Course Introduction Methodological reform (revolution?) taking place Goal: more accurately inform theory, practice, and future research Content objectives: conceptual and practical (but mostly conceptual) Inform participants’ current and future research efforts Motivate future inquiry with a methodological focus Not stats-heavy, technical; assumed: basic knowledge of descriptive and inferential statistics (e.g., M, SD, t test, ANOVA) Examples mostly from second language (L2) research Lecture, all-group discussion, and small-group discussion ask Qs at any time! TheoryResearchPractice
Course Overview Monday/today: Statistical power, effect sizes, and fallacies of statistical significance Tuesday: Meta-analysis and the synthetic approach Wednesday: Assessing methodological quality Thursday: Replication research Friday: Data transparency, reporting practices, and visualization techniques
Statistical power, effect sizes, and fallacies of statistical significance Luke Plonsky Current Developments in Quantitative Research Methods Day 1
Review of Common Stats: Comparing Means t test ANOV A from Bialystok & Miller (1999) Mean scores (DV) Groups (IV)
Review of Common Stats : Correlations from DeKeyser (2000) Question: What is the relationship between two (continuous) variables? Positive, negative, curvilinear Strong, weak, moderate, none
A Model of Research Conduct a study (e.g., the effects of A on B) p < 0.05p > 0.05 Important finding / Get published! Modify relevant theory, research, practice Trash What’s wrong with this picture?
(Another quick review) Q: Wait, real quick: What’s a p value? A 1 : The probability of similar results (e.g., differences between groups; relationship between variables) given NO difference between groups / no relationship between variables A 2 : NOT an indication of the magnitude, importance, direction, or replicability of an effect/relationship WHAT WE REALLY WANT TO KNOW! Also: Observed p values vary as a function of sample size (N), effect size (e.g., Cohen’s d), and variance.
OK, on to the Controversy (Anderson et al., 2000) 60+ years and 400+ articles (e.g,., Schmidt, 1996; Thompson, 2001) “The almost universal reliance on merely refuting the null hypothesis as the standard method for corroborating substantive theories … is a terrible mistake, is basically unsound, poor scientific strategy, and one of the worst things that ever happened in the history of psychology” (Meehl, 1967, p. 72). APA Task Force on Statistical Inference (Wilkinson & TFSI, 1999) AL: strict (dogmatic?) adherence to NHST; very little discussion until recently (Crookes, 1991; Ellis, 2006; Larson-Hall, 2010; Lazaraton, 1991; Nassaji, 2012; Norris, 2013; Norris & Ortega, 2000, 2006; Oswald & Plonsky, 2010; Plonsky, 2011, 2012, 2013; Plonsky & Gass, 2011) http://oak.ucc.nau.edu/ldp3/bib_nhst.html http://oak.ucc.nau.edu/ldp3/bib_nhst.html
Wilkinson & TFSI (1999) Purpose: “to initiate discussion in the field about changes in current practices of data analysis and reporting” General recommendations: be transparent; calculate power a priori; inspect data descriptively and visually; simpler analyses are best Specifics: report exact p values; report (contextualized) ESs for all tests; CIs
NHST is Unreliable The effects of A and B are always different—in some decimal place—for any A and B. Thus asking ‘are the effects different?’ is foolish (Tukey, 1991, p. 100). StudyN1N1 N2N2 M 1 (SD 1 )M 2 (SD 2 )pd 15515 (3)18 (4).22650.85 215 15 (3)18 (4).02760.85 345 15 (3)18 (4).00010.85 ↑ ↓ ↑ ↓ ↑ ↑ The (nil) hypothesis that d = 0 is (almost) always false! (Cohen, 1994)
NHST is Unreliable (Cont’d) Same goes for p values based on correlations Remember: r =.30 = 0.30 = 0.30 p =.05
NHST is Unreliable (Cont’d) “[with NHST] … tired researchers, having collected data on hundreds of subjects, then conduct a statistical test to evaluate whether there were a lot of subjects, which the researchers already know, because they collected the data and know they are tired.” Thompson, 1992, p. 436
NHST is Crude and Uninformative Continuous data yes/no dichotomy p values say nothing about: Replicability Theoretical or practical importance Magnitude of effects p >.05 ≠ zero effect size: The absence of evidence for differences is not evidence for equivalence (Kline, 2004, p. 67) Large p values can correspond to large effects and vice versa Other explanations for p >.05? small sample/low power/high sampling error; small (i.e., hard-to- detect effect size; unreliable instruments; weak treatment; other hidden variables; … Appropriate for a limited period of exploratory research (Should be an) inverse relationship between theoretical maturity and reliance on p
From Papi & Abdollahzadeh (2012) What could these t test and resulting p values possibly contribute here? NHST is Crude and Uninformative
Do you see any similar patterns here? (Hint: look at the p values and ESs) p >.05 but sizeable d Key p <.05 but not large d Taylor et al. (2006) p >.05 w/neg. d
NHST is Arbitrary …surely, God loves the.06 nearly as much as the.05 (Rosnow & Rosenthal, 1989, p. 1277) How much more (or less) would we know if the conventional alpha level was.03 (or.15)? What if tests of statistical significance never existed? (Harlow et al., 1997)
Adherence to NHST (and p values) constrains progress of theory inefficient research efforts NHST & publication bias (Rothstein, et al., 2005) Scenario: 100 intervention studies; H 0 is true (i.e., no difference between treatments A and B with alpha.05) (At least) 5 studies will find p <.05 95 studies will sit unpublished, or be re-run until p <.05 (jelly beans cause acne)jelly beans cause acne Type 1 error (false positive) in published studies = 100% Treatment effects (which are nil) become grossly overestimated Conduct a study (e.g., the effects of A on B) p < 0.05p > 0.05 Important finding / Get published! Modify relevant theory, research, practice Trash NHST is Counter-productive
Summary (Quantitative) linguistics research relies heavily on NHST, which is… highly controversial at best and possibly dangerous and to-be-avoided; unreliable; crude and uninformative; arbitrary; and counter-productive OK, but what we can do to improve?
Power (Or: a possible solution to our obsession with p values?)
Statistical Power What is it? Why does it matter? How many participants do I need? (A very practical and common question)
What kind of power is needed vs. typical? Table 2 in Cohen (1992) Are these Ns typical in linguistics research? d=0.2 d=0.5 d=0.8
What kind of power is needed vs. typical? Plonsky & Gass (2011) 2% conducted a power analysis Median d = 0.65 + median n = 22 Overall post hoc power =.56 Plonsky (2013) 1% (6/606 studies) conducted a power analysis median d =.71 (inflated?) + median n = 19 Overall post hoc power =.57 What does this mean for Internal validity (and, hence, external validity/generalizability)? Past research? Theory-building? Practical implications? Availability bias in meta-analyses?
The “Power Problem” in L2 Research (Plonsky, 2013, in press) Rarely analyze power Small samples Heavy reliance on NHST (median = 18) Effects not generally very large Omission of non-statistical results Rarely check assumptions Rarely use multivariate statistics
Tools for Power Analysis Cohen’s (1988, 1992) power tables A priori Conceptually? Practically: http://danielsoper.com/statcalc3/calc.aspx?id=47 http://danielsoper.com/statcalc3/calc.aspx?id=47 Post hoc Conceptually? Practically: http://danielsoper.com/statcalc3/calc.aspx?id=49 http://danielsoper.com/statcalc3/calc.aspx?id=49
What if you can’t get enough power? This may be the case when, for example… You’re studying a very small or hard-to-find population (L3 learners of Swahili with L1 Korean) You have limited funding for running participants Your phenomenon/relationship/effect of interest is small (i.e., hard to detect) Your advisor says you can’t use the PSY participant pool Avoid or limit inferential stats Form less (sub)groups less contrasts Focus on descriptives (including effect sizes and CIs) ‘Bootstrap’ the data?
Bootstrapping Random re-sampling from observed data to produce a simulated but more stable outcome (see Larson-Hall & Herrington, 2010) (More) robust to: outliers, non-normal data common Larson-Hall & Herrington (2010) ANOVA: p>.05 between NSs (n=15) and 3 learner groups (n=14, 15, 15) Tukey post hocs: p <.05 ONLY between NSs and Group A (p =.002); p b =.407; p c =.834 Bootstrapped post hoc tests p <.05 for all three groups p values non-statistical due to a lack of power; Type II error Plonsky et al. (in press) Re-analyzed raw data from 26 primary L2 studies 4 (of 16) Type I ‘misfits’ (i.e., 25% Type I ‘misfit’ rate) 0 Type II ‘misfits’ Too much power (via large N) inflated findings?
BUT EVEN WITH GREATER POWER VIA BOOTSRTAPPING, OUR RESULTS ARE STILL BASED ON THE FLAWED NOTION OF STATISTICAL SIGNIFICANCE
EFFECT SIZES! (Or: a MUCH BETTER solution to our obsession with p values)
Effect Sizes Questions we’ll address What are they? How do we calculate them? What advantages do ESs provide over p values? How can we interpret ESs?
What is an effect size? A quantitative indication of the strength of a relationship or an effect Common effect sizes Standardized mean differences (Cohen’s d) M 1 -M 2 / SD pooled (see Excel macro for calculating d) Correlation coefficients (e.g., r) Shared variance (R 2, eta 2 ) Odds Ratios (likelihood of A given B) Percentages
Why Effect Sizes? - An alternative to NHST (p) - Null Hypothesis Significance Testing (p) vs. Effect Sizes (d) Unreliable: result dependent on sample size (e.g., Kline, 2009) ESs: not dependent on N Crude and uninformative: a) forces continuous data into a yes/no dichotomy; b) tells us nothing about practical significance or magnitude (e.g., Cohen, 1994) ESs: Express magnitude/size of relationship (i.e., WHAT WE REALLY WANT TO KNOW) Arbitrary: …surely, God loves the.06 nearly as much as the.05 (Rosnow & Rosenthal, 1989, p. 1277) ESs: Continuous and can be compared/combined across studies 36
Research Questions and Their Answers Using NHST vs. ESs Think of a study you read recently or one that you’re working on. What were the RQs? Where they phrased dichotomously (Do …? Is there a difference …?)? If so, what kind of answer can come from such a RQ? How might the findings differ with an emphasis on magnitude rather than presence/absence of a relationship or effect?
Why Effect Sizes? - Journal Requirements - APA Publication Manual, 6 th Edition Three major L2 Journals: Language Learning, TESOL Quarterly, Modern Language Journal Plonsky & Gass (2011): 0% (1980s) 0% (1990s ) 27% (2000s) Plonsky (2013): 3% (1990s) 42% (2000s) So now effect sizes get reported more often… ?
…but very rarely do we interpret them What do they mean anyway? What implications do these effect have for future research, theory, and practice? What does d = 0.50 (or 0.10, or 1.00…) mean? small How big is ‘big’? And how small is ‘small’?
ESs: Summary ESs are best understood in relation to other, field-specific effects d ≈ 0.40 (small) d ≈ 0.70 (medium) d ≈ 1.00 (large) …if people interpreted effect sizes [using fixed benchmarks] with the same rigidity that.05 has been used in statistical testing, we would merely be being stupid in another metric (Thompson, 2001, pp. 82–83). Additional considerations: Theoretical and methodological maturity (over time) SD units Research setting (lab vs. classroom; SL vs. FL) Length/intensity of treatment Manipulation of IVs Publication bias Sample size / sampling error Instrument reliability Empirically-based, field-specific scale for d values in L2 research
A Revised Model of Research Conduct a study (e.g., the effects of A on B) p < 0.05 d = ? p > 0.05 d = ? More precise and reliable estimate of effects Modify relevant theory, research, practice Trash Accumulation of results (via meta-analysis)
Based on our discussion today, what changes would you suggest to the field?
10 Suggestions for Reform 1. A diminished reliance on NHST / p-values 2. Drop the “significant” from “statistically significant” 3. Focus on the practical and theoretical importance of results 4. Better educate ourselves and future generations of researchers Emphasize: ESs, alternatives to NHST, synthetic-mindedness in primary research De-emphasize NHST 5. ESs (for all findings, not only when p <.05) 6. CIs (for all findings, not only when p <.05) “a quiet but insistent reminder that no knowledge is complete or perfect” (Sagan, 1996) 7. Replication (to mitigate effects of low power) 8. Examine data visually 9. Meta-analysis / a synthetic approach 10. Initiative from the top down
Further Reading Beyond significance testing (Kline, 2013) The cult of statistical significance (McCloskey, 2008) Understanding the new statistics (Cumming, 2012) Effect sizes for research (Grissom & Kim, 2012, 2nd ed.) Statistical power analysis for the behavioral sciences (Cohen, 1988, 2nd ed.)
Connections to Other Topics to be Discussed this Week Meta-analysis (relies on ES) rather than p values (TUESDAY) Replication (THURSDAY) Reporting practices (full descriptives including ES, always; data transparency, etc.) (FRIDAY)
Tomorrow: Meta-analysis Motivation for and benefits of (conceptual understanding) Procedures/techniques (practical understanding)
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