Data Collection Methods

• Data collection involves gathering and measuring information on variables of interest systematically to answer research questions, test hypotheses, and evaluate outcomes.

Types of Data

• Primary data are collected firsthand and are considered original.
• Secondary data have been previously collected by someone else and have undergone statistical processing.

Methods of Primary Data Collection

• Observation
• Interview
• Questionnaire
• Case Study
• Survey

Observation Method

• Observation Method involves collecting data from the field through observation, either personally or by an observer.
• Advantages: Eliminates subjective bias; Provides current information; Independent of respondent's variable.
• Disadvantages: Time consuming; May provide limited information; Can be affected by unforeseen factors.

Types of Observation

• Structured Observation: observation done by characterizing style of recording the observed information, standardized conditions of observation, definition of the units to be observed, selection of pertinent data of observation
• Example: An auditor performing inventory analysis in store.
• Unstructured Observation: observation done without any thought before observation
• Example: Observing children playing with new toys.
• Participant Observation: Observer is a member of the group being observed
• Advantages: Observation of natural behavior; Closeness with the group; Better understanding
• Non-participant Observation: Observer observes people without giving any information to them.
• Advantages: Objectivity and neutrality; More willingness of the respondent
• Controlled Observation: Observation takes place in natural condition. It is done to get spontaneous picture of life and persons.
• Uncontrolled Observation: observation takes place according to definite prearranged plans, with experimental procedure then it is controlled observation generally done in laboratory under controlled condition.

Interview Method

• Method of collecting data through oral-verbal stimuli, with replies in terms of oral-verbal responses.

Types of Interviews

• Personal Interviews: interviewer asks questions in a face-to-face contact
• Structured Interviews: set of pre-decided questions.
• Unstructured Interviews: no pre-determined questions are followed
• Focused Interviews: attention is focused on the given experience of the respondent and its possible effects
• Clinical Interviews: concerned with broad underlying feelings or motivations, or with the course of an individual's life experience.
• Group Interviews: a group of 6 to 8 individuals is interviewed.
• Qualitative and Quantitative Interviews: divided on the basis of subject matter i.e. whether qualitative or quantitative.
• Individual Interviews: interviewer meets a single person and interviews him.
• Selection Interviews: is done for the selection of people for certain jobs.
• Depth Interviews: deliberately aims to elicit unconscious material relating to personality dynamics and motivations.
• Telephonic Interviews: contacting samples via telephone.

Questionnaire Method

• Questionnaire Method is popular in large inquiries, with questionnaires mailed to respondents expected to read, understand, and answer questions independently.
• Advantages: Low cost, even with a large geographical area; Answers are in the respondent's own words, reducing bias; Provides adequate time for answers; Non-approachable respondents can be conveniently contacted; Large samples can be used.
• Disadvantages: Low return rate of questionnaires; Slowest method of data collection; Difficult to ensure the expected respondent completes the form.

Case Study Method

• Case Study Method involves an intensive investigation of a particular unit.
• Advantages: Less costly and time-consuming; Advantageous when exposure data is expensive or hard to obtain; Useful when studying dynamic populations with difficult follow-ups.
• Disadvantages: Subject to selection bias; Do not allow calculation of incidence (absolute risk)

Survey Method

• Survey Method is a common approach for diagnosing and solving social problems through surveys.
• Advantages: Relatively easy to administer; Can be developed in less time compared to other methods; Cost-effective.
• Disadvantages: Respondents may not provide accurate, honest answers; Closed-ended questions may have lower validity; Data errors due to non-responses may exist.

Secondary Data Sources

• Publications of central, state, and local governments
• Technical and trade journals
• Books, magazines, and newspapers
• Industry, bank, and stock exchange reports and publications
• Reports by research scholars and universities
• Public records

Factors to Consider Before Using Secondary Data

• Reliability of data: Who, when, which methods, at what time etc.
• Suitability of data: Object, scope, and nature of original inquiry should be studied, as if the study was with different objective then that data is not suitable for current study
• Adequacy of data: Level of accuracy.
• Area differences then data is not adequate for study

Selection of Proper Method for Data Collection depends on:

• Nature, scope and object of inquiry
• Availability of funds
• Time factor
• Precision required

Designing a Survey

• Surveys can assess opinions or test hypotheses.
• Steps for designing a survey;
  • Determine the goal of your survey: What question do you want to answer?
  • Identify the sample population: Whom will you interview?
  • Choose an interviewing method: face-to-face interview, phone interview, self-administered paper survey, or internet survey.
  • Decide what questions you will ask in what order, and how to phrase them
• Conduct the interview and collect the information.
• Analyze the results by making graphs and drawing conclusions.

Experiment design

• When conducting an experiment, the goal is to discover something new or find an explanation of why something happens
• It's important that the experiment maintains internal and external validity.
• The experiment must follow all of the steps of the scientific method.

Probability

• Sample Space: The set of all possible outcomes of a statistical experiment, denoted by the symbol S.
• Element: Each outcome in a sample space.
• Event: Any collection of sample points, is called a subset of a sample space.
• Complement: The complement of an event A with respect to S is the subset of all elements of S that are not in A, denoted by the symbol A'.
• Intersection: The intersection of two events A and B, denoted by the symbol A ∩ B, is the event containing all elements that are common to A and B.
• Mutually Exclusive: Two event A and B are mutually exclusive, or disjoint, if A ∩ B = Ø that is, A and B have no elements in common
• Union: The union of events A and B, denoted by A∪B, is the event containing all the elements that belong to A or B or both.

Counting Sample Points

• Rule 1: If operations can be performed in n ways, and if for each of these ways a second operation can be performed in n2 ways, then two operations can be performed in n₁n2 ways
• Rule 2: The number of permutations of n objects is n!
• Rule 3: The number of permutationsof n distinct objects taken r at a time is n!/(n-r)!
• Rule 4: The number of distinct permutations of n things soo of which aaare one of a kind, .n2 of a second kind, ---, nk of nth kind is n!/(n₁!n₂!...nk!)
• Rule 5: The number of combinations ofn distinct objects taken r at a time is n!/((r!(n-r)!)

Random Variables and Probability Distribution

• A random variable is a function that associates a real number with each element in the sample space
  • X denotes a random variable
  • x denotes its cases
• Types
  • Discrete - if a sample space contains finite number of possibilities
  • Continuous - if a sample space contains infinite number of possibilities
• Examples:
  • Number of automobiles accidents per year in Q.C.
  • Length of time to play 15 holes of golf
  • Amount of milk produced yearly by a particular cow
  • Number of eggs laid each month by a hen
  • Length of grain produced per hectare.

Discrete Probability Distribution

• A discrete random variable assumes each of its values with a certain possibility.
• The set of ordered pairs (x, f(x)) is a probability function probability mass function or probability distribution.
• Three key Principles
  • f(x)≥ 0
  • Σx f(x) = 1
  • P(X = x) = f(x)

Cumulative Distribution Function

• The cumulative distribution function F(x) of a discrete random variable x with probability distribution f(x) is:
• F(x) = P(X ≤ x) = Σt≤x f(t), for -∞ < t < +∞

Joint Probability Distribution

• If X and Y are two discrete random variables, the probability distribution for their simultaneous occurrence can be represented by a function with values f(x,y) for any pair values of (x,y) within the range of the random variables X and Y.
• Function f(x,y) is a joint probability distribution of probability mass function of the discrete random variables X and Y, with the following principles
  • f(x,y) ≥ 0 for all (x,y)
  • ΣxΣy f(x, y) = 1
  • P(X = x, Y= y) = f(x,y)
  • For any region A in the xy-plane P((X,Y) ∈ A) = ΣΣA f(x, y)