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Manpower Planning and Resourcing Q.I Discuss the components and ranges of Manpower Planning A. 1. The first essential component in manpower is its development. This component involves optimal development of human resources through formal and informal educations and creation of congenial conditions for rising technical, managerial, Economics and other skills. Arousing of public enthusiasm or development of motivation is also an essential part of manpower development. Then come towards manpower utilization. This stage involves optimal and effective utilization of various categories of manpower classified on the basis of education and sex. The development manpower turns from a stock into a flow as a consequence of proper utilization. Next component is manpower forecasting. The future demand of manpower along their essential characteristics is lorecast using relevant statistical techniques. The various methods used are: through conduct ofjitys and seeking the opinion of employers; the use of incremental labor- output ratio; density quotient method that estimates the number of people required to do a certain job in an efficient manner; Productivity change method and lastly through international comparisons. Matching of manpower supply with manpower demand; this phase involves matching manpower supplies with manpower demand keeping in view the nature of manpower requirements and its characteristics. This takes us into the orbit of policy action. Last component is provision of productive and gainful employment. It involves provision of productive and gainful employment to the new entrants in the job market. Q.2Write a note on human resource value accounting. A.2 Human is the core factor and which is required to be recognized prior to any other ‘M’s But till now an urgent need based modification is required while identifying and measuring data about human resources. In this paper my objective is to identify the extensive use of Lev & Schwartz model of Human resource accounting, in spite of several criticized from various sides regarding its applicability. Further more, it also portrays the applicability in wide variety of organization of such model (some pubic sector units and IT based sector). Human is the buzzword in the modem knowledge based society. It is the most vital input on which the success & failure of the organization very much depend upon. Starting from the classical economist to modern human capital economist such development in considered being a continuous process. It is one of the most important ‘M’ associated, which is considered while taken care of 4M’s associated with any organization and they are money machines. Materials and men. However, the most interesting thing is that the first three are recognized and find a place in the assets side of the Balance sheet of the organization. However, in case of fourth one ambiguity prevails among the accountant. In spite of its usefulness has been acclaimed is various literature over the decades but its application still remain a suspectable issue, the IASB and the ASB in different countries have not been able to formulate any specific accounting standard for measurement & reporting of such valuable elements. It is a popular phenomenon among the Indian corporate world is to disclose information relating to human resource in annual statements. In this context, it is necessary to conduct a study to assess the disclosure pattern of HRA information in Indian corporate World. It first promulgated by BHEL (Bharat Heavy Electrical Ltd), a leading public enterprise, during the financial year 1972-73. Later other leading public and private sector Organization in the subsequent years also adopted it. Some of them are Hindustan Machine Tools Ltd. (HMTL). Oil and Natural Gas Corporation Ltd. (ONGC), NTPC, Cochin Refineries Ltd. (CRL), Madras Refineries Ltd.,(MRL), Associated Cement Company Ltd.(ACC) and Infosys Technologies Ltd.(ITL). However, adaptability of various models (mainly Lev and Schwartz model, Flamholtz model and Jaggi and Lev model) and discount rate fixation and disclosure pattern

Ic. Either age wise, skill wise etc in BHEL, SAIL, MMTC (Minerals & Metals Trading Corporation Of India Ltd.) HMTL, NIP make it clear, that there has been no uniformity among Indian enterprises regarding HRA disclosure. Meaning of Human Resource Accounting: HRA has been defined by American Accounting Association’s committee —“HRA is the process of identifying & measuring data about human resources & communicating this information to interested parties”. Stephen knauf defined HRA as “The measurement & quantification of human organizational inputs such as recruiting, training, experience & commitment.” According to Eric. G flamholtz HRA represents-”Accounting for people as an organizational resource. It is the measurement of the cost & value of people for the organization”. Hence, it can be said that, it is the process of developing financial assessment for people within organization & society & monitoring of these assessment through time, it deals with. Although HR valuation has important implication for external financial reporting, in the contemporary economic scenario valuing HR has been greater significance for internal HRM decision. Problem Statement: Understanding the way of valuation of human resources by using Lev & Schwartz model and how valuation of such asset are related with the other financial variables for financial reporting purpose. Research Objectives: The main objectives of the study are: i) To asses the way of presenting HRA information in the financial statement by selected companies ii) To identify’ HRA methods and models (mainly the extensive use of Lev & Schwartz model) which are used to arrive at human resource value. iii) How human resource are related with the other accounting variables for human financial reporting in selected companies. The ways of presentation of HRA information disclosed by some of the companies Name of the HRA introduce Model Discount organization in the year rate (in%) BHEL 1973 — 74 Lev & Schwartz model 12 SAIL 1983 — 84 Lev & Schwartz model with 14 Some refinements as suggested by Eric.G. Flamholtz& Jaggi and Lev MMTC 1982 — 83 Lev & Schwartz model 12 ONGC 1981 — 82 Lev & Schwartz model 12.25 NTPC 1984 — 85 Lev & Schwartz model 12 INFOSYS 1999 Lev & Schwartz model 12.96 2006-07 Lev & Schwartz model 14.97 Source: Secondary PRODUCTIVITY & PFRFORMANCF INDICATORS RRJTA RR VA HR VAt HR PTIHR KR’ VA uRN. .1 ri.ployen MIEL / -. SALt - - - - I I I I I I ONGC - - - N11’C I - - flffDSYS - - - I I I — Source: Secondary

Terminology used: 1) PBT-Proflt Before Tax 2) HR- Human Resource 3) TA-Total Assets 4) Turn-Turnover ( or Sales) 5) FA-Fixed Assets 6) VA- Value Added Models of Human Capital Valuation Many models have been created to value human capital. Some are based on historic costs while some are based on future earnings. But each has its own limitations and one model has proved to be more valid than other. Although the Lev and Schwartz model has been the most widely use model for its ease of use & convenience. The Lev & Schwortz Model The Lev and Schwartz model states that the human resource of a co is the summation of value of all the Net present value (NPV) of expenditure on employees. The human capital embodied in a person of age r is the present value of his earning from employment Under this model, the following steps are adopted to determine HR Value. i) Classification of the entire labor force into certain homogeneous groups like skilled, unskilled, semiskilled etc. and in accordance with different classed and age wise.eg. In Infosys the classification is based on software professionals & support staff etc. ii) Construction of average earning stream for each group.eg. At Infosys Incremental earnings based on group! age have been considered. iii) Discounting the average earnings at a predetermined rate in order to get present value of human resource’s of each group. iv) Aggregation of the present value of different groups which represent the capitalized future earnings of the concern as a whole, 1(t) vr= (L-+ r) Where, V, = the value of an Individual r years old 1(t) = the individual’s annual earnings up to retirement t retirement age r = a discount rate specific to the cost of capital to the company. Critical appraisal of the Lev & Schwartz, wdel: — 1] It is essentially an input measure .it ignores the output i.e. productivity of employees. 2] Service state of each individual employee is not considered. 3] The training expenses incurred by the company on its employees are not considered. 4] The attrition rate in organization is also ignored. 5] Factors responsible for higher earning potentiality of each individual employees like seniority, bargaining capacity, skill, experience etc. that may cause differential salary structure are also ignore. Conclusion The conceptual thinking about valuation human resources is still in a developing stage. The accounting bodies all over the world accept no model of HR accounting. However, still we find some application of Lev & Schwartz model is most public sector units and IT based sectors. En knowledge based sectors where human resources are considered the key elements for monitoring the business activities to attend their goals successfully, may not overlooked this side. Hence, the government along with that should take considering the great significance of HRA proper initiation Other professional & accounting bodies both at the national & international levels for the measurement & reporting of such valuable assets.

Q.3 Write a note on Individual and Organizational knowledge conversion process. A.3 the definition of knowledge as the understanding gained from experience, and applied to new situations, ties knowledge closely to the individual. This implies that the term organizational knowledge is a mere metaphor to denote the aggregate knowledge of an organization’s employees. After all, an organization cannot have a brain or a memory to have knowledge. However, if an organization’s knowledge is merely the aggregate knowledge and brainpower of its employees, then how do we classify organizational behavioral patterns reflected in databases, records, and hundreds of practices and operations? What about the wisdom gleaned from the organizations past experiences and transactions, and the insight gained from contact (relationships and networks) with customers, suppliers, and possibly competitors? However, all these resources have been created and are still maintained by individual employees, a considerable part of them remains with the organization after employees leave at the end of the day. There is no doubt that the knowledge of a newly established organization with few members is that of its employees. But as organizations grow in size and life span, organizational knowledge takes other forms as well. As the organization grows, its knowledge base surpasses the knowledge of its individual members, to include experiences and behavioral routines that develop because of the application of knowledge to an insurmountable number of settings. These behavioral patterns and routines have stored in them past experiences, and hence knowledge or wisdom, that affect the organization’s modals operandi and the way it responds to the changing environment. In addition to these routines and practices, an organization has a wealth of in formation resources that it collected and codified through the years. This represents the informational platform, which the employees process to produce more knowledge. Hence is part of the organizational knowledge base. The value of information databases lies in their potential to facilitate the generation of new knowledge by employees and thus should be based on their learning needs and the competencies that the organization plans to develop. That is why knowledge managers refer it to as the knowledge base, since it provides the basic knowledge resources that an employee needs to advance on the learning curve. The interaction between the individual knowledge and the various forms of organizational knowledge, and the conversion from one form to the other, is what creates value in an organization. However, like the information/knowledge interface, it is hard to determine with any precision when individual knowledge ends and organizational knowledge begins. This is because of the complex nature of knowledge. Human and organizational behavior. To clarify the matter, KM practitioners Tacit Explicit Tacit to Tacit Explicit to Tacit Socialization Internalization Tacit to Explicit to Externalization Explicit EXHIBIT 5.1 Tacit/Explicit Knowledge Conversions created the concept of tacit/explicit knowledge, which incorporates both dichotomies (information/knowledge and individual/organizational knowledge) in a manner that enables an organization to understand the knowledge and value creation process. Under the tacit/explicit distinction, explicit knowledge includes all that can be codified or expressed in documents, manuals, and databases. Tacit knowledge. On the other hand, encompasses all that cannot be clearly articulated but is the real source of knowledge and the basis of decision-making. En addition to experience, skills, and competence, tacit knowledge includes intuition and things that the employee “just knows.” The most efficient and effective way to pass this knowledge is through personal contact. To enable effective decision-making. KM practitioners search for ways by which an organization can locate, externalize, and capture the tacit knowledge of its employees. Once captured. The tacit knowledge is converted

into explicit knowledge, by being codified, and later shared. However, other individual/organizational or tacitlexplicit knowledge conversions take place as well. Nonaka and Takeuchil4 explain that there are four modes of knowledge conversions based on the tacit explicit concept. As illustrated in Exhibit 5.1. First. Knowledge can be converted from tacit to tacit through mentoring, apprenticeship, and other forms of personal contact (i.e. socialization). Second, knowledge can be converted from tacit to explicit when the individual articulates the basis of her or his decision and thus conveys Knowledge (i.e. externalization). Then there is the internalization of knowledge wherein explicit is transferred to tacit knowledge when the employee learns from the organization’s codified knowledge (reports, manuals. etc). Finally, explicit is transferred to other explicit knowledge where documents or information arc shared and added to the organizational information database. These fotlr modes of knowledge conversions on the individual/organizational interface and the informational knowledge conversion in the human brain arc what KM tries to boost to maximize value creation. Misunderstanding of these knowledge relations and conversions lies at the heart of so many failed KM initiatives. It is important to note that KM is not only about implementing a number of solutions to minimize organizational memory loss, prevent the brain drain, and supplement IT tools, though many organizations use it just for this purpose. Using it restrictively limits the potential of KM in advancing the whole organization on its journey to become a learning organization. British Petroleum proved that by implementing a robust KM program whereby the whole organization was transformed to a “big brain,” boosting its overall performance extensively and pulling it from the brink of bankruptcy.15 KNOWLEDGE MANAGEMENT - A MEANS TO AN END The ability of an organization to learn, accumulate knowledge from its experiences, and reapply this knowledge is itself a skill or a competence that, beyond the core competencies directly related to delivering its product or service, may provide strategic advantage. - Michael Zack, Northeastern University Professor 15 The competence to generate knowledge resources, being deeply embedded in the organization’s practices, routines, and brainpower of its people, can hardly be imitated by competition, and hence can be the source of sustainable competitive performance. Consequently, the ability to manage knowledge effectively becomes a critical organizational competence for achieving the organizational mission. It is the ability to recognize the availability of knowledge resources within the whole organization, develop them through transfer, and deploy and redeploy them to meet strategic objectives. To develop KM as a core competence, a number of changes are needed at both the strategic and operational levels. On the strategic level, a shift in the organizational vision is necessary if the organization is to get on the road to becoming a knowledge/learning organization. For leadership to steer the organization in that direction, the organization should envision itself as a knowledge machine or a big brain. To manage knowledge effectively, however, leadership’s commitment alone is not sufficient. Two things are needed at the strategic level to implement KM - (I) applying a gap analysis, also known as a knowledge audit, to the organizational knowledge resources to ascertain what the organization knows and needs to know; and (2) adopting the knowledge strategies that will enable the organization to meet its goals or mission, taking into account the strengths and weaknesses of its knowledge resources. On the operational level, many changes need to be implemented that affect the structure of the organization, including the IT infrastructure, its culture, the use of practices and tools, and the job design. These changes will be discussed next.

Q.l Explain demand forecasting in detail. A.1 Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market. Necessity for forecasting demand Often forecasting demand is confused with forecasting sales. However, failing to forecast demand ignores two important phenomena. There is a lot of debate in the demand planning literature as how to measure and represent historical demand, since the historical demand forms the basis of forecasting. Should us USC the history of outbound shipments or customer orders or a combination of the two to proxy for demand. Stock effects The effects that inventory levels have on sales. In the extreme case of stockouts, demand coming into your store is not converted to sales due to a lack of availability. Demand is also untapped when sales for an item are decreased due to a poor display location, or because the desired sizes are no longer available. For example, when a consumer electronics retailer does not display a particular flatscreen TV, sales for that model are typically lower than the sales for models on display. In addition, in fashion retailing, once the stock level of a particular sweater falls to the point where standard sizes are no longer available, sales of that item are diminished. Market response effect The effect of market events that are within and beyond a retailer’s control. Demand for an item will likely rise if a competitor increases the price or if you promote the item in your weekly circular. The resulting sales increase reflects a change in demand because of consumers responding to stimuli that potentially drive additional sales. Regardless of the stimuli, these forces need to be factored into planning and managed within the demand forecast. In this case, demand forecasting uses techniques in causal modeling. Demand forecast modeling considers the size of the market and the dynamics of market share versus competitors and its effect on firm demand over a period. In the manufacturer to retailer model, promotional events are an important causal factor in influencing demand. These promotions can be modeled with intervention models or use a consensus process to aggregate intelligence using internal collaboration with the Sales and Marketing functions. Methods No demand forecasting method is 100% accurate. Combined forecasts improve accuracy and reduce the likelihood of large enors. Methods that rely on qualitative assessment Forecasting demand based on expert opinion. Some of the types in this method are. • Unaided judgment • Prediction market • Delphi technique • Game theory • Judgmental bootstrapping • Simulated interaction • Intentions and expectations surveys • Conjoint analysis

Methods that rely on quantitative data • Discrete Event Simulation • Extrapolation • Quantitative analogies • Rule-based forecasting • Neural networks • Data milling • Causal models • Segmentation Q.2 Discuss the various attraction strategies A.2 Whenever I meet with a manager or broker to discuss their growth plans and objectives. I always ask what their talent attraction strategy is. I often get a blank or somewhat confused look. Most brokers and managers are former agents and one thing we are not known for is our diligent business planning and strategizing. Therefore, it is no surprise that we carry that trait over into our growth plans when we become managers or brokers. Here are some questions I ask to get them thinking: • what is your target incremental GCI goal this year from new agents added to your company? • How many agents will you have to attract in order to reach that target? • What methods are you using to generate inquiries from candidates considering a career in real estate and those already licensed? • How will you effectively respond, qualify and follow up with these inquiries? • Do you have a talent attraction presentation that is customizable to the candidate and their concerns and interests? • What strategies do you use to target and contact existing agents from other companies? • Does your value proposition differentiate between Baby Boomers, Gen X and Gen Y candidates? • What plans do you have in place to train and develop new agents? • How quickly will a new agent in your office make their first transaction (listing or sale)? How long has it historically taken other new agents? Knowing the answers to these questions should help anyone in charge of attracting talent to their real estate office begin to formulate a strategy and plan. You may have noticed that I have not used the word “recruit” up until this point. We have purposely chosen to remove that word from our vocabulary and replace it with “talent attraction”. I’ll explain more in a future blog. Fashion people live for being ahead of the curve, spotting emerging trends and being knowledgeable before anyone else knows it. As the Fall/Winter 2007/08 Fashion week at Bryant Park in New York City drew to a close in early February, 1 wondered what are the parallels that can be drawn between life trends and fashion trends? Does what we choose to wear reflect who we are, who we want to be or what we would like to be doing? How much of what we do really speaks to our true passions and soul’s purpose? Today, there is a trend toward going deeper and seeking work that is meaningful and answers the question. “What am I here for?” Many are reviewing what their definition of success really means. For some the outward symbols of success are just veneer. True success comes from the inside out from living one’s life from a place of imagination and purpose. Though they can make the trip enjoyable, fame or a corner office, a public persona or a closet full of designer goodies may not be enough to sustain us through life’s journey. How many of us have made choices that have left us feeling empty, asking the question. “Is that all there is?” Ever notice when you feel drained and droopy, just going through the motions, feeling stuck? That loss of power is often a message something just is not quite right. These “messages” if ignored tend to grow louder and more frequent until something occurs, forcing us to pay attention and make a change. What if we get to the top of the hill and discover it was not

what we wanted after all? What next? Alternatively, if your work is satisfying but the rest of your life is totally out of control and you have no time for yourself, you family, or friends? You want to take time for your life but haven’t even got time for lunch. Whatever is going on in your life, you will get a fresh perspective with the guidance of a professional coach, which could be just the jumpstart you need. In just one session, you will notice something has shifted. Professional coaches are trained to use hundreds of skills to guide you to create the life you want and have graduated from an International Coach Federation accredited program such as: Coach University, Coaches Training Institute or the NYU Executive Coaching Program to name a few. Coaches work in partnership with you. Working with a coach means you are ready and able to focus on creating an extraordinary life. One of the most frequent questions is, “What is the difference between a coach, consultant and a therapist?” Coaches arc neither consultants nor therapists. Therapists and consultants are often seen as experts in their fields. Therapy focuses on healing wounds from the past. Professional coaching is not a substitute for therapy and a professional coach does not work on therapy related issues. If emotional pain or distress is present, then a therapist would be recommended. Professional coaching clients have worked through and accepted their pasts, and are ready to concentrate on creating an exceptional future in the present. Consultants are focused on results, and are hired to produce a certain outcome. Consultants are often authorities in a particular field and advise their clients how to solve specific problems. Professional coaches are focused on people and connections, and are hired to support the client as he or she achieves a desired outcome. Coaches guide clients to their own solutions vs. consultants who are hired to make recommendations. Coaches and clients have a peer relationship. An expert coach has been trained to connect with the client, listen deeply, observe, assess, support and guide. An expert coach works with clients to help them focus on that which matters most and to be accountable to take the actions required for their success. Are you ready to find out what’s next for you? Do you want to have a life that fits who you are? Do you want more life balance, a better job, a new relationship or to improve an old one? Is there a project you’ve wanted to work on or a business idea you were hatching? Do you want to start an exercise program or get a complete life makeover? Working with one of the professional coaches may just be the thing you are seeking. A coach can help you to accelerate the process, improve your performance and be more fulfilled in every area of your life. Coaches work with clients via telephone, making it easy to get started and convenient to arrange. Portable too! Working with a coach you might just start something that will fit you like a glove. Nancy Mendes is a Coach University trained professional Attraction Coach who has helped hundreds of clients since 2000. She works with women who want to be audacious and bold, live rich, play big and do well. She has been an executive in corporate America at such companies as Merrill Lynch, Elizabeth Arden and The Fashion Group International. Her Audacious Confidence groups really help clients to ramp up their attraction quotient so they can play a bigger, more profitable and audacious game Q.3Explain the types of validity. A.3 In psychology, validity has two distinct fields of application. The first involves L validity, a concept that has evolved with the field of psychometrics: “Validity refers to the degree to which evidence and theory support the interpretations of test scores entailed by proposed uses of tests”. The second involves research design. Here the term refers to the degree to which a study

supports the intended conclusion drawn from the results. In the Campbellian tradition, this refers to the degree of support for the conclusion that the causal variable caused the effect. In contrast to test validity, assessment of the validity of a research design generally does not involve data collection or statistical analysis but rather evaluation of the design in relation to the desired conclusion on the basis of prevailing standards and theory of research design. Test validity Reliability and validity an early definition of test validity identified it with the degree of correlation between the test and a criterion. Under this definition, one can show that reliability of the test and the criterion places an upper limit on the possible correlation between them (the so-called validity coefficient). Intuitively, this reflects the fact that reliability involves freedom from random error and random errors do not correlate with one another. Thus, the less random error in the variables, the higher the possible correlation between them. Under these definitions, a test cannot have high validity unless it also has high reliability. However, the concept of validity has expanded substantially beyond this early definition and the classical relationship between reliability and validity need not hold for alternative conceptions of reliability and validity. Within classical test theory. Predictive or concurrent validity (correlation between the predictor and the predicted) cannot exceed the square root of the correlation between two versions of the same measure — that is, reliability limits validity. Construct validity Evidence involves the empirical and theoretical support for the interpretation of the construct. Such lines of evidence include statistical analyses of the internal structure of the test including the relationships between responses to different test items. They also include relationships between the test and measures of other constructs. As currently understood, construct validity is not distinct from the support for the substantive theory of the construct that the test is designed to measure. As such, experiments designed to reveal aspects of the causal role of the construct also contribute to construct validity evidence. Content validity Evidence involves the degree to which the content of the test matches a content domain associated with the construct. For example, a test of the ability to add two-digit numbers should cover the full range of combinations of digits. A test with only one-digit numbers, or only even numbers, would not have good coverage of the content domain. Content related evidence typically involves subject matter experts (SME’s) evaluating test items against the test specifications. Criterion Validity evidence involves the correlation between the test and a criterion variable (or variables) taken as representative of the construct. For example, employee selection tests are often validated against measures of job performance. Measures of risk of recidivism among those convicted of a crime can be validated against measures of recidivism. If the test data and criterion data are collected at the same time, this is referred to as concurrent validity evidence, lithe test data is collected first in order to predict criterion data collected at a later point in time, and then this is referred to as predictive validity evidence. Construct validity Construct validity refers to the totality of evidence about whether a particular operationalization of a construct adequately represents what is intended by theoretical account of the construct being measured. (Demonstrate an element is valid by relating it to another element that is supposedly valid.)

There are two approaches to construct validity- sometimes referred to as ‘convergent validity’ and ‘divergent validity’ (or discriminant validity). Convergent validity Convergent validity refers to the degree to which a measure is correlated with other measures that it is theoretically predicted to correlate with. Discriminant validity Discriminant validity describes the degree to which the operationalization does not correlate with other operationalization that it theoretically should not be correlated with. Content validity Content validity is a non-statistical type of validity that involves “the systematic examination of the test content to determine whether it covers a representative sample of the behavior domain to be measured” (Anastasia & Urbana, 1997 p. 114). A test has content validity built into it by careful selection of which items to include (Anastasia & Urbana. 1997). Items are chosen so that they comply with the test specification, which is drawn up through a thorough examination of the subject domain. Fox craft et al. (2004. p. 49) note that by using a panel of experts to review the test specifications and the selection of items the content validity of a test can be improved. The experts will be able to review the items and comment on whether the items cover a representative sample of the behavior domain. Representation validity Representation validity is also known as translation validity. Face validity Face validity is an estimate of whether a test appears to measure a certain criterion; it does not guarantee that the test actually measures phenomena in that domain. Indeed, when a test is subject to faking (malingering), low face validity might make the test more valid. Face validity is very closely related to content validity. While content validity depends on a theoretical basis for assuming if a test is assessing all domains of a certain criterion (e.g. does assessing addition skills yield in a good measure for mathematical skills. To answer this you have to know, what different kinds of arithmetic skills mathematical skills include) face validity relates to whether a test appears to be a good measure or not. This judgment is made on the “face” of the test, thus it can also be judged by the amateur. Criterion validity Criterion-related validity reflects the success of measures Isled for prediction or estimation. There are two types of criterion-related validity: Concurrent and predictive validity. A good example of criterion-related validity is in the validation of employee selection tests; in this case scores on a lest or battery of tests is correlated with employee performance scores. Concurrent validity Concurrent validity refers to the correlates with other measures of time. Going back to the selection administered to current employees performance reviews.

degree to which the operationalization the same construct that are measured at the same test example. This would mean that the tests are and then correlated with their scores on

Predictive validity Predictive validity refers to the degree to which the operationalization can predict (or correlate with) with other measures of the same construct that are measured at some time in the future. Again, with the selection test example. This

would mean that the tests are administered to applicants, all applicants are hired, their performance is reviewed at a later time, and then their scores on the two measures are correlated. Statistical conclusion validity Campbell and Stanley (1963) define internal validity as the basic requirements for an experiment to be interpretable — did the experiment make a difference in this instance? External validity addresses the question of generalizability to whom can we generalize this experiment’s findings? Internal validity internal validity is an inductive estimate of the degree to which conclusions about causes of relations are likely lobe true, in view of the measures used the research setting And the whole research design. Good experimental techniques in which the effect of an independent variable on a dependent variable is studied under highly controlled conditions, usually allow for higher degrees of internal validity than, for example, Single- case designs. Eight extraneous variables can interfere with internal validity: 1. History, the specific events occurring between the first and second measurements in addition to the experimental variables 2. Maturation, processes within the participants as a function of the passage of time (not specific to particular events), e.g. Growing older, hungrier, More tired, and so on. 3. Testing, the effects of taking a test upon the scores of a second testing. 4. Instrumentation, changes in calibration of a measurement tool or changes in the observers or scorers may produce changes in the obtained measurements. 5. Statistical regression, operating where groups have been selected on the basis of their extreme scores. 6. Selection, biases resulting from differential selection of respondents for the companion groups. 7. Experimental mortality, or differential loss of respondents from the comparison groups. 8. Selection-maturation interaction, etc. e.g. in multiple-group quasiexperimental designs Intentional validity To what extent did the chosen constructs and measures adequately assess what the study intended to study? External validity The issue of external validity concerns the question to what extent one may safely generalize the (internally valid) causal inference (a) from the sample studied to the defined target population and (b) to other populations (i.e. across time and space). Four factors jeopardizing external validity or representativeness are: 1. Reactive or interaction effect of testing, a pretest might increase the scores on a posttest 2. Interaction effects of selection biases and the experimental variable. 3. Reactive effects of experimental arrangements, which would preclude generalization about the effect of the experimental variable upon persons being exposed to it in non-experimental settings 4. Multiple-treatment interference, where effects of earlier treatments are not erasable. Ecological validity Ecological validity is whether the results can be applied to real life situations. This issue is closely related to external validity and covers the question to which degree your experimental findings mirror what you can observe in the real world (ecology science of interaction between organism and its environment).

Typically, in science, there arc two domains of research: Passive-observational and active experimental. The purpose of experimental designs is to test causality, so that you can infer A causes B or B causes A. But sometimes, ethical and/or mythological restrictions prevent you from conducting an experiment (e.g. how does isolation influence a child’s cognitive functioning?) Then you can still do research, but it’s not causal, it’s co relational, A occurs together with B. Both techniques have their strengths and weaknesses. To get an experimental design you have to control for all interfering variables. That’s why you conduct your experiment in a laboratory setting. While gaining internal validity (excluding interfering variables by keeping them constant) you lose ecological validity because you establish an artificial lab setting. On the other hand with observational research you can’t control for interfering variables (low internal validity) but you can measure in the natural (ecological) environment, thus at the place where behavior occurs.