Replication data for: On Political Methodology (doi:10.7910/DVN/TTW7YI)

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Replication data for: On Political Methodology

doi:10.7910/DVN/TTW7YI

Harvard Dataverse

2008-10-05

4

King, Gary, 2008, "Replication data for: On Political Methodology", https://doi.org/10.7910/DVN/TTW7YI, Harvard Dataverse, V4

Replication data for: On Political Methodology

doi:10.7910/DVN/TTW7YI

King, Gary (Harvard University)

1991

Harvard Dataverse

Harvard Dataverse

2006

1991

https://doi.org/10.7910/DVN/TTW7YI

Social Sciences

"Politimetrics" (Gurr 1972), "polimetrics" (Alker 1975), "politometrics" (Hilton 1976), "political arithmetic" (Petty [1672] 1971), "quantitative Political Science (QPS)," "governmetrics," "posopolitics" (Papayanopoulos 1973), "political science statistics (Rai and Blydenburgh 1973), "political statistics" (Rice 1926). These are some of the names that scholars have used to describe the field we now call "political methodology." The history of political methodology has been quite fragmented until recently, as reflected by this patchwork of names. The field has begun to coalesce during the past decade; we are developing persistent organizations, a growing body of scholarly literature, and an emerging consensus about important problems that need to be solved. <br /> <br /> I make one main point in this article: If political methodology is to play an important role in the future of political science, scholars will need to find ways of representing more interesting political contexts in quantitative analyses. This does not mean that scholars should just build more and more complicated statistical models. Instead, we need to represent more of the essence of political phenomena in our models. The advantage of formal and quantitative approaches is that t hey are abstract representations of the political world and are, thus, much clearer. We need methods that enable us to abstract the right parts of the phenomenon we are studying and exclude everything superfluous <br /> <br /> Despite the fragmented history of quantitative political analysis, a version of this goal has been voiced frequently by both quantitative researchers and their critics (Sec. 2). However, while recognizing this shortcoming, earlier scholars were not in the position to rectify it, lacking the mathematical and statistical tools and, early on, the data. Since political methodologists have made great progress in these and other areas in recent years, I argue that we are now capable of realizing this goal. In section 3, I suggest specific approaches to this problem. Finally, in section 4, I provide two modern examples, ecological inference and models of spatial autocorrelation, to illustrate these points. <br /> <br /> See also: <a href= "http://gking.harvard.edu/category/research-interests/methods/unifying-statistical-analysis" target="_blank">Unifying Statistical Analysis</a>

This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use.

King, Gary. 1991. On Political Methodology. Political Analysis 2: 1–30. (replication dataset: ICPSR s1053): <a href="http://j.mp/jFDaDu" target="_blank">Link to article</a>

King, Gary. 1991. On Political Methodology. Political Analysis 2: 1–30. (replication dataset: ICPSR s1053): <a href="http://j.mp/jFDaDu" target="_blank">Link to article</a>

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ACROSPIN.G

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program file to rotate cloud point with ACROSPIN from GAUSS

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agghis.asc

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Aggregated data (to the year level) in ASCII form

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agghis.cmd

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Documentation for aggregated data

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agghis.dat

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Aggregated data (to the year level) in Gauss form

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agghis.prg

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Gauss Program filefor aggregated data

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BOOT.G

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proc to do bootstrapping on a supplied proc (will compute the standard deviations of the statistics computed by the supplied proc)

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CDFNORM.G

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program file to plot cumulative distribution function of the normal distribution

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COMBIN.G

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program for number combinations of n choosen x at a time

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COUNT.PRG

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program file

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COVAR.G

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program file

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DELIFALL.G

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Program file producing output: each existing vect or, whose symbol is named in vars, will be delif'd with condition t

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DENS.G

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KERNEL DENSITY ESTIMATION (A smooth version of a histogram)

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EXIST.G

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Proc format file

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figures.prg

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Gauss program file to draw all figures

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FTOSM.G

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Proc format file: matrix field to string

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GETIT.G

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Proc format file

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GRAPH.PRG

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external proc graphset

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HYPERG.G

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program file

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IN.G

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program file

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innov.asc

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Cumulative number of new methods per year (coded from above)

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ISMISSM.G

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program file

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LAG.G

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program file

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LNG.G

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log of the gamma function of x

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LOADA.G

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program file

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LOADASC.G

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program file

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LOGIT.G

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program file

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LOGITI.G

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program file

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LOLS.G

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Procedure for computing LOLS coefficients using data from a GAUSS data set on disk. This is designed for quick lols analysis when options are not required

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LOWESS.G

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Local Regression and optional Robust Fitting and Symmetric Errors

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MAT2STR.G

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program file

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MAXLIK.PRG

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program file

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MEANCAT.G

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program file

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MEANSE.G

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program file

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MEANSE0.G

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program file

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methhis.asc

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Individual level data in ASCII form

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methhis.cmd

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Documentation for individual level data

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methhis.dat

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Individual level data in Gauss form

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methhis.doc

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Documentation for methhis.asc. The data are coded from articles in the APSR, 1906 through 1988.

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methhis.prg

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Gauss program file for individual level data

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MKMISSM.G

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program file

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opm.zip

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Data in original file formats

application/octet-stream

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orig.asc

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Data on number of articles that use original data, and number of articles that use government data

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PDFNORM.G

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program file

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PLINE.G

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program file

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PLINES.G

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program file

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polmeth.pdf

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Article based on this study: On Political Methodology

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PRTPARM.G

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neatly and briefly prints relevant output from maxlik

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PRTV.G

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will print out the vectors concatinated horizontally

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RC.G

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program file

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read.me

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Detailed information on the files in this study

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REG.G

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program file

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REGRAND.G

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regression with approximate randomization

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REGV.G

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same as reg.g except arguments are lists of variable names

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REVC.G

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program file

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RNDMN.G

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program file

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RNDMNBAK.G

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program file

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RNDNB.G

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negative binomial random numbers

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RNDP.G

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random poisson numbers

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ROBUST.G

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Calcuates heteroskedasticity-consistent covariance matrix

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SCALM.G

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program file

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SCALZERO.G

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program file

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SEBAR.G

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draw vertical standard error bar at x,y coordinates

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SECAT.G

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program file

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SELIFALL.G

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each existing vector, whose symbol is named in vars, will be selif'd with condition ck

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SEQAS.G

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create vector of PTS evenly spaced points between STRT and ENDD, not including the end points

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SEQASE.G

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create vector of PTS evenly spaced points between STRT and ENDD, including the end points

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STR2MAT.G

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program file

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STRSECTM.G

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matrix version of strsect

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STRSTRIP.G

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program file

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SUBDAT.G

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Returns a submatrix of a data set

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SUBDATD.G

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creates vectors in memory from a dataset on disk

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SUBDATV.G

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creates vectors in memory from a dataset on disk

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SYM2IND.G

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program file

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TOKEN2.G

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To extract the first token from a string

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TRIMALL.G

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each existing vector, whose symbol is named in vars, will be trimr'd ttop rows from top and tbot rows from bot

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TRIPLE.G

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triple scatter plot

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VARGETM.G

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program file

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VARIANCE.G

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program file

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WHITE.G

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program file

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XYLABC.G

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Scatter plot w/labels for points, CUSTOMIZED

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XYM.G

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Graphs all columns of X against each other

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