Practising Psychometric Tests Online

Total Page:16

File Type:pdf, Size:1020Kb

Practising Psychometric Tests Online Practising psychometric tests online Many websites offer practice psychometric tests online. Practicing different tests can help you prepare for tests an employer may request you take as part of their selection process. Assessment Day SHL offers practice tests in verbal and numerical, verbal and abstract Assessment Day offers two free tests numerical reasoning, situational reasoning tests. each for numerical reasoning, verbal judgement, critical reasoning, and reasoning, situational judgment, many other types of practice tests. Psychometric Success inductive reasoning, and in-tray exercises. Cubiks www.psychometric-success.com Download 16 different paper tests on practicetests.cubiks.com 1. Go to www.assessmentday.co.uk numerical, verbal, abstract reasoning, Cubiks provides practice tests on 2. Select the type of test you want to spatial, technical and clerical ability. verbal and numerical reasoning for practice Manager/Graduate and Business 3. Scroll down and click on Start Free University of Kent Careers Support levels, as well as a Test 1 / 2 downloadable leaflet with diagrammatic You can also ‘like’ Assessment Day on www.kent.ac.uk/careers/psychotests.ht reasoning sample questions. Facebook to get access to more free m tests: www.facebook.com/AssessmentDayTe OPC The University of Kent’s Careers sts/app_208195102528120#!/Assessm Service has developed sample tests to www.theopc.co.uk/tests/practicetests entDayTests practice online, in areas from verbal, numerical and logical reasoning to The Occupational Psychology Centre Graduates First computer aptitude, teamwork and has developed psychometric tests and leadership. http://www.graduatesfirst.com/cp/city/lo personality questionnaires for the fields gin.aspx of Customer Service, Professional, Job Test Prep Register with your City email address Safety, Station and Transport. Take to access free numerical, verbal and their practice tests on numerical, verbal www.jobtestprep.co.uk logical reasoning tests. and fault-finding. Most of Job Tests Prep’s practice tests require payment, but they offer a free Kenexa/PSL Institute of Psychometric Coaching numerical, verbal and inductive reasoning test and a personality test. www.psl.com/practice www.psychometricinstitute.co.uk Click on Free Psychometric Tests to Kenexa offers a free numerical and The Institute of Psychometric Coaching access. verbal practice test. offers guidance and practice courses. You can practice tests for free by PathMotion SHL clicking Personality Free Test or Aptitude Free Tests. Free samples www.pathmotion.com/verbal-and- www.shldirect.com/practice_tests.html include personality questionnaire and numerical-reasoning-tests Practising psychometric tests online Register with PathMotion to access Bar Standards Board thinking and matrix manipulation, and four free numerical and verbal tests. free samples are not available online. www.barstandardsboard.org.uk/media/ Search for IPAT on in the Resources 1344440/watsonglaser_form_ab_exam NumericalTestPractice.com section of CareersHub for some ple_questions.pdf sample questions. There is also some www.numericaltestpractice.com This is a downloadable pdf of a sample information on the test here: Click “Try NTP for Free” and then “Try question from the Watson Glaser test, /www.practiceshltests.com/companies- 1 Timed Numerical Test for Free” to provided by the Bar Standards Board. using-shl/ibm and a sample is available access a free practice test for a fee here: Banking and Finance www.jobtestprep.co.uk/ibm.aspx. Practice Aptitude Tests.com www.practiceaptitudetests.com Bloomberg Aptitude Test (BAT) Free tests for numerical, verbal and diagrammatic reasoning. Click on the about.bloomberginstitute.com/files/201 test type to access two free tests for 3/04/BATSampleQuestions.pdf each type. The BAT is not required for a selection process, but it can be a way to set E-Financial Careers yourself apart from the crowd. The test is offered for free at City University www.efinancialcareers.co.uk/numerical London several times a year. It _test.htm focusses on business, finance and Offers three free numerical tests of the banking issues. Take a look at the style used by investment banks. You guide and sample questions here. need to create a free account in order Check the CareersHub Events section to access the tests. for test sessions at City University London. Watson-Glaser (Law) The Watson Glaser Critical Thinking Business test is often uses by law firms as part of their recruitment process. The test GMAT measures your ability to think critically, make inferences and deductions, The GMAT is increasingly being used recognise assumptions and evaluate by business schools around the world arguments. as part of the admissions process. Take free practice tests at the following Linklaters sites: www.linklatersgraduates.co.uk/critical- www.platinumgmat.com/gmat-practice- thinking-test test/ Linklaters provides some sample questions from the Watson-Glaser www.mba.com/the-gmat/download- Critical Thinking test, which is used by free-test-preparation-software.aspx law firms as part of their recruitment processes. www.kaptest.co.uk/gmat/sample- questions Hogan Lovells graduates.hoganlovells.com/apply_now Computing /critical_thinking_test/ IPAT(O) Hogan Lovells provides samples of Watson-Glaser critical thinking IBM has developed a proprietary in- questions. house test to measure programming aptitude. The test focusses on quick .
Recommended publications
  • 1.2. INDUCTIVE REASONING Much Mathematical Discovery Starts with Inductive Reasoning – the Process of Reaching General Conclus
    1.2. INDUCTIVE REASONING Much mathematical discovery starts with inductive reasoning – the process of reaching general conclusions, called conjectures, through the examination of particular cases and the recognition of patterns. These conjectures are then more formally proved using deductive methods, which will be discussed in the next section. Below we look at three examples that use inductive reasoning. Number Patterns Suppose you had to predict the sixth number in the following sequence: 1, -3, 6, -10, 15, ? How would you proceed with such a question? The trick, it seems, is to discern a specific pattern in the given sequence of numbers. This kind of approach is a classic example of inductive reasoning. By identifying a rule that generates all five numbers in this sequence, your hope is to establish a pattern and be in a position to predict the sixth number with confidence. So can you do it? Try to work out this number before continuing. One fact is immediately clear: the numbers alternate sign from positive to negative. We thus expect the answer to be a negative number since it follows 15, a positive number. On closer inspection, we also realize that the difference between the magnitude (or absolute value) of successive numbers increases by 1 each time: 3 – 1 = 2 6 – 3 = 3 10 – 6 = 4 15 – 10 = 5 … We have then found the rule we sought for generating the next number in this sequence. The sixth number should be -21 since the difference between 15 and 21 is 6 and we had already determined that the answer should be negative.
    [Show full text]
  • There Is No Pure Empirical Reasoning
    There Is No Pure Empirical Reasoning 1. Empiricism and the Question of Empirical Reasons Empiricism may be defined as the view there is no a priori justification for any synthetic claim. Critics object that empiricism cannot account for all the kinds of knowledge we seem to possess, such as moral knowledge, metaphysical knowledge, mathematical knowledge, and modal knowledge.1 In some cases, empiricists try to account for these types of knowledge; in other cases, they shrug off the objections, happily concluding, for example, that there is no moral knowledge, or that there is no metaphysical knowledge.2 But empiricism cannot shrug off just any type of knowledge; to be minimally plausible, empiricism must, for example, at least be able to account for paradigm instances of empirical knowledge, including especially scientific knowledge. Empirical knowledge can be divided into three categories: (a) knowledge by direct observation; (b) knowledge that is deductively inferred from observations; and (c) knowledge that is non-deductively inferred from observations, including knowledge arrived at by induction and inference to the best explanation. Category (c) includes all scientific knowledge. This category is of particular import to empiricists, many of whom take scientific knowledge as a sort of paradigm for knowledge in general; indeed, this forms a central source of motivation for empiricism.3 Thus, if there is any kind of knowledge that empiricists need to be able to account for, it is knowledge of type (c). I use the term “empirical reasoning” to refer to the reasoning involved in acquiring this type of knowledge – that is, to any instance of reasoning in which (i) the premises are justified directly by observation, (ii) the reasoning is non- deductive, and (iii) the reasoning provides adequate justification for the conclusion.
    [Show full text]
  • A Philosophical Treatise on the Connection of Scientific Reasoning
    mathematics Review A Philosophical Treatise on the Connection of Scientific Reasoning with Fuzzy Logic Evangelos Athanassopoulos 1 and Michael Gr. Voskoglou 2,* 1 Independent Researcher, Giannakopoulou 39, 27300 Gastouni, Greece; evatha@gmail.com 2 Department of Applied Mathematics, Graduate Technological Educational Institute of Western Greece, 22334 Patras, Greece * Correspondence: voskoglou@teiwest.gr Received: 4 May 2020; Accepted: 19 May 2020; Published:1 June 2020 Abstract: The present article studies the connection of scientific reasoning with fuzzy logic. Induction and deduction are the two main types of human reasoning. Although deduction is the basis of the scientific method, almost all the scientific progress (with pure mathematics being probably the unique exception) has its roots to inductive reasoning. Fuzzy logic gives to the disdainful by the classical/bivalent logic induction its proper place and importance as a fundamental component of the scientific reasoning. The error of induction is transferred to deductive reasoning through its premises. Consequently, although deduction is always a valid process, it is not an infallible method. Thus, there is a need of quantifying the degree of truth not only of the inductive, but also of the deductive arguments. In the former case, probability and statistics and of course fuzzy logic in cases of imprecision are the tools available for this purpose. In the latter case, the Bayesian probabilities play a dominant role. As many specialists argue nowadays, the whole science could be viewed as a Bayesian process. A timely example, concerning the validity of the viruses’ tests, is presented, illustrating the importance of the Bayesian processes for scientific reasoning.
    [Show full text]
  • Argument, Structure, and Credibility in Public Health Writing Donald Halstead Instructor and Director of Writing Programs Harvard TH Chan School of Public Heath
    Argument, Structure, and Credibility in Public Health Writing Donald Halstead Instructor and Director of Writing Programs Harvard TH Chan School of Public Heath Some of the most important questions we face in public health include what policies we should follow, which programs and research should we fund, how and where we should intervene, and what our priorities should be in the face of overwhelming needs and scarce resources. These questions, like many others, are best decided on the basis of arguments, a word that has its roots in the Latin arguere, to make clear. Yet arguments themselves vary greatly in terms of their strength, accuracy, and validity. Furthermore, public health experts often disagree on matters of research, policy and practice, citing conflicting evidence and arriving at conflicting conclusions. As a result, critical readers, such as researchers, policymakers, journal editors and reviewers, approach arguments with considerable skepticism. After all, they are not going to change their programs, priorities, practices, research agendas or budgets without very solid evidence that it is necessary, feasible, and beneficial. This raises an important challenge for public health writers: How can you best make your case, in the face of so much conflicting evidence? To illustrate, let’s assume that you’ve been researching mother-to-child transmission (MTCT) of HIV in a sub-Saharan African country and have concluded that (claim) the country’s maternal programs for HIV counseling and infant nutrition should be integrated because (reasons) this would be more efficient in decreasing MTCT, improving child nutrition, and using scant resources efficiently. The evidence to back up your claim might consist of original research you have conducted that included program assessments and interviews with health workers in the field, your assessment of the other relevant research, the experiences of programs in other countries, and new WHO guidelines.
    [Show full text]
  • Psychology 205, Revelle, Fall 2014 Research Methods in Psychology Mid-Term
    Psychology 205, Revelle, Fall 2014 Research Methods in Psychology Mid-Term Name: ________________________________ 1. (2 points) What is the primary advantage of using the median instead of the mean as a measure of central tendency? It is less affected by outliers. 2. (2 points) Why is counterbalancing important in a within-subjects experiment? Ensuring that conditions are independent of order and of each other. This allows us to determine effect of each variable independently of the other variables. If conditions are related to order or to each other, we are unable to determine which variable is having an effect. Short answer: order effects. 3. (6 points) Define reliability and compare it to validity. Give an example of when a measure could be valid but not reliable. 2 points: Reliability is the consistency or dependability of a measurement technique. [“Getting the same result” was not accepted; it was too vague in that it did not specify the conditions (e.g., the same phenomenon) in which the same result was achieved.] 2 points: Validity is the extent to which a measurement procedure actually measures what it is intended to measure. 2 points: Example (from class) is a weight scale that gives a different result every time the same person stands on it repeatedly. Another example: a scale that actually measures hunger but has poor test-retest reliability. [Other examples were accepted.] 4. (4 points) A consumer research company wants to compare the “coverage” of two competing cell phone networks throughout Illinois. To do so fairly, they have decided that they will only compare survey data taken from customers who are all using the same cell phone model - one that is functional on both networks and has been newly released in the last 3 months.
    [Show full text]
  • The Problem of Induction
    The Problem of Induction Gilbert Harman Department of Philosophy, Princeton University Sanjeev R. Kulkarni Department of Electrical Engineering, Princeton University July 19, 2005 The Problem The problem of induction is sometimes motivated via a comparison between rules of induction and rules of deduction. Valid deductive rules are necessarily truth preserving, while inductive rules are not. So, for example, one valid deductive rule might be this: (D) From premises of the form “All F are G” and “a is F ,” the corresponding conclusion of the form “a is G” follows. The rule (D) is illustrated in the following depressing argument: (DA) All people are mortal. I am a person. So, I am mortal. The rule here is “valid” in the sense that there is no possible way in which premises satisfying the rule can be true without the corresponding conclusion also being true. A possible inductive rule might be this: (I) From premises of the form “Many many F s are known to be G,” “There are no known cases of F s that are not G,” and “a is F ,” the corresponding conclusion can be inferred of the form “a is G.” The rule (I) might be illustrated in the following “inductive argument.” (IA) Many many people are known to have been moral. There are no known cases of people who are not mortal. I am a person. So, I am mortal. 1 The rule (I) is not valid in the way that the deductive rule (D) is valid. The “premises” of the inductive inference (IA) could be true even though its “con- clusion” is not true.
    [Show full text]
  • 1 a Bayesian Analysis of Some Forms of Inductive Reasoning Evan Heit
    1 A Bayesian Analysis of Some Forms of Inductive Reasoning Evan Heit University of Warwick In Rational Models of Cognition, M. Oaksford & N. Chater (Eds.), Oxford University Press, 248- 274, 1998. Please address correspondence to: Evan Heit Department of Psychology University of Warwick Coventry CV4 7AL, United Kingdom Phone: (024) 7652 3183 Email: E.Heit@warwick.ac.uk 2 One of our most important cognitive goals is prediction (Anderson, 1990, 1991; Billman & Heit, 1988; Heit, 1992; Ross & Murphy, 1996), and category-level information enables a rich set of predictions. For example, you might not be able to predict much about Peter until you are told that Peter is a goldfish, in which case you could predict that he will swim and he will eat fish food. Prediction is a basic element of a wide range of everyday tasks from problem solving to social interaction to motor control. This chapter, however, will focus on a narrower range of prediction phenomena, concerning how people evaluate inductive “syllogisms” or arguments such as the following example: Goldfish thrive in sunlight --------------------------- Tunas thrive in sunlight. (The information above the line is taken as a premise which is assumed to be true, then the task is to evaluate the likelihood of the conclusion, below the line.) Despite the apparent simplicity of this task, there are a variety of interesting phenomena that are associated with inductive arguments. Taken together, these phenomena reveal a great deal about what people know about categories and their properties, and about how people use their general knowledge of the world for reasoning.
    [Show full text]
  • Structured Statistical Models of Inductive Reasoning
    CORRECTED FEBRUARY 25, 2009; SEE LAST PAGE Psychological Review © 2009 American Psychological Association 2009, Vol. 116, No. 1, 20–58 0033-295X/09/$12.00 DOI: 10.1037/a0014282 Structured Statistical Models of Inductive Reasoning Charles Kemp Joshua B. Tenenbaum Carnegie Mellon University Massachusetts Institute of Technology Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describe 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabi- listic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning. Keywords: inductive reasoning, property induction, knowledge representation, Bayesian inference Humans are adept at making inferences that take them beyond This article describes a formal approach to inductive inference the limits of their direct experience.
    [Show full text]
  • A Philosophical Treatise of Universal Induction
    Entropy 2011, 13, 1076-1136; doi:10.3390/e13061076 OPEN ACCESS entropy ISSN 1099-4300 www.mdpi.com/journal/entropy Article A Philosophical Treatise of Universal Induction Samuel Rathmanner and Marcus Hutter ? Research School of Computer Science, Australian National University, Corner of North and Daley Road, Canberra ACT 0200, Australia ? Author to whom correspondence should be addressed; E-Mail: marcus.hutter@anu.edu.au. Received: 20 April 2011; in revised form: 24 May 2011 / Accepted: 27 May 2011 / Published: 3 June 2011 Abstract: Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results and is based on solid philosophical foundations, the requisite technical knowledge necessary for understanding this framework has caused it to remain largely unknown and unappreciated in the wider scientific community. The main contribution of this article is to convey Solomonoff induction and its related concepts in a generally accessible form with the aim of bridging this current technical gap. In the process we examine the major historical contributions that have led to the formulation of Solomonoff Induction as well as criticisms of Solomonoff and induction in general. In particular we examine how Solomonoff induction addresses many issues that have plagued other inductive systems, such as the black ravens paradox and the confirmation problem, and compare this approach with other recent approaches.
    [Show full text]
  • Relations Between Inductive Reasoning and Deductive Reasoning
    Journal of Experimental Psychology: © 2010 American Psychological Association Learning, Memory, and Cognition 0278-7393/10/$12.00 DOI: 10.1037/a0018784 2010, Vol. 36, No. 3, 805–812 Relations Between Inductive Reasoning and Deductive Reasoning Evan Heit Caren M. Rotello University of California, Merced University of Massachusetts Amherst One of the most important open questions in reasoning research is how inductive reasoning and deductive reasoning are related. In an effort to address this question, we applied methods and concepts from memory research. We used 2 experiments to examine the effects of logical validity and premise– conclusion similarity on evaluation of arguments. Experiment 1 showed 2 dissociations: For a common set of arguments, deduction judgments were more affected by validity, and induction judgments were more affected by similarity. Moreover, Experiment 2 showed that fast deduction judgments were like induction judgments—in terms of being more influenced by similarity and less influenced by validity, compared with slow deduction judgments. These novel results pose challenges for a 1-process account of reasoning and are interpreted in terms of a 2-process account of reasoning, which was implemented as a multidimensional signal detection model and applied to receiver operating characteristic data. Keywords: reasoning, similarity, mathematical modeling An important open question in reasoning research concerns the arguments (Rips, 2001). This technique can highlight similarities relation between induction and deduction. Typically, individual or differences between induction and deduction that are not con- studies of reasoning have focused on only one task, rather than founded by the use of different materials (Heit, 2007). It also examining how the two are connected (Heit, 2007).
    [Show full text]
  • 9. Causal Inference*
    9. Causal inference* The desire to act on the results of epidemiologic studies frequently encounters vexing difficulties in obtaining definitive guides for action. Weighing epidemiologic evidence in forming judgments about causation. "The world is richer in associations than meanings, and it is the part of wisdom to differentiate the two." — John Barth, novelist. "Who knows, asked Robert Browning, but the world may end tonight? True, but on available evidence most of us make ready to commute on the 8.30 next day." — A. B. Hill Historical perspective Our understanding of causation is so much a part of our daily lives that it is easy to forget that the nature of causation is a central topic in the philosophy of science and that in particular, concepts of disease causation have changed dramatically over time. In our research and clinical practice, we largely act with confidence that 21st century science has liberated us from the misconceptions of the past, and that the truths of today will lead us surely to the truths of tomorrow. However, it is a useful corrective to observe that we are not the first generation to have thought this way. In the 1950s, medical and other scientists had achieved such progress that, according to Dubos (1965:163), most clinicians, public health officers, epidemiologists, and microbiologists could proclaim that the conquest of infectious diseases had been achieved. Deans and faculties began the practice of appointing, as chairs of medical microbiology, biochemists and geneticists who were not interested in the mechanisms of infectious processes. As infectious disease epidemiology continues to surge in popularity, we can only shake our heads in disbelief at the shortsightedness of medical and public health institutions in dismantling their capabilities to study and control infectious diseases, whose epidemics have repeatedly decimated populations and even changed the course of history.
    [Show full text]
  • 1 Phil. 4400 Notes #1: the Problem of Induction I. Basic Concepts
    Phil. 4400 Notes #1: The problem of induction I. Basic concepts: The problem of induction: • Philosophical problem concerning the justification of induction. • Due to David Hume (1748). Induction: A form of reasoning in which a) the premises say something about a certain group of objects (typically, observed objects) b) the conclusion generalizes from the premises: says the same thing about a wider class of objects, or about further objects of the same kind (typically, the unobserved objects of the same kind). • Examples: All observed ravens so far have been The sun has risen every day for the last 300 black. years. So (probably) all ravens are black. So (probably) the sun will rise tomorrow. Non-demonstrative (non-deductive) reasoning: • Reasoning that is not deductive. • A form of reasoning in which the premises are supposed to render the conclusion more probable (but not to entail the conclusion). Cogent vs. Valid & Confirm vs. Entail : ‘Cogent’ arguments have premises that confirm (render probable) their conclusions. ‘Valid’ arguments have premises that entail their conclusions. The importance of induction: • All scientific knowledge, and almost all knowledge depends on induction. • The problem had a great influence on Popper and other philosophers of science. Inductive skepticism: Philosophical thesis that induction provides no justification for ( no reason to believe) its conclusions. II. An argument for inductive skepticism 1. There are (at most) 3 kinds of knowledge/justified belief: a. Observations b. A priori knowledge c. Conclusions based on induction 2. All inductive reasoning presupposes the “Inductive Principle” (a.k.a. the “uniformity principle”): “The course of nature is uniform”, “The future will resemble the past”, “Unobserved objects will probably be similar to observed objects” 3.
    [Show full text]