IT’S ALL ABOUT THE DATA: CHALLENGES AND SOLUTIONSIN THE STUDY OF NASCENT ENTREPRENEURS

GERGANA MARKOVA*, JOHN PERRY and STEVEN M. FARMER

Department of Management, Barton School of BusinessWichita State University, 1845 Fairmount, Wichita, KS 67260-0048

*gergana.markova@wichita.edu

Received September 2010Revised March 2011

Entrepreneurship research has been criticized for a lack of methodological rigor, although evidencesuggests that from a methodological perspective, it is improving (Davidsson, 2006). In this paper, wesystematically review the methods used in the study of nascent entrepreneurs to identify challengesassociated with the data used in these studies. We also review the field’s achievements — notably, thesuccessful use of representative sampling of populations of nascent entrepreneurs — and we raiseconcerns about the predominant use of secondary data sets and the use of scales originally developedfor large, established firms. Drawing on methodological advancements in other fields, we offer sug-gestions related to study design, data collection, sampling and measurement. Although some of thechallenges we note are inherent to the nature of entrepreneurship, we hope our discussion can helpresearchers design better studies and better interpret their findings.

Keywords: Data; nascent entrepreneurs; methods; PSED; GEM.

1. Introduction

Derived from multiple disciplines (Ireland and Webb, 2007), entrepreneurship research hasgreatly benefitted from the diverse methodologies of other fields (i.e., economics, strategicmanagement, sociology). Each of these fields has employed its own assumptions andconventions in positing and testing research questions and faced its own methodologicalchallenges. But entrepreneurship is often seen as a unique discipline (Low, 2001).Therefore, it is of no surprise that critiques of the methodologies and data analytic tech-niques specific to the study of entrepreneurship have been offered (e.g., Chandler andLyon, 2001; Dean et al., 2007; Davidsson, 2004, 2006; Low and MacMillan, 1988).Because advancement in any field is likely to be influenced by the information used forresearch inquiries, a potential source of methodological challenges in entrepreneurshipmay originate from the data used. Although theory drives the need for certain types ofdata, the nature of the data actually acquired determines the research questions that can orcannot be tested and the analytical strategies that can be employed.

Journal of Developmental EntrepreneurshipVol. 16, No. 2 (2011) 169–198© World Scientific Publishing CompanyDOI: 10.1142/S1084946711001781

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A particularly important area of entrepreneurship study — one that has seen tre-mendous growth in recent years — is nascent entrepreneurship. Nascent entrepreneurshipis concerned with the efforts of individuals and small groups to start a new firm (Reynolds,2007). In 1985, Gartner claimed researchers knew little about this venture creation process.Despite the numerous studies generated since then, there is much to be discovered. Webelieve a critical examination of the data itself is crucial for the further advancement of thisfield. For instance, much of the knowledge gained about nascent entrepreneurship in thepast 15 years has come from a small number of high-profile longitudinal data collectionsbased on random sampling using relatively large samples. In many ways, these sorts ofdata represent an ideal not often attained in the behavioral sciences. Nevertheless, there areunrecognized concerns when much of the knowledge in a field is drawn from a limited setof sources.

There have been a large number of reviews of the progress of the entrepreneurshipfield. Most have focused on theoretical and paradigm development and similar conceptualissues (e.g., Aldrich, 2001; Busenitz et al., 2003; Davidsson and Wiklund, 2001; Gartner,1989, 2001; Ucbasaran et al., 2001; Venkataraman, 1997; Vesper, 1983). Fewer reviewshave focused on methodological and data analytic issues in entrepreneurship (e.g.,Chandler and Lyon, 2001; Davidsson, 2004, 2006; Dean et al., 2007; Low and Mac-Millan, 1988; Shaver, 2007). Of these, only Davidsson (2006) provided an extendedassessment of methods and analytic considerations that focused on the nascent entrepre-neurship field. Chandler and Lyon (2001), in their review of research design and constructmeasurement, did not cross data sources by construct validation assessment; therefore,they could provide only a limited assessment of how data sources have affected dataquality. Dean et al. (2007) noted an increase in longitudinal and international samples inentrepreneurship (not strictly nascent) over time; however, their main focus was not ondata per se but on the analytic techniques used to interpret data. Altogether, limitedattention has been paid to issues concerning the sources of data collected and theiradvantages and limitations. An exception is Davidsson (2004) discussion of sampling andselection biases in entrepreneurial study and attendant operationalization issues. However,neither this work, nor the others cited, has focused comprehensively on data use issues.

Therefore, the purpose of this paper is to examine the role of data and associatedmethodological issues in nascent entrepreneurship research. In particular, our goal is todescribe how data are being used in nascent entrepreneurship and assess the advantagesand disadvantages of current data practices that can shape the future of the field. Weparticularly focus on the types and sources of data and the nature of the research questionsthe data are intended to address. In structuring our review and assessment, we draw on anestablished framework for deriving valid inferences from data (Shadish et al., 2002; Cookand Campbell, 1979). We ask, what is the extent to which the results of a study can begeneralized across populations of persons, settings, treatments and outcomes? Unless dataare collected from an entire population, valid (i.e., generalizable) inference requires thatdata be collected from representative samples, across theoretically appropriate physicaland temporal settings, using diverse representations of predictor and outcome constructs.Being able to draw valid inferences for relationships of interest is a sine qua non for

170 G. Markova, J. Perry & S. M. Farmer

advancing theory and knowledge in any field. But because of the expense, time and effortinvolved, and lack of clear knowledge of treatments and outcomes, most studies do notrandomly sample across populations of persons, settings, treatments and outcomes. Thedata actually collected — be it from primary data collection based on a specific theoreticperspective, or experimental data, or data from a large random sample intended mostly forsecondary use — can satisfy some but not other conditions for making inferences to alarger population. We intend to examine the prevalence of different data sources in nascententrepreneurship research, bearing in mind the tradeoffs inherent in their use that speak toour ability to advance theory and knowledge. Where concerns are raised, we draw onmethodological advancements in other fields such as psychology, sociology, economics,education and strategic management to offer suggestions that can facilitate future study innascent entrepreneurship.

The paper is organized as follows. First, we briefly define nascent entrepreneurship andoutline the major research questions in this subfield. Next, we identify how theory buildingand testing is based on researchers’ abilities to draw valid inferences from data, and brieflydescribe how the elements of the Shadish et al. (2002) framework (persons, settings,treatments and outcomes) can apply to the study of nascent entrepreneurship. Followingthis, we describe the nature of data used in the extant literature by conducting anexhaustive search for published work in nascent entrepreneurship. We focus on identifyingthe nature of the data used (e.g., census, large archival database, primary data, etc.) and theresearch questions the data are intended to answer. The trends we uncover are discussed interms of threats to valid inferences in nascent entrepreneurship research, and we proposeways to address the gaps and improve validity of inferences in specific aspects of datagathering. Finally, based on our findings, we take a big-picture approach to discuss theinherent tradeoffs and matches in the use of theory, data and methods in nascent entre-preneurship research. We conclude by offering ideas for advancing future research in thefield.

2. A Brief Overview of Nascent Entrepreneurship

2.1. Nascent entrepreneurs

Nascent entrepreneurs are individuals who are at various stages of developing newbusinesses that are not yet operational (Katz and Gartner, 1988). Reynolds and his col-leagues (Reynolds et al., 2004) defined nascent entrepreneurs as individuals who havetaken steps within the last 12 months toward creating a venture but have not yet paidemployees for more than 3 months. Other definitions of nascent entrepreneurship also exist(e.g., Delmar and Davidsson, 2000). Many empirical nascent entrepreneurship studies areinterested in the individual characteristics and behaviors of the entrepreneurs, butresearchers have also examined behaviors in the pre-startup phases such as planning,obtaining resources, networking, registration and other activities (Carter et al., 1996;Reynolds, 1997). Based on these and other studies, researchers have found that nascententrepreneurs are different than novice and established entrepreneurs (Alsos and Kolver-eid, 1998; Robichaud et al., 2007; Westhead et al., 2003) and that they face unique

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challenges. Since the first use of the term nascent fifteen years ago (Davidsson, 2006),many nascent entrepreneurship topics have been examined, and some of these are outlinedbelow.

2.2. Typical research questions in nascent entrepreneurship

The study of nascent entrepreneurs has been concerned with several major researchquestions (Davidsson, 2006). For instance, researchers have sought to identify individualcharacteristics that lead some individuals to become nascent entrepreneurs (Delmar andDavidsson, 2000; Diochon et al., 2005a; Robichaud et al., 2007; Sardy and Alon, 2007).Among the individual differences, a special place has been allocated to gender (Carteret al., 2003; Menzies et al., 2006; Robichaud et al., 2007), race (Singh et al., 2008; Singhand Crump, 2008), and ethnicity (Delmar and Davidsson, 2000). Also, researchers havequestioned how nascent entrepreneurs engage in the first steps of the discovery andexploitation processes in the initial stages of venture creation. Third, researchers havequestioned the link between contextual factors such as bureaucracy (Sorensen, 2007),social networks (Klyver, Hindle and Meyer, 2008) and entrepreneurial intensity. Finally,some researchers have examined entrepreneurial teams and growth aspirations in newventures (Davidsson and Henrekson, 2002; Liao and Welsch, 2005). A more detailedsynthesis of the major research questions addressed in published empirical studies isoutlined in Table 1.

2.3. Drawing valid inferences: The interdependence of theory and data

The ultimate purpose of any scientific inquiry is to advance theory development, becausetheory allows deduction to new instances, settings and entities. According to (Whetten,1989) the major building blocks of theory are the answers to the questions what, how andwhy. For theory to develop and advance, it must be tested and refined. Therefore, anempirical study makes the greatest contribution when it can address these questions. To betested, a theory needs to be falsifiable (Cook and Campbell, 1979). Moreover, a goodtheory is often plausible (Weick, 1995) and so it can lead to prediction and understanding(Dubin, 1976). Therefore, data play a crucial role in developing and testing new theories.Moreover, any theoretical contribution, major or minor, can be made possible or inhibitedby the data used for its testing.

Data have a dual relation with theory. On one hand, data allow us to build theory fromthe ground-up by exploring relationships that exist in practice. On the other hand, data arenecessary to deductively test and expand theory. As researchers, we want to determinewhether our findings apply to persons or cases beyond the immediate subjects included inthe study, and to broader populations in other places, other countries and other times.Moreover, we want our findings to apply to the larger meaning of the concepts and notonly to the immediate operationalization of these concepts.

But how valid is what we learn from a single empirical study? What gives us confidencethat the result of a single statistical model can be passed on to practitioners and students?According to Shadish et al. (2002) (see also Cook and Campbell, 1979; Cronbach et al.,

172 G. Markova, J. Perry & S. M. Farmer

Table 1. Review of the methodologies used in nascent entrepreneurship studies.

Data Source/Sample Analysis Employed:Longitudinal

Analysis Employed:Cross-sectional

General Research Questions

US PSED I— a panel study of830 individualswho were workingto create abusiness in1998–2000, and acomparisongroup of 431non-entrepreneurs;5500 items werecollected over fourwaves (3 years);publicly available— PSED sets inother countriesmimic the contentand data collectionprocedure

Brush, Manolova, andEdelman, 2008;Carter, Gartner,and Reynolds,1996; Cassar,2007; Gelderen,Thurik, andBosma, 2005;Hongwei andRuef, 2004; Liao,Welsch, andTan, 2005;Lichtenstein,Carter, Dooley,and Gartner, 2007;Newbert, 2005;Parker andBelghitar, 2006;Singh and Crump,2008; Singh,Knox, and Crump,2008; Tornikoskiand Newbert,2007

Brush, Edelman, andManolova, 2008;Carter, Gartner,Shaver, andGatewood, 2003;Cassar, 2006;Edelman, Manolova,and Brush, 2008;Honig and Karlsson,2004; Kim, Aldrich,and Keister, 2006;Liao and Gartner,2006; Liao andWelsch, 2003, 2005;Liao, Welsch, andTan, 2005; Reynolds,Carter, Gartner, andGreene, 2004; Ruef,Aldrich, and Carter,2003; Sardy and Alon,2007; Schjoedt andShaver, 2007; Shaver,Gartner, Crosby,Bakalarova, andGatewood, 2001;Singh and Crump,2008; Singh andLucas, 2005

1. Descriptive studies —descriptive statistics withdetailed explanation aboutthe data collection

2. Comparative studies —nascent entrepreneursversus non-entrepreneurs,non-nascent, betweenmales and females,between black and white,across nationalities andethnicities, technology andnon-technology nascententrepreneurs,homemakers andnon-homemakers

3. Identify factors related tothe start-up process,survival and growth effectof planning and perceivedenvironmental uncertainty;effect of householdincome, education,managerial experience;effect of business plan,initial location, legitimacyseeking behaviors

4. Identify reasons to start abusiness — careerreasons, effect of job andlife satisfaction

5. Identify characteristics ofentrepreneurs — risktolerance, education

6. Describe start-up activities,sequence of activities, for-mation process, gestationpaths and patterns — useof various theoreticalperspectives (a dynamiccapabilities perspective)

7. Comparison betweenpractice and textbookprescriptions

Study of Nascent Entrepreneurs 173

Table 1. (Continued)

Data Source/Sample Analysis Employed:Longitudinal

Analysis Employed:Cross-sectional

General Research Questions

Norwegian PSED Rotefoss andKolvereid, 2005

Alsos and Kolvereid,1998

1. Comparison betweennovice, serial and parallelbusiness founders

2. Identify external factors— effect on aspiring,nascent, and fledglingentrepreneurs

Dutch PSED Gelderen, Thurik, andBosma, 2005

1. Identify success and failurefactors for start-ups

Canadian PSED Diochon, Menzies,and Gasse, 2005b,2007, 2008;Menzies, Diochon,and Gasse, 2004;Menzies, Diochon,Gasse, and Elgie,2006

Diochon, Menzies, andGasse, 2005a, 2007;Robichaud, Zinger,and LeBrasseur, 2007

1. Identify impact of humancapital on firm gestation

Swedish PSED Chandler, Honig, andWiklund, 2005;Davidsson andHenrekson, 2002;Davidsson andHonig, 2003;Delmar andShane, 2003;Honig, Davidsson,and Karlsson,2005

Davidsson andHenrekson, 2002;Delmar andDavidsson, 2000;Delmar and Shane,2004; Honig, 2001

1. Identify characteristics ofnascent entrepreneurs —descriptive studies oflearning strategies

2. Factors that affectgestation, growth andsurvival — social andhuman capital for nascententrepreneurs; planning;legitimizing activities;macro factors

Multiple PSEDsamples

Delmar and Davidsson,2000

1. Comparison of prevalenceof entrepreneurialactivities; replicatesfindings in othercountries

REM/SOEPGermany— closely relatedto GEM— secondary data,similar to GEM,collected for policypurposes; 12,000respondents

Mueller, 2006; Wagner,2003, 2006, 2007;Wagner, 2007

Environmental effectsfor breeding nascententrepreneursCharacteristics ofentrepreneursComparative study —

compare women and men

174 G. Markova, J. Perry & S. M. Farmer

Table 1. (Continued)

Data Source/Sample Analysis Employed:Longitudinal

Analysis Employed:Cross-sectional

General Research Questions

GEM— initiated in1999 with 10countries, grew toinclude 35countries in 2005and 42 countriesin 2007, includingUS— Data collectionover time with atleast 2,000 adultsin each country— Differentstudies usedifferent numberof countries

Amorós and Cristi,2008; Koellinger,2008; Koellinger,Minniti, andSchade, 2007;Sternberg andWennekers, 2005

Acs, 2006; Acs and Varga,2005; Arenius andMinniti, 2005;Bergmann andSternberg, 2007; DeClercq and Arenius,2006; De Waal, 2004;Frederick, 2004a,2004b; 2004c;Frederick andBygrave, 2004;Hessels, Van Stel,Brouwer, andWennekers, 2007;Hindle and Klyver,2007; Justo, Maydeu,and De Castro, 2008;Klyver, Hindle, andMeyer, 2008;Koellinger andMinniti, 2006;Koellinger, Minniti,and Schade, 2007;Levie, 2007; Levie andAutio, 2008; Maritz,2004a, 2004b; Minnitiand Langowitz, 2007;Minniti and Nardone,2007; Morales-Gualdrón and Roig,2005; O’Gorman andTerjesen, 2006;Reynolds et al., 2005;Stel, Carree, andThurik., 2005; Stel,Storey, and Thurik,2007; Sternberg andLitzenberger, 2004;Szerb, Rappai, Makra,and Terjesen, 2007;Szerb, Terjesen, andRappai, 2007; Tomincand Rebernik, 2006;Uhlaner and Thurik,2007; Verheul, Stel,and Thurik, 2006;Wennekers,Wennekers, Thurik,and Reynolds, 2005;Wong, Ho, andAutio, 2005

1. Descriptive studies —detailed description of thedata collection, some basiccomparative statisticsacross data collectionswithin GEM

2. Effects of entrepreneurialactivities on macro andeconomic development

3. Comparative studies —between nascent, new andestablished businesses;across countries: betweenentrepreneurs and non-entrepreneurs; Necessityversus opportunity innascent activities; acrosscountries, cultural factors,gender comparison

4. Characteristics of nascententrepreneurs and theireffect on start-upprocesses and growth —

effect of attitude;Knowledge, personal,demographic, perceptions

5. Environmental factorsrelevant to start-ups —macro factors, culturalfactors, clusters

6. Effect of financing on newventures characteristics oninformal investment,innovation

7. Decision making ofnascent entrepreneurs

Study of Nascent Entrepreneurs 175

Table 1. (Continued)

Data Source/Sample Analysis Employed:Longitudinal

Analysis Employed:Cross-sectional

General Research Questions

Spanish GEM Lafuente, Vaillant, andRialp, 2007

Effect of entrepreneurshiprole models effect onentrepreneurial activities.

New Zealand GEM Cruickshank andEden, 2005; Edenand Cruickshank,2004

Wilson and Mitchell,2004; Zhu, Frederick,and Walker, 2004

Comparative studies —between men and womennascent entrepreneurs;different ethnic groups,and home basedentrepreneurs

Danish GEM Klyver, 2007; Klyver andTerjesen, 2007

Comparison of socialnetworks of males andfemales entrepreneurs atdifferent venture stages

British householdpanel survey

Henley, 2007 Relationship betweenentrepreneurial aspirationsand self-employment

Census data— DenmarkStatistics institute(IDA)

Sorensen, 2007 Effect of bureaucracy onentrepreneurial entry

Census data— nationalstatistics data inItaly (INPS) aboutnew born firms— data from 1987until 1993

Vivarelli andSantarelli, 2007

Investigates the types ofnascent entrepreneurs thatare more successful

Census data— based on firmregistration data

Kirchhoff and Phillips,1988

Theorizes from trends inofficial statistics, noreported relationships,but simple descriptivetables to determinemacro effects on firm birth

176 G. Markova, J. Perry & S. M. Farmer

Data Source/SampleDescription

Analysis Employed: Longitudinalor Cross-Sectional

General Research Questions

Primary probabilistic sample— 1169 Austrian nascententrepreneurs and newbusiness owner

Korunka, Frank, Lueger, and Mugler,2003 — cross sectional

Identify personal character-istics and environmentalfactors and compare thesample with other Euro-pean countries; looks atbusinesses soon aftercreation

Primary probabilistic sample— collected in variousregions of Italy

Minniti, 2005 — cross sectional Compare entrepreneurialactivities in differentregions and identifiessocial and demographicfactors that impact them

Primary probabilistic sample— 223 new venture-organizing efforts initiatedin the first 9 months of1998

Shane and Delmar, 2004 —

cross-sectionalDoes completing a business

plan before talking tocustomers and beginningmarketing improveventure performance?

Primary probabilistic sample‘— two regions in US,several data collectionsovertime, similarquestions as PSED, highresponse rate

Reynolds, 1997; Reynolds and Miller,1992 — longitudinal

Identified gestation events andtemporal sequences ofgestation events, compareacross the samples, can beclassified as longitudinal,no relations are tested,mainly descriptive

Primary non-probabilisticsamples— student population— convenient sample— policy capturingapproach

Bishop and Nixon, 2006; Drnovsek andGlas, 2002; Lee and Jones, 2008;Poh, Lena, and Maw, 2008; Vivarelli,2004 — cross sectionalSibin, Matthews, and Grace,2007 — longitudinalMosey and Wright,2007 — multicase approach

Identify effects of individual,environmental andeconomic factors onstart-ups and post-start-upperformance; Relationshipbetween need forachievement, businessgoals and persistence;Comparison of edu-cational approaches usingmulti-case study; Self-efficacy comparisonbetween business studentsand innovators;Comparative study usingpolicy capturing approachand within subject designwith a sample of MBAstudents Development ofsocial capital from humancapital among academicentrepreneurs

Table 1. (Continued )

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1980; Cronbach, 1982) we derive this confidence from the representativeness of fourelements of an empirical design — namely persons, settings, treatments and outcomes. Ourability to apply the empirical results beyond a specific context is also known as externalvalidity (or generalizability, used interchangeably hereafter). Short et al. (2002) note that “therepresentativeness of a sample determines the generalizability of findings extrapolated fromthe sample.” But whereas most researchers may think about representative data in terms of arepresentative sample of respondents, empirical relationships have more than one aspect ofsampling. As such, researchers need to attain representativeness across all four elements toclaim externally valid findings. We use this framework to discuss how the use of data shapesinquiries in the field of nascent entrepreneurship.

In the following paragraphs, we briefly describe the four elements necessary for validinferences, providing instances of their application in the study of nascent entrepreneur-ship. Following that, we describe the results of a review of data use in nascent entrepre-neurship research, with particular attention to the implications of data use for making validinferences about findings.

2.4. Persons

Person validity refers to the ability of a study to generalize from the sample of individualsfrom whom data were collected to a larger target population of individuals, and acrosssubpopulations within the target population. In the study of nascent entrepreneurship, thesubjects of interest generally refer to the populations of three groups of individuals. Thefirst group consists of individuals who are considering starting a business and/or are takingactions to do so but are not yet established business owners. The second group is a broaderpopulation of individuals involved in entrepreneurial activities at any stage including serialentrepreneurs, novice business owners, or corporate intrapreneurs. And the third group isthe broader population in the labor market including individuals who are thinking ofstarting a business and those who are not thinking of starting a business. In other words,the third group refers to everyone eligible to be involved in business.

A nascent entrepreneurship study’s research question determines the potential popu-lation of interest. For example, when policy makers want to know what measures canincrease interest in nascent entrepreneurial activities, they should test their hypotheses on asample representative of the target population of adults eligible to work because thetreatments are intended to stimulate interest among this population. However, if theresearch question is about decision-making of individuals who are already involved instarting a business, the applicable population is limited to individuals who are initiating anew venture rather than the more general population of work-eligible adults.

2.5. Settings

Can the results of a study be generalized across settings? Settings are often considered interms of physical location (i.e., geographically, across or within regions, countries, cul-tures, industries, etc.), or temporally across different time periods (e.g., 1980s versus2000s). Representativeness in settings means that the results of research are invariant

178 G. Markova, J. Perry & S. M. Farmer

across different setting dimensions that are theoretically important. For instance, nascententrepreneurship data collected from surveys in the 1990s were based on the meaning thatrespondents applied to the words and concepts used to study their activities at that time. Ifsimilar data were collected 100 years earlier, the definition of what constitutes a nascententrepreneur would have included a farmer planning to buy his own land as opposed tocontinuing to work on the family land. Furthermore, the use and meaning of words islikely to evolve over time. This evolution is critical to what meaning is conveyed torespondents while information is gathered. How many of the questions used in today’ssurveys would have made sense to this 1890s farmer? The issue of meaning is alsopertinent to the next two elements — treatments and outcomes.

2.6. Treatments

The third element of interest, treatment, broadly refers to the factors, causes and predictorsin an inference relationship. Based on the choice of empirical approach, treatment can berepresented through manipulation or measurement. Entrepreneurship researchers havewidely preferred measured variables to predict variables of interest. Experiments are rare(Davidsson, 2006). The most broadly used representations of treatment are individual orcontextual factors. Some of these factors have indisputable representation (e.g., gender,nationality). However, perceptual or contextual variables are not indisputable in theiroperationalizations and therefore, are subject to threats of validity. Another way ofrepresenting a treatment is to use a scenario to initiate certain cognitive or emotionalresponses toward an outcome of interest. When this is done, the choice of oper-ationalization is likely to determine the extent of valid inferences associated with theresearch question. Given that constructs in general can be represented in more than oneway, validity of inference is demonstrated when diverse representations (i.e., measures)obtain similar results.

2.7. Outcomes

The final element necessary to draw valid inferences is outcomes. Outcomes refer to thedependent variables of interest. In nascent entrepreneurship studies, several dependentvariables have been frequently studied. A particularly important one concerns establishingan individual as a nascent entrepreneur as opposed to not being a nascent entrepreneur(e.g., Delmar and Davidsson, 2000). A second dependent variable that has been studied iswhether an individual engages in nascent entrepreneurship activities such as opportunitydiscovery, seeking resources, marketing, making a product, etc. A third dependent variablethat has been studied is whether a business has successfully been launched. The rep-resentation of each of these dependent variables allows or limits drawing valid inferences.For example, assume we are interested in whether the educational level of an entrepreneuris related to the success of the entrepreneur’s venture. Success can be represented byseveral operationalizations including venture launch, existence after a certain period oftime, or venture growth. Each of these definitions is a representation of what success maymean. To complicate things even further, each of these definitions can be measured

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(or represented) in different ways. For example, growth might be measured in terms ofnumber of employees, number of customers, or sales. Thus, when a conceptual relationbetween the educational level of a nascent entrepreneur and the success of his/her ventureis tested, researchers are likely to consider only a limited set of measures of each concept(usually the available operationalizations). However, they will often draw inferences for thehigher level conceptual relationship being tested. If the researchers are using a dataset withonly one of the possible representations of an abstract construct, they should not translatetheir results into inferences about the conceptual relationship. Therefore, the nature of thedata limits the degree to which researchers are able to draw valid inferences. In the fol-lowing section, we review the use of data in nascent entrepreneurship and how the extantapproaches in the published literature determine the research advancements in the field.

3. Review of Data Use in Nascent Entrepreneurship Research

To understand the current state of data usage in nascent entrepreneurship, we system-atically identified and analyzed peer-reviewed published articles on the topic of nascententrepreneurs and pre-start venture activities. More specifically, we were interested inactivities that occur prior to “firm birth.” Firm birth is an admittedly imprecise point whichis sometimes designated by an initial listing in a business registry or some form of legalregistration (Reynolds, 2007) and by the existence of individuals working toward regis-tration. We used two approaches to identify the articles. First, we searched academicdatabases using search terms such as “nascent entrepreneur,” “nascent entrepreneurship”and “start-up.” This search was not limited to specific journals or disciplines. We usedEBSCOhost and EBSCO MegaFile as the main search engines to identify articles. Both ofthese database services allow access to hundreds of multidisciplinary databases, includingBusiness Source Premier and Academic Search Premier. They also allow referral to titlesnot available in full-text form within their indexed databases. Using EBSCOhost andEBSCO MegaFile allowed us to be confident about the depth of our search. Second, wecarefully searched through all publications in nine management and entrepreneurshipjournals listed by Chandler and Lyon (2001) in their review of entrepreneurship meth-odology. We did this to ensure the sample we were developing would represent the state ofthe field. The nine journals we searched included: Entrepreneurship: Theory and Practice,Journal of Business Venturing, Strategic Management Journal, Academy of ManagementJournal, Academy of Management Review, Organization Science, Management Science,Journal of Management and Administrative Science Quarterly. We also searched fouradditional journals in which nascent entrepreneurship researchers publish frequently(Entrepreneurship and Regional Development, Journal of Developmental Entrepreneurship,Journal of Small Business Economics and Small Business Economics). Finally, we usedGoogle Scholar and the same identification terms to verify that we identified the correcttarget articles. We chose a broader approach for literature identification in comparison toprevious methodological reviews to capture the ongoing trends relevant to our interest.

The search rendered 114 empirical articles published as a result of a peer-reviewedpublication process. We excluded theoretical articles, reviews and online reports. We also

180 G. Markova, J. Perry & S. M. Farmer

limited our investigation only to the nascent stages of venture creation (prior to firmestablishment), and did not include articles on self-employment (which we view asdifferent from venture creation). Because our focus was on entrepreneurial activity, wealso excluded research on entrepreneurial intentions. Although our search was thorough,we recognize that we may have missed a number of relevant studies. Nevertheless, webelieve the articles included in our sample (see Table 1) represent the vast majority of thecurrently published empirical work in nascent entrepreneurship.

In our review, we identified the source of the data (e.g., primary data collection bysurvey, secondary data, census, experimental, etc.), whether the study was cross-sectionalor longitudinal and the research questions identified in the studies. This allowed us toidentify how researchers were using data to answer specific research questions, and thusassess the potential match between research question and data usage. To systematicallyanalyze the collected literature, we initially broke our sample into categories for classifi-cation as suggested by prior methodological reviews (Chandler and Lyon, 2001; Deanet al., 2007). The papers identified for our study were systematically categorized based ondata source, design, variables of interest, reliability and validity analyses, level of anal-ysis, sampling procedure and sample characteristics, analytical procedure and researchquestions. Because the results suggested little variability across some of these categories,we decided to streamline the final classification by collapsing or excluding categories.First, papers were organized by data source (i.e., PSED, GEM, Swedish PSED, primarydata collection). Second, within this categorization, we separated the studies according totheir designs such as longitudinal or cross-sectional. Because a majority of the papers(approximately 90%) use panel data and described the panel data, we wanted to clearlydemonstrate the use of the panel data. We considered longitudinal studies those thatincluded (a) variables measured at more than one point of time or (b) looked at processvariables or sequence of gestation activities. Third, we synthesized the research questionseach study addressed into categories to be able to clearly connect the data and study designused with the type of research questions these studies have addressed. Our conclusions areoutlined in the following section.

To obtain a clear picture of the published research practices, we separated the studies thatused the panel data in cross-sectional fashion from those that used more than one timeobservation (i.e., longitudinal studies). More specifically, papers were classified as usinglongitudinal designwhen the analysis involved either (a) data points of the same variable overtime or (b) process variables and sequences of gestation activities. Studies that simplycompare variables at one point of time or report analyses in which the independent variable ismeasured at one point and the dependent variable at sequential point of time were classifiedas cross-sectional. This classification is particularly informative for studies that base theiranalyses on panel data collection, but use the observations in a cross-sectional fashion.

3.1. Overview of the findings

After reviewing the studies, we found a distinct “data approach.” Of the 114 studies wereviewed, a large majority — 102 studies — used secondary data, and 95 of them used

Study of Nascent Entrepreneurs 181

data from two large data collection initiatives. That is, there is an overwhelming use (83%)of data from publicly available large datasets such as the Panel Study of EntrepreneurshipDynamics (PSED I) dataset (Liao and Gartner, 2006; Reynolds et al., 2004; Sardy andAlon, 2007; Schjoedt and Shaver, 2007; Shaver et al., 2001), the PSED II dataset (Rey-nolds and Curtin, 2008), the Global Entrepreneurship Monitor (GEM) dataset (Koellingeret al., 2007), the Swedish PSED dataset (Davidsson and Henrekson, 2002; Delmar andDavidsson, 2000; Eckhardt et al., 2006), the Canadian PSED dataset (Diochon et al.,2005b), and others (i.e., the Australian PSED dataset, the Kauffman Firm Survey dataset).Short descriptions of these databases are provided in Table 1 and more information aboutother relevant datasets can be found at http://research.kauffman.org. Although we engagethis issue in more depth later, at this juncture, we simply point out that because empiricalfindings and theory advancement are highly dependent on the nature of the data, it seemsthat learning about nascent entrepreneurs has been shaped by several large data collectioninitiatives.

We also found the studies based on the PSED and GEM data sets have been fruitfulfor comparative and descriptive studies. As a result of these initiatives, researchers haveidentified distinct characteristics of nascent entrepreneurs and differentiated them fromother types of entrepreneurs. They have outlined factors that facilitate or inhibit venturebirths (i.e., contextual factors), and they have pointed out potential reasons for engaging inventure creation (for more details, see Table 1). These relationships have also been stu-died across various settings, mainly across countries. Therefore, in the sense of theShadish et al. (2002) framework, representativeness across persons and partially acrosssettings have been established. However, few of these efforts have been concerned withthe implications stemming from the representativeness of the other elements inthe framework such as treatment and outcomes. Moreover, the complexity of thesedata initiatives has limited the propensity to test any given theory in any depth(Davidsson, 2006). We acknowledge that in such large-scale data collections, it might beimpractical to gather long measures capturing complex constructs or multiple oper-ationalizations of conceptual variables. Therefore, in the following section, we discuss theuse of data in nascent entrepreneurship in terms of the trade-offs between theory, data, andmethods.

4. Learning from the Data: Threats to Valid Inferences in NascentEntrepreneurship Research and Some Suggestions

Based on the review and findings in Table 1, we synthesized the relationships between thetypes of data collection we found and the ability to make valid inferences and generate andtest theory (Table 2). The table outlines the potential of these types of data collection andsampling. We acknowledge that some of these potentials have been realized by some ofthe studies listed in Table 1, and examples are included. However, not all of the studieshave realized the potential their data approaches allow. For example, whereas a non-probability sample may allow testing theory in depth and developing rigorous measures,not all of the published studies have done so.

182 G. Markova, J. Perry & S. M. Farmer

Table

2.Trade-offsof

typesof

data

interm

sof

valid

ityof

inferences

andtheory.

Typeof

Data

%of

Studies

Examples

Validity

Persons

Validity

Settin

gsValidity

Treatments

and

Outcome

Theory

Generation

Theory

Testin

gTrade-O

ffs

Census

2.6%

Kirchho

ffand

Phillips,19

88;

Sorensen,

2007;

Vivarelliand

Santarelli,2007

Highest

Low

tomod

erate

Low

Moderateto

high

Low

Trendsapplicable

tothe

who

letarget

populatio

nbu

tno

ttheory

driven;

limitedmeasuresused

limittheory

testing

Secondary/archival—

prob

abilistic

86.8%

PSE

Ddata

sets*GEM

data

sets*

High

Mod

erateto

high

Low

tomod

erate

Moderate

Moderate

Trendsapplicableto

thetarget

populatio

nor

multip

lepo

pulatio

nbu

tthe

measureslim

ittheability

totestor

generate

theory

Primary—

prob

abilistic

4.4%

Minniti,

2005

;Reyno

lds,19

97;

Reyno

ldsand

Miller,19

92

High

Low

tomod

erate

Moderate

Moderate

High

Allo

wsmoretheory

testing

anddevelopm

entbut

limitedability

toinferto

multip

lesettings

Primary—

non-probabilistic

samples

4.4%

Bishopand

Nixon

,20

06;

Sibin,Matthew

s,andGrace,20

07

Low

Low

Moderateto

high

Low

tomod

erate

High

Allo

wstheory

testingwith

questio

nableinferenceto

person

sandsettings

Experim

ental

0%Low

Low

High

High

High

Allo

wsin-depth

theory

generatio

nandtestingat

costof

inability

toinferto

person

sandsettings

Casestud

ies

1.7%

Lee

andJones,

2008;Mosey

and

Wrigh

t,20

07

Low

Low

Low

High

Low

Primarily

useful

forinitial

theory

developm

ent

*-comprehensive

listof

studiesisincluded

inTable

1.

Study of Nascent Entrepreneurs 183

As noted, use of large secondary/archival data sets based on probabilistic collection(such as the PSED efforts) has been predominant. Therefore, the advantages and dis-advantages of such an approach may also tend to dominate empirical efforts in the field.This form of data collection has numerous advantages, particularly concerning person andsetting validity. Although not noted in the Table 2, many of the secondary/archival datasets are longitudinal, which enhances temporal validity. Where such efforts may beweaker, though, is in generating high quality measurement. This is probably the result ofpractical needs to limit the depth and breadth of measurement of any particular construct orset of constructs, in favor of a more broadband approach in such studies. Nevertheless, itcan hamper validity tied to treatments and outcomes insofar as triangulation of resultsamong multiple measures of the same construct and using constructs that are themselvesrepresented fully in their content domains, becomes less likely. The same set of practicalneeds — that creation of these probabilistic data sets usually requires the combined effortsof many researchers over time — also limits the ability to test any given theoreticapproach in-depth (Davidsson, 2006). Theory testing, as with measurement, is noted morein its breadth than in its depth. Of course, other types of data sources, which have adifferent set of strengths and weaknesses, can be used to supplement these types of studies.A concern we raise for the field is that the extremely heavy reliance on secondary/archival/probabilistic data sets has greatly limited the use of complementary alternatives, poten-tially hampering the development of improved measurement and theory in nascententrepreneurship research. In effect, there may be “too much of a good thing” here. Below,we examine these different data sources in more depth, and offer some suggestions forfuture consideration.

4.1. Census data

Rarely available in behavioral science, census data have the great advantage of includingthe entire population of persons. This eliminates the need for sampling or demonstratingrepresentativeness of the sample. A census approach is feasible for populations with morerestricted characteristics (i.e., registered business owners). Some researchers have usedavailable data collections from governmental agencies such the United States CensusBureau or Labor department. For example, Wagner (2003) used German planning infor-mation and (Mueller, 2006) used German Socioeconomic Panel (SOEP) data from theGerman Social Insurance Statistics. The collection of this type of data is costly andprobably not feasible for perceptual and complex variables. Entrepreneurship researchers(Kirchhoff and Phillips, 1988; Sorensen, 2007) adjust data intended for other purposes(i.e., official statistics data) and variables ought to be adapted for research questions.

4.2. Secondary/archival data

The use of archival or secondary data has been typical in all areas of entrepreneurshipresearch. Ireland et al. (2005) found among the 50 entrepreneurship articles published inthe Academy of Management Journal between 1963 and 2005, 29 reported analysis basedon secondary data. Shaver (2007) stated that among the 34 articles published in a major

184 G. Markova, J. Perry & S. M. Farmer

entrepreneurial journal in one year, fifteen presented data from archival databases collectedfor different purposes than they were used for. By contrast, fourteen used primary datacollected by the authors for that study. Our review of nascent entrepreneurship shows aneven stronger trend toward the use of secondary data in the field (approximately 88% of allstudies reviewed).

It is important to bear in mind that large data collections are difficult and costly,requiring the efforts of large teams of researchers and the logistical and financial support oflarge organizations. As a result, when such databases are created, and particularly whenthey are made publicly available, there is immense interest in using these data to answervarious research questions. These data sets are particularly attractive for researchersbecause of their availability, size and probabilistic sampling.

The use of probabilistic sampling provides confidence that the sample is representativeof the target population of persons. For instance, the creators of the PSED claim, withsome justification, that the sample is representative of the U.S. population with over-sampling minority and women (Gartner et al., 2004). Moreover, the PSED efforts incor-porated multiple observations over time. Thus, sampling occurred over temporal settingswithin a limited period. Another broadly used dataset is GEM which consists of severalsamples of working adults from the general population in numerous countries. As a resultof this multi-country design, GEM assures representativeness not only across persons butalso across 42 countries (sampling across physical settings).

For the study of nascent entrepreneurship, large databases designed to represent thepopulation of nascent entrepreneurs offer great advantages in terms of providing adescriptive picture of current business activities. This allows a depiction of the individualcharacteristics of entrepreneurs that is applicable to the population of nascent entrepre-neurs, and it permits investigation of the effects of macroeconomic phenomena onentrepreneurial activities. Furthermore, entrepreneurship scholars have noted that progresstoward academic legitimacy as a field depends greatly on the ability to create new,entrepreneurship-specific theory (Busenitz et al., 2003). The descriptive accuracy of large,representative samples has special importance for informing grounded theory developmentthat can lead to such field-specific conceptualizations. Moreover, because an assessment oftheir accuracy relative to population parameters can be made, these large databasesfacilitate studies related to policy making and regional development of entrepreneurialcapabilities. Although it often has been noted that the results of organizational research canbe less than useful for practice, the recent emphasis on generating and using large archivaldatabases to study nascent entrepreneurship represents a major leap forward.

Still, it should be apparent from our prior discussions that no single approach towarddata and methods can fully realize the goal of making truly valid inferences. There areseveral major disadvantages that emerge concerning the predominant use of large sec-ondary datasets as a source of knowledge about the field, even those that are longitudinaland representatively sampled on persons. First, theory may be adjusted to the availabledata. Ideally, such large-scale data collections ought to be designed to test theory. Of thecurrently available public datasets, the PSED efforts come closest to this ideal, insofar asmany aspects of the data collections were shaped by efforts to measure variables that

Study of Nascent Entrepreneurs 185

operationalize explicit conceptual models (e.g., taking gender into account in new firmcreation; Carter and Brush, 2004). Still, two factors conspire to limit the usefulness of suchdata collections in testing theory. The first is the sheer size of the initiatives. For instance,over 100 individuals were involved in generating the design and items for the PSED I.This means reconciliation among the desires and interests of this large group was inevi-tably a political process (Reynolds and Curtin, 2004). Therefore, no single set ofresearchers would likely be able to test their ideas as thoroughly as they would wish, interms of constructs measured, and even the number of measures representing a givenconstruct. The second factor is that the pool of theory specific to nascent entrepreneurshipis itself limited (cf. Davidsson, 2006).

Second, whereas we can be relatively confident these data are likely to yield validinferences in terms of persons and settings, the representations of treatments and outcomes(i.e., measures) has not fared so well. This is an inherent tradeoff in an enterprise of thisscope, but our earlier discussions highlight that person validity is not necessarily moreimportant to inference than treatment or outcome validity. The use of available oper-ationalizations of both treatment and outcomes may not have been theoretically driven orpsychometrically validated, particularly for researchers who were not the dataset creators.Such measures may meet the face validity test, but rarely undergo stringent psychometricevaluation. Moreover, these variables may have been calculated from categorical variablesthat are perfectly valid for the purposes intended (e.g., open a bank account). But whenthey are combined into continuous variables through a mathematical procedure to create ameasure meant to represent a latent construct, the resulting measure can lack validation ortheoretical guidance. For example, if a researcher is investigating the effect of legitimizingactivities using PSED, she may choose to add together data items such as ‘opening a bankaccount,’ ‘separate business location’ and ‘sign indicating the business.’ These questionshave yes or no answers and being combined together would generate a discreet number ofvariations of the new variable. Thus, the operationalization of legitimacy is limited by theavailable items in the data set.

Our concern here is heightened by recent evidence that validations of measures inentrepreneurship research are less frequent than are desired (Chandler and Lyon, 2001).Moreover, recent research has noted new entrepreneurship scholars may not be adequatelytrained in key validation techniques such as exploratory or confirmatory factor analysis(Dean et al., 2007). Researchers also often use a single item measure (such as a simplequestion) to measure complex perceptual constructs. We believe multi-item measurementcan more appropriately capture a content domain, increase variability and thus, sensitivityof measurement, and allow assessment of reliability. There has been some progress in thisarea. For instance, early efforts to identify who was or was not a nascent entrepreneurrelied on a single screening question. However, more recent work has utilized severalscreening items (Davidsson, 2006).

Third, there may be a tendency to focus on relationships that emerge as statisticallysignificant (significance bias), echoing Shaver (2007) comment on entrepreneurshipresearch that “research questions are too often dictated by what analyses are possible.” Aswith other research endeavors, it may be that only relationships found to be significant are

186 G. Markova, J. Perry & S. M. Farmer

transformed into research questions. However, the patina of representative sampling mayserve to obscure the need to serve theory.

Finally, there are concerns arising from the fact that a very large share of ourknowledge of nascent entrepreneurship has been derived from so few sources. Shaver(2007) recognized this issue as a concern for the larger entrepreneurship field, declaringthat “the resulting literature is top-heavy, with too many papers, over too many years,relying on slightly different analyses conducted on entirely too few databases.” A problemhere is that continued use of the same database, even if the data are entirely representativeof a broader population (and our previous discussion highlights limitations in this), canlead to inflation of Type I error in the results and therefore, increase the probability offinding relationships based on chance. This can occur even if the same variables are notused across studies because the same individual cases are being used repeatedly, and thusthe same biases, limitations and other quirks specific to the members of that sample mayalso be repeated across different studies. Given the effort and resources for these data setsto be assembled, we do not advocate abandoning their use. Rather, we warn that thefindings should be interpreted in terms of potential statistical artifacts.

4.3. Primary probabilistic data

Fewer studies in the field have utilized primary probabilistic data collections. Such datainitiatives allow more theory, driven design and measurement while still having highrepresentativeness of the target population of persons. However, such endeavors are costlyand generally involve fewer physical or temporal settings. For example, Korunkaet al. (2003) collected data in Austria; Poh et al. (2008) collected data in Singapore andReynolds and Miller (1992) collected data in two separate states in the United States.Limited resources may not allow replication across places or time. This trade-off isbalanced by the use of more sophisticated measures and the opportunity to test in-depththeoretical perspectives. Thus, we advocate more studies using primary probabilisticsamples that can enhance the validity of inferences across persons, treatments andoutcomes.

4.4. Primary non-probabilistic data

Nonprobability sampling is another way to identify participants for research studies. Thissampling occurs when participants are not selected randomly but, rather, are selected on apredetermined criterion. Such a process does not warranty representation of the largerpopulation as suggested by probability theory. Despite a possible prima facie claim to lackof representation, further consideration is warranted. It is advocated in social sciences thatprobabilistic samples allow more rigorous and accurate findings but there are situationswhen it is not feasible or theoretically imperative to randomly select participants. We lookat two types of non-probability sampling below.

Convenience samples generally lack the representativeness of large random samples.One way to overcome this deficiency is to compare the demographics of the sample withthose of the target population. Even if representativeness across persons is hard to establish

Study of Nascent Entrepreneurs 187

in convenience sampling, such research endeavors can address other questions larger datacollections may not allow. Purposive sampling is another example of nonprobabilitysampling in which participants are recruited without the use of random selection, but basedon a previously determined criterion or characteristic.

Although lacking representativeness of the target population, nonprobability samplesmay allow the use of more detailed measures and research designs impractical in largeprobabilistic data collections. Researchers can use smaller convenience samples to developand improve measures before initiating major data collection efforts. As the field matures,research could increase the use of multidimensional scales and experimental manipulationto better represent treatments and outcomes. In this way, validity of inferences can beachieved across studies (Shadish et al., 2002) by improving the representativeness of allelements. This allows for better understanding of the unique processes that occur during‘firm gestation.’

Convenience samples may further allow building upon past findings to extend infer-ences beyond persons and settings. Studies with large representative samples have alreadydepicted a typical entrepreneur. Thus, if a researcher wants to test decision-making schemaof entrepreneurs, and doing so will require extensive involvement of the sample, he or shemay choose to recruit fewer individuals who fit the modal instance of a nascent entre-preneur (i.e., modal instance sampling). Similarly if researchers are interested in the studyof the cognitions of entrepreneurs, they may choose to purposefully target individuals withcertain characteristics (e.g., taking actions toward starting a business) until the samplereaches a desired size. This approach has been successfully applied in the study of crosscultural cognition (Mitchell et al., 2000; Mitchell et al., 2002).

4.5. Experiment and quasi-experiment

Experimental methods have the advantage of being able to manipulate the treatment andbetter control other factors. As such, they can allow drawing causal conclusions. Withinthe noted constraints of our search, we were not able to locate any peer-reviewed publishedresearch that used experimental or quasi-experimental research designs to study nascententrepreneurs. Such study designs assure better internal validity but they are particularlychallenging to implement given the characteristics of the population of interest. However,they are highly applicable and may be helpful for studying decision-making or cognitionsof venture creators. Such designs may also be applicable for studying the effects ofentrepreneurial aspirations on individuals. Additionally, educational settings may provideparticularly good settings for such investigation, because experimental control would bepossible with such a subject pool where one is able to control or match on relevantvariables and catch individuals at a reasonable starting point concerning entrepreneurialaspirations.

4.6. Other suggestions

Several study design approaches successfully utilized in other areas of behavioral sciencehave found little or no application in the study of nascent entrepreneurs. For example, we

188 G. Markova, J. Perry & S. M. Farmer

found only one paper that uses the case study method (Lee and Jones, 2008). Case studies,whether single or multi-cases, are likely to provide detailed information about a limitednumber of subjects. Inherent in the nature of the case inquiry is the ability to make onlylimited inferences about broader populations of persons or settings. However, case studiesare the perfect vehicle for the initial stages of theory development. Using multiple cases ofheterogeneous examples, researchers can identify factors that set apart such examples. Forexample, in-depth analyses of entrepreneurs who initiated ventures but attained differentsuccess over time may be informative about the cognitive and emotional paths of venturecreators. Another way to minimize the inability to generalize findings is to sample typicalcases of the population of interest (similar to the modal instance sampling). Similarly,interesting theoretical ideas can originate from investigating extreme cases (e.g., fastsuccess of a venture) as opposed to the average case.

Another example is the use of diaries or experience sampling (Judge and Ilies, 2004;Judge et al., 2006), which has been used to study mood and emotions at work and at home.Such data collections would allow a more precise capturing of the cognitive-emotionalprocesses at the early stages of venture creation. This approach is likely to overcome themajor shortcomings of archival data or cross-sectional surveys.

Another approach successfully applied in other fields to explain complex cognitiveprocesses is cognitive mapping (or mental model mapping). Examples of such studies canbe found in the areas of information decision-making (Kolkman et al., 2005), in theinteractions of users and data bases (Keng and Xin, 2006), and even in the study of foresteconomics (Tikkanen et al., 2006). Moreover, the technique has evolved to allow group-level cognitive mapping (Tegarden and Sheetz, 2003). Krueger (2003) recommendedcognitive mapping could be applied to understand entrepreneurial scripts and schematachange, and how they relate to each other. However, this approach has seen limited use inentrepreneurship research in general (Fausnaugh and Hofer, 1993; Russell, 1999) andalmost no use in exploring nascent entrepreneurial cognitions. A notable exception isJenkins and Johnson (1997) use of cognitive mapping to contrast entrepreneurial inten-tions with outcomes.

Another method that has found limited application in studies of nascent entrepre-neurship is policy capturing (Aiman-Smith et al., 2002; Karren and Barringer, 2002).Policy capturing is concerned with how individuals use and weight available informationwhen making decisions. It has been used to understand decision-making in numerousfields (e.g., job choice, Slaughter et al., 2006; family business succession, Shepherd andZacharakis, 2000). In entrepreneurship, this methodology has been used to investigateventure capital decisions (Shepherd, 1999a,b; Shepherd and Zacharakis, 2003; Zacharakiset al., 2007) but has rarely been applied to understand nascent entrepreneurs’ decisionmaking. (For an exception, see Bishop and Nixon, 2006.) It is likely that policy capturingmethods could be particularly helpful in investigating, among other things, how nascententrepreneurs actually develop entrepreneurial cognitive structures such as arrangements,willingness and ability scripts (Mitchell et al., 2000; Mitchell et al., 2002).

Finally, overall data quality can be improved by refocusing some of the measures repre-senting treatments and outcomes. Most of the measures used in nascent entrepreneurship have

Study of Nascent Entrepreneurs 189

been developed for large established businesses. Rarely have such borrowed measuresbeen validated for the context of small and emerging businesses. Two problems may stemfrom this practice. First, the respondents in large businesses tend to be professionalmanagers, unlike many nascent entrepreneurs. Second, the wording or even the content ofthe measures may not be applicable because starting businesses often have simpleorganizational structures and are very informal. Therefore, we suggest research effortsshould be allocated to re-validate scales to ensure readability and applicability to the targetsample of nascent entrepreneurs.

5. Discussion and Conclusion

We have identified data-related methodological issues that have presented challenges tothe research in nascent entrepreneurship, and we have made suggestions for how toaddress these challenges that have been successfully applied in other fields. We realizesome of the challenges stem from the nature of the object of research inquiry. But we hopeour discussion will provide guidance to researchers as they design future studies andinterpret their findings.

A main contribution of this study has been the uncovering of a prevalent trend in thefield — the use of large secondary datasets. The use of secondary datasets such as thePanel Study of Entrepreneurial Dynamics and Kauffman Firm Survey is logical giventhe low base rate of entrepreneurial activity that makes generation of representativesamples difficult. Because of this, their use has the potential to strongly influence thedirection of the field. There are distinct advantages in using these large datasets, some ofwhich have multiple observations over time. Using these datasets can facilitate thedevelopment of research projects that might otherwise be delayed by having to initiatedifficult, costly and time-consuming data collections on a study-by-study basis. However,datasets inherit their creators’ assumptions and attendant design limitations, and theyprompt inquiries that are limited by the available information. Therefore, they may besusceptible to threats to generalizability that have not been widely recognized.

Why is generalizability so important? Understanding venture creation and the indi-viduals involved in this process is critical to answering some of the key questions oforganizational science. Every organization, regardless of its later complexity, begins as anew venture and the first steps its founders take are likely to influence its future.

Despite our literature search, we realize there might be published papers or manuscriptsthat did not find their place in our tables. These omissions may bias our conclusions.However, the purpose of this paper is to depict some ongoing trends as opposed topresenting a complete list of all research efforts in the nascent entrepreneurship literature.We also realize our suggestions are subject to limitations stemming from the process ofknowledge creation and academic exploration. In the organizational studies fields, noveltyor originality in publishing is highly valued and statistical significance is often necessaryfor quantitative research to appear in journals. Also, ethnocentric bias may restrict authors’motivations to seek representation across settings (e.g., countries) when cross-culturalfindings are not different enough from prior research in other settings. As such, we rarely

190 G. Markova, J. Perry & S. M. Farmer

see replication studies being published, especially when prior work has reported any nullfindings. This is particularly unfortunate because as we have tried to demonstrate, gen-eralizability requires multiple forms of constructive replication: same study with differentpopulations, same study in different settings and the same study with different concep-tualizations and operationalizations of the constructs. Thus, we have modest expectationsresearchers will discontinue assuming generalizability at the end of a single study.However, we hope that with the use of a diverse sampling, not only across persons, butalso across settings, treatments and outcomes, the joint effort of nascent entrepreneurshipresearchers across studies will reach a point of greater validity.

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