| { | |
| "paper_id": "O06-1018", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:07:47.116105Z" | |
| }, | |
| "title": "MiniJudge: Software for minimalist experimental syntax", | |
| "authors": [ | |
| { | |
| "first": "James", | |
| "middle": [], | |
| "last": "Myers", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Chung Cheng University", | |
| "location": { | |
| "settlement": "Minhsiung", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "MiniJudge is free online open-source software to help theoretical syntacticians collect and analyze native-speaker acceptability judgments in a way that combines the speed and ease of traditional introspective methods with the power and statistical validity afforded by rigorous experimental design. This paper shows why MiniJudge is useful, what it feels like to use it, and how it works.", | |
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| "abstract": [ | |
| { | |
| "text": "MiniJudge is free online open-source software to help theoretical syntacticians collect and analyze native-speaker acceptability judgments in a way that combines the speed and ease of traditional introspective methods with the power and statistical validity afforded by rigorous experimental design. This paper shows why MiniJudge is useful, what it feels like to use it, and how it works.", | |
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| "section": "Abstract", | |
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| "text": "Linguistics is a science because linguists test hypotheses against empirical data, but this testing is done in a much more informal way than in almost any other science. Theoretical syntacticians, for example, violate protocols standard in the rest of the cognitive sciences by acting simultaneously as experimenter and subject, and by showing little concern with the issues of experimental design and quantitative analysis deemed essential in most sciences. Linguists recognize that their informally-collected data are often inconclusive; controversial native-speaker judgments are commonplace problems in both research and teaching. From my conversations with syntacticians, I get the sense that they would appreciate a tool for collecting judgments more reliably, yet this tool should be one that permits them to maintain their traditional focus on theory rather than method. reveal MiniJudge's inner workings, which involve some underused or novel statistical techniques.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Currently the only implementation of MiniJudge is MiniJudgeJS, which is written in JavaScript, HTML, and the statistical language R (www.r-project.org).", | |
| "cite_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Though some readers may wonder why we should bother with judgments when we can simply analyze corpora, judgments and corpus data are actually complementary performance windows into linguistic competence, with their own strengths and weaknesses (see e.g. Penke & Rosenbach, 2004) . The question is how we can extract the maximum value out of judgments in the easiest possible way.", | |
| "cite_spans": [ | |
| { | |
| "start": 254, | |
| "end": 278, | |
| "text": "Penke & Rosenbach, 2004)", | |
| "ref_id": "BIBREF15" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Balancing speed and reliability in syntactic judgment collection", | |
| "sec_num": "2." | |
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| "text": "Phillips and Lasnik (2003:61) are entirely right to emphasize that the \"[g]athering of native-speaker judgments is a trivially simple kind of experiment, one that makes it possible to obtain large numbers of highly robust empirical results in a short period of time, from a vast array of languages.\" Even Labov (1996:102) , who generally favors corpus data, admits that \" [f] or the great majority of sentences cited by linguists,\" native-speaker intuitions \"are reliable.\" Yet as Phillips and Lasnik (2003:61) also point out, \"it is a truism in linguistics, widely acknowledged and taken into account, that acceptability ratings can vary for many reasons independent of grammaticality.\" Unfortunately, in actual practice linguists don't take the distinction between \"acceptability\" and \"grammaticality\" as seriously as they know they should, and their \"trivially simple methods\" become merely simple-minded (Sch\u00fctze, 1996) .", | |
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| "start": 305, | |
| "end": 321, | |
| "text": "Labov (1996:102)", | |
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| "end": 375, | |
| "text": "[f]", | |
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| "end": 923, | |
| "text": "(Sch\u00fctze, 1996)", | |
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| "section": "Experimental syntax", | |
| "sec_num": "2.1." | |
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| "text": "Since the problem of detecting competence in performance is precisely the problem faced by experimental cognitive scientists every day (e.g., testing vision theories with optical illusions), a reasonable response to the syntactician's empirical challenges would be to adopt the protocols standard in the rest of the experimental cognitive sciences: multiple stimuli and subjects (naive ones rather than the bias-prone experimenters themselves), systematic controls, factorial designs, continuous response measures, filler items, counterbalancing, and statistical analysis. When judgments are collected with these more careful protocols, they often reveal hitherto unsuspected complexity. Recent examples of the growing experimental syntax literature include Sorace & Keller (2005) and Featherston (2005) ; Cowart (1997) is a user-friendly handbook.", | |
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| "start": 758, | |
| "end": 780, | |
| "text": "Sorace & Keller (2005)", | |
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| "start": 785, | |
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| "text": "Featherston (2005)", | |
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| "section": "Experimental syntax", | |
| "sec_num": "2.1." | |
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| "text": "Full-fledged experimental syntax is complex, forcing the researcher to spend a lot of time on work that is not theoretically very interesting. The complexity of an experiment should actually be proportional to the subtlety of the effects it is trying to detect. Very clear judgments are detectable with traditional \"trivially simple\" methods; very subtle judgments may require full-fledged experimental methods. But in the vast area in between, a compromise seems appropriate, where methods are powerful enough to yield statistically valid results, yet are simple enough to apply quickly: a minimalist experimental syntax (see Table 1 ). Maximum of two binary factors Very few sentence sets (about 10)", | |
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| "text": "Table 1", | |
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| "section": "Minimalist experimental syntax", | |
| "sec_num": "2.2." | |
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| "text": "Random sentence order Very few speakers (about [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] Order treated as a factor in the statistics While conducting a minimalist experiment is much simpler than conducting a full-fledged judgment experiment (an explicit guide is given in Myers 2006) , some steps may still be overly complex and/or intimidating to the novice experimenter, in particular the design of the experimental sentences and the statistical analysis. The purpose of the MiniJudge software is to automate these steps.", | |
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| "text": "Myers 2006)", | |
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| "section": "Minimalist experimental syntax", | |
| "sec_num": "2.2." | |
| }, | |
| { | |
| "text": "To show how MiniJudge is used, I describe a recent application of it to a morphosyntactic issue in Chinese; for another example, see MJInfo.htm#resultshelp, reachable through the MiniJudge homepage.", | |
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| "section": "Using MiniJudge", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "MiniJudge has also been used to run syntax experiments on English and Taiwan Sign Language, as well as to run pilots for larger studies and to help teach basic concepts in experimental design.", | |
| "cite_spans": [], | |
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| "section": "Using MiniJudge", | |
| "sec_num": "3." | |
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| "text": "He (2004) presents an interesting observation regarding the interaction of compound-internal phrase structure and affixation of the plural marker men. Part of his paradigm is shown in Table 2 , where V = verb and O = object (based on his (2) & (4), pp. 2-3). ", | |
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| "start": 184, | |
| "end": 191, | |
| "text": "Table 2", | |
| "ref_id": "TABREF1" | |
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| "section": "Goal of the experiment", | |
| "sec_num": "3.1." | |
| }, | |
| { | |
| "text": "MiniJudgeJS is simply a JavaScript-enabled HTML form. Input and output are handled entirely by text areas; generated text includes code to run statistical analyses in R. Like the rest of the MiniJudge family, MiniJudgeJS divides the experimental process into the steps listed in Table 3 . Table 2 are shown in the first row of Table 5 . The user only has to find parallel substitutes for four segments, rather than having to construct whole new sentences while keeping track of the factorial design (Table 5 also shows the segments needed to generate the new sets in Table 4 ).", | |
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| "start": 279, | |
| "end": 286, | |
| "text": "Table 3", | |
| "ref_id": "TABREF2" | |
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| "start": 289, | |
| "end": 296, | |
| "text": "Table 2", | |
| "ref_id": "TABREF1" | |
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| "start": 327, | |
| "end": 334, | |
| "text": "Table 5", | |
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| "start": 499, | |
| "end": 507, | |
| "text": "(Table 5", | |
| "ref_id": "TABREF4" | |
| }, | |
| { | |
| "start": 567, | |
| "end": 574, | |
| "text": "Table 4", | |
| "ref_id": "TABREF3" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "The MiniJudgeJS interface", | |
| "sec_num": "3.2." | |
| }, | |
| { | |
| "text": "The segmentation and set generation processes are designed to work equally well in English-like and", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "The MiniJudgeJS interface", | |
| "sec_num": "3.2." | |
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| "text": "Chinese-like orthographies. Of course, since MiniJudge knows no human language, it sometimes makes strange errors, so users are allowed to correct its output, or even to generate new sets manually. After the user has corrected and approved the master list of sentences, it can be saved to a file for use in reports (as I am doing here). In the present experiment, the master list contained 48 sentences (12 sets of 4 sentences each). This is an unusually large number of sentences for a MiniJudge experiment; significant results have been found with experiments with as few as 10 sentences.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "The MiniJudgeJS interface", | |
| "sec_num": "3.2." | |
| }, | |
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| "text": "In order to run a MiniJudge experiment, the user must make three decisions. The first concerns the maximum number of speakers to test. It is possible to get significant results with as few as 7 speakers, but in the present experiment, I generated 30 surveys. As it turned out, only 18 surveys were returned.", | |
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| "section": "Running the experiment", | |
| "sec_num": "3.4." | |
| }, | |
| { | |
| "text": "The second decision concerns whether surveys will be distributed by printed form or by email. In", | |
| "cite_spans": [], | |
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| "section": "Running the experiment", | |
| "sec_num": "3.4." | |
| }, | |
| { | |
| "text": "MiniJudgeJS, printing surveys involves saving the them from a text area and printing them with a word processor. MiniJudgeJS cannot send email automatically, so emailed surveys must be individually copied and pasted. In the present experiment, I emailed thirty students, former students, or faculty of my linguistics department who did not know the purpose of the experiment.", | |
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| "section": "Running the experiment", | |
| "sec_num": "3.4." | |
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| "text": "The final decision concerns the instructions, which the user may edit from a default. MiniJudgeJS requires that judgments be entered as 1 (yes) vs. 0 (no); in the current version, if surveys are to be collected electronically, these judgments must be typed before each sentence ID number. Chinese instructions for the VOmen experiment were written with the help of Ko Yu-guang. Each survey starts with the instructions, followed by a speaker ID number (e.g., \"##02\"), and finally the survey itself, with each sentence numbered in the order seen by the speaker. Because the speakers' surveys intentionally hide the factorial design, the experimenter must save this information separately in a schematic survey file. This file is meant to be read only by MiniJudgeJS; as an example, the first line of the schematic survey file for the present experiment is explained in Table 6 . the first three lines of the data file for the VOmen experiment are shown in Table 7 . However, since R is a command-line program, and its outputs can be unintelligible without statistical training, MiniJudgeJS handles the interface with it. The user merely enters the name of the data file, decides whether or not to test for syntactic satiation (explained below in section 3.5.2), and pastes the code generated by MiniJudgeJS into the R window. After the last line has been processed by R, the code either will generate a warning (that the file was not found or was not formatted correctly), or if all went well, will display a simple interpretive summary report. A much more detailed technical report is also saved automatically; this report is explained, step by step for the novice user, in", | |
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| "start": 868, | |
| "end": 875, | |
| "text": "Table 6", | |
| "ref_id": "TABREF5" | |
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| "start": 955, | |
| "end": 962, | |
| "text": "Table 7", | |
| "ref_id": "TABREF6" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Running the experiment", | |
| "sec_num": "3.4." | |
| }, | |
| { | |
| "text": "MJInfo.htm#resultshelp.", | |
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| "section": "Running the experiment", | |
| "sec_num": "3.4." | |
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| "text": "When the data file containing the 18 completed surveys in the VOmen experiment was analyzed using the R code generated by MiniJudgeJS, the summary report in Figure 1 was produced. The factor VO had a significant negative effect. The factor men had a significant negative effect. Order had a significant negative effect. There were no other significant effects.", | |
| "cite_spans": [], | |
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| "start": 157, | |
| "end": 165, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF2" | |
| } | |
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| "eq_spans": [], | |
| "section": "A null result?", | |
| "sec_num": "3.5.1" | |
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| "text": "The above results do not take cross-item variability into account because no confound between items and factors was detected (p > .2). Although He (2004) makes no predictions relating to satiation, the unexpected null result noted in section 3.5.1 suggests that it may be worthwhile trying out a more complex analysis that includes interactions with order. Running this analysis simply involves telling MiniJudgeJS that we want to test for satiation (by clicking a checkbox), and then pasting the generated code into R. Doing this with the VOmen data resulted in the two new lines in Figure 2 being added to the significance summary.", | |
| "cite_spans": [ | |
| { | |
| "start": 144, | |
| "end": 153, | |
| "text": "He (2004)", | |
| "ref_id": "BIBREF10" | |
| } | |
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| "text": "Figure 2", | |
| "ref_id": "FIGREF3" | |
| } | |
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| "eq_spans": [], | |
| "section": "A null result?", | |
| "sec_num": "3.5.1" | |
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| "text": "The interaction between VO and men had a significant positive effect. The interaction of VO * men with Order had a significant negative effect (satiation). ", | |
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| "section": "A null result?", | |
| "sec_num": "3.5.1" | |
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| { | |
| "text": "MiniJudgeJS, as with all future versions in the MiniJudge family, is free and open source. The JavaScript and R code can be modified freely by downloading the HTML file and opening it in a text editor, and both are heavily commented to make them easier to follow. In this section I give overviews of the programming relating to material generation and statistical analysis.", | |
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| "section": "The inner workings", | |
| "sec_num": "4." | |
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| "text": "As described in section 3.3, MiniJudgeJS can assist with the generation of additional sentence sets.", | |
| "cite_spans": [], | |
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| "section": "Material generation", | |
| "sec_num": "4.1." | |
| }, | |
| { | |
| "text": "This involves two major phases: segmenting the prototype sentences into the largest repeated substrings, and substituting new segments for old segments in the new sentence sets.", | |
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| "section": "Material generation", | |
| "sec_num": "4.1." | |
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| "text": "The first step is to determine whether the prototype sentences contain any spaces. If they do, words are treated as basic units, and capitalization is removed from the initial word and any sentence-final punctuation mark is also set aside (for adding again later). If there are no spaces (as in Chinese), characters are treated as basic units. Next, the boundaries between prototype sentences are demarcated to indicate that cross-sentence strings can never be segments. The algorithm for determining other segment boundaries requires the creation of a lexicon containing all unique words (or characters) in the prototype corpus. If the algorithm detects that items from the corpus and from the lexicon match only if one of the items is lowercase, this item is recapitalized. Versions of the prototype sentences with \"word-based\" capitalization is later used when old segments are replaced by new ones.", | |
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| "section": "Material generation", | |
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| "text": "The most crucial step in the segmentation algorithm is to check each word (or character) in the lexicon to determine whether or not it has at least two neighbors on the same side in the corpus. For example, suppose the prototype set consisted of the sentences \"A dog loves the cat. The cat loves a dog.\" The lexical item \"loves\" has two neighbors on the left: \"dog\" and \"cat\". Thus a segment boundary should be inserted to the left of \"loves\" in the corpus. Similarly, the right neighbor of \"loves\" is sometimes \"the\" and sometimes \"a\"; hence \"loves\" will be treated as a whole segment. By contrast, the lexical item \"cat\" always has the same item to its left (once sentence-initial capitalization is removed):", | |
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| "section": "Material generation", | |
| "sec_num": "4.1." | |
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| "text": "\"the\". Similarly, the right neighbor of \"the\" is always \"cat\". Thus \"the cat\" will be treated as a segment, and the same logic applies to \"a dog\". The prototype segments are thus \"a dog\", \"loves\", \"the cat\".", | |
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| "section": "Material generation", | |
| "sec_num": "4.1." | |
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| { | |
| "text": "The final phase involves substituting the user-chosen new segments for the prototype segments.", | |
| "cite_spans": [], | |
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| "section": "Material generation", | |
| "sec_num": "4.1." | |
| }, | |
| { | |
| "text": "This is done using JavaScript's built-in regular expression functions, which only became available with Netscape 4 and Internet Explorer 4.", | |
| "cite_spans": [], | |
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| "section": "Material generation", | |
| "sec_num": "4.1." | |
| }, | |
| { | |
| "text": "The statistical analyses conducted by MiniJudgeJS involve several innovations: the use of GLMM, the inclusion of order and interactions with order as factors, the use of JavaScript to communicate with R, the use of R code to extract key values from R's technical output so that a simple report can be generated, and the use of R code to compare by-subject and by-subject-and-item analyses to decide whether the latter is really necessary. In this section I describe each of these innovations in turn.", | |
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| "section": "Statistical analysis", | |
| "sec_num": "4.2." | |
| }, | |
| { | |
| "text": "As explained in section 3. scores, which are reliable only if the number of observations is greater than 50 or so, but in actual practice, 50 judgments are trivial to collect (e.g., 5 speakers judging 10 sentences each). Second, like regression in general, GLMM assumes that the correlation between the dependent and independent variables is not perfect, so it is paradoxically unable to confirm the significance of perfect correlations.", | |
| "cite_spans": [], | |
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| "section": "GLMM", | |
| "sec_num": "4.3.1" | |
| }, | |
| { | |
| "text": "Third, like logistic regression (but unlike ANOVA or ordinary regression), it is impossible to calculate GLMM coefficients and p values perfectly; they can only be estimated. Unfortunately, the best way to estimate GLMM values is extremely complicated and slow, so R uses \"simpler\" yet less accurate estimation methods. Currently, R provides two options for estimating GLMM coefficients: the faster but less accurate penalized quasi-likelihood approximation, and the slower but more accurate Laplacian approximation. MiniJudgeJS uses the latter.", | |
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| "section": "GLMM", | |
| "sec_num": "4.3.1" | |
| }, | |
| { | |
| "text": "The function in the lme4/Matrix packages used for GLMM is lmer, which can also handle linear mixed-effect modeling (i.e., repeated-measures linear regression). The syntax is illustrated in Figure 3 , which shows the commands used to run the final analyses described above in section 3.5.2.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 189, | |
| "end": 197, | |
| "text": "Figure 3", | |
| "ref_id": "FIGREF4" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "GLMM", | |
| "sec_num": "4.3.1" | |
| }, | |
| { | |
| "text": "\"Factor1\" and \"Factor2\" are variables whose values are set in the R code to represent the actual factors.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GLMM", | |
| "sec_num": "4.3.1" | |
| }, | |
| { | |
| "text": "The use of categorical data is signaled by setting the distribution family to \"binomial\". The name of the loaded data file is arbitrarily called \"minexp\" (for minimalist experiment). The first function treats only subjects as random, while the second function treats both subjects and items as random. The choice to test for satiation or not is determined by the user; based on this choice, JavaScript generates different versions of the R code. The choice to run one-factor or two-factor analyses is determined by the R code itself by counting the number of factors in the data file. Both analyses in Figure 3 are always run, and then compared with another R function described below in 4.3.5. glmm1 = lmer(Judgment ~ Factor1 * Factor2 * Order + (1|Speaker), data = minexp, family = \"binomial\", method = \"Laplace\") glmm2 = lmer(Judgment ~ Factor1 * Factor2 * Order + (1|Speaker) + (1|Sentence), data = minexp, family = \"binomial\", method = \"Laplace\") effect: early judgments (when comparison is impossible) will be different from later judgments. Thus factoring out order effects in the statistics serves roughly the same purpose as counterbalanced lists.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 602, | |
| "end": 610, | |
| "text": "Figure 3", | |
| "ref_id": "FIGREF4" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "GLMM", | |
| "sec_num": "4.3.1" | |
| }, | |
| { | |
| "text": "JavaScript is much more powerful than many programmers realize. In fact, a key inspiration for Unfortunately, the necessary statistical programming is quite formidable.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JavaScript as an R interface", | |
| "sec_num": "4.3.3" | |
| }, | |
| { | |
| "text": "Instead, in MiniJudgeJS the role of JavaScript in the statistical analysis is mainly as a user-friendly GUI. Since the statistics needed for a MiniJudge experiment is highly standardized, very little input is needed from the user, but the potential to use JavaScript to interface with R in more flexible ways is there. This would help fix a major limitation with R, whose command-line interface is quite intimidating for novice users, and whose online help leaves a lot to be desired (cf. Fox, 2005 ).", | |
| "cite_spans": [ | |
| { | |
| "start": 489, | |
| "end": 498, | |
| "text": "Fox, 2005", | |
| "ref_id": "BIBREF7" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JavaScript as an R interface", | |
| "sec_num": "4.3.3" | |
| }, | |
| { | |
| "text": "Of course, using JavaScript as an interface has its limitations, the most notable of which are the built-in security constraints that prevent JavaScript from being able to read or write to files, or to communicate directly with other programs. For example, it's impossible to have JavaScript run R in the background, to save users the bother of copying and pasting in R code. This is why we are currently exploring other versions of MiniJudge. One that has made some progress is MiniJudgeJava, written by", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JavaScript as an R interface", | |
| "sec_num": "4.3.3" | |
| }, | |
| { | |
| "text": "Chen Tsung-ying in Java using its own platform-independent GUI tools. Interfacing with R is likely to remain tricky, however, unless we create something like MiniJudgeR, written in R itself, or figure out how to program GLMM directly in JavaScript.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JavaScript as an R interface", | |
| "sec_num": "4.3.3" | |
| }, | |
| { | |
| "text": "GLMM is a high-powered statistical tool, unlikely to be used by people who don't already have a strong background in statistics, and so the outputs generated by R are not understandable without such a background. Since MiniJudge is intended for statistical novices, extra programming is needed to translate R output into plain language. For MiniJudgeJS, the most crucial portion of R's output for GLMM is the matrix containing the regression coefficient estimates and p values, like that shown in Figure 4 (from the VOmen experiment, without testing for satiation MiniJudgeJS \"sinks\" lmer's displayed output to an offline file, and then reads this file back in as a string (the offline file becomes the permanent record of the detailed analysis). The string is then searched for the string \"(Intercept)\" which always appears at the upper left of the value matrix. The coefficient is the first value to the right of this, and the p value is the fourth value (skipping \"<\", if any).", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 497, | |
| "end": 505, | |
| "text": "Figure 4", | |
| "ref_id": "FIGREF5" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "R code to simplify output", | |
| "sec_num": "4.3.4" | |
| }, | |
| { | |
| "text": "If the p value associated with a factor or interaction is less than 0.05, a summary line is generated that gives the actual factor name and the sign of the estimate, as in Figures 1 and 2 above. The R code generates the summary table counting the number of yes judgments for each category (see Figure 1) directly from the data file itself.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 172, | |
| "end": 187, | |
| "text": "Figures 1 and 2", | |
| "ref_id": "FIGREF2" | |
| }, | |
| { | |
| "start": 294, | |
| "end": 303, | |
| "text": "Figure 1)", | |
| "ref_id": "FIGREF2" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "R code to simplify output", | |
| "sec_num": "4.3.4" | |
| }, | |
| { | |
| "text": "MiniJudgeJS runs both by-subject and by-subject-and-item analyses, but it reports only the first in the main summary unless it finds that the more complex analysis is really necessary. This approach differs from standard psycholinguistic practice, where both by-subject and by-item analyses are always run. A commonly cited reason for always running a by-item analysis is that it is required to test for generality across items, just as a by-subject analysis tests for generality across subjects. However, this logic is based on a misinterpretation of Clark (1973) , the paper usually cited as justification. The second problem with the standard justification for performing obligatory by-item analyses, as Raaijmakers et al. (1999) emphasize, is that the advice given in Clark (1973) actually applies only to experiments without matched items, such as an experiment comparing a random set of sentences with transitive verbs (\"eat\" etc) with a random set of sentences with unrelated intransitive verbs (\"sleep\" etc).", | |
| "cite_spans": [ | |
| { | |
| "start": 552, | |
| "end": 564, | |
| "text": "Clark (1973)", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 707, | |
| "end": 732, | |
| "text": "Raaijmakers et al. (1999)", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 772, | |
| "end": 784, | |
| "text": "Clark (1973)", | |
| "ref_id": "BIBREF4" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "Such sentences will differ in more than just the crucial factor (transitive vs. intransitive), so even if a difference in judgments is found, it may actually relate to uninteresting confounded properties (e.g., the lexical frequency of the verbs). However, if lexically matched items are used, as in the VOmen experiment, there is no such confound, since items within each set differ only in terms of the experimental factor(s). If items are sufficiently well matched, taking cross-item variation into account won't make any difference in the analysis (except to make it much more complicated), but if they are not well matched, ignoring the cross-item variation will result in misleadingly low p values.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "Nevertheless, if we only computed models that take cross-item variation into account, we might lose useful information. After all, a high p value does not necessarily mean that there is no pattern at all, only that we have failed to detect it. Thus it may be useful to know if a by-speaker analysis is significant even if the by-sentence analysis is not. Such an outcome could mean that the significant byspeaker result is an illusion due to an uninteresting lexical confound, but it could instead mean that if we do a better job matching the items in our next experiment, we will be able to demonstrate the validity of our theoretically interesting factor. Thus MiniJudge runs both types of analyses, and only chooses the by-subjects-and-items analysis for the main report if a statistically significant confound between factors and items is detected. The full results of both analyses are saved in an off-line file, along with the results of the statistical comparison of them.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "The R language makes it quite easy to perform this comparison. The model in which only speakers are treated as random is a special case of the model in which both speakers and sentences are treated as random. This means the two GLMM models can be compared by a likelihood ratio test using ANOVA (see Pinheiro & Bates, 2000) . As with the output of the lmer function, the output of the lme4", | |
| "cite_spans": [ | |
| { | |
| "start": 300, | |
| "end": 323, | |
| "text": "Pinheiro & Bates, 2000)", | |
| "ref_id": "BIBREF17" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "package's anova function makes it difficult to extract p values, so again the output is \"sunk\" to the offline analysis file to be read back in as a string. Only if the p value is below 0.05 is the more complex model taken as significantly better. If the p value is above 0.2, MiniJudgeJS assumes that items and factors are not confounded and reports only the by-subjects-only analysis in the main summary.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "Nevertheless, MiniJudgeJS, erring on the side of caution, gives a warning if 0.2 > p > 0.05. In any case, both GLMM analyses are available for inspection in the offline analysis file. Each analysis also includes additional information, generated by lmer, that may help determine which one is really more reliable, including variance of the random variables and the estimated scale (compared with 1); these details are explained in MJInfo.htm#resultshelp.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "In the case of the VOmen experiment, the comparison of the two models showed that the bysubjects-only model was sufficient (p = 1). This is unsurprising, given that the materials were almost perfectly matched, and that the by-items table showed no outliers among the sentence judgments.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "The final problem with the standard justification for automatic by-item analyses is one that even Raaijmakers et al. (1999) fail to point out. Namely, since repeated-measures regression models make it possible to take cross-speaker and cross-sentence variation into account at the same time, without throwing away any data, they are superior to standard models like ANOVA. To learn more about how advances in statistics have made some psycholinguistic traditions obsolete, see Baayen (2004) .", | |
| "cite_spans": [ | |
| { | |
| "start": 98, | |
| "end": 123, | |
| "text": "Raaijmakers et al. (1999)", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 477, | |
| "end": 490, | |
| "text": "Baayen (2004)", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By-subject and by-item analyses", | |
| "sec_num": "4.3.5" | |
| }, | |
| { | |
| "text": "MiniJudge, currently implemented only in the form of MiniJudgeJS, is software for theoretical syntacticians without any experimental training who want to collect and interpret judgments quickly and reliably. Though MiniJudgeJS is limited in some ways, in particular in how it interfaces with R, it is still quite easy to use, as testing by my students has demonstrated. Moreover, it is unique, offering syntacticians power that they cannot obtain any other way. Behind this power are original programming and statistical techniques. Finally, MiniJudgeJS is an entirely free, open-source program (as will be all future versions). Anyone interested is invited to try it out, save it for use offline, and contribute to its further development.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusions", | |
| "sec_num": "5." | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "Pezzullo and Harald Baayen also provided helpful information on programming and statistical matters.Of course I am solely responsible for any mistakes.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgements", | |
| "sec_num": "6." | |
| } | |
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| "FIGREF0": { | |
| "text": "This is where MiniJudge comes in. MiniJudge (www.ccunix.ccu.edu.tw/~lngproc/MiniJudge.htm) is software to help theoretical syntacticians design, run, and analyze linguistic judgment experiments quickly and painlessly. Because MiniJudge experiments involve testing the minimum number of speakers and sentences in the shortest amount of time, all while sacrificing the least amount of statistical power, I call them \"minimalist\" experiments. In this paper I first argue why a tool like MiniJudge is necessary. I then walk through a sample MiniJudge experiment on Chinese. Finally, I", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "text": "Surveys themselves are randomized individually to prevent order confounds, as is standard in psycholinguistics. The randomization algorithm, taken from Cowart (1997:101), results in every sentence having an equal chance to appear at any point in the experiment (by randomization of blocks), while simultaneously distributing sentence types evenly and randomly.", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "text": "Default results summary generated by MiniJudgeJS for the VOmen experiment", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF3": { | |
| "text": "New lines in results summary when satiation was tested in the VOmen experiment As hoped, factoring out the interactions with order revealed a significant interaction between the factors [VO] and[men]. This shows that the ratio difference seen inFigure 1is indeed statistically reliable (the detailed report file shows p = 0.02), thus vindicating He's empirical claim. This new analysis also detected satiation in the VOmen effect; it was this interaction with order that had obscured evidence for the VOmen effect in the default analysis.This experiment thus not only provided reliable evidence in favor of the empirical claim made byHe (2004), but it also revealed three additional patterns not reported by He: overall lower acceptability for VO forms relative to OV forms, overall lower acceptability of men forms, and the satiability of the VOmen effect. Detecting satiation, and the VOmen effect it obscured, depended crucially on the use of careful experimental design and statistical analysis, and would have been impossible using traditional informal methods. Despite this power, the MiniJudge experiment was designed, run, and analyzed within a matter of days, rather than the weeks required for full-fledged experimental syntax.", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "text": "R commands for computing GLMM when testing satiation in a two-factor experiment4.3.2 Order as a factorMiniJudgeJS includes order as a factor whether or not the user tests for satiation, to compensate for the fact that MiniJudge experiments use no counterbalanced lists of sentences across subgroups of speakers.List counterbalancing is used in full-fledged experimental syntax so that speakers don't use an explicit comparison strategy when judging sentences from the same set (a comparison strategy may create an illusory contrast or have other undesirable consequences). However, comparison can only occur when the second sentence of a matched pair is encountered. If roughly half of the speakers get sentence type [+F] first and half get [-F] first, then on average, judgments for [+F] vs. [-F] are only partially influenced by a comparison strategy. The comparison strategy (if any) will be realized as an order", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF5": { | |
| "text": "GLMM output generated by lmer for the VOmen experiment without testing for satiation Unfortunately, the output of the lmer function is a list object, containing only the parameters used to compute the estimates and p values, not the values themselves. Thus the R code generated by", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "TABREF0": { | |
| "type_str": "table", | |
| "content": "<table><tr><td>Binary yes/no judgments</td><td>No counterbalancing of sentence lists</td></tr><tr><td>Experimental sentences only (no fillers)</td><td/></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "Defining characteristics of minimalist experimental syntax" | |
| }, | |
| "TABREF1": { | |
| "type_str": "table", | |
| "content": "<table><tr><td colspan=\"2\">The VOmen paradigm of He (2004)</td></tr><tr><td>[+men]</td><td>[-men]</td></tr><tr><td>[+VO] *zhizao yaoyan zhe men</td><td>zhizao yaoyan zhe</td></tr><tr><td>make rumor person PLURAL</td><td>make rumor person</td></tr><tr><td>[-VO] yaoyan zhizao zhe men</td><td>yaoyan zhizao zhe</td></tr><tr><td>rumor make person PLURAL</td><td>rumor make person</td></tr><tr><td colspan=\"2\">Some speakers shown He's starred and non-starred examples are willing to agree with his judgments,</td></tr><tr><td colspan=\"2\">but it's likely that the star pattern has biased them. It may also be that He's generalization works for the</td></tr><tr><td colspan=\"2\">few examples he cites, but fails in general. My goal, then, was to use MiniJudge to generate more</td></tr><tr><td>examples to test systematically on native speakers.</td><td/></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "" | |
| }, | |
| "TABREF2": { | |
| "type_str": "table", | |
| "content": "<table><tr><td>I. Design experiment</td><td>II. Run experiment</td><td>III. Analyze experiment</td></tr><tr><td>Choose experimental factors</td><td>Choose number of speakers</td><td>Download and install R</td></tr><tr><td colspan=\"2\">Choose set of prototype sentences Write instructions for speakers</td><td>Enter raw results</td></tr><tr><td colspan=\"2\">Choose number of sentence sets Print or email survey forms</td><td>Generate data file</td></tr><tr><td colspan=\"2\">Segment prototype set (optional) Save schematic survey file</td><td>Save data file</td></tr><tr><td>Replace segments (optional)</td><td/><td>Generate R code</td></tr><tr><td>Save master list of test sentences</td><td/><td>Paste R command code into R</td></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "The steps used by MiniJudge experiment begins by choosing the experimental factors. In the case of the VOmen claim, the paradigm inTable 2is derived via two binary factors: [\u00b1VO] (VO vs. OV) and [\u00b1men] (with or without men suffixation). As noted above, He's observation doesn't relate to each factor separately, but" | |
| }, | |
| "TABREF3": { | |
| "type_str": "table", | |
| "content": "<table><tr><td colspan=\"2\">Table 4. Extending the VOmen paradigm of He (2004)</td></tr><tr><td>[+men]</td><td>[-men]</td></tr><tr><td>[+VO] *chuanbo bingdu yuan men</td><td>chuanbo bingdu yuan</td></tr><tr><td>spread virus person PLURAL</td><td>spread virus person</td></tr><tr><td>[-VO] bingdu chuanbo yuan men</td><td>bingdu chuanbo yuan</td></tr><tr><td>virus spread person PLURAL</td><td>virus spread person</td></tr><tr><td>[+VO] *sheji shipin ren men</td><td>sheji shipin ren</td></tr><tr><td>design ornaments person PLURAL</td><td>design ornaments person</td></tr><tr><td>[-VO] shipin sheji ren men</td><td>shipin sheji ren</td></tr><tr><td>ornaments design person PLURAL</td><td>ornaments design person</td></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "below, regardless of any additional influences from pragmatics, frequency, suffixlikeness (zhe vs. the others), or freeness (ren vs. the others); the stars here represent what He might predict (lexical content for the new sets was chosen with the help of Ko Yu-guang and Zhang Ning)." | |
| }, | |
| "TABREF4": { | |
| "type_str": "table", | |
| "content": "<table><tr><td colspan=\"2\">Set 1 (prototype) segments: zhizao</td><td>yaoyan</td><td>zhe</td><td>men</td></tr><tr><td>Set 2 segments:</td><td>chuanbo</td><td>bingdu</td><td>yuan</td><td>men</td></tr><tr><td>Set 3 segments:</td><td>sheji</td><td>shipin</td><td>ren</td><td>men</td></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "Prototype segments and new segments for the VOmen experiment" | |
| }, | |
| "TABREF5": { | |
| "type_str": "table", | |
| "content": "<table><tr><td>File line:</td><td>01</td><td>20</td><td>05</td><td>01</td><td>-VO</td><td>-men</td></tr><tr><td colspan=\"2\">Explanation: speaker ID</td><td>sentence ID</td><td>set ID</td><td>order in</td><td>value of first</td><td>value of</td></tr><tr><td/><td>number</td><td>number</td><td>number</td><td>survey</td><td>factor</td><td>second factor</td></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "The structure of the schematic survey information file for the VOmen experiment MiniJudgeJS extracts judgments from the surveys and creates a data file in which each row represents a single observation, with IDs for speakers, sentences, and sets, presentation order of sentences, factor values (1 for [+] and -1 for[-]), and judgments. As an example," | |
| }, | |
| "TABREF6": { | |
| "type_str": "table", | |
| "content": "<table><tr><td colspan=\"3\">Speaker Sentence Set</td><td>Order</td><td>VO</td><td>men</td><td>Judgment</td></tr><tr><td>1</td><td>2 0</td><td>5</td><td>1</td><td>-1</td><td>-1</td><td>1</td></tr><tr><td>1</td><td>4 5</td><td>1 2</td><td>2</td><td>1</td><td>1</td><td>0</td></tr><tr><td colspan=\"2\">3.5. Analyzing the results</td><td/><td/><td/><td/><td/></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "First three lines of data file for the VOmen experiment For novice experimenters, the most intimidating aspect of psycholinguistic research is statistical analysis. MiniJudge employs quite complex statistical methods that are unfamiliar even to most psycholinguists, yet hides them behind a user-friendly interface. Data from a MiniJudge experiment are both categorical and repeated-measures (grouped within speakers). Currently the best available statistical model for repeated-measures categorical data is generalized linear mixed effect modeling (GLMM), which can be thought of as an extension of logistic regression (see e.g. Agresti et al., 2000). GLMM poses serious programming challenges, so MiniJudgeJS passes the job to R, the world's foremost free statistical package (R Development Core Team, 2005). R is an open-source near clone of the proprietary program S (Chambers & Hastie, 1993), and like S, is a full-featured programming language. Its syntax is a mixture of C++ and Matlab, and of course it has a wide variety of built-in statistical functions, including many user-written packages. The specific R package used by MiniJudgeJS for GLMM is lme4 (and its prerequisite package Matrix), authored by Douglas Bates and Deepayan Sarkar, and maintained by Douglas Bates. R has a simple GUI interface, and by default, the Windows version nativizes (e.g., in Chinese Windows, menus and basic messages are in Chinese)." | |
| }, | |
| "TABREF8": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "num": null, | |
| "text": "providing a diagnostic for performance effects (a position taken byGoodall 2004). On the other hand, satiability may differ due to differences between the components of competence itself, thus permitting a new grammatical classification tool (a position taken byHiramatsu 2000)." | |
| }, | |
| "TABREF9": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "num": null, | |
| "text": "5, generalized linear mixed effect modeling (GLMM) is conceptually akin to logistic regression, which is at the core of the sociolinguistic variable-rule analyzing program VARBRUL and its descendants (Mendoza-Denton et al. 2003), but unlike logistic regression, GLMM regression equations also include random variables (e.g., the speakers); see Agresti et al. (2000). One major advantage of a regression-based approach is that no data are thrown away. Moreover, since each observation is treated as a separate data point, GLMM is usually not affected much by missing data, but only if they are missing non-systematically (this is why participants in MiniJudge experiments are requested to judge all sentences, guessing if they're not sure).Though GLMM is the best statistical model currently available for repeated-measures categorical" | |
| }, | |
| "TABREF10": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "num": null, | |
| "text": "JavaScript-enabled HTML file written by John C. Pezzullo. Using only basic platform-universal JavaScript, the page collects data, reformats it, estimates logistic regression coefficients via a highly efficient maximum likelihood estimation algorithm, and generates chi-square values and p values. Thus a JavaScript-only version of MiniJudgeJS is conceivable, without any need to pass work over to R." | |
| }, | |
| "TABREF11": { | |
| "type_str": "table", | |
| "content": "<table><tr><td/><td>Estimate</td><td>Std. Error</td><td>z value</td><td>Pr(>|z|)</td></tr><tr><td>(Intercept)</td><td>-0.0810381</td><td>0.2330613</td><td>-0.3477</td><td>0.728057</td></tr><tr><td>Factor1</td><td>-0.8090969</td><td>0.0886143</td><td>-9.1305</td><td>< 2.2e-16</td></tr><tr><td>Factor2</td><td>-0.9741367</td><td>0.0891447</td><td>-10.9276</td><td>< 2.2e-16</td></tr><tr><td>Order</td><td>-0.0192680</td><td>0.0059976</td><td>-3.2126</td><td>0.001315</td></tr><tr><td colspan=\"2\">Factor1:Factor2 0.0119932</td><td>0.0877194</td><td>0.1367</td><td>0.891250</td></tr></table>", | |
| "html": null, | |
| "num": null, | |
| "text": "). The trick is to extract the estimates (the signs of which provide information about the nature of the pattern) and the p values (which indicate significance) in order to generate a simple summary containing no numbers at all." | |
| }, | |
| "TABREF12": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "num": null, | |
| "text": "First, it is wrong to think that by-item analyses check to see if any item behaves atypically (i.e., is an outlier). For parametric models like ANOVA, it is quite possible for a single outlier to cause an illusory significant result, even in a by-item analysis (categorical data analyses like GLMM don't have this weakness). To test for outliers, there's no substitute for checking the individual by-item results manually. MiniJudge helps with this by reporting the by-sentence rates of yes judgments in a table saved as part of the offline analysis file; items with unusually low or high acceptability relative to others of their type stand out clearly. In the case of the VOmen experiment, this table did not seem to show any outliers." | |
| } | |
| } | |
| } | |
| } |