Sources of data Flashcards

1
Q

PRIMARY, SECONDARY DATA

A

Primary data - collected specifically for the purpose of the survery. You are aware of the possible shortfalls and limitations. It`s expensive to collect and store.

Secondary data - collected for some other purpose. Must be ACCURATE and RELIABLE.

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2
Q

DISCRETE, CONTINOUS DATA VARIABLES

A

Discrete - can take countable number of values (nr. of goals on the match, can only be whole number)

Continuous - take on any value (height)

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3
Q

INTERNAL, EXTERNAL SOURCE

A
  1. INTERNAL
    - Accounting records (cost, general, purchase, sales ledger)
    - Data relating to personnel (payroll)
    - Production department (ascribing costs to the physical information produced by the source)
    - Service business - accounting, solicitors
  2. EXTERNAL
    - Received from customers and suppliers

a) Primary SOURCE
- As close as you can get to the origin of the item of the data

b) Secondary SOURCE
- Second-hand data

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4
Q

SOURCES OF 2ND DATA

A
  1. Banks (money supply, government debt, financial transactions)
  2. Government (population data)
  3. Financial newspapers (forex, interest rates, gilts)
  4. Trade Journals (industry, competitors` products, industry costs and prices)
  5. Websites
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5
Q

PROS and CONS of 2nd DATA

A

PROS

  • Cheap
  • Large quantity
  • Quick to obtain
  • Great for analyzing the past and patterns

CONS

  • Inadequacies, limitations
  • Out of date
  • Not relevant
  • Incorrect
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6
Q

ECONOMIC ENVIRONMENT

A

Affecting firms at national and global level, both in general level of economic activity and in particular variables, such as exchange rates, interest rates, inflation.

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7
Q

MACROECONOMIC FACTORS THAT INFLUENCE COMPANIES

A
  1. Overall growth or fall in GDP - increased/decreased demand for goods/services. When there is expansion we try to identify the demand. In the recession we are focused on costs, competition, profitability.
  2. Local economic trends - type of industry, labour rate, house prices.
  3. Inflation - disrupting decision making, wages go up
  4. Interest rate - costs of borrowing, how much consumers afford to spend
  5. Tax levels - how much profit to retain/give to shareholders, VAT - how much do customers pay for the product
  6. Government spending - ifluenced if you are supplier of givernment
  7. The business cycle - expansion, contraction
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8
Q

3 Vs of BIG DATA

A
  1. Volume
    - Scale of information
  2. Velocity (hitrost)
    - Timeliness
  3. Variety
    - Structured and unstructured
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9
Q

BIG DATA - overall

A
  • Collection
  • Analysis
  • Make predictive models (only digitalized data can be used)
  • Find trends, understand customers
  • Focus resources more effectively to make better decisions - increase profit, decrease costs
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10
Q

DECISION MAKING AND BIG DATA

A

Decision based on big data made:

  • Quicker
  • More flexibility, respond earlier
  • Based on current situation, can take potential future situations into account
  • Hard data evidence that can be quantified
  • Collaborative basis (data can be shared, presented)
  • Higher probability of out of the box decisions (all factors taken into account)
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11
Q

BENEFITS OF BIG DATA ANALYTICS

A
  • Analysis of vast quantities in relative quickly time
  • Improving decision making
  • Focus on individual customer
  • Cost reduction
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12
Q

SAMPLING, CENSUS (pros and cons)

A

Data are often collected from sample, not population. If the whole population is examined = CENSUS.

Cons of census

  • Costly
  • Data may be out of date by the time you complete

Pros of sample

  • Once a certain sample size is reached, very little accuracy is gained by examining more items
  • Possible to ask more questions
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13
Q

CHOICE OF SAMPLE

A

Completness

  • Covering all areas of population in sample
  • Lack of completness = BIAS
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14
Q

(NON)-PROBABILITY SAMPLING METHOD

A
  1. Probability - known chance of each member of the population appearing in the sample.
  2. Non-probability - change of each member of population appearing in the sample is not known. QUOTA sampling.
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15
Q

PROBABILITY SAMPLING METHOD

A
  1. Random
  2. Stratified random
  3. Systematic
  4. Multistage
  5. Cluster
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16
Q

RANDOM SAMPLING, SAMPLING FRAME

A
  • Every item in the population has an equal chance of being included
  • Random sample is not perfect sample
  • SAMPLING FRAME = numbered list of all items in the population

CONS

  • Full range of variation
  • Unrepresentative sample is possible
  • Costly
  • Numbering is laboring
  • Difficult to obtain data
  • Adequate sampling frame could not exist
17
Q

STRATIFIED RANDOM

A

Dividing population into strata or categories. Random samples are then taken from each stratum or category.

PROS

  • Representative
  • Reflecting population
  • Conclusions about each stratum can be made
  • Increased precision, less variation

CONS
- Prior knowledge of each items in population is required

18
Q

SYSTEMATIC

A

Selecting every n-th item after a random start.

PROS

  • Easy
  • Cheap

CONS

  • Biased sample
  • Not completely random (some chances have a zero chance of being selected)
19
Q

MULTISTAGE

A

Dividing population into number of sub-populations and then selecting a small sample of these sub-populations at random.

PROs

  • Fewer investigators are needed
  • Not so costly

CONs

  • Bias
  • Not truly random (rest of the populations cannot be in the sample when areas are chosen). Selected areas should reflect the full range of diversity.
20
Q

CLUSTER

A
  • Non-random
  • Selecting one definable subsection of population as sample, that subsection is taken to be representative of the population.

PROs

  • Alternative to multistage
  • Inexpensive

CONs
- Bias

21
Q

QUOTA SAMPLING

A
  • Non-probability sampling method
  • Interview all the people until quota is met

PROs

  • Cheap, administratively easy
  • Much larger sample can be studies
  • No sampling frame required
  • May be the only possible approach
  • Yields enough accurate information

CONs

  • Bias
  • Sampling error due to its non-random nature