MODULE TWO UNIT ONE TYPES OF DATA ANALYSIS

Question 1

QUALITATIVE RESEARCH

Answer 1

Is related to verbal, written, observational, or narrative data. Aims to develop a deep understanding of a particular event or phenomenon through the inerpretation of spoken and written words, perceptions, and feelings.

Question 2

QUALITATIVE RESEARCH

CONTINUED

Answer 2

Uses specfic structured techniques & stragegies to understand data related to words, perceptions, or feelings, rather than numbers. Examples are: Focus groups, interviews, open-ended questionaires & unstructured observations.

Qualitative - credited in providing rich, in-depth data. On other hand, advocates of quantitative research criticise qualitative methods for laccking generalisability, being valnerbale to interpretation biases & being difficult to replicate.

Question 3

QUANTITATIVE RESEARCH

Answer 3

Data that is in the form of numerical values or quantities of some kind is related to quantitative research. Questions like “How many..”, “How often..” and “How much…” can be answered using this method and procedures.

Question 4

QUANTITATIVE RESEARCH

CONTINUED

Answer 4

Involves turning raw numbers into meaningful informtion through the application of rational & critical thinking, using a variety of statistical procedures. Examples are: Survey research, experimental research, content analysis, secondary analysis, correlation & regression analysis.

Question 5

QUANTITATIVE APPROACHES TO RESEARCH

VS QUALITATIVE APPROCHES TO RESEARCH

Answer 5

• Evaluation of objective data
• Focus on variables
• Less in-depth: examines specfic aspects of a topis
• Uses statistical tests for analysis
• Relies on a larger sample size
• Relies on more standardised methods of statistical analysis and fixed design
• Less dependent on context
• More generalisable

Question 6

QUALITATIVE APPROCAHES TO RESEARCH

VS QUANTITATIVE APPROACHES TO RESEARCH

Answer 6

• Evaluation of subject data
• Focus on interactive process
• More in-depth: examines breadth and depth of topic
• No statistical tests used for thematic analysis
• Less generalisable
• Smaller sample sizes often used to colelct data
• More varied techniques in data analysis & flexible design
• More dependent on context

Question 7

MIXED METHOD APPROACH

Answer 7

Limitations in either quantitative or qualitative research can be mitigated by carefully plamming a mixed method research study.

Mixed method approaches to research have emerged out of the tensions between qualitative and quantitiative research paradigms.

Question 8

MIXED METHOD APPROACH

CONTINUED

Answer 8

Focuses on collecting, analyzing, & mixing both quantitaive and qualitative data in a single study or series of studies. Its central premise is that the use of quantitative and qulaitative approaches , in combination, provides a better understanding of research problems then either approach alone.

Question 9

MIXED METHOD APPROACH

CONTINUED

Answer 9

Is becoming more commonplace in business research. However, it significantly increases the complexity of a research project for a number of reasons: Involves a variety of data collection methods & analysis techniques, resulting in it being more costyly & time-consuming. Both methods require different skils sets, few people are proficient in both types. Involves different types of logic, combining the two areas of research can make the interpretation of the research findings more complicated.

Mixed method approaches require careful planning, & a good understaning of both qualitative and quantitative techniques.

Question 10

TYPES OF QUANTITATIVE DATA

Answer 10

The scale of measure is a classification system, or taxonomy, that is used to describe the nature of the numbers assigned to a variable. The four scales of measurement used when doing quantitative research: 1. Ratio, 2. Interval, 3. Ordinal, 4. Nominal.

Question 11

QUANTITATIVE DATA LEVEL OF MEASUREMENT - NOMINAL

Answer 11

Data is classified or group and cannot be ranked or ordered.

EG: Make of car, Gender

DISCRETE DATA

Question 12

QUANTITATIVE DATA LEVEL OF MEASUREMENT - ORDINAL

Answer 12

Data is placed in discrete and ordered categories that are ranked.

EG: Position in a race, The Likert Scale

DISCRETE DATA

Question 13

QUANTITATIVE DATA LEVEL OF MEASUREMENT - INTERVAL

Answer 13

Meaningful differences between variables that fall on a continuum.

EG: Temperature, IQ Score

CONTINUOUS DATA

Question 14

QUANTITATIVE DATA LEAVEL OF MEASUREMENT - RATIO

Answer 14

Meaningful differences between variables that fall on a continuum that has a true zero point.

EG: Income, Number of customers

CONTINUOUS DATA

Question 15

CONTINOUS DATA: RATIO & INTERVAL SCALES

Answer 15

Continuos data is data that can be measured, & the distance between the values on the scale of measurement have equal quantitative meaning. Two types of continous variables: interval & ratio.

In other words, continuos data can take on any value within a specfic range of possible values: it is data that fales wintin a continuum or a scale that can be subdivided into finer inrements.

Question 16

CONTINUOS VARIABLES: INTERVAL

Answer 16

An interval variable differs from ratio variable that a variable on the interval scale has no clearly defined, or absolute zero.

In other words, there is no clear definition 0.0

Question 17

CONTINUOS VARIABLES: RATIO

Answer 17

Height is an example of a ratio variable. Monthly income is another example of the ratio scale of measure.

Question 18

DISCRETE DATA: ORDINAL AND NOMINAL SCALES

Answer 18

Can be counted, but is not necssarily measured on a scale. Only a finite number of values is possible, & the values cannot be meaningfully subdivided. Two types of discrete data: Ordinal data and nominal data.

Question 19

DISCRET DATA: ORDINAL DATA

Answer 19

This refers to data in which variables fall into natural, ordered categories. Variables can be ranked, but the distances between the categories is not mathematically defined.

Question 20

DISCRETE DATA: NOMINAL DATA

Answer 20

This is data that places people, onjects, or other entities into mutally exclusive categories, that cannot be ranked. There fore, this type of data tells us which category a subject belongs to, & not how much of a certain characteristic the subject possesses.

Question 21

STRUCTERED, UNSTRUCTURED & SEMI STRUCTURED DATA

Answer 21

Another way to classify data is according to the level of structure or the level of organisation of the data.

The terms structured, unstructed and semi sturctured data are particiulary relevant in the feild of big data.

Question 22

STRUCTURED DATA

Answer 22

Is organised data that is uploaded into a relational database or spreadsheet.

Data organised into fixed fields, easily retrieved via search options or algorthithms.

Question 23

UNSTRUCTURED DATA

Answer 23

Represents around 80% of all data, as it often includes text & multimedia files.

Doesn’t fit into a specfic database or spreadsheet structure.

Question 24

SEMI-STRUCTURED DATA

Answer 24

Does not conform to the formal structure of data models associated with relational databases or other forms of data tables. This data does contain tags, or other markers to seperate semanticelements & enforce hierarchies of records and fields within the data.

Therefore, it is not completely unstructured.

Question 25

TYPES OF ANALYSIS & DATA ANALYTICS

Answer 25

The purpose of the research is the most important determinant for the type of data analysis that should be applied. Both dara analysis & more complex analytics techniques are used for:
* Describe a current situation.
* Diagonse past pitfalls.
* Discover trends & patterns in unexplored data.
* Predict future events;
* Inform business decision-making.

Question 25

TYPES OF ANALYSIS & DATA ANALYTICS

Answer 25

The purpose of the research is the most important determinant for the type of data analysis that should be applied. Both dara analysis & more complex analytics techniques are used for:
* Describe a current situation.
* Diagonse past pitfalls.
* Discover trends & patterns in unexplored data.
* Predict future events;
* Inform business decision-making.

Question 25

TYPES OF ANALYSIS & DATA ANALYTICS

Answer 25

The purpose of the research is the most important determinant for the type of data analysis that should be applied. Both dara analysis & more complex analytics techniques are used for:
* Describe a current situation.
* Diagonse past pitfalls.
* Discover trends & patterns in unexplored data.
* Predict future events;
* Inform business decision-making.

Question 26

TYPES OF ANALYSIS & DATA ANALYTICS

Answer 26

The purpose of the research is the most important determinant for the type of data analysis that should be applied. Both dara analysis & more complex analytics techniques are used for:
* Describe a current situation.
* Diagonse past pitfalls.
* Discover trends & patterns in unexplored data.
* Predict future events;
* Inform business decision-making.

Question 26

TYPES OF ANALYSIS & DATA ANALYTICS

Answer 26

The purpose of the research is the most important determinant for the type of data analysis that should be applied. Both dara analysis & more complex analytics techniques are used for:
* Describe a current situation.
* Diagonse past pitfalls.
* Discover trends & patterns in unexplored data.
* Predict future events;
* Inform business decision-making.

Question 27

DESCRIPTIVE ANALYSIS

Answer 27

Aim to yeild a detailed description of a certain behaviour, event, relationship, or object of study without necessarily analysing the cause or reasons for it’s existance. Descriptive analyses simply aim to describe phenomena as they are hypothesised to exist. Descriptive statistics will answer: ‘WHO” & “WHERE” questions, not why and how. Following goals & outcomes:
* Provide a detailed, highly accurate picture.
* Create a set of categories or classify types.
* Clarify a sequence of steps or stages.
* Document a casual process or mechanism.

Question 28

INFERENTIAL ANALYSIS

Answer 28

Uses sample dats to make genralistaions about a whole population. Inferential statictics is used to draw conclusions about an additonal population outside of the data set. There are two major areas of inferential statistics:
* Estimating parametres: Taking a statistic from the sample data & using it to infere something about a population parametre.
* Hypothesis tests: Using sample data to answer a question specfically related to a research questions.

Inferential statistics approaches can inform business decision-making, & help businesses identify trends & patterns in the data that can be generalised.

Question 28

INFERENTIAL ANALYSIS

Answer 28

Uses sample dats to make genralistaions about a whole population. Inferential statictics is used to draw conclusions about an additonal population outside of the data set. There are two major areas of inferential statistics:
* Estimating parametres: Taking a statistic from the sample data & using it to infere something about a population parametre.
* Hypothesis tests: Using sample data to answer a question specfically related to a research questions.

Inferential statistics approaches can inform business decision-making, & help businesses identify trends & patterns in the data that can be generalised.

Question 29

DATA ANALYTICS

Answer 29

Data analysis is a necassary subcomponent of larger field of study known as data analytics. Analutics involves a number of different approaches, & may rely on sophisticated software to run complex analyses.

Question 30

PREDICTIVE ANALYTICS

Answer 30

Is a branch of analytics that looks towards the future & uses data to ake statistical predictions about future events or trends.

Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabillistic in nature (Wu, 2013)

Question 31

PRESCRIPTIVE ANALYTICS

Answer 31

Is an emerging branch of data analytics, & is more advanced & technical that predictive analysics. Prescriptive analytics goes one step further than predictive analytics in that it not only provides models that predict future events, but it also suggest a range of actions & provide the predicted outcome for each action.