Producing Good Research: Behavioral and Otherwise American Accounting Association Auditing Doctoral Consortium Clearwater, Florida. January 14, 2004 Jane Jollineau Kennedy University of Washington

Todays Outline I.What is good research (experimental and otherwise)? II.Why do experiments? III.Finding a topic in Behavioral (auditing) research IV.Common Pitfalls

I. What is good research? A.Good topic B.Good design

A. What is a good research topic? Problem is described clearly, comprehensively Important constituents care about it! Problem is challenging but not impossible Possible to evaluate solutions or interpret the direction of results The results will add to our knowledge about the problem and have theoretical and/or practical ramifications

For every research idea/paper write 3 paragraphs answering the following: 1.What problem or issue will be addressed? 1.Why is the problem or issue important? 1.How you will address the problem (and what do you expect to find)? Tip: Kinneys 3 Paragraphs (footnote 18 in his 1986 TAR article)

My Example Paper for Today Frank Hodge, TAR, October 2001: Hyperlinking unaudited information to audited financial statements: effects on investor judgments Test of Kinneys 3 questions

Q1: Hodge 2001 This study investigates whether firms can influence investors perceptions of their financial reports by hyperlinking unaudited information to information in the audited financial statements.

Hodge 2001 Q1 continued Specifically, does hyperlinking optimistic, unaudited information about a firms prospects to audited financial statements lead investors to: –Misclassify unaudited info. as audited, –Inflate the credibility of unaudited info., –Judge the firms earnings potential to be higher, relative to viewing the same info. in hardcopy format? Will a warning help?

Q2: Who cares about this problem? Management SEC FASB AICPA Accounting/auditing researchers System designers(?)

Q3: Method and results? Method: –Used an experiment. Why? Data not observable from investors investment decisions. Can control info. and extraneous factors re. people, tasks, and environment. Results: –Hyperlinked info was perceived as more credible –Firm judged to have greater earnings potential –Simple labels (Un)audited reduced the effects

B. What is good Research design? Theory answers why? Construct Validity Internal Validity External Validity Giving yourself a decent chance of testing your theory and finding results

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 Model & Definitions (Kinney 86)

A simple model {V -3, V -2, V -1 } Y1 Y1 X0X0 Z0Z0 ?

Building a Research Model Control Other potentially influential variables: Vs and Zs Operational X Operational Y Operational Theoretical X Theoretical Y Conceptual Independent Dependent

For X to cause Y : X and Y are correlated Alternative explanations are 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 represent X and Y Reason to believe that X : Y relation generalizes to other persons, times, and settings.

...threats to Predictive Validity Statistical Conclusion Validity (5) Internal Validity (4) Construct Validity (2 and 3) External Validity (1)

Threats to Validity (contd) 5: Stat. Val. 4: Internal Validity 2:Construct Validity 3: Construct Validity 1: Ext. Val. Control Other potentially influential variables: Vs and Zs Operational X Operational Y Operational Theoretical X Theoretical Y Conceptual Independent Dependent

Research Model for Hodge 2001 Financial Decisions Assoc. of unaudited Information with Audited F/S 1 Hyperlinked Letter to F/S Credibility and Earnings Judgments 23 5 Accounting & finance courses; investing experience 4 Conceptual Operational Control Independent Dependent

To achieve Internal Validity: account for Vs and Zs by: random assignment of participants to treatment/control estimate statistically and remove (covariate analysis, regression) match on Vs hold constant by design/selection (special case of matching)

The Researchers Problem (Kinney 1986) or Giving Yourself a Chance to find Results = risk that data incorrectly accepts new theory = risk that data incorrectly rejects new theory = true effect of X on Y = residual variation given research design (i.e., after effects of Vs and Zs) n = available sample size All five are related through a single, simple formula

Researchers Problem (continued) is fixed at.05 or.10 by convention is the risk of failing to detect true phenomenon (you want to minimize ) = f(X) = f(Vs, Zs) n is semi-fixed by data availability or cost = f ( n )

Graphically... (small, small okay) _ Y| H 0, n, _ Y| H A, n, 0 Accept H 0 Reject H 0 Y

Graphically (large, large okay) _ Y| H 0, n, _ Y| H A, n, 0 Accept H 0 Reject H 0 Y

Graphically … (small, large _ Y| H 0, n, _ Y| H A, n, 0 Accept H 0 Reject H 0 Y yikes!)

What to do? Need Power! Increase sample size (not always possible) Improve research design –Theory –Have you identified and measured all the major Vs and Zs? –Have you manipulated the Xs at appropriate levels that allow you to find effects? –Manipulation checks?

II. Why use experiments? Causal theory can be tested (not just association) Manipulate the variables of interest and control or randomize on others Can get at process not just outcomes Ex ante research is possible Some conditions that do not exist in natural settings can be created in the lab

(Difficult but) Necessary Choices in Experiments 1.Professional participants? 2.Monetary incentives? 3.Between- or within-subjects design (see Schepanski, Tubbs, and Grimlund 1992)? 4.Level of analysis, individual or market (see Libby, Bloomfield & Nelson 2002)?

1. Choice of participants? Theory should dictate this choice Negative externalities of using professionals when not needed: limited resource (Libby, Bloomfield & Nelson 2002) Selection bias is stronger with professionals than with more available groups like students (Peecher & Solomon 2001) Hodge 2001 used MBA students as investors Elliott, Hodge and Kennedy (2004)

2. Monetary incentives? Dictated by theory Want participants to take the task seriously Many results driven more by ability than incentives (running a 4-minute mile) Incentives do not necessarily mitigate judgment bias– in some cases reduce (see Camerer & Hogarth 1999) Incentives are not always possible or practical in real life either! Hodge 2001 paid a flat rate for participation

3. Within- vs. Between-? Enhanced statistical power, subjects are their own control– less variance Increases salience of treatment effects Vulnerable to carry-over effects Can help demonstrate the (un)intentional nature of cognition Requires proper analysis Hodge 2001 used a between-subject design

Hodge 2001 Experimental Design 3 x 1 between-subjects Control: Hardcopy of unaudited letter and audited financial statements Treatment 1: Audited financial statements on computer with hyperlinked unaudited letter Treatment 2: same as 1. Above, except that when the hyperlink was activated a notice popped up saying (Un)audited.

4. Individuals or Markets? Theory dictates Markets are expensive Markets can have less observations, therefore less power (unless repeated measures) Research shows that biases are not driven out by markets (may be reduced though) Hodge 2001 studies individual judgments but ultimately wants to say something about behavior in the market – systematic individual results aggregate to market behavior?

III. Finding a topic in behavioral auditing research? Think broadly! Is Hodge 2001 auditing research? You want to get the big potatoes! Audits have a supply and a demand side Many financial reporting issues are of concern to auditors and investors Audit environment has changed greatly

Finding a topic in behavioral audit research? (contd) Enron and other accounting scandals have emphasized many problems such as: –lack of independence –engagement risk –incentives to report aggressively –incentives to please the client Sarbanes-Oxley Act Read, read, read…

IV. Common Pitfalls Topic fails Yawn Test Predicting the null hypothesis Accepting the null hypothesis Low power tests Poor motivation; small incremental contribution Running experiments too early Not getting adequate feedback before submitting

Top 5 Takeaways 1.Have fun! 2.Seek lots of input at each stage 3.Use a well-thought out design 4.Choose the research method that best informs the question 5.Choose an interesting topic

Helpful Papers on Research Bonner, S., Accounting Horizons, Dec Camerer, C, & R. Hogarth. JRU, Kinney, W., The Accounting Review, Apr Libby, R. & M. Lipe, JAR, 30, Libby, R., Bloomfield, R., & M. Nelson. AOS, Nov., Maines, L., BRIA, Supplement Peecher, M. & I. Solomon, IJA, 2001.

Q & A?