Theory, Literature review, and Hypothesis Formulation

Theory

Framework based on facts

Descripting theory

Following rational logical thinking

Criterias of good theories


  1. Can explain phenomenon
  2. Clear and concise
  3. Logical and fit with the facts or general belief
  4. Can predict possiblities of phenomenon
  5. Recent

Reliable theory

Its validity can be proven with new or more comprehensive data.

Thinking framework

A diagram that outline the flow of logic of a study.

Theoretical framework


  1. can be proposed on:
  2. Available theories
  3. New theories, or modification of available theories

Literature review

Should show all relevant documents, both published and unpublished.

Information required


  1. Subject specific
  2. Research method

Steps required


  1. Identify and access published and unpublished materials on the topic of intererset
  2. Extract systematically the relevant information
  3. Write up the literature review

Example


  1. Introducing subject behavior that take risks and company performance
  2. Why it is important to study
  3. Identifying problems to study

Hypotheses

assumption, idea or belief about a phenomenon, relationship, or situation that is not yet known the truth. Statement about the relationship between 2 or more variables.

Hypotheses requirement


  1. Simple, specific, and clear
  2. Verifiable
  3. Relevant
  4. Operationable

Category of hypotheses


  1. Research hypothesis
  2. Alternate hypothesis

Common mistakes regarding hypothesis formulation

  1. wrong selection of framework
  2. Sampling error
  3. Data collection error
  4. Wrong analysis
  5. Bad concluion

2 kinds of error


  1. type 1 or alpha error, rejecting hypotesis zero that is true
  2. type 2 or beta error, accepting hypotesis zero that is wrong


Data source


  1. Primary data, directly collected data
  2. Secondary data, available data from third parties, whether micro or macro data

Type of data


Time series
Cross section, relationship between variables


Population

Aggregation of the whole elements, must be clear about the contents, unit, scope and time. Finite population can be studied, while infiine one is impossible to study.


Sample

Representation of population.


Reason for sampling


  1. Limited resource (save the cost)
  2. Faster 
  3. More data could be collected
  4. Information accuracy is good
  5. For destructive cases

Types of sampling


  1. Probability sampling
  2. Non-probability sampling

Probability sampling


  1. simple random
  2. stratified
  3. systematic
  4. cluster
  5. Probability proportional to size/area


Non-probability sampling


  1. Convinience sampling
  2. Quota sampling
  3. Snowball sampling

Primer data collection

depends on:

  1. Study objective
  2. Available resource
  3. researchers expertise
  4. availability of respondent

Data analysis


  1. Editing, 
  2. Coding, 
  3. Entry, digitalizing the data

Data presentation


  1. Frequency distribution
  2. One way tabulation
  3. Cross tabulation
  4. Descriptive

2 Type of tabulation


  1. Simple tabulation
  2. Complex tabulation









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