bUsIness AnaLYtICs 2. BIVARIATE ANALYSIS
Lesson #4 ➢ Two variables (Bivariate)
➢ E.g. Gender & CGPA
QUANTITATIVE DATA ANALYSIS
3. MULTIVARIATE ANALYSIS
1.0 INTRODUCTION
• Quantitative analysis involves the techniques by ➢ Several variables (Multivariate)
which researchers convert data to numerical forms ➢ E.g. Age, education, and prejudice
and subject them to statistical analyses. 3.0 UNIVARIATE ANALYSIS
• Involve techniques.
Univariate analysis is the analysis of a single variable.
• Involve the task of converting data into knowledge.
• Myths: Because Univariate analysis does not involve
➢ Complex analysis and BIG WORDS impress relationships between two or more variables, its purpose
people. is more toward descriptive rather than explanatory.
➢ Analysis comes at the end after all the data 3.1 DISTRIBUTION
are collected.
➢ Data have their own meaning. Frequency distribution is counts of the number of
responses to a question or to the occurrence of a
2.0 QUANTIFICATION OF DATA phenomenon of interest. (Polonsky & Waller, 2011, p.
189)
o The numerical representation and manipulation of
observations for the purpose of describing and Obtained for all the personal data or classification
explaining the phenomena that those observations variables. (Babbie, 2010, p. 428)
reflect. (Babbie, 2010, p. 422) Gives researcher some general picture about the
2.1 DATA PREPARATION dispersion, as well as maximum and minimum response.
EDITING 1. What is your religious preference?
o Data must be inspected for completeness & _1 Protestant _2 Catholic _3 Jewish _4 None _5 Other
consistency.
o E.g. a respondent may not answer the question about
marriage.
o But in other questions, the respondent answers that
s/he has been married for 10 years and has 3
children.
MISSING DATA
o Elimination of questionnaire (missing >10% of the total
response). 3.2 CENTRAL TENDENCY
CODING & DATA ENTRY Present data in form of an average:
o Involves quantification (process of converting data into 𝒔𝒖𝒎 𝒐𝒇 𝒗𝒂𝒍𝒖𝒆𝒔
1. MEAN = 𝒕𝒐𝒕𝒂𝒍 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒄𝒂𝒔𝒆𝒔
numerical form). E.g. Male -1, Female – 2
2. MODE = most frequently occurring attribute
DATA TRANSFORM 3. MEDIAN = middle attribute in the ranked
Changing data into new format. E.g. reduce 5 Likert-type distribution of observed attribute
Scale into 5 categories. 3.3 DISPERSION
• Distribution of values around some central value,
2.2 TYPES OF VARIABLES ANALYSIS such an average.
• Example measure of dispersion:
1. UNIVARIATE ANALYSIS
➢ Range: The distance separating the highest from
➢ One variable (Univariate) the lowest value.
➢ E.g. Age, gender, income, etc.
, ➢ Variance: To describe the variability of the 4.2 HANDLING “DON’T KNOWS”
distribution.
Whether to include or exclude the ‘don’t knows’ is harder
➢ Standard Deviation: An index of the amount of to decide.
variability in a set of data.
Higher SD means data are more dispersed.
Lower SD means that they are more bunched
together.
3.4 CONTINUOUS & DISCRETE VARIABLES
CONTINUOUS VARIABLE
• A variable can take on any value between two
specified values.
• An infinite number of values. 4.3 NUMERICAL DESCRIPTIONS IN QUALITATIUVE
• Also known as quantitative variable RESEARCH
• E.g. Income & Age The discussions are also relevant to qualitative studies.
• Scale: Interval & Ratio
The findings off in-depth, qualitative studies often can be
DISCRETE VARIABLE verified by some numerical testing.
• A variable whose attributes are separate from
one another.
• Also known as qualitative variable
• E.g. Marital status, gender, & nationality
• Scale: Nominal & Ordinal
4.0 SUBGROUP COMPARISON
Bivariate and multivariate analyses aimed primarily at
explanation. 5.0 BIVARIATE ANALYSIS
Before turning into explanation, we should consider the • In contrast to univariate analysis, subgroup
case of subgroup description. comparisons involve two variables.
• Subgroup comparisons constitute a kind of
bivariate analysis – the analysis of two variables
simultaneously.
• However, as with univariate analysis, the purpose of
subgroup comparisons is largely descriptive.
• Most bivariate analysis in social research adds on
another element: determining relationships
Subgroup comparisons tell how different groups between the variables themselves.
responded to this question and some pattern in the
results.
4.1 “COLLAPSING” RESPONSE CATEGORIES
Combining the two appropriate range of variation to get
better picture or meaningful analyses.
• Table describes the church attendance of men and
women as reported in 1990 General Social Survey.
• It shows comparatively & descriptively – that
woman in the study attended church more often as
compared to men.
• However, the existence of explanatory bivariate
analysis tells a somewhat different story. It
suggests: gender has an effect on the church
attendance.
Lesson #4 ➢ Two variables (Bivariate)
➢ E.g. Gender & CGPA
QUANTITATIVE DATA ANALYSIS
3. MULTIVARIATE ANALYSIS
1.0 INTRODUCTION
• Quantitative analysis involves the techniques by ➢ Several variables (Multivariate)
which researchers convert data to numerical forms ➢ E.g. Age, education, and prejudice
and subject them to statistical analyses. 3.0 UNIVARIATE ANALYSIS
• Involve techniques.
Univariate analysis is the analysis of a single variable.
• Involve the task of converting data into knowledge.
• Myths: Because Univariate analysis does not involve
➢ Complex analysis and BIG WORDS impress relationships between two or more variables, its purpose
people. is more toward descriptive rather than explanatory.
➢ Analysis comes at the end after all the data 3.1 DISTRIBUTION
are collected.
➢ Data have their own meaning. Frequency distribution is counts of the number of
responses to a question or to the occurrence of a
2.0 QUANTIFICATION OF DATA phenomenon of interest. (Polonsky & Waller, 2011, p.
189)
o The numerical representation and manipulation of
observations for the purpose of describing and Obtained for all the personal data or classification
explaining the phenomena that those observations variables. (Babbie, 2010, p. 428)
reflect. (Babbie, 2010, p. 422) Gives researcher some general picture about the
2.1 DATA PREPARATION dispersion, as well as maximum and minimum response.
EDITING 1. What is your religious preference?
o Data must be inspected for completeness & _1 Protestant _2 Catholic _3 Jewish _4 None _5 Other
consistency.
o E.g. a respondent may not answer the question about
marriage.
o But in other questions, the respondent answers that
s/he has been married for 10 years and has 3
children.
MISSING DATA
o Elimination of questionnaire (missing >10% of the total
response). 3.2 CENTRAL TENDENCY
CODING & DATA ENTRY Present data in form of an average:
o Involves quantification (process of converting data into 𝒔𝒖𝒎 𝒐𝒇 𝒗𝒂𝒍𝒖𝒆𝒔
1. MEAN = 𝒕𝒐𝒕𝒂𝒍 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒄𝒂𝒔𝒆𝒔
numerical form). E.g. Male -1, Female – 2
2. MODE = most frequently occurring attribute
DATA TRANSFORM 3. MEDIAN = middle attribute in the ranked
Changing data into new format. E.g. reduce 5 Likert-type distribution of observed attribute
Scale into 5 categories. 3.3 DISPERSION
• Distribution of values around some central value,
2.2 TYPES OF VARIABLES ANALYSIS such an average.
• Example measure of dispersion:
1. UNIVARIATE ANALYSIS
➢ Range: The distance separating the highest from
➢ One variable (Univariate) the lowest value.
➢ E.g. Age, gender, income, etc.
, ➢ Variance: To describe the variability of the 4.2 HANDLING “DON’T KNOWS”
distribution.
Whether to include or exclude the ‘don’t knows’ is harder
➢ Standard Deviation: An index of the amount of to decide.
variability in a set of data.
Higher SD means data are more dispersed.
Lower SD means that they are more bunched
together.
3.4 CONTINUOUS & DISCRETE VARIABLES
CONTINUOUS VARIABLE
• A variable can take on any value between two
specified values.
• An infinite number of values. 4.3 NUMERICAL DESCRIPTIONS IN QUALITATIUVE
• Also known as quantitative variable RESEARCH
• E.g. Income & Age The discussions are also relevant to qualitative studies.
• Scale: Interval & Ratio
The findings off in-depth, qualitative studies often can be
DISCRETE VARIABLE verified by some numerical testing.
• A variable whose attributes are separate from
one another.
• Also known as qualitative variable
• E.g. Marital status, gender, & nationality
• Scale: Nominal & Ordinal
4.0 SUBGROUP COMPARISON
Bivariate and multivariate analyses aimed primarily at
explanation. 5.0 BIVARIATE ANALYSIS
Before turning into explanation, we should consider the • In contrast to univariate analysis, subgroup
case of subgroup description. comparisons involve two variables.
• Subgroup comparisons constitute a kind of
bivariate analysis – the analysis of two variables
simultaneously.
• However, as with univariate analysis, the purpose of
subgroup comparisons is largely descriptive.
• Most bivariate analysis in social research adds on
another element: determining relationships
Subgroup comparisons tell how different groups between the variables themselves.
responded to this question and some pattern in the
results.
4.1 “COLLAPSING” RESPONSE CATEGORIES
Combining the two appropriate range of variation to get
better picture or meaningful analyses.
• Table describes the church attendance of men and
women as reported in 1990 General Social Survey.
• It shows comparatively & descriptively – that
woman in the study attended church more often as
compared to men.
• However, the existence of explanatory bivariate
analysis tells a somewhat different story. It
suggests: gender has an effect on the church
attendance.
