Planning for Research Success: Three paragraphs, Five Boxes, and Power (or “If all else fails, try concepts and theory”) William R. Kinney, Jr. The PhD Project - ADSA Atlanta, Georgia August 2, 2014

Outline Background and X, Y, V, and Z - for planning The scholarly researcher’s problem Using 3 paragraphs and 5 boxes to address threats to research validity (Runkle and McGrath meet Cook and Campbell plus Kinney and Libby) Hints on how to get your paper published in a top-tier scholarly journal

The Three Paragraphs: Ask yourself (then tell the reader): 1. What am I trying to “find out?” (usually does X cause Y to vary) 2. Why is it important to “find out?” 3. How do I plan to “find out?” (method, data, analysis) (One page, double-spaced, 12 pt. font, normal margins, and include an informative title!)

1. Background and framework Nothing you don’t know or couldn’t figure out with slight effort. “Facts” are real world observables. “Problems” are facts or relationships between facts that you don’t like or understand. “Theories” are ideas about causal relationships between facts (or what causes the problem “facts”) “Hypotheses” are predictions of real world observables that should occur if your theory is descriptive of the real world.

Getting started Suppose that you have an idea (a problem): you observe undesirable “facts” or peculiar “facts” or claims What research barriers must be overcome: Availability of: Causal theories (or basis for prediction)? Data? Estimation methods? Research design (how to combine the above)? Exposition (if you can’t explain it, you fail)? Who will want to read your paper (and why)? What is your comparative advantage? Hint: “One gets the biggest potatoes on the first pass through the field” (Irish agricultural economics principle per Frank O’Connor, University of Iowa)

Accounting research domain Auditing KPS Laws, regs, governance Contracts/ incentives Professional standards Firm organization, mores External enforcement Culture, markets, traditions Professional structure

Predictions from: (in order of scholarly desirability) Abstract causal theories: economics, politics, regulation, information, finance, behavior, management strategy, organizations Authorities’ prescriptions/assertions: Congress, SEC, PCAOB, EC, NYSE Assertions of interest groups: AICPA, IAASB, US CC, IIA, IAG, AEI, audit firms No theories: no one knows the “facts”

A Framework Y = f ( X, Vs, Zs ) Y = phenomenon to be explained X = your (new) theory about a cause of Y Vs = prior causes of Y Zs = contemporaneous causes of Y

How does X (treatment) get there? Experiments vs. Archival studies Y1 X0 ? {V-3, V -2, V -1 } Z0 Random assignment or independent of V vs. Self selection or V(s) determine X

2. Scholarly Researcher’s Problem:  = risk that data incorrectly “accepts” new theory  = risk that data incorrectly “rejects” new theory  = true size of X effect on Y  = residual variation in Y given research design (i.e., after effects of Vs and Zs) n = available sample size All five are related through a single, simple formula

The Simple formula: (Za + Zb) . s d n = ( ) 2 or: d n s 1/2 Zb = - Za

* Researcher’s Design Problem n is semi-fixed by data availability or cost *  = f ( X : Y relation) 3 paragraphs/ 5 boxes  = f ( Vs, Zs)  is fixed at .05 or .10 by journal editors  is the researcher’s risk of failing (you want to minimize )  = f ( n ) - - + -

Graphically . . . (small d, small s  b okay) _ Y| H0, n, s _ Y| HA, n, s b Y Accept H0 Reject H0

Graphically . . . (large d, large s  b okay) Accept H0 Reject H0 a d _ Y| HA, n, s b _ Y| H0, n, s Y

Graphically . . . (small d, large s  b yikes!) Accept H0 Reject H0 a d _ Y| HA, n, s b _ Y| H0, n, s Y

3. Facilitating research using 3 paragraphs and 5 boxes Three paragraphs (common sense – or Kinney) Five boxes (predictive validity – or Libby) Use both to help you do your own work, anticipate comments of others, quickly evaluate work of others An example

Analyze Threats to Validity using Predictive Validity Boxes If all else fails . . . Theoretical X (X ) Y (Y) Conceptual 1 Independent Dependent 2 3 Operational X Y 5 Control Other potentially influential variables Vs and Zs 4

Empirically, it is believable that X causes Y if: Validity: Statistical Conclusion (5) Internal (4) Construct (2 and 3) External (1) X and Y are correlated Vs and Zs ruled out by design, including Y causes X X and Y caused by an omitted V or Z Reason to believe that operational X and Y measure X and Y Reason to believe that X : Y relation generalizes to other persons, times, and settings.

Auditor independence and non-audit services: Was the U. S Auditor independence and non-audit services: Was the U.S. government right? (Kinney, Palmrose, Scholz 2004) Does an audit firm’s dependence on fees for FISDI, internal audit, and certain other services to an audit client reduce financial reporting quality? The answer is important because a) the Sarbanes-Oxley Act presumes so, banning such services to audit clients, and b) some registrants now voluntarily restrict tax and other legally permitted services. Using fee data from 1995-2000 for restating and similar non-restating registrants, we find no consistent association between fees for FISDI or internal audit services with restatements, but find significant positive association between unspecified services fees and restatements and significant negative association between tax services and restatements.

Five boxes for KPS example . . . Independent Dependent Auditor dependence on client 1 Lower quality financial reporting Conceptual Operational Control Non-audit fees Restatement 2 3 5 Industry, size, audit policies, acquisitions, etc. 4

Causal theory vs. Policy using five boxes (causal theory testing) Theoretical X (X ) Y (Y) Conceptual 1 Independent Dependent + 2 3 Operational X Y 5 + Control Other potentially influential variables Vs and Zs 4

Causal Theory vs. Policy using five boxes (policy testing) Independent Dependent 1 New policy Desired behaviors Conceptual + identical Need Creativity, Theory? 2 3 Operational X Y 5 0, < 0 Control Other potentially influential variables Vs and Zs 4

Estimating d with small and large n _ Y| n = 3,000 _ Y| n = 30 Y ^ Smallest “Important” amount d

4. Hint one: Your main contribution is: 1. New data 2. New estimation 3. New theory (or new problem) Whatever it is, Exploit it!

Hint Two: Broaden your contribution (and reader interest) by: 1. Making your theory elaborate 2. Using multi-methods and multi-measures 3. Generalizing your approach across contexts, disciplines, cultures, and time

Hint Three: In introducing your paper, tell the reader: 1. What (specific) problem will be addressed (usually expressed conceptually) 2. Why the (specific) problem is important 3. How you address the (specific) problem and what you find (One page, double-spaced, 12 pt. font, normal margins, and include an informative title!)

Order of importance of clear and compelling exposition Title Abstract Introduction Conclusions . . . rest of text

Referees are not (entirely) stoopid – Sometimes: They are right They are misguided, but you misguided them They are wrong, but remind you that you were making a different (and important) point They are simply wrong and nothing need be done (extremely rare, in my experience) Always “attend to” their comments – you will benefit.

Remember . . . Plan for research success write the three paragraphs before you do the work minimize b ex ante maximize validity ex ante Make your unique contribution apparent to all Your present model of the world is simplified – be alert to revision via new problems, theories, data, and methods (read, take courses, attend seminars in potentially related areas for new theories, monitor data sources, scan for useful research tools)

References Cook, and D. Campbell, Quasi-experimentation design & analysis issues for field settings. Houghton Mifflin Co. (New York) 1971 (Validity types). Kinney, W., "Empirical Accounting Research Design for Ph.D. Students," The Accounting Review, April 1986 (3 paragraphs and integration). Libby, R., Accounting and human information processing: Theory and applications. Prentice-Hall, Inc., (Englewood Cliffs) 1981 (Boxes). Runkel, P., and J. McGrath, Research on human behavior – A systematic guide to method. Holt, Rinehart, and Winston, Inc., (New York) 1972 (Boxes). Simon, J., and P. Burstein, Basic Research Methods in Social Science (3rd ed.). Random House, (New York) 1985 (Chapter 3 – X, Y, Vs, Zs). Ziliak, S., and D. McCloskey, The Cult of Statistical Significance. The University of Michigan Press, (Ann Arbor) 2008 (power).