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Hypothesis....Phd in Management, HR, HRM, HRD, Management


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Phd in Management, HR, HRM, HRD, Management

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Hypothesis....Phd in Management, HR, HRM, HRD, Management

  1. 1. w.e.l.c.o.m.e good morning /an mm bagali JAIN University CMS Business School / Bangalore "Quantitative and Qualitative for Research" Department of Management Studies Cambridge Institute of technology November 29, 2013.
  2. 2. • Setting • Objectives • Hypothesis • Sampling • Questionnaire • Analysis • Results elements of research study
  3. 3. Focus for the day H y p o t h e s i s
  4. 4. Some thinking – Do we require always – How big X small the statement should be – Should it always be proves X disproved – When to develop X construct
  5. 5. Some statements That, children's in US watch an average of 3hrs of TV / week Most people who come to courtroom are innocent The Tax law have an effect on the Revenue That larger firms are more efficient in conducting R and D
  6. 6. hy·poth·e·sis /hīˈpäTHəsis/ Noun A supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation A proposition made as a basis for reasoning, without any assumption of its truth Synonyms supposition - assumption - presumption
  7. 7. Hypothesis is derived form the Greek words  “hypo” means under  “tithemi” means place
  8. 8. Hypothesis A statement of the predicted relationship between two or more variables • Tentative theory or supposition set up and adopted provisionally as a basis of explaining certain facts or relationships and as a guide in the further investigation of other facts or relationships • A hypothesis is written in such a way that it can be disproven (null) or proven (alternative) by valid and reliable data
  9. 9. meaning Under known facts of the problem to explain relationship between ......... a guess but experienced guess based on some facts … a hunch, assumption, suspicion, assertion or an idea about a phenomena, relationship, or situation, the reality of truth of which one do not know Researcher calls these assumptions, assertions, statements, or hunches hypotheses and they become the basis of an inquiry. Results observed X Results you expect
  10. 10. thus, Written statement Drawn from experience/observation Constructed / formulated Data analysis Questionnaire
  11. 11. Purpose • Allow theoretical propositions to be tested in the real world • Guide the research design • Dictate the type of statistical analysis for the data • Provide the reader with an understanding of the researchers expectations about the study before data collecting begins
  12. 12. precautions properly formulated Ho and H1 one tailed or two tailed Null or Alternative Rejection or Acceptance
  13. 13. characteristics a tentative proposition unknown validity specifies relation between two or more variables simple, specific, and contextually clear capable of verification related to the existing body of knowledge prove X disprove accept X reject
  14. 14. advantage Hy • Bringing clarity to the research problem  provides a study with focus  signifies what specific aspects of a research problem is to investigate  what data to be collected and what not to be collected  enhancement of objectivity of the study  formulate the theory  enable to conclude with what is true or what is false  The format of the questionnaire
  15. 15. The rationale or sources of hypothesis • From the researchers own experiences • From previous research studies • From theoretical propositions • Literature available • Observation • Discussions • Historical studies and evidences
  16. 16. Ethical Issue Hypothesis should always be written before the study and should not be changed after the study results are examined – Don‟t change – Don‟t alter – Don‟t add
  17. 17. Variables – Independent – Dependent – Controlled
  18. 18. Terms to know – M= Mean – DV= Observed phenomena – A= significant level – S= Sample Standard Deviation – T= t test for degree of freedom (normal population) – X= Sample mean – a= alpha-level of significance – B= beta
  19. 19. Hy Null Rejection region Significance Sampling distribution Independent variable Dependent variables Level of significance
  20. 20. Types of Hypothesis Descriptive Hy: The magnitude, trend or behaviour of population under the study: • Eg: The attrition rate in BPO is almost 40% • The literacy rate in Blore is 90% – Rational Hy: States the expected relationships between two variables, i.e.: increase, decrease, less than or more than…. Eg: Higher the exhaustion / stress experience by BPO professionals, higher the turnover intention
  21. 21. Stating Hy are used to state the relationship(s) between two variables and may be stated as : – Null Hy (one tailed) – Non Directional – Directional (Two tailed)
  22. 22. Formulating Null and Alternative Hy Directional Hy: The population parameters is structured to be Greater than / Equal to / Less than / called as ONE tailed test(one sided) Non Directional Hy: The population parameter is structured to be equal to a specified value called as TWO tailed test(two sided)
  23. 23. Criteria while designing hypothesis • Declaration form • Uni-dimensional (two variables at a time) • Measurable • Based on literature / theories • Statistical testing
  24. 24. Classifications of hypothesis Typologies Simple or complex: A Simple hypothesis: concerns the relationship between one independent( cause) and one dependent variable (effect).
  25. 25. A complex hypothesis: Concerns a relationship where two or more independent variables, two or more dependent variables, or both, are examined in the same study (multivariate)
  26. 26. Hypothesis are used to state the relationship between two variables and may be stated as Null hypotheses (no relationship between two variables). Nondirectional hypotheses (we don‟t know or won‟t speculate about the direction of the relationship between two variables). Directional hypotheses. We state the direction of the relationship between two variables.
  27. 27. Null and research hypothesis Null hypothesis (Ho)= Statistical hypothesis; predict that no relationship exists between variables (Rejection intention) Research hypothesis(H1)= Alternative hypothesis; state the expected relationship between variables (Acceptance intention)
  28. 28. Steps in Testing Hypothesis
  29. 29. • As researchers and management professionals, one must understand the principles and concepts behind the use of various statistical methods • Generalizations from data may be based on models that require assumptions that may not be appropriate to the situation • Understand the role of ‘uncertainty’
  30. 30. How statistics helps research • Understand the effects of variability and chance • How many subjects to study • How long to study a situation • Are my findings consistent with my hypotheses or can they be explained by chance or variation • Obtain estimates of important parameters • Summarize quantifiable information • Describe - with precision and accuracy • Build the evidence of which relationships are likely not due to chance
  31. 31. Examples: • lifetime of light bulbs • quality of textile garments • is training effective? • what factors predict a successful micro-lending? • how do I discover fraud in credit card transactions?
  32. 32. So what do these 5 examples have in common? • Dealing with quantifiable information • Information obtained on several instances/subjects, not just a single one • Admit the presence of variability among instances • Have uncertainty from not observing the entire population of subjects/instances • Presence of chance is acknowledged • Models (approximations of reality) are used • Models of association: correlation, time series, linear multiple variable regression • Data must fulfill some assumptions/requirements for each model
  33. 33. Step 1: State the H0 and H1 » Rejection X Acceptance » Write / state / construct in such a way that, Null gets rejected / Alternative gets accepted » And, null is the basis for argument » We either "Reject H0 in favour of H1" or "Do not reject H0"; we never conclude "Reject H1", or even "Accept H1".
  34. 34. Alternative Hy • "Do not reject H0", this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against H0 in favour of H1. • Rejecting the null hypothesis then, suggests that the alternative hypothesis may be true.
  35. 35. Step 2 : Significance level and Sample size » 0.05 level / 5 % level » Big or small or what??? Fixed probability of wrongly rejecting the null hypothesis H0, if it is in fact true.
  36. 36. Step 3: Determination of a test statistics » Correlation » Regression » Multivariate » Time series » Survival analysis » Students „t‟ test » Z test (normal distribution)
  37. 37. Step 4: Determination of a Critical Region(CR) » Rejection region (RR) » Try to reject null hy
  38. 38. Step 5: Computing the value of the test statistics and collect the data Independent and dependent and controlled samples
  39. 39. variables Computing the value of the test statistics and collect the data • The scale of measurement determines how a variable is described, analyzed and interpreted , Description • Tell possible values, or range of values • Tell likely values to observe in a population • Tell the central tendency, variability, shape in a sample • Tell the observed frequency of values in a sample • Quantify the relationships with other variables • Analysis • Infer characteristics of the population from the sample values • Compare groups with respect to their distribution of this variable • Establish how it relates to other variables • Interpretation • Are characteristics and relationships meaningful / important? • Are they statistically significant?
  40. 40. Step 6: Making Decision and Conclusions Rejections Acceptance How you conclude results
  41. 41. errors Type 1 error In a hypothesis test, a type I error occurs when the null hypothesis is rejected when it is infact true; that is, H0 is wrongly rejected Type 2 error A type II error occurs when the null hypothesis H0, is not rejected when it is in fact false.
  42. 42. Relationships specify How the value of one variable changes in relation to another May be either positive, negative, or the two variables may not have any relationship to one another
  43. 43. Level of Significance The level of significance for rejecting the statistical null hypothesis should always be stated before data are collected The level of significance usually set at (.05). this means that the researcher is willing to risk being wrong 5% . Generally the aim of the researcher is to reject the null hypothesis because this provides support for the research hypothesis. Fix it to : 0.05 level
  44. 44. Test Statistics Mathematical formula to test Null Hy p value Significance level variance Standard Deviation
  45. 45. • P value – The observed level of significance, is the smallest level at which Ho can be rejected – The decision rules for rejecting Ho in the p-value approach are : • If p-value is greater tha or equal to „a’ , you do not reject the null hy; • If p-value is less than ‘a’, you rekect the null hy
  46. 46. Thus, Hypothesis Criteria • Is written in a declarative sentences • Is written in the present tense. There is a positive relationship between the number of times children have been hospitalized and their fear of hospitalization • Contains the population • Contains the variables • Is empirically testable
  47. 47. A relook Does the study contain a hypothesis or hypotheses? Is each hypothesis clearly worded and concise? Is the hypothesis written in a declarative sentences? Is each hypothesis directly tied to the study problem?
  48. 48. Does each hypothesis contain the population and at least two variables? Is it apparent that each hypothesis contain only one prediction? if the study contains research questions, are the questions precise and specific? Do the research questions further delineate the problem area of the study?
  49. 49. Example of hypothesis formulation – Title : Employee empowerment – Objective: The investigation is an empirical research work undertaken to understand how a model company can be created with innovative workplace programme and policies. – It was also intended to understand the impact of such innovative practices on empowerment and how such processes could change the very face of the organisation and help it remain at the top of the business
  50. 50. Hy formed • Ha1 Individual and organisational achievements can be gained through the sense of belonging; • Ha2 A sense of Organisational life through climate shapes behavior and moulds positive attitude towards organisational growth and development leading to employee empowerment; • Ha3 Access to information about the mission, value, goals and objectives of an organization is positively related to empowerment; • Ha4 If an organization aspires for fundamental changes, it must change the fundamentals; and • Ha5 Empowerment at workplace makes leaders redundant.
  51. 51. Supported by …… – Data collection – Questionnaire formulation – Style of data collection – Analysis – Conclusions
  52. 52. • Concluding remarks • If you know the principles of statistics, you will understand how it can help you improve the management of processes that are subject to uncertainty – from variability, sampling, chance • If you know the methods of statistics, you will know that there are multiple options and methods to address the same issue – all are based on models, and thus all are incorrect – but some models are more useful than others • If you are clever, you will know that cheaters like to cheat others – but you will not be cheated !!
  53. 53. Thank you, all Any questions or comments about the presentation can be sent to