus on Investigative and Artistic interests. Six studies found Agreeableness predicts interest in jobs that focus on Social interest; six studies showed Conscientiousness predicts interests in jobs that focus on Conventional interests; and one study found Neuroticism predicts interests in jobs that focus on Investigative interests (see Table A-2 for the citations). One discrepancy in this research has been Costa and McCrae’s claims that the FFM applies uniformly to all adult ages, but Mroczek, Ozer, Spiro, and Kaiser (1998) found substantial differences between the structures emerging from older individuals as compared to undergraduate students, in that the five factor structure failed to emerge in the student sample (i.e., agreeableness failed to emerge) as it did with the older sample.All of the links discussed between the FFM of personality and the RIASEC interest types provide the foundation for hypotheses 1a – 1e. Hypotheses 1a – 1e will add to the literature, previously discussed, by assessing current college students early in their college careers. These are the people who have the potential to be most influenced by vocational and career counselors. Given that research has found a discrepancy in FFM profiles of younger and older individuals, it is important to test its efficacy in predicting vocational interests.Hypothesis 1a (H1a): Extraversion will significantly positively correlate withSocial and Enterprising types, but not Realistic, Investigative, Artistic, andConventional types.Hypothesis 1b (H1b): Openness to Experience will significantly positivelycorrelate with Investigative and Artistic types, but not Realistic, Conventional,Social, and Enterprising types.Hypothesis 1c (H1c): Agreeableness will significantly positively correlate withSocial types, but not Realistic, Conventional, Enterprising, Investigative, andArtistic types.Hypothesis 1d (H1d): Conscientiousness will significantly positively correlatewith Conventional types, but not Realistic, Enterprising, Investigative, Social, and Artistic types.Hypothesis 1e (H1e): Neuroticism will significantly negatively correlate withInvestigative types, but not Realistic, Enterprising, Conventional, Social, andArtistic types.Criticisms and Limitations of the FFMThe whole enterprise of science depends on challenging accepted views, and the FFM has become one of the most accepted models in personality research. Many critiques of the FFM ask “Why are there five and only five factors? Five factor protagonists say: it is an empirical fact…via the mathematical method of factor analysis, the basic dimensions of personality have been discovered.” (McCrae & Costa, 1989, p. 120). Has psychology as a science achieved a final and absolute way of looking at personality or is there a way to further our conceptualization of personality? In the article by Costa and McCrae (1997) explaining the anticipated changes to the NEO in the new millennium, they anticipate only minor wording modifications and simplifications. Thus it appears as if the FFM is viewed as a final or almost final achievement (Block, 2001). One claimed benefit of the FFM is evidence of heritability is strong for all 5 factors, but evidence is strong for all personality factors studied; it does not single out the Costa and McCrae factors (Eysenck, 1992). In other words, all the criteria suggested by Costa and McCrae are necessary but not sufficient to mark out one model from many which also conform to this criteria.The debate that has been most prominent over the past 15 years, and which has probably attracted the most attention, concerns the number and description of the basic, fundamental, highest-order factors of personality. Evidence from meta-analyses of factorial studies provide evidence that three, not five personality factors, emerge at the highest level of analysis (Royce & Powell, 1983; Tellegen & Waller, 1991; Zuckerman, Kuhlman, & Camac, 1988; Zuckerman, Kuhlman, Thornquist, & Kiers, 1991).Altogether, Eysenck (1992) has surveyed many different models, questionnaires and inventories, reporting in most cases a break-down into 2 or 3 major factors; but never 5. Additionally, Jackson, Furnham, Forde, and Cotter (2000) and Tellegen (1985) have contradicted Costa and McCrae’s (1995) assertions that a five-factor model seems most appropriate, with results showing that a three-factor solution is both more clear and parsimonious.Another critique of the FFM lies in its development. The initial factor-analytic derivations of the Big Five were not guided by explicit psychological theory, and therefore some have asked the question, “Why these five?” (e.g., Revelle, 1987; Waller & Ben-Porath, 1987). As Briggs (1989) points out, the original studies leading to the FFM “prompted no a priori predictions as to what factors should emerge, and a coherent and falsifiable explanation for the five factors has yet to be put forward” (p. 249).A further developmental critique of the FFM is the lack of lower order factors. Theoretically, factors exist at different hierarchical levels, and the FFM only measures five higher order factors (Block, 2001). The FFM operates at a broadband level to measure the main (i.e., higher order) categories of traits (McAdams, 1992). Within each of the five categories, therefore, may be many different and more specific traits, as traits are nested hierarchically within traits (McAdams, 1992).Another limitation of the FFM lies in researchers’ inability to consistently link the personality traits to the Holland interest types. Research has found that although there is a significant overlap between the FFM and RIASEC interest types, the RIASEC types do not appear to be entirely encompassed by the Big-Five personality dimensions (Carless,1999; Church, 1994; DeFruyt & Mervielde, 1999; Tokar, Vaux, & Swanson, 1995). Three personality dimensions in the FFM predict the RIASEC types, but there is less evidence to support that the other two predict the RIASEC types. Specifically, there appears to be significant overlap with Conscientiousness, Openness, and Extraversion in predicting the RIASEC interest types, but less research has been able to find links between Agreeableness and Neuroticism with the RIASEC interest types. This is a limitation of the FFM in relating to vocational interests (Costa, McCrae, & Holland, 1984; Gottfredson, Jones, & Holland, 1993; Tokar, Vaux, & Swanson, 1995).Looking Beyond the FFM: Tellegen’s Big Three Model of PersonalityIn light of these criticisms of the FFM, it seems attention could be paid to alternate models of personality to investigate the dimensions underlying Holland’s interest types. The literature base is sparse here, and alternative personality models warrant further study, particularly with regard to vocational interests (Blake & Sackett, 1999; Church, 1994; Larson & Borgen, 2002; Staggs, Larson, & Borgen, 2003). One such model is Tellegen’s Big Three which addresses many of the criticisms of the FFM.Many vocational psychology researchers use the Big Five model of personality, often measured by the NEO-PI or NEO-PI-R, but less often the Big Three model of personality measured by the Multidimensional Personality Questionnaire (MPQ) is used (Tellegen, 1985; Tellegen & Waller, 1991). This model of personality resulted from ten years of research on focal dimensions in the personality literature (Tellegen, 1985; Tellegen & Waller, 1991). Tellegen’s (1985; Tellegen & Waller, 1991) Big Three modeldefines three higher order factors. These represent the clusters of items from a factor analysis that composed the three higher order traits. The lower order factors consist of items clustered in each of the higher order factors. The higher order factors are: Positive Emotionality (PEM), Negative Emotionality (NEM), and Constraint (CT) (Tellegen, 1985; Tellegen & Waller, 1991). These higher order traits correlate minimally with one another and encompass 11 lower order traits. Refer to Table A-3 for a description of the three higher order traits and the 11 lower order traits.There are only three published studies that have examined the Big Three model as relating to vocational interests. Blake and Sackett (1999) reported that the Artistic type moderately related with the MPQ Absorption lower order trait (Larson & Borgen, 2002). The Social type negatively related to the MPQ Aggression lower order trait; the Enterprising type related moderately to the MPQ Social Potency lower order trait; and the Conventional type related moderately to the MPQ Control lower order trait.Staggs, Larson, and Borgen (2003) also analyzed the lower order traits, specifically, as opposed to the higher order factors of PEM, NEM, and CT. They identified seven personality dimensions that have a substantial relationship with vocational interests: Absorption predicted interest in Artistic occupations; Social Potency predicted interest in Enterprising occupations; Harm Avoidance predicted interest in science and mechanical activity occupations; Achievement predicted interest in science and mathematic occupations; Social Closeness predicted interest in mechanical activity occupations; Traditionalism predicted interest in religious activities; and Stress Reaction predicted interest in athletic careers. Staggs, Larson, and Borgen (2003) used a collegestudent sample, but were not studying the RIASEC types; they were using a different conceptualization of vocational interests as measured by the Strong Interest Inventory which measures General Occupational Themes.Larson and Borgen (2002) found that the PEM factor was more strongly correlated with Social interests than with Enterprising interests; however, PEM did strongly correlate with all six RIASEC types (p < .001). This finding shows strong evidence that the PEM higher order trait relates to the RIASEC types. Larson and Borgen (2002) also found that the CT factor was negatively related to Realistic and Artistic interest types, and that the NEM factor was negatively related to Artistic interest types. Larson and Borgen (2002) utilized a sample of “gifted” adolescent students, which is a very limited and non-generalizable sample. In contrast, the current study tests a freshman college student sample, which is more generalizable to the population of students who are seeking vocational guidance.The links between the MPQ and the RIASEC interest types are under-researched. Although some vocational research has utilized the MPQ, more needs to be done to determine the relationships between Tellegen’s Big Three and Holland’s RIASEC interest types. However, the research provides support for the idea that there are alternative personality dimensions (i.e., Tellegen’s Big Three), outside of the FFM, that can significantly predict vocational interests, in particular the RIASEC types. Hypotheses 2a – 2c will test relationships between the Tellegen’s Big Three (as measured by the MPQ-BF) and the RIASEC interest types.Hypothesis 2a (H2a): The PEM factor will significantly positively correlate with all six RIASEC types.Hypothesis 2b (H2b): The CT factor will significantly negatively correlate withRealistic and Artistic types, but not with Investigative, Social, Enterprising, andConventional types.Hypothesis 2c (H2c): The NEM factor will significantly negatively correlate with Artistic types, but not with Investigative, Social, Enterprising, Realistic, andConventional types.Comparing Tellegen’s Big Three and the FFMAn earlier discussion proposed criticisms of the FFM. In light of these criticisms an alternate personality model was considered: Tellegens’ Big Three, measured by the MPQ. This model of personality resolves all the previous criticisms of the FFM: five versus 3 factors, lack of lower order factors, and model development issues.Tellegen’s (1985) understanding of personality differs from the conception of the FFM. Tellegen believes personality can be summed by three overriding traits or factors versus the 5 factors of the FFM. This is an inherent difference in the two models of personality, which guided the development of instruments used to measure these models, in terms of a three versus a five factor structure. Furthermore, Tellegen (1985) utilized a bottom-up approach to development of the MPQ, in which constructs were based on iterative cycles of data collection and item analyses designed to better differentiate the primary scales. In contrast, Tupes and Chrtistal (1961) emphasized deductive, top-down。