Introduction: Data & measurement

Transcription

1 Introduction: & measurement Johan A. Elkink School of Politics & International Relations University College Dublin 7 September 2015

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4 Definition: N N refers to the number of cases being studied, at the unit of analysis level. Qualitative Case studies N = 1 Comparative methods small N Quantitative Large N large N The choice between qualitative and quantitative methods depends on data availability and a number of trade-offs or priorities in the analysis e.g. generalizability (breadth) vs accuracy (depth) (see Gerring, 2001, 2012).

5 Descriptive vs inferential statistics Descriptive statistics: numerically or graphically summarizing a specific set of data. Inferential statistics: drawing conclusions about a population on the basis of numerical or graphical information on a subset of the population.

6 Introductory comments Syllabus Objective: Lectures and labs Grading and homework Plagiarism Textbook Polity IV score Log of GDP per capita

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8 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely?

9 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely? individual

10 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely? individual country

11 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely? individual country country

12 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely? individual country country electoral districts

13 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely? individual country country electoral districts parties

14 Unit of analysis The unit of analysis refers to the level of the observations at which you are drawing conclusions. Are older people more likely to vote? Are richer countries more likely to be democratic? Does district magnitude affect proportionality? Do rural areas have lower turnout? Are left-wing parties more likely to support European integration? Are junior ministers more likely to resign prematurely? individual country country electoral districts parties ministers

15 Definition: N N refers to the number of cases being studied, at the unit of analysis level. Qualitative Case studies N = 1 Comparative methods small N Quantitative Large N large N The choice between qualitative and quantitative methods depends on data availability and a number of trade-offs or priorities in the analysis e.g. generalizability (breadth) vs accuracy (depth) (see Gerring, 2001, 2012).

16 Example data set Age Vote Party Education Sex 1 21 Yes FF 4 Male 2 30 No 3 Female 3 80 Yes FG 3 Male 4 50 Yes Lab 2 Male 5 33 No 5 Female 6 20 No 2 Female 7 43 Yes FF 5 Female 8 42 Yes FF 2 Male FF = Fianna Fail; FG = Fine Gael; Lab = Labour Education: 1 = none; 2 = primary; 3 = secondary; 4 = tertiary; 5 = post-graduate

17 Example data set District System Magnitude Seats Threshold Proportionality 1 PR Yes PR No STV No FPTP No FPTP No PR Yes STV No PR Yes MIXED Yes 0.6 PR = proportional representation; STV = single transferable vote; FPTP = first past the post; MIXED = mixed electoral system

18 Missing values In observed data, there are often missing values particular data that is not available for particular cases. Generally, these need to be excluded from statistical analysis and thus identified in the data set. For many data sets, in particular for survey data, missing data is often identified by numerical coding schemes the analysis can easily misinterpret these as numbers instead of missing!

19 Example data set Age Vote Party Education Sex 1 21 Yes FF 4 Male 2 30 No 3 Female 3 80 Yes FG 3 Male 4 50 Yes Lab 2 Male 5 33 No 5 Female 6 20 No 2 Female 7 43 Yes FF 5 Female 8 42 Yes FF 2 Male FF = Fianna Fail; FG = Fine Gael; Lab = Labour Education: 1 = none; 2 = primary; 3 = secondary; 4 = tertiary; 5 = post-graduate

20 Example data set (missing) Age Vote Party Education Sex 1 21 Yes FF 4 Male Female 3 80 Yes FG 3 Male 4 50 Yes Lab 2 Male 5 33 No 6 20 No 2 Female 7 43 Yes FF 5 Female 8 42 Yes FF 2 FF = Fianna Fail; FG = Fine Gael; Lab = Labour Education: 1 = none; 2 = primary; 3 = secondary; 4 = tertiary; 5 = post-graduate

21 Example data set (missing) Age Vote Party Education Sex 1 21 Yes FF 4 Male Female 3 80 Yes FG 3 Male 4 50 Yes Lab 2 Male 5 33 No 6 20 No 2 Female 7 43 Yes FF 5 Female 8 42 Yes FF 2 FF = Fianna Fail; FG = Fine Gael; Lab = Labour Education: 1 = none; 2 = primary; 3 = secondary; 4 = tertiary; 5 = post-graduate

22 Variables A variable is an attribute that has two or more divisions, characteristics, or categories. The opposite is a constant, which is an attribute that does not vary. (Argyrous, 1997, 3)

23 Random variables A random variable assigns a particular numerical value to each possible outcome of an experiment or random phenomenon. A realized or observed variable is the actual value of the variable after the experiment or phenomenon. What you see in a data set are thus the observed or measured values on a particular underlying random variable. (Mood, Graybill and Boes, 1974, 53); (?, 245)

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25 Definition Conceptualisation: defining the variable of interest in qualitative or substantive terms. Operationalisation: defining the variable in terms of the operations used to measure a variable for individual cases. (Argyrous, 1997, 5 6)

26 (Adcock and Collier, 2001, 531)

27 is the process of determining and recording which of the possible traits of a variable an individual case exhibits or possesses. A case is an entity that displays or possesses the traits of a given variable. A population is the set of all cases of interest. A sample is a subset of the population. (Argyrous, 1997, 3 4)

28 Levels of measurement Categorical Nominal categories Ordinal... in particular order Scale Interval... with meaningful distance Ratio... with meaningful zero Examples: geographical distance, turnout (voter), left-right orientation (party), committee membership (MP), education level (voter), GDP per capita (country), UN membership (country), Likert scale

29 Levels of measurement Categorical Nominal categories Ordinal... in particular order Scale Interval... with meaningful distance Ratio... with meaningful zero Examples: geographical distance, turnout (voter), left-right orientation (party), committee membership (MP), education level (voter), GDP per capita (country), UN membership (country), Likert scale

30 Levels of measurement Categorical Nominal categories Ordinal... in particular order Scale Interval... with meaningful distance Ratio... with meaningful zero Examples: geographical distance, turnout (voter), left-right orientation (party), committee membership (MP), education level (voter), GDP per capita (country), UN membership (country), Likert scale

31 Levels of measurement Categorical Nominal categories Ordinal... in particular order Scale Interval... with meaningful distance Ratio... with meaningful zero Examples: geographical distance, turnout (voter), left-right orientation (party), committee membership (MP), education level (voter), GDP per capita (country), UN membership (country), Likert scale

32 Levels of measurement Categorical Nominal categories Ordinal... in particular order Scale Interval... with meaningful distance Ratio... with meaningful zero Examples: geographical distance, turnout (voter), left-right orientation (party), committee membership (MP), education level (voter), GDP per capita (country), UN membership (country), Likert scale

33 Levels of measurement Categorical Nominal categories Ordinal... in particular order Scale Interval... with meaningful distance Ratio... with meaningful zero Examples: geographical distance, turnout (voter), left-right orientation (party), committee membership (MP), education level (voter), GDP per capita (country), UN membership (country), Likert scale A discrete variable is measured by a unit that cannot be subdivided. It has a countable number of values. A continuous variable is measured by units that can be subdivided infinitely. It can take any value in a line interval. (Argyrous, 1997, 11)

34 Percentages and proportions A proportion is calculated as the number of cases in a particular category (n) divided by the total number of cases (N): n N. A percentage is calculated as the proportion times 100%: n N 100%.

35 Percentages and proportions A proportion is calculated as the number of cases in a particular category (n) divided by the total number of cases (N): n N. A percentage is calculated as the proportion times 100%: n N 100%. E.g. 3 out of 20 is 3 20 = = 1.5%.

36 Exercise: proportions What proportion of crimes in Town A relate to burglary? Which town has the highest homicide rate? Town A Town B Population 20, ,213 Homicide Robbery Auto theft Rape Burglary total (Healey, 1996, 52)

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38 comparison Source: 12 June 2015

39 comparison (log scale) Source: 12 June 2015

40 and code For the sake of replicability and transparency, saving commands is key in the use of statistical software. preparation transformation Descriptives Analysis Including clarifying commentary. software format SPSS.sps Stata.do R.R Python.py

41 SPSS Developed by social scientists and extensively used in sociology and political science. pros Good documentation and supports Large user-base Can link to R and Python Designed for survey data Easy graphical user interface cons Very expensive... but declining rapidly Limited programming functionality Single data set Not very cutting-edge

42 SPSS windows

43 SPSS View

44 SPSS Variable View

45 SPSS Output

46 SPSS Syntax Editor

47 Stata Developed by epidemiologists and extensively used in economics and political science. pros Superb documentation and supports Extensive package library Large user-base cons Expensive Slightly less cutting-edge Low usage outside academia Awkward programming language Single data set

48 Stata windows

49 Stata do-file editor

50 R Developed by statisticians and extensively used in political science, data science, statistics, etc. pros cons Free software Variable documentation quality Very extensive package library Inconsistent interfaces Real programming language Steep learning curve at start Large and active user-base No graphical user interface 1 Multiple data sets Highest quality graphics But note RStudio.

51 RStudio windows

52 RStudio windows

53 RStudio data view

54 Adcock, Robert and David Collier validity: a shared standard for qualitative and quantitative research. American Political Science Review 95(3): Argyrous, George Statistics for social research. Basingstoke: MacMillan. Gerring, John Social science methodology: A critical framework. Cambridge: Cambridge University Press. Gerring, John Social science methodology: A unified framework. Cambridge: Cambridge University Press. Healey, Joseph F Statistics: a tool for social research. Wadsworth. Mood, A.M., F.A. Graybill and D. Boes Introduction to the Theory of Statistics. New York: McGraw-Hill.