Lectures 1-4: Datasets, Variables, Distributions, Estimation, Quantitative Methods

Question 1

What is a variable?

Answer 1

A variable represents a characteristic for each case within a dataset, which can be described using more than one value – e.g. a variable might record incomes or unemployment rates.

Question 2

What is a nominal (a.k.a. categorical) variable?

Answer 2

A variable with distinct categories that does not tell you anything about the relationship between them, and cannot be ranked in terms of value or order – e.g. birthplace, religion.

Question 3

Are nominal variables and categorical variables the same thing?

Answer 3

Yes.

Question 4

What is an ordinal variable?

Answer 4

A variable with categories than can be ordered or ranked according to some sort of criterion (but where you cannot specify the precise size of the interval between any two categories) – e.g. a ranking of low-, semi- or high-skilled workers.

Question 5

What is an interval (a.k.a. ratio) variable?

Answer 5

A variable on a scale, with an exact distance between any pair of values. It may be either continuous (e.g. height, income) OR discontinuous/discrete (e.g. indivisible units such as numbers of factories or people).

Question 6

Are interval variables and ratio variables different?

Answer 6

No. They are the same thing.

Question 7

What is a dummy variable?

Answer 7

A variable that cannot be measured but can still be used by assigning values that represent two (or more) categories – e.g. where 0 = no and 1 = yes.

Question 8

What is an independent (a.k.a. explanatory) variable?

Answer 8

A variable that explains your dependent variable.

Question 9

What is a dependent (a.k.a. response) variable?

Answer 9

It represents a phenomenon that you want to understand through comparison to other variables (i.e. your independent variables).

Question 10

What are univariate (a.k.a. descriptive) statistics?

Answer 10

They capture the distribution of an individual variable; univariate analysis is the simplest form of statistical analysis.

Question 11

What types of methods would you use to investigate univariate statistics?

Answer 11

For qualitative variables: frequency, mode and median. For quantitative variables: mean, median, mode, standard deviation, etc.

Question 12

What are bivariate statistics?

Answer 12

They capture the relationship between 2 variables – e.g. racism and income.

Question 13

What types of methods would you use to investigate bivariate statistics?

Answer 13

For qualitative variables: crosstabulate, Cramer’s V, logistic/multinomial regression. For quantitative variables: correlation (if the independent variable is quantitative), simple regression.

Question 14

What are multivariate statistics?

Answer 14

They capture or model the relationships among 3 or more variables.

Question 15

What types of methods would you use to investigate multivariate statistics?

Answer 15

For qualitative variables: logistic/multinomial regression. For quantitative variables: multiple regression.

Question 16

What is statistical inference?

Answer 16

The process of analysing data to deduce the properties of an underlying distribution.

Question 17

What is a dataset?

Answer 17

A series of units (individuals, household..) with one or more characteristics (variables). For each variable there is a sequence of observations, each with its own value

Question 18

What do cross-sectional dataset capture?

Answer 18

They capture the characteristics of a comparable unit at a single point in time. The observations vary by the characteristics, not by unit type. Example: 1 round of ESS

Question 19

What do times-series dataset capture?

Answer 19

Time series capture repeated observations at different time periods. For example: inflation in one country over several decades

Question 20

What do cross-sectional times-series (CSTS) dataset capture?

Answer 20

They capture fixed and non-sampled units at different time periods. For example: inflation in several countries over several decades

Question 21

What is a panel data?

Answer 21

A dataset that captures sampled units at different time intervals. Example Electoral Panels (same individuals over several elections)

Question 22

What is a rolling cross-section dataset?

Answer 22

Dataset that captures sampled units (called cohorts) at different time intervals. Ex: Several rounds of ESS (different individuals but with same characteristics of age, nationality…)

Question 23

What is the mean?

Answer 23

It is the average of all values

Question 24

What is the median?

Answer 24

It is the value in the middle. There is the same number of observations above and below it

Question 25

What is the mode?

Answer 25

The value that occurs the most

Question 26

What is a quartile?

Answer 26

The n/4 quarter of the values that fall below the median. Ex. The first quartile has ¼ of the values below the median. The 3rd quartile has ¾ of the values below the median

Question 27

What is an interquartile range?

Answer 27

The distance between the 1st & 3rd quartile

Question 28

What is the mean deviation?

Answer 28

It measures how much each value differs from the mean. It is the average of all differences

Question 29

What is the variance?

Answer 29

It is the mean of the squared deviations. The variance gets rid of the possible negative signs when calculating the mean deviation. It captures better the greater spread of the values. There is a disadvantage: it is expressed in “square units”

Question 30

What is the Standard deviation?

Answer 30

The typical deviation from the mean

Question 31

What is the coefficient of variation?

Answer 31

It allows to compare standard deviation of series measured in different units

Question 32

What is a normal distribution or normal curve?

Answer 32

It takes the form of a bell-shaped. The curve is symmetrical and unimodal, so the mean, the median and the mode are identical

Question 33

What is the skewness?

Answer 33

It measures the extent to which a distribution is asymmetrical. It is given by the relationship between the mean and the median: the greater the skewness, the greater the difference between the mean and the median

Question 34

When is a distribution skewed to the right (positive skewness)?

Answer 34

When the tail of the curve stretches to the right containing a small number of very large values.

Question 35

When is a distribution skewed to the left (negative skewness)?

Answer 35

When the tail of the curve stretches to the left with a small number of very low values.

Question 36

What is the Kurtosis?

Answer 36

It measures the size of the distribution tails. It approaches to 3 in quasi-normal distributions.

Question 37

What is the empirical law of the normal distribution?

Answer 37

A constant proportion of all the cases lie in a given distance from the mean measured in terms of standard deviation