RESEARCH METHODOLOGY LECTURE 6 VARIABLES AND CONSTRUCTING HYPOTHESES

RESEARCH METHODOLOGY LECTURE 6 VARIABLES AND CONSTRUCTING HYPOTHESES 1 Mazhar Hussain Dept of Computer Science ISP, Multan Mazhar. hussain@isp. edu. pk

ROAD MAP Introduction Chosing your research problem Chosing your research advisor Literature Review Plagiarism Variables in Research Construction of Hypothesis Research Design Writing Research Proposal Writing your Thesis Data Collection Data Representation Sampling and Distributions Paper Writing Ethics of Research 2

CONCEPT "A general idea referring to a behavior or characteristic of an individual, group, or nation". For example, pain, happiness, richness, effectiveness, dignity…etc. 3

VARIABLE "An attribute that is observable, measurable, and has a dimension that can vary". For example, temperature is a variable that is observable, measurable, and varies from high to low. 4

CONCEPTS VS. VARIABLES Concepts are mental images or perceptions � Meaning vary from individual to individual Variables are measurable � With varying degrees of accuracy A variable can be measured, a concept can not be. Concepts in your study? � Need to convert them to variables 5

CONCEPTS, INDICATORS & VARIABLES Concepts in your study – How to measure? Identify the indicators �A set of criteria reflective of the concept Convert the indicators to variables Example � Concept – Rich � Indicator – Income & Assets � Income(in $) is also a variable � Assets House, Cars, Investements Convert each of these into dollars Based on total income and total value of assets, decide whether a given person is rich or not. 6

CONVERTING CONCEPTS TO VARIABLES Concept Indicators Variables Decision level Rich 1. Income 2. Assets 1. Income/year 2. Total value: 1. Home 2. Cars 1. If >$100, 000 2. If > $250, 000 High academic achievement 1. Marks exam 2. Marks practical 1. Percentage 2. Percentage 1. If > 80% 2. If > 80% 7

TYPES OF VARIABLES Classification can be based on: � The causal relationship � The design of study � The unit of measurement 8

TYPES – FROM VIEW OF CAUSATION Change variables – Independent variables � The cause responsible for brining about a change in a phenomenon or situation � Variable that is believed to cause or influence the dependent variable Outcome variables – Dependent variables � Variable that is influenced by the independent variable. Extraneous variables � Variables affecting the cause-and-effect relationship 9

EXAMPLES Does Smoking Cause Lung cancer ? Does Nursing care Cause Rapid recovery ? Does Drug (a) Cause Improvement ? Cause Effect Independent variable Dependent variable 10

EXAMPLES • Extraneous Variable that confound the relationship between the dependent and independent variables, thus it needs to be controlled. E. g. , "air pollution" is an extraneous variable interferes with studying the relationship between smoking "independent variable" and lung cancer "dependent variable". 11

VARIABLES – VIEWPOINT OF STUDY DESIGN Active variables � Variables that do not pre-exist, pre-exist so, the researcher has to create them. � These variables can be manipulated, changed or controlled. Attribute Variables �A pre-existing characteristic or attribute which the researcher simply observes and measures � These variables cannot be manipulated, changed or controlled 12

EXAMPLE Study designed to measure the effectiveness of three teaching models A, B, C Researcher may change the teaching model No control on the characteristics of the student population – age, gender or motivation to study 13

VARIABLES – MEASUREMENT VIEWPOINT Categorical Variables (Qualitative) Continuous Variables (Quantitative) 14

VARIABLES – MEASUREMENT VIEWPOINT Categorical Variables Measured on nominal scales Two types � Dichotomous Variables Vary in only two values. E. g. alive or dead, day or night etc. � Polytomous Variables More than two categories E. g. Religion – Muslim, Christian, Jew 15

VARIABLES – MEASUREMENT VIEWPOINT Continuous Variables Continuity in measurement – take any value on the scale on which they are measured E. g. age, income etc. 16

The material in these slides is based on the following resources. REFERENCES Research Methodology, Ranjit Kumar, Chapter 5 17

RESEARCH METHODOLOGY CONSTRUCTING HYPOTHESES 18 Mazhar Hussain Dept of Computer Science ISP, Multan Mazhar. hussain@isp. edu. pk

ROAD MAP Introduction Chosing your research problem Chosing your research advisor Literature Review Plagiarism Variables in Research Construction of Hypothesis Research Design Writing Research Proposal Writing your Thesis Data Collection Data Representation Sampling and Distributions Paper Writing Ethics of Research 19

HYPOTHESIS Hypothesis � Brings clarity, specificity and focus to research problem Possible to conduct a study without hypothesis as well Hypothesis – how to construct � Arise from ‘hunches’ or ‘educated guesses’ 20

HYPOTHESIS - EXAMPLES Betting on a horse race � Hunch – Horse#6 will win � Hunch is true or false – Only after the race Distribution of smokers � Hunch – more male smokers at your workplace than female smokers � Test the hunch – ask them � Conclude – hunch was right or wrong 21

HYPOTHESIS - EXAMPLES Public health �A disease is very common in people coming from a specific sub-group of population � To find every possible cause – enormous time and resources � Perform a study – collect information to verify your hunch � Verificiation – hunch correct or not 22

HYPOTHESIS Researcher – does not know about a phenomenon, situation or a condition But – does have a hunch, assumption or guess Conclude through verification Hunch may be � Right � Wrong � Partially right 23

HYPOTHESIS - DEFINITIONS A tentative statement about something, the validity of which is usually unknown A proposition that is stated in a testable form and that predicts a particular relationship between two or more variables. A hypothesis is written in such a way that it can be proven or disproven by valid and reliable data – it is in order to obtain these data that we perform our study. 24

HYPOTHESIS From the definitions, a hypothesis has certain characteristics: It is a tentative proposition Its validity is not known In most cases, it specifies a relationship between two or more variables. 25

HYPOTHESIS - CONSIDERATIONS A hyothesis should be simple, specific and clear � No ambiguity in the hypothesis – makes verification difficult � Unidimensional – should test one relationship at a time � Must be familiair with the subject area (literature review) before suggesting the hypothesis 26

HYPOTHESIS - CONSIDERATIONS The average of male students in the class is higher than that of female students Clear Specific Testable 27

HYPOTHESIS - CONSIDERATIONS A hypothesis should be capable of verification � Data collection and analysis � Hypothesis cannot be tested? � May forumulate hypothesis for which methods of verification not available You may end up developing a technique A hypothesis should be operationalisable � Expressed in terms that can be measured 28

TYPE OF HYPOTHESIS Categories of hypothesis � Research hypothesis Your hypothesis which you want to test � Alternative hypothesis Specify the relationship that will be considered as true in case the research hypothesis proves to be wrong. 29

WAYS OF FORMULATING HYPOTHESIS There is no significant difference in the proportion of male and female smokers in the study population A greater proportion of females than males are smokers in the study population A total of 60% of females and 30% of males in the study population are smokers There are twice as many female smokers as male smokers in the study population 30

WAYS OF FORMULATING HYPOTHESIS Hypothesis of No Difference � When you formulate a hypothesis stipulating that there is no difference between two situations, groups or outcomes � There is no significant difference in the proportion of male and female smokers in the study population 31

WAYS OF FORMULATING HYPOTHESIS Hypothesis of Difference �A hypothesis in which a researcher stipulates that there will be a difference but does not specify its magnitude �A greater proportion of females than males are smokers in the study population 32

WAYS OF FORMULATING HYPOTHESIS Hypothesis of Point-Prevalence �A researcher has enough knowledge about the behaviour/situation � Able to express the hypothesis in quantitative units �A total of 60% of females and 30% of males in the study population are smokers 33

WAYS OF FORMULATING HYPOTHESIS Hypothesis of Association � Expressed � Twice as a relationship as many female smokers as male smokers 34

HYPOTHESIS TESTING Hypothesis testing - H 0 � Null hypothesis Usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or "this product is not broken". � Alternative hypothesis Negation of null hypothesis, for example, "this person is not healthy", "this accused is guilty" or "this product is broken ". � Errors depend directly on null hypothesis. 35

HYPOTHESIS TESTING True state of nature Your Decision H 0 is True H 0 is False Reject H 0 Accept H 0 36

HYPOTHESIS TESTING Your Decision True state of nature H 0 is True H 0 is False Reject H 0 Type I error Correct Decision Accept H 0 Correct Decision Type II error 37

HYPOTHESIS TESTING H 0 is True H 0 is False Reject H 0 Type I error Correct Decision Accept H 0 Correct Decision Type II error H 0 = This person is healthy Telling the person that he is sick when infact he was healthy Type I error Telling the person that he is sick when infact he was sick Correct Telling the person that he is healthy when infact he was sick Type II error Telling the person that he is healthy when infact he was healthy Correct Traditionally probability of type I errors is denoted by α and that of type II errors by β 38

HYPOTHESIS TESTING H 0 = Defendent is Innocent 39

EXAMPLE – AIRPORT TRAVELERS Your Decision True state of nature Innocent Terrorist False positive True positive Innocent True Negative False negative 40

EXAMPLE: FACE DETECTION True Positives False Negative False Positives True Negative (Rest of the image) 41

EXAMPLE: FACE DETECTION How many faces were detected out of total? Recall = 3/4= 75% Did system detected extra objects other than faces? Precision = 3/6 = 50% 42

EXAMPLE - BIOMETRICS Biometric access control system � Finger print, iris, face, hand geometry etc. Enrollment � Enroll all the authorized users – take their finger prints, facial images or iris scans etc. Validation �A person arrives � Take data (finger print, iris, face) � Compare with database � If matched with an individual – Allow � Else - Decline 43

EXAMPLE - BIOMETRICS Enrollment What kind of errors the system can make? 44 http: //www. idteck. com/support/biometrics. asp

EXAMPLE The FRR is the frequency that an authorized person is rejected access 45 The FAR is the frequency that a non authorized person is accepted as authorized

EXAMPLE - BIOMETRICS Challenge How to find a similarity threshold value for acceptance/rejection Find system response to a large number of inquires from authorized as well as unauthorized users. Record similarity scores of authorized and unauthorized cases Plot respective histograms/distributions 46

The material in these slides is based on the following resources. REFERENCES Research Methodology, Ranjit Kumar, Chapter 6 http: //en. wikipedia. org/wiki/Type_I_and_type_II_errors http: //www. intuitor. com/statistics/T 1 T 2 Errors. html http: //www. fingerprint-it. com http: //fingerchip. pagesperso-orange. fr 47