Alhassanain(p) Network for Heritage and Islamic Thought

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Part Seven/ Interdisciplinary Perspectives

Chapter Thirty-Five

Handbook of Social Psychology, D. Gilbert, S. Fiske, & G. Lindzwey (eds.), New York: McGraw Hill. (1998)

Social Psychology and World Politics

Philip E. Tetlock

The Ohio State University

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Table of Contents

[Note] 5

[Introduction] 6

I. Standards of Evidence and Proof 9

A. Grounds for Scientific Pessimism 9

B. Grounds for Scientific Optimism 13

II. Psychological Challenges to Neorealist Rationality 18

A. Neorealism 18

B. Cognitivism 20

C. Are Psychologists Biased to Detect Bias? 31

D. Motivational processes 32

E. Placing Psychological Processes 37

F. Reprise 42

III. Psychological Challenges to Deterrence 43

A. Deterrence 43

B. Testing, Clarifying and Qualifying Deterrence 45

C. Influence Strategies 46

D. Reprise 51

IV. Nation-State Under Siege 53

A. Centrifugal and Centripetal Forces 53

B. Subnational Fragmentation 54

C. Beyond anarchy 57

D. Beyond Anarchy 59

V. Concluding Thoughts 62

Bibliography 67

[Note]

Chapter to appear in D. Gilbert, S. T. Fiske & G. Lindsay (Eds.), Handbook of social psychology (4th edition). New York: McGraw-Hill. Comments should be sent to the author at the Department of Psychology, 1885 Neil Ave., 142 Townsend Hall, The Ohio State University, Columbus OH, 43210. Portions of this chapter were completed while the author was a fellow at the Center for Advanced Study in the Behavioral Sciences. Preparation of this chapter was assisted by research grants from the MacArthur Foundation, the Institute on Global Conflict and Cooperation and the National Science Foundation as well as by a training grant from NSF to the Mershon Center at The Ohio State University. I thank Peter Suedfeld, Susan Fiske, Dan Gilbert, Ole Holsti, Bob Jervis, Peter Katzenstein, Ned Lebow, Yaacov Vertzberger, Herb Kelman, Bruce Russett, Paul t’Hart, Robyn Dawes, Randall Schweller, Rick Herrmann, Don Sylvan, Alan Fiske, and Bill Boettcher for helpful comments on earlier versions of this chapter.

[Introduction]

Does social psychology add to our understanding of war and peace among nations? For many social psychologists, the answer is an unequivocal "yes." What could be more obvious? Social psychology explores the causes of the thoughts, feelings and actions of human beings. International relationsis ultimately the product of the thoughts, feelings and actions of human beings who decide to arm or disarm nations, to engage in ethnic cleansing or to build pluralistic polities, and to treat international trade as a zero-sum or positive-sum game. It seems to follow that, insofar as social psychology achieves its explanatory goals, it cannot help but shed light on the central problems of world politics.

This reductionist syllogism, however, proves too much. By the same "logic," we could just as easily absorb psychology into neurophysiology or biochemistry into quantum mechanics. It is not enough to posit the relevance of the ostensibly more basic discipline: it is necessary to demonstrate its relevance. Moreover, demonstrating relevance is no small task. Skeptics stand ready to challenge the generalizability of laboratory findings (the external-validity controversy), to criticize social psychologists for not trying hard enough to bridge the gap between detailed case studies and abstract theory-testing (the idiographic-nomothetic controversy), to scold social psychologists for failing to respect the unbridgeable gap between descriptive and prescriptive propositions (the “is” - “ought” controversy), and to question the explanatory usefulness of "soft" micro constructs such as beliefs and values in a domain dominated by "hard" macro constraints such as determined domestic interest groups, unforgiving international creditors and lethal new weapons technologies in the hands of potential adversaries (the level-of-analysis controversy). From this standpoint, it is not at all obvious that social psychology plays an essential explanatory role in world politics. The scholarly community is well-advised to turn to other sources of theoretical guidance: for example, rational actor models derived from game theory and micro-economics (Bueno de Mesquita and Lalman, 1992; Waltz, 1979) or normative models derived from institutional economics and theories of international "regimes" (Keohane, 1984) or even Marxist analyses of center-periphery relations in the international system (Wallerstein, 1984).

There are thus two polar-opposite starting points for this chapter. One takes the relevance of social psychology as self-evident; the other takes the irrelevance of social psychology as equally self-evident. On reflection, most scholars would probably reject both starting points for staking out far too simplistic positions on the complex problem of how to weave together explanations that span the micro-macro continuum: from the individual decision-maker to small group dynamics to accountability constraints of organizations to domestic political competition to the international balance of power. Although theories grounded in these different levels of analysis do occasionally make contradictory predictions, the prevailing tendency today is to think "systemically" about the interconnections across levels of analysis and to stress the complementarity rather than the exclusivity of levels of analysis (Jervis, 1976).

In this spirit, the current chapter explores efforts over the last 27 years (the time of the last Handbook review on this topic--Etzioni, 1969) to assess the relevance of social psychology to world politics. The first section addresses the problem of setting standards of evidence and proof for causal claims in a domain where: (a) key events occur only once; (b) there are typically many plausible causal candidates; (c) experimental control is impossible and statistical control is often problematic; (d) investigators must therefore rely on speculative thought experiments concerning how events would have unfolded under alternative circumstances. Our task is further complicated by the emotionally and ideologically charged conclusions that investigators sometimes draw. When the null hypothesis is "nuclear deterrence played no role in preventing a Soviet-American war,” the routine scientific act of trading off Type I versus Type II errors becomes a defining political statement in itself. This combination of causal ambiguity, political controversy, and moral engagement makes doing "normal science" on world politics potentially hazardous to one's scientific reputation. No matter what one does, one runs the risk of standing accused of either political naiveté or of surreptitiously advancing an activist agenda or of clinging to an outmoded positivist philosophy of science that upholds the reactionary ideal of value-neutrality. The resolution to the trilemma proposed here emphasizes multi-method research programs in which investigators: (a) rigorously ground psychological propositions in political contexts; (b) self-critically practice "turnabout" thought experiments in which they ask each other whether they apply the same standards of evidence to politically opposing claims.

The second section examines the now familiar “rationality” debate as that controversy plays itself out in the study of world politics. Since Thucydides, self-styled realists have argued that world politics obeys a distinctive logic of its own. There is little leeway in the cut-throat arena of international competition for slow learners who allow personal beliefs, needs, or ideals to cloud their vision of looming threats. Accordingly, there is little need for psychological explanations. National leaders either respond in a timely manner to shifting balances of power (whether calibrated in strategic nuclear warheads or gross domestic products) or they are rapidly replaced by more realistic leaders. The choice is between rational updating of expectations in calculating Bayesian fashion and being selected out of the game in ruthless Darwinian fashion. The chapter explores: (a) why these purely systemic explanations are helpful for understanding broad historical patterns but are hardpressed to explain specific foreign policy decisions; (b) how psychological models can complement the explanatory strengths and weaknesses of systemic theories. The chapter also cautions us not to view the knowledge transfer as a one-directional flow from “basic” psychological truths to the applied international context; the macro context can set both normative and empirical boundary conditions on psychological generalizations, reminding us that response tendencies that look dysfunctional in the laboratory may be functional in many international settings as well as that response tendencies that look empirically robust in the laboratory may disappear at institutional and international levels of analysis.

The third section deals with interstate influence. Deterrence theorists--who enjoyed remarkable influence over American foreign policy in the post World War II period--take an extremely parsimonious view of the workings of diplomacy and negotiation. From this perspective, too much subtlety can be a dangerous thing when it communicates weakness and vacillation to potential aggressors. One prevails by possessing the necessary power and by clearly communicating the resolve to use it under specified circumstances. This section examines some psychologically richer conceptions of the influence process that emphasize the need to strike reasonable balances between the goals of deterrence (don't let the other side exploit you) and of reassurance (don't exacerbate the worst-case fears of the other side). It also examines evidence from multiple methods that clarifies when different influence tactics are likely to elicit desired reactions from the other side.

Thus far, the focus has been on fundamental processes--decision making and social influence--that are a safe bet to play key roles in almost every conceivable scenario for the next century. World politics, is, however, in a state of flux. The nation-state, it is frequently claimed, is under siege by both sub-national forces of ethnic fragmentation and supra-national forces of economic integration. An empirically comprehensive analysis can no longer treat the nation-state as the unchallenged decision-making unit. People may direct their loyalties elsewhere. And a theoretically balanced analysis can no longer assume that international relations are inherently anarchic and lawless. Accordingly, the fourth section of this chapter targets theories that address the powerful centrifugal and centripetal forces operating on the international system. The focus shifts to the complexities of applying theories of group identification and of distributive and procedural justice to a rapidly transforming world. Key questions for the attentive public become "Who am I?", "To what groups do I belong?", "What is a fair distribution of burdens and benefits of group membership within and across national boundaries?", and "When should we coordinate the policies of our nation with those of others to provide the international equivalent of public goods in such diverse domains as health, commerce, environmental protection, peace, and human rights?”

I. Standards of Evidence and Proof

In epistemology, as in life, it is helpful to set one's aspiration level high, but not too high. On the one hand, the early pioneers of the scientific approach to world politics were probably too optimistic (Richardson, 1960). We will never have clock-like Newtonian laws of world politics that predict the waxing and waning of great powers or the trajectories of arms races. On the other hand, post-modernist debunkers of scientific approaches have probably gone too far (Ashley, 1988). Predicting political events is not as hopeless as predicting the shape, color and size of the clouds in the sky next week. That said, however, there are good reasons for supposing that there is limited potential for discovering powerful laws that support accurate long-term forecasts (Almond and Genco, 1978; Jervis, 1992b).

A. Grounds for Scientific Pessimism

(1) the tape of history runs only once. Psychologists have grown accustomed to the inferential luxury of control groups that allow them to assess whether the hypothesized cause really made a difference. In world politics, the control groups exist--if "exist" is the right word--only in the imaginations of political observers who try to reconstruct how events would have unfolded if the hypothesized cause had taken on a different value in an alternative world. Could we have averted World War II if Churchill rather than Chamberlain had confronted Hitler at Munich in 1938? Could we have triggered World War III if Kennedy had followed his more hawkish advisors and launched air strikes against Soviet missile sites in Cuba in 1962? Would the newly industrializing countries of the 1970's and 1980's have grown even more rapidly if their governments had pursued less interventionist economic policies? Time-machines fantasies to the side, there is no way to rerun history and experimentally manipulate the presence or absence of a "key" personality, event, or policy.

Whereas experimental control is simply impossible, statistical control is possible in principle, but often deeply problematic in practice. For many categories of questions--such as the role of nuclear weapons in preserving the long peace (1945-1991) between the U.S.A. andU.S.S.R.( Gaddis, 1993)--there are too many confounding variables and too few degrees of freedom to disentangle competing casual claims. Judicious selection of comparison cases and meticulous process tracing of decision-making records can sometimes tip the scales of plausibility in these statistically indeterminate cases (George, 1980; Khong, 1991), but any causal inferences will still ultimately rest on counterfactual assumptions about what would have happened in possible worlds in which the hypothesized independent variables took on alternative values from those in the actual world. The dependence on thought experiments is not, however, adequately appreciated (Fearon, 1991) and is regarded by many as embarrassing, undercutting the validity of all casual claims about world politics. This reaction is understandable but exaggerated. To say that debates over security issues ultimately reduce to competing counterfactual scenarios is not to concede that anything goes. It is possible to articulate standards of evidence and proof even for counterfactual arguments (Tetlock & Belkin, 1996). Among other things, good counterfactual arguments should have clearly specified antecedents, consequents and connecting principles, should not rewrite massive stretches of history, should rely on connecting principles that are consistent with well-established theoretical and empirical generalizations, and should contain the seeds of testable hypotheses in the actual world.

(2) many plausible causal candidates. When scholars perform thought experiments to explore what might have happened under this or that contingency, they often discover a plethora of possibilities. Half a dozen schools of thought may stand ready to advance their preferred causal candidates and to assert confidently that they know how events would have worked out if the hypothesized causes had taken on different values. These causal candidates are drawn from the full spectrum of levels of analysis. It is instructive to observe their interplay in two actual controversies: the debates over blame for World War I and credit for the Asian "tigers."

(I) World War I. For nearly a century, scholars have debated the origins of the ironically labeled "war to end all wars." Some claims invoke "butterfly-effect" counterfactuals that, in the spirit of chaos and complexity theory, stress the role of quasi-random contingencies in shaping events (cf. Gaddis, 1993). A classic example is the precipitating event--such as the assassination of Archduke Ferdinand in June 1914--which can be easily "undone" by simply positing that the driver of the royal carriage possessed a map of Sarajevo and did not make a fateful wrong turn that gave the Serbian assassins who had just botched the job a remarkable second chance to do it right. Other claims invoke cause-effect generalizations drawn from traditional disciplines. Some psychobiographers suggest that German foreign policy would have been more prudent if Kaiser Wilhelm had not been so insecure (perhaps because of his withered arm) and determined to assert his manhood (Kohut, 1982). Students of crisis decision-making suggest that time pressure and information load promoted simplification and rigidity of thought (perhaps preventing policy-makers from generating complex compromise agreements that might have averted war--Holsti, 1972). Students of military doctrine and organization assign blame to the widespread "cult of the offensive" that led key planners to believe that the side which mobilized first possessed a decisive strategic advantage (Snyder, 1984). Students of identity-politics trace the conflict to the inherent instability of multi-ethnic empires such as Austro-Hungary and to the resulting power vacuum. Neorealist analysts of international systems point to the inherent instability of multipolar balances of power and the threat to that balance posed by the rapid growth of German industrial and military strength. In this view, World War I was a conflagration waiting to happen. The Sarajevo assassination was but one of countless sparks that could have easily set off the same underlying conflict.

(ii) The emergence of the "Asian tigers”. Almost no social scientists in the 1950's predicted that the astonishing growth rates of East Asian economies in the late 20th century. But almost all social scientists in the 1990's can generate four or five reasons for the inevitability of those same growth rates. Advocates of cultural explanations can point to the "work-ethic" character traits inculcated by Confucianist family values: hard work, in-group loyalty, and willingness to subordinate immediate gratification of individual desires to long-term group goals (Pye, 1985). Advocates of strategic trade can point to government subsidies and nurturance of "infant industries" in high-growth-potential sectors of the economy (Johnson, 1993; Tyson, 1993). Advocates of neoclassical economic theory can point to intense competition within many of these "infant industries" and to tax laws and low-inflation macro-economic policies that encourage high savings rates that, in turn, provide large pools of low-interest investment capital (Friedman, 1992). Defenders of authoritarian government as an essential transitional phase toward prosperity can point to the importance of suppressing unions and maintaining low wage rates in labor-intensive industries as an early source of comparative advantage in international trade.

What do World War I and the economic miracles of East Asia have in common? In each case, we find a long list of distinct but interrelated causal candidates, each of which rests on difficult-to-test assumptions about what would have had to be different to alter the observed outcome. In each case, an event that virtually no one predicted appears, with benefit of theoretical hindsight, hopelessly over-determined (Fischhoff, 1975; Hawkins & Hastie, 1990). Finally, in each case, people often find it very difficult to recapture the sense of uncertainty that prevailed before the historical outcome was known. We exaggerate the degree to which we “knew it all along.” Our capacity to assimilate known outcomes from the past to favorite causal schemata vastly exceeds our ability to predict unknown outcomes in the future (cf. Dawes, 1993).

(3) the interrelatedness of potential causes. In the ideal thought experiment, we manipulate one cause at a time and gauge its impact. Part of what makes "assassination" counterfactuals so popular among chaos and complexity theorists is that it seems so easy to rewrite one or two trivial details of history, to hold all else constant, and to observe big effects. All we need to do is to suppose that Lee Harvey Oswald was not quite so good a marksman and most of us share the intuition that we would have a rather different list of American presidents from 1963 onward. The thought experiment seems to illustrate the "sensitive dependence on initial conditions" of major events (McCloskey, 1991).

Thought experiments are, however, more problematic than this laboratory model suggests. Causes rarely exist in isolation from each other. When we tamper with one potential cause, we almost always alter a host of others, thereby creating confounding variables (Jervis, 1996). For instance, if we counterfactually posit slower German growth rates in pre-1914 Europe, we simultaneously change the entire geopolitical calculus. Perhaps Britain would no longer perceive Germany as the power most likely to achieve European hegemony but rather would see France as the primary threat (as in Napoleonic times) or Russia (as after 1945). A shift in the domestic political or economic condition of one state may have far-reaching ramifications on alliance structures. Game theorists have been most explicit in modeling these sorts of "ripple effects" by mapping out the best responses available to other players in the event that one player (for whatever reason) deviates from the equilibrium path (Bueno de Mesquita, 1996; Weingast, 1996). To reach determinate counterfactual conclusions, however, these game theorists must make heroic assumptions both about the rationality of the players and about the assumptions that the players themselves make about each other's rationality (necessary for identifying equilibrium strategies via backward induction).

If world politics is best represented as systems embedded within systems and if it is generally inappropriate even to imagine manipulatingan hypothesized cause in isolation from the causal network within which it is embedded, we confront an extraordinary dilemma. For the more densely interconnected the potential causes, the less possible it becomes to trace the impact of any change even after the fact, less still to predict it before the fact. From this standpoint, the most popular research strategy for disentangling cause and effect in world politics--the comparative case method--is deeply, perhaps fatally, flawed. Searching for several cases that are similar except for one "independent variable" is systematically misleading. Not only is there no random assignment to conditions, there is the problem of path-dependence (Jervis, 1996). Our location in the historical flow of events is consequential. What we do now is shaped by what happened earlier. These earlier branching-point events have taught us particular lessons and values. For example, when we compare the consequences of pursuing deterrence versus reassurance policies (a popular comparison in political psychology), we need to factor into our intuitive causal model the variety of reasons why people found it reasonable to resort to deterrence in certain cases but to reassurance in others. Deterrence may be an effect as well as a cause--a sign in itself of how far the relationship had already deteriorated. Interpretive controversies of this sort surface frequently in security debates.

The strong form of the "system-effects” argument leaves us teetering on the brink of policy nihilism, with no way to tell what might have happened if we had listened to one or the other faction in a policy dispute. Of course, the strong form of the argument may be too strong. In the cosmic matrix of causal interconnections, most entries may be close to zero (cf. Pattee, 1973), in which case the indeterminacy problems are less acute. The only sure fact is that no one knows for sure. Firm opinions must be based on metaphysical guesses.

(4) counterfactuals are often politically controversial. A fourth factor complicates efforts to make sense of world politics. Most debates over counterfactual scenarios engage partisan political motives. For instance, defenders of the Reagan defense build-up of the 1980's argued that, without it, the Soviet political establishment would never have accepted as radically a reformist leader as Gorbachev--a leader whose policies of glasnost and perestroika "led to" the disintegration of the Soviet state (Pipes, 1993). Critics of the Reagan administration argued that the defense build-up was either an irrelevancy or an impediment to Soviet reformers and--in either case--an egregious waste of national treasure (Garthoff, 1994; Lebow & Stein, 1994). For our purposes, the key point is this: one can neither sensibly defend nor compellingly criticize a policy initiative without making assumptions about how things would have worked out differently if the government had done something else.

Short of adopting a stance of radical agnosticism toward all policy initiatives, something few social scientists are willing to do, there is no avoiding the daunting task of setting thresholds of proof for counterfactual thought exercises. One must judge the likelihood of making Type I versus Type II errors (e.g., concluding that deterrence works when it does not versus concluding that it does not work when it does) and the risks associated with each error, all the while under partisan pressure to tip the scales of plausibility in one direction or another.

(5) too much is at stake to confess ignorance. Putting political pressures aside, human beings find it dissonant to acknowledge that they are making extraordinarily consequential decisions with little knowledge of the likely consequences. Insofar as people need to believe that they are masters of their destiny or, at least, that they live in a comprehensible and predictable world, they will bolster their confidence in preferred counterfactual scenarios and downplay rival scenarios. They will also tend to dismiss indeterminacy arguments as nihilistic carping. Indeed, they will find it aversive to be reminded of the soft counterfactual underbelly of their belief systems. Who wants to be told that deeply held convictions rest on speculative assumptions about what might have happened in imaginary worlds?

In sum, there are powerful temptations--cognitive, emotional, and political--to claim to know more than one does or even can about world politics. Part of the problem is how few causal claims can withstand systematic scientific scrutiny. There are usually many plausible accounts and imperfect means of gauging their relative credibility. Part of the problem is the magnitude of the policy consequences and the awkwardness of confessing how little one knows when so much is at stake. The scholarly study of world politics seems to require a superhuman capacity to tolerate ambiguity and to resist the siren calls of moral-political advocacy.

This argument might be mistaken for a counsel of despair, perhaps even a postmodernist critique of the quest for causal laws in both social psychology and history (cf. Gergen, 1978). It is one matter, however, to acknowledge candidly the difficulty of the task and quite another to abandon it altogether and replace it with a hermeneutic agenda. Awareness of epistemological traps and political temptations helps to calibrate realistic aspiration levels for research. The classic goal--well-defined covering laws that allow us to deduce past events and to predict future ones with great accuracy (Hempel,1965)-- is outside our reach now and perhaps forever. A more modest goal--cumulative multi-method research programs that allow us to identify recurring patterns in the past and to anticipate some future events with greater than chance accuracy--is within our reach. Indeed, the proof is the research literature.

B. Grounds for Scientific Optimism

Given the scale of the indeterminacy problems in world politics, how can researchers build a persuasive case for a particular hypothesis? This chapter argues for a three-pronged strategy in which: a) researchers draw upon middle-range theoretical generalizations that rest on cumulative empirical work in other fields as sources of causal hypotheses; b) researchers make good-faith (reasonably value-neutral) efforts to test the applicability of these generalizations to various facets of world politics; c) researchers remain vigilant to the possibility that the transfer of knowledge is not one-directional and that "basic" psychological findings may have to be seriously qualified or even reversed by political, economic, and cultural moderator variables.

With respect to the first requirement, the wide assortment of "basic-process" chapters in the volume should serve as evidence that cumulative bodies of work exist on such relevant topics as judgment and choice (Dawes, Handbook Chapter) bargaining and negotiation (Pruitt, Handbook Chapter) social identity maintenance and intergroup conflict (Brewer & Brown, Handbook Chapter) and perceptions of fairness within and across group boundaries (Tyler & Smith, Handbook Chapter). Prima facie relevance is not enough, however, to clinch the case. Thoughtful theorists deny the relevance of laboratory studies on one or another of the following grounds: a) selection arguments (to make it into high-level political roles, one must be a lot more mature and rational than the typical sophomore participant in laboratory studies); b) motivational arguments (policy-makers are especially motivated to make rational decisions because the stakes are so high); c) accountability-constraint arguments (even if policy-makers were prone to the same effects as laboratory participants, they work within complex institutional systems of checks and balances that prevent those effects from being translated into policy).

Each argument raises subtle issues concerning boundary conditions for experimental findings. Here the key point is the necessity of obtaining independent evidence that the hypothesized psychological processes are indeed operating in the political world. The crucial question becomes one of multi-method convergence: Do we obtain similar functional relationships between the conceptual independent and dependent variables studied in laboratory and real-world settings? Consider three examples:

1) laboratory studies have revealed that people tend to shift into "simpler" modes of cognitive processing as information load, time pressure, and threat exceed some optimal point (Gilbert, 1989; Kruglanski & Freund, 1983; Streufert and Streufert, 1978; Svenson and Maules, 1994). Archival researchers, relying on content analyses of political statements, have concluded that similar processes have occurred in numerous international crises (Raphael, 1982; Suedfeld, 1992a; Wallace & Suedfeld, 1992; Walker & Watson, 1989, 1994).

2) laboratory studies of bargaining and influence reveal that threats often either do not work as intended or even backfire. One recurring theme is the relative effectiveness of some version of the tit-for-tat strategy that protects one from exploitation but leaves the door open for reconciliation. Qualitative case studies and quantitative event-analytic studies of international relations often converge on strikingly similar conclusions (Leng, 1993), as do computer simulations that pit all logical combinations of strategies against each other (Axelrod, 1984).

3) laboratory studies of judgmental biases reveal a host of mistakes that people apparently make in drawing causal attributions, estimating relationships among variables, revising views in response to new evidence, and attaching confidence to their judgments (Fiske & Taylor, 1991; Nisbett & Ross, 1980). Many case studies of the foreign policy decision-making process suggest that policy-makers fall prey to the same effects (Jervis, 1976; Lebow, 1981; Snyder, 1984; Vertzberger, 1986; Wirtz, 1991).

When it arises, and it does frequently in this chapter, multi-method convergence is generally taken as an encouraging sign. The working assumption is that theoretical generalizations that pass radically different tests stand a better chance of capturing robust regularities than do generalizations whose support is confined to one genre of research. Controlled experiments reassure us of the internal validity of the original causal claim whereas field methods--case studies, content analysis, event analysis, codifying expert judgment--reassure us that the hypothesized process holds up in the hurly-burly of world politics.

There is much to recommend this methodological division of labor and I have indeed endorsed it elsewhere (Tetlock, 1983). But there are good reasons for caution. First, convergence is sometimes spurious. Field researchers may conclude that policy-making that only superficially resembles a laboratory analog is the product of the same underlying process. For instance, policy makers may appear to rely on crude rules of thumb in drawing lessons from history (a finding consistent with laboratory work on analogical reasoning -- Gilovich, 1981). Policy makers may, however, actually possess a farmore subtle grasp of the situation. They may be using simple historical arguments such as "no more Munichs" or "no more Vietnams" to achieve impression management goals: to rally support from wavering political constituencies and to pre-empt potential critics. In a similar vein, policy makers may not be unaware of value trade-offs or of contradictory evidence but may find it politically useful to refuse to acknowledge them. Distinguishing perceptual-cognitive from impression management explanations is often a tricky judgment call even in controlled laboratory settings (Tetlock & Manstead, 1985); the indeterminacy problems are obviously more severe in historical case studies.

Political psychologists, nonetheless, frequently make such judgments. Holsti (1989) made such judgments in assessing whether crisis-induced stress really impairs policy reasoning or whether policy makers are trying to influence the calculations of other national leaders by persuading them that such impairment has occurred (see Schelling, 1966, on the rationality of occasionally appearing irrational). Stein (1991) made similar sorts of judgments in assessing whether leaders of states that challenge deterrence are allowing motives and wishes to inflate the perceived odds of success or whether they are trying to intimidate the status quo power by persuading it that the challengers have no choice (given their public commitments) but to persevere with confrontational policies. Fischhoff (1991) and Sagan (1985) grappled with a similar dilemma in the domain of nuclear command, control and communications systems. How can we assess whether decision makers responsible for operating these systems overestimate their ability to simultaneously avoid Type I errors (falsely conclude that an attack is occurring) and Type II errors (falsely conclude that an attack is not occurring) or whether these decision makers are self-consciously promoting a functional fiction that the system is in sound working order (so that the public will not panic and that adversaries will not test the system) . In short, it is no simple matter to distinguish true from spurious multi-method convergence.

There is also a second reason for caution. Multimethod divergence sometimes occurs. It is easy to identify real-world exceptions to most laboratory-based generalizations. Decision makers sometimes draw flexible, multidimensional lessons from history (Neustadt and May, 1986), confront trade-offs even in highly stressful situations (Maoz, 1981), and display a willingness to change their minds in response to new evidence (Breslauer & Tetlock, 1991). Multimethod divergence of this sort does not, of course, mean that one set of findings must be right and the other wrong. When different research methods yield different results, there are lots of possible explanations. How people think may depend on a variety of boundary conditions: individual differences in intellectual capacity, cognitive style, and interpersonal style, cultural background, and institutional variables such as the nature of the decision making task, small groups processes and role and accountability relationships. Each class of variables--by itself or in combination with others--may explain inconsistencies in the evidence.

Finally, a third cautionary comment merits mention. There is inevitably an element of subjectivity in judgments of multimethod convergence and divergence which creates potential for both the appearance and reality of political bias. There are no hard and fast rules that specify how quickly one should conclude that "convergence is specious" or "divergence constitutes falsification." When the hypotheses at stake are as politically consequential as "conservatives are more prone to cognitive biases than liberals” (Kanwisher, 1989) or "deterrence typically does not work" (White, 1984), the stage is set for epistemic mischief on an epic scale. Most social scientists are well known to be liberal in their political sympathies (Lipsett, 1982) and the suspicion inevitably arises -- justifiably or not -- that they are selectively raising and lowering standards of evidence to favor certain hypotheses (cf. Tetlock, 1994). Under these inauspicious circumstances, the scholarly community can best protect its reputation for reasonably fair and value-neutral scholarship by affirming its commitment to a level field for hypothesis-testing. "Turnabout” thought experiments should become routine mental exercises in the peer review process in which skeptics are encouraged to make the case, for example, that "if liberals instead of conservatives displayed a certain judgmental tendency, we would have been more reluctant to label the tendency a bias" or "if the predictions of the conflict-spiral rather than deterrence theory had been refuted, we would have more vigorously challenged the logical adequacy of the test". The key questions become: Have the rules of evidence been "stacked against" unpopular points of view? Do we require exceptionally strong evidence to challenge popular hypotheses and accept remarkably weak evidence to reject unpopular hypotheses? Do we, in brief, fall prey to the same theory-driven biases of thought of which we accuse others? And what scientific accountability mechanisms can we create to check such biases and adjudicate claims of epistemic double standards in so methodologically eclectic a field as world politics?

Notwithstanding these qualifications, multi-method triangulation still provides the soundest basis for causal claims in world politics. In the following sections, I shall be explicit about the evidential basis for various claims, giving extended attention to those that enjoy multi-method support.

Volume 15 Number 1

©The Author(s) 2013

Predictors of School Readiness in Literacy and Mathematics: A Selective Review of the Literature

Sandra M. Linder, Ph.D.

M. Deanna Ramey

Serbay Zambak

Clemson University, School of Education

Abstract

This paper presents findings from a selective review of the literature related to predictors of school readiness in literacy and mathematics. School readiness was defined as what children are expected to know and do in a variety of academic domains and processes of learning prior to entering a formal classroom setting. Seven themes emerged, based on a review of selected empirical research published over a sixteen-year period. Twenty-four predictors of success for school readiness were categorized under these themes. Implications for practice and recommendations for future research are presented.

Introduction

Young children are increasingly entering academically rigorous school settings where an emphasis on accountability and standards has replaced an emphasis on child development. However, many young children enter school unprepared for both academic and social expectations. Research suggests (Aunola, Leskinen, Lerkkanen, & Nurmi, 2004) that if students enter kindergarten at a disadvantage, early gaps in understandings of literacy or mathematics tend to be sustained or widened over time; this appears to be particularly true for children of poverty (McLoyd & Purtell, 2008). It is imperative for the field to identify strategies that move young children toward becoming independent and reflective learners, to increase the likelihood of their school success in later years.

In order to achieve this vision, we must first identify the specific characteristics or factors that enable certain children to enter formal schooling at an advantage while others enter at a disadvantage. Since the 1950s, researchers have investigated how external factors can influence or predict student success in school, and particularly school readiness (Milner, 1951), but a comprehensive list of factors that may affect cognitive, social, emotional, or language development in the school-age years has yet to be compiled. This literature review focuses on school readiness in the areas of literacy and mathematics. Its purposes are to provide stakeholders such as parents, caregivers, and teachers with insight into factors that research has identified as possibly contributing to children’s successful entry into formal schooling and to enable them to identify whether particular children are affected by these factors.

Many definitions of school readiness can be found in the research literature. For some, school readiness relates to students’ cognitive abilities (Nobel, Tottenham, & Casey, 2005). For others, readiness is more related to maturational, social, and emotional domains of development (Ray & Smith, 2010) or to whether or not students have the tools necessary to work effectively in a classroom setting (Carlton & Winsler, 1999). For the purposes of this study, school readiness was defined as children’s preparedness for what they are expected to know and do in academic domains and processes of learning when they enter a formal classroom setting. Rather than focusing on specific activities such as counting to ten or saying the alphabet, this definition considers such components as children’s social-emotional characteristics, cognitive processes related to conceptual understanding, and their ability to communicate about their understandings.

Methods

A systematic review of the literature was conducted over three months during the spring of 2011. The question guiding the literature review was: What predictors of school readiness in mathematics and literacy have been identified by empirical research in education?

Data Collection and Analysis

The research team determined parameters for conducting searches by first examining already published literature reviews or meta-analyses relating to early childhood literacy or mathematics and relating to issues of school readiness. Four criteria emerged for articles to be included: (1) publication after 1995; (2) publication in a reputable peer-reviewed journal; (3) grounding in empirical research; and (4) use of rigorous research methods. These criteria are similar to those used in examples found in the preliminary review of the literature (Justice, 2003; La Paro & Pianta, 2000); however, many previous analyses were limited to large scale quantitative studies. During the preliminary review, meta-analyses of this literature published in 1995 or before were identified (Bus, Ijzendoorn, & Pellegrini, 1995). Therefore, this literature review focused on research following those publications to determine if any changes have occurred.

Having established parameters, the research team searched the literature to compile articles relevant to the research question. Both criterion and snowball sampling methods were used to identify literature. For criterion sampling, the research team conducted electronic searches of a variety of databases and search engines to identify articles that met the established parameters. Snowball sampling involved examining reference sections from theoretical articles related to school readiness, school achievement, early childhood mathematics, and early childhood literacy to find empirical research relevant to the research question. Snowball sampling was also conducted on the reference sections for each empirical study identified in the review to determine if additional sources could be included.

Finally, the research team conducted preliminary readings of the articles to obtain an overall understanding of the data. Following this analysis, articles were clustered based on similarity of findings. After clustering, articles underwent a secondary analysis to establish predictors of school readiness in mathematics and literacy.

Results

In general, literature relating to predictors of success in early childhood literacy was more prevalent than literature relating to early childhood mathematics. Therefore, more predictors of success relating to literacy were identified in this review. It is likely then that the findings do not encompass all potential predictors of school readiness in mathematics.

Seven themes emerged from the literature review regarding factors associated with school readiness in mathematics and literacy: (1) child care experience; (2) family structure and parenting; (3) home environment; (4) learning-related skills; (5) social behavior; (6) mathematical and literacy-based tasks; and (7) health and socioeconomic status. The sections that follow describe findings relating to each of these themes.

Child care experience. Several studies reviewed noted correlations between children’s exposure to high-quality child care and their performance on measures of school readiness in literacy and mathematics. In a longitudinal study conducted by the NICHD Early Child Care Research Network (2002), participation in high quality, center-based child care was associated with higher language performance (NICHD, 2002). However, increased time spent in child care did not increase language performance, and a higher number of hours spent in child care was associated with increased behavior problems, as reported by caregivers (NICHD, 2002).

Ramey and Ramey (2004) reported the results of multiple randomized controlled trials investigating experiences in preschool education and their connection to school readiness. The authors identified seven types of experiences that are “essential to ensure normal brain and behavioral development and school readiness” (2004, p. 474). These experiences should: “(1) encourage exploration, (2) mentor in basic skills, (3) celebrate developmental advances, (4) rehearse and extend new skills, (5) protect from inappropriate disapproval, teasing, and punishment, (6) communicate richly and responsively, and (7) guide and limit behavior” (Ramey & Ramey, 2004, p. 474).

The authors indicate that children’s exposure to high-quality child care built around these types of experiences can better prepare children for school. Magnuson and colleagues (2004) also examined the relationship between quality of care and school readiness and, in particular, how different types of preschool experiences may affect children of economically advantaged and disadvantaged families. Child care was categorized as parental care, center-based care, Head Start, or other non-parental care. Using a sample from the Early Childhood Longitudinal Study, Kindergarten Class (ECLS-K), the authors found that children who attended center-based programs before kindergarten performed better in math and reading than children who experienced only parental care. Having attended center-based programs was associated with greater benefits for children from “disadvantaged” families than for those with higher economic status, including enhancement of mathematics performance (Magnuson, Meyers, Ruhm, & Waldfogel, 2004).

High quality child care was not always defined in the literature reviewed for this study, but some of the literature did examine aspects of high-quality care. Klein, Starkey, Clements, Sarama, and Iyer (2008) examined the effects of a preschool mathematics curriculum on children’s levels of school readiness. Their findings suggest that use of high-quality curricula implemented with fidelity can lead to higher levels of school readiness in mathematics (Klein, Starkey, Clements, Sarama, & Iyer, 2008). Bracken and Fischel (2007) examined the impact of a supplementary literacy-based curriculum on Head Start preschoolers’ mathematics and literacy achievement and social and behavior skills. More students displayed positive behavior and social skills when engaging with the supplementary curriculum; these skills were associated with higher levels of performance on literacy tasks (Bracken & Fischel, 2007). Characteristics of instruction have also been considered in determining child care quality. Chien and colleagues (2010) investigated the types of engagement young children could encounter in child care settings (free play, group or individual instruction, and scaffolded learning). Children in settings with more free play showed smaller gains than their peers on literacy and mathematics indicators at the preschool level. Individual instruction tended to be a stronger predictor of success on preschool assessments (Chien, Howes, Burchinal, Pianta, Ritchie, Bryant, Clifford, Early, & Barbarin, 2010). However, that study focused only on the types of engagement as predictors of achievement success and did not discuss the potential positive implications of free play and group instruction on other domains of development.

Parenting Style and Family Structure. Parenting styles, parent and child relationships, and family structure were considered as factors potentially related to school readiness in some of the studies in this literature review. Hill (2001) examined the relationship between parenting styles and kindergarten children’s school readiness in African-American and Euro-American families with comparable socioeconomic status. Maternal warmth or acceptance was found to be positively related to children’s performance on a pre-reading measure, while “short temper” and lack of patience were associated with lower scores. Also positively related to children’s performance were teachers’ perception of the extent to which parents valued education, and the quality of parent involvement (high quality was characterized by primarily parent-initiated involvement; lower quality by primarily teacher-initiated parent involvement). Mothers’ expectations for grades were positively related to children’s performance on the pre-reading measure. Hill (2001) also compared parenting styles to kindergarten children’s performance on a measure of quantitative concepts. Again, maternal warmth and high expectations for good grades were associated with higher scores on the pre-mathematics measure, while lack of patience was connected to lower scores. However, no significant relationship was found between children’s performance and teacher-parent contact; the teacher-parent relationship alone did not predict better performance.

Wu and Qi (2006) examined the relationship between parenting styles and African American children’s achievement in the areas of reading, math, and science. They found that parents’ perceptions of children’s abilities and expectations for good grades were strong predictors of success for students at all grade levels. These predictors were just as strong as parents’ socioeconomic status (Wu & Qi, 2006). While parental involvement is commonly cited in the literature as a strong predictor of success, in this study, parental involvement was not shown to have a large impact on student achievement. Wu and Qi (2006) reported that their study “found limited positive effects of school-based parental involvement and, in addition, some negative effects of home-based parental involvement on achievement test scores” (p. 426). Lahaie (2008) found parental involvement to be a predictor of success for children of immigrants; that study’s analysis of data from the ECLS-K indicated a correlation between higher levels of parental involvement and young children’s higher proficiency in English and mathematics.

Family structure has also been cited as an important predictive factor relating to school readiness in mathematics and literacy. For example, Entwisle and Alexander (1996) investigated the relationship between children’s literacy and mathematics school readiness and parent configuration, or family type, in a random sample of Baltimore children. Mothers who were single parents were found to have lower expectations for their children’s grades in both reading and mathematics than mothers in two-parent families. However, regardless of family type, children in families with greater economic resources and who had a parent or parents with high expectations for success “consistently outperformed other children in reading and math” (Entwisle & Alexander, 1996, p. 341).

Home Environment. The research literature on school readiness includes several studies of the relationship between daily home activities and school readiness. Clarke and Kurtz-Costes (1997) examined the educational quality of the home environment and the influence of television-watching on readiness. They interviewed children and caregivers of low-income, African-American families and compared these data to school readiness assessments. Negative correlations were found between the amount of time spent watching television and number of books in the home, and between television viewing time and amount of parent-child instructional interactions. More television viewing time also predicted lower scores on readiness assessments (Clarke & Kurtz-Costes, 1997). Wright and colleagues (2001) investigated the relations between young children’s television viewing experiences and their performance on tests of school readiness and vocabulary. Television programming was divided into 4 categories: (1) child-audience, informative or educational; (2) child-audience, fully animated cartoons with no informative purpose; (3) child-audience, other programs (neither of the above); and (4) general-audience programs. According to the authors, “for very young children [2-3], viewing informative programming designed for children was associated with subsequent letter-word skills, number skills, receptive vocabulary and school readiness” (Wright, Huston, Murphy, St. Peters, Pinon, Scantlin, & Kotler, 2001, p. 1361). The authors found this difference to be stable across the study; young children who frequently watched educational television at ages 2 and 3 performed better on a battery of tests at age 3 than did infrequent viewers. However, children who were frequent viewers of non-educational cartoons or general-audience programs at ages 2 and 3 had lower scores than infrequent viewers.

A longitudinal study of children’s reading abilities and the literacy environment in the home (Burgess & Hecht, 2002) found that the home literacy environment (HLE) was significantly related to young children’s oral language ability, word decoding ability, and phonological sensitivity. The authors define the home literacy environment in two ways: (1) Passive HLE, or “those parental activities that expose children to models of literacy usage (e.g., seeing a parent read a newspaper)” (2002, p. 413), and (2) Active HLE, or, “those parental efforts that directly engage the child in activities designed to foster literacy or language development (e.g., rhyming games, shared readings)” (2002, p. 413). In a study in the Netherlands, Leseman and de Jong (1998) examined three issues related to home literacy: the potential influence of affective factors, such as cooperation, co-construction, or social-emotional constructs; the influence of contextuality, or cultural or social background factors; and causality, the impact of home literacy on language development. They found that home literacy environment factors determined children’s school literacy achievement when controlling for confounding factors. Their findings suggest that combining exposure to literacy in the home with co-construction opportunities increased the predictive value of home literacy in relation to early literacy achievement.

Learning-related characteristics. “Learning-related characteristics” include children’s behaviors and dispositions related to engaging in tasks as well as their strategies for completing tasks. McClelland, Morrison, and Holmes (2000) studied the relationship between work-related social skills and student performance in kindergarten classrooms and again in second grade. Examples of children’s work-related social skills included the ability to follow directions, take turns in group activities, and stay on task. When child demographic information (e.g., IQ, entrance age, ethnicity, parental education level, and home literacy environment) was controlled, findings showed that work-related skills contributed to children’s academic success in mathematics. Children with poor work-related skills performed significantly worse in mathematics upon school entry and at the end of second grade (McClelland, Morrison, & Holmes, 2000). McClelland, Acock, and Morrison (2006) later examined the influence of learning-related skills in kindergarten on academic math and reading success in elementary school. In this study, the math and reading abilities of children rated as having poor learning-related skills were compared to children rated as having high learning-related skills. Findings suggested that learning-related skills such as self-regulation and social competence predicted math and reading achievement between kindergarten and sixth grade. These effects were strongest between kindergarten and second grade but were still significant through sixth grade (McClelland, Acock, & Morrison, 2006).

Social behavior. Connections between school readiness and children’s temperament, or the innate aspects of their personality, have been addressed in the research literature. A child’s tendency to display characteristics such as being active or sociable may be correlated with school readiness; Chang and Burns (2005) examined the connection between temperament and attention skills for children attending Head Start. Findings from their multiple regression analysis indicate that temperament and motivational development are related to levels of attention in young children, similar to findings from research conducted with older children (Chang & Burns, 2005).

Konold and Pianta (2005) examined the predictive value of particular cognitive processes and social behaviors related to self-regulation on typically-developing children’s kindergarten and first grade achievement. The authors developed six normative profiles of patterns of school readiness: (1) attention problems; (2) low cognitive ability; (3) low-to-average social and cognitive skills; (4) social and externalizing problems; (5) high social competence; and (6) high cognitive ability and mild externalizing (Konold & Pianta, 2005). Findings suggest that cognitive ability and social skills should be considered predictors of school readiness, and that although these factors are interrelated, they can operate independently of each other in terms of their predictive value. For example, children with high cognitive abilities performed better on achievement measures, regardless of social skills, while students with average cognitive ability and higher social competence also tended to perform at higher levels than did those with average to low cognitive ability and average social competence (Konold & Pianta, 2005).

Normandeau and Guay (1998) investigated the relationship between cognitive self-control and prosocial behaviors such as collaboration and effective communication in kindergarten-age children. Cognitive self-control was correlated with increased student achievement, which was evidenced when following these children to the end of first grade. Aggressive behaviors were negatively correlated to cognitive self-control while prosocial behaviors had a positive correlation. Children who displayed more aggressive behaviors tended to have less self-control when attempting to complete school tasks, which led to poorer student achievement (Normandeau & Guay, 1998). Dobbs and colleagues (2006) examined the relationship of prosocial behaviors to mathematics skills in preschoolers. The authors found that when students participated in an early math intervention, which consisted of over 85 mathematical tasks that their teachers could select to implement, they were less likely to display negative behaviors such as aggression or a lack of attention.

Performance on mathematical and literacy-based tasks. Correlations between young children’s readiness-related literacy and mathematics skills and their experience with mathematics- and literacy-based tasks were explored in some of the literature reviewed during this study. Tasks might include such activities as examining concepts about print (literacy) and playing number games or block building (mathematics). Siegler and Ramani (2008) examined the role that playing numerical board games could play in preparing children in low-income families for school. They found that the numerical ability of children from affluent families was significantly higher than the numerical ability of children from impoverished families; however, the gap between groups in terms of their understanding of numerical magnitude was closed as a result of the intervention (Siegler & Ramani, 2008). Following this study, Ramani and Siegler (2008) sought to determine if playing linear numerical board games had an impact on a broader range of mathematical topics and whether this impact was stable over time by exploring informal board game play in the home environment (Ramani & Siegler, 2008). They reported the positive connection between informal board game play in the home environment and numerical ability. Playing card games and video games did not have the same results (Ramani & Siegler, 2008).

Building spatial sense through block play has also been considered as a potential predictor of success in terms of school achievement in the elementary years and beyond. Hanline, Milton, and Phelps (2009) examined the relationship between block play at the preschool level and later school success in math and reading. Although no significant relationships were identified in this study between block play and later math achievement, a significant relationship was identified between block play and later reading ability. Higher levels of sophistication in young children’s representations through block construction correlated with greater success in reading during the early elementary years (Hanline, Milton, & Phelps, 2009). While block play may not be a predictor of mathematics success at the early elementary level, it has been found to be a predictor of success for later school achievement in mathematics. Wolfgang, Stannard, and Jones (2001) reported the positive predictive relationship of levels of preschool block play (as determined by the Lunzer Five Point Play Scale) and mathematics achievement during middle and high school. Similar findings were reported regarding construction-type play with LEGOs and later school achievement (Wolfgang, Stannard, & Jones, 2001).

Health and socioeconomic status. Characteristics of child and parent health have long been cited in the literature as possible correlates of children’s school readiness, and are sometimes included as confounding variables when authors are attempting to identify alternative predictors (such as child care or parent-child interactions). Janus and Duku (2007) examined five constructs they identified as having a potential impact on school readiness: (1) socioeconomic status, (2) family structure, (3) parent health, (4) child health, and (5) parent involvement. Their Early Development Instrument, an assessment of school readiness, was built around these five factors in an effort to determine which of the five factors would be most relevant in predicting school readiness. Based on this assessment, health (including current health and low-birth weight) and gender of the child (boys are twice as likely to struggle with school readiness compared to girls) were the strongest predictors. In addition, children from low-income families were twice as likely to have difficulty with school readiness as children from middle- or high-income families.

Patrianakos-Hoobler and colleagues (2009) also examined risk factors related to health of premature infants in relation to the children’s eventual school readiness. They found that boys born premature were twice as likely as girls to display lower school readiness levels. Lower readiness was also identified for premature “infants born to black mothers” as compared to “infants born to nonblack mothers” (Patrianakos-Hoobler, Msall, Marks, Huo, & Schreiber, 2009, p. 4). Socioeconomic status emerged as the “strongest barrier to achieving school readiness” (Patrianakos-Hoobler, et al., 2009, p. 5).

Low socioeconomic status has been consistently negatively correlated to school readiness in the research literature. In 1997, Stipek and Ryan studied the cognitive differences and motivation of economically advantaged and disadvantaged children at school entry. Significant cognitive differences were found relative to number skills, problem solving, and memory. Economically disadvantaged children had as much motivation for learning as economically advantaged children. However, economically advantaged children showed higher levels of concern regarding performance and decreased levels of enjoyment as the study progressed (Stipek & Ryan, 1997).

Discussion and Recommendations

Definitions of school readiness have long been under contention, and it is unclear whether the view that students should be ready for school rather than schools being ready for children is developmentally appropriate. This systematic review of empirical research literature published after 1995 and before 2013 identified seven themes for which correlates of school readiness could be categorized. Table 1 describes the 24 predictors that were categorized under each of these themes.

Table 1

Predictors of school readiness in literacy and mathematics

While the above table describes factors that hold potential for predicting young children’s school readiness, risk factors were also identified in the literature. These factors include health risks such as low birth weight, prematurity, or general health issues, as well as demographic criteria such as gender (some studies have indicated that boys are more likely to struggle than girls), family structure (single mothers tend to have lower grade expectations for their children), maternal education level (not finishing high school) or the occupation of the head of household (due to the level of income associated with this occupation). In addition, low parental income or socioeconomic status and belonging to a minority group (including African American and Hispanic ethnicities) have often been identified as risk factors for school success.

Parents, caregivers, and teachers of young children as well as the children themselves are the primary stakeholders who would benefit from early interventions designed to enhance school readiness for young children. Initiatives focusing on building positive parent-child relationships and enhancing readiness-related aspects of the home environment have the potential to influence students’ readiness and later school achievement. Future research is needed on the roles parents play in children’s academic success. Specifically, little research can be found regarding parent involvement at the early childhood and primary levels and the influence of role models on children’s positive behavior and dispositions relative to school readiness. Research on the home environment is also necessary, including the increasing role of digital technologies and how they may influence family dynamics and, in turn, children’s future school success. Further investigations of the effects of implementing literacy- and mathematics-based tasks in the home, such as increasing math talk or encouraging children to build structures in a variety of shapes, may enhance what is currently understood about how home environments affect readiness.

Given the apparent correlation between child care quality and children’s school readiness, initiatives to improve early childhood teacher quality and the overall quality of public and private child care programs could have the potential to promote children’s school readiness in literacy and mathematics. The research identified in this review did not yield consistent definitions for high-quality child care. Future studies comparing types of child care settings could help to clarify what is high-quality care, considering such components as teacher quality and instructional practices, classroom environments, and curricula (e.g., presentation of mathematical and literacy-based tasks), and the longitudinal effects of such factors on student success.

The research included in this review was not consistent regarding types of assessments used to measure children’s school readiness. Many studies indicated that an effective tool for measuring school readiness has yet to be developed (Kilday & Kinzie, 2009). Recommendations for future research include the development and validation of a school readiness assessment that measures constructs across domains of development. Once such an assessment has been validated, it could be used for further investigation of the factors identified in this review.

Finally, initiatives specifically focused on young children could include interventions that enhance prosocial behaviors, motivation toward learning, and academic skills. Such interventions could occur in the context of the home, community, or child care setting. Some factors in school readiness and success that are specifically related to demographics (e.g., socioeconomic status, entrance age, belonging to a minority group) or health (e.g., birth weight) are difficult to isolate for the purposes of interventions that might enhance or reduce their influence on children’s school readiness and success. Often these factors occur simultaneously with additional factors within the child, parent, or teacher as described above; thus, future research on ways that some predictors may mediate the effects of demographic or health-related risk factors could be particularly helpful to the field.

Given the importance attached to children’s school readiness, any research that sheds further light on its components and processes is likely to enable adult stakeholders to better discern what constitutes the best possible environments and experiences for children. These environments and experiences can provide young children with the foundation for success on whatever paths they choose in the future.

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Author Information

Dr. Sandra M. Linder is an assistant professor of early childhood mathematics education at Clemson University. Her research centers on improving early childhood teacher quality and student understandings in mathematics.

Sandra M. Linder, Ph.D.

Assistant Professor of Early Childhood Mathematics Education

Coordinator of Early Childhood Education

Clemson University, School of Education

407F Tillman Hall

Clemson, SC 29634-0705

864-656-5102

sandram@clemson.edu

M. Deanna Ramey is a Ph.D. student in curriculum and instruction at Clemson University, studying literacy and early childhood education. Her research interests include young children's experiences with informational text, motivation and engagement, and self-regulation.

V. Serbay Zambak is a Ph.D. student in curriculum and instruction at Clemson University with an emphasis on mathematics education. He has taught mathematics in İstanbul and Amsterdam and has supported practicing teachers' instructional technology skills for mathematics education.

Volume 15 Number 1

©The Author(s) 2013

Predictors of School Readiness in Literacy and Mathematics: A Selective Review of the Literature

Sandra M. Linder, Ph.D.

M. Deanna Ramey

Serbay Zambak

Clemson University, School of Education

Abstract

This paper presents findings from a selective review of the literature related to predictors of school readiness in literacy and mathematics. School readiness was defined as what children are expected to know and do in a variety of academic domains and processes of learning prior to entering a formal classroom setting. Seven themes emerged, based on a review of selected empirical research published over a sixteen-year period. Twenty-four predictors of success for school readiness were categorized under these themes. Implications for practice and recommendations for future research are presented.

Introduction

Young children are increasingly entering academically rigorous school settings where an emphasis on accountability and standards has replaced an emphasis on child development. However, many young children enter school unprepared for both academic and social expectations. Research suggests (Aunola, Leskinen, Lerkkanen, & Nurmi, 2004) that if students enter kindergarten at a disadvantage, early gaps in understandings of literacy or mathematics tend to be sustained or widened over time; this appears to be particularly true for children of poverty (McLoyd & Purtell, 2008). It is imperative for the field to identify strategies that move young children toward becoming independent and reflective learners, to increase the likelihood of their school success in later years.

In order to achieve this vision, we must first identify the specific characteristics or factors that enable certain children to enter formal schooling at an advantage while others enter at a disadvantage. Since the 1950s, researchers have investigated how external factors can influence or predict student success in school, and particularly school readiness (Milner, 1951), but a comprehensive list of factors that may affect cognitive, social, emotional, or language development in the school-age years has yet to be compiled. This literature review focuses on school readiness in the areas of literacy and mathematics. Its purposes are to provide stakeholders such as parents, caregivers, and teachers with insight into factors that research has identified as possibly contributing to children’s successful entry into formal schooling and to enable them to identify whether particular children are affected by these factors.

Many definitions of school readiness can be found in the research literature. For some, school readiness relates to students’ cognitive abilities (Nobel, Tottenham, & Casey, 2005). For others, readiness is more related to maturational, social, and emotional domains of development (Ray & Smith, 2010) or to whether or not students have the tools necessary to work effectively in a classroom setting (Carlton & Winsler, 1999). For the purposes of this study, school readiness was defined as children’s preparedness for what they are expected to know and do in academic domains and processes of learning when they enter a formal classroom setting. Rather than focusing on specific activities such as counting to ten or saying the alphabet, this definition considers such components as children’s social-emotional characteristics, cognitive processes related to conceptual understanding, and their ability to communicate about their understandings.

Methods

A systematic review of the literature was conducted over three months during the spring of 2011. The question guiding the literature review was: What predictors of school readiness in mathematics and literacy have been identified by empirical research in education?

Data Collection and Analysis

The research team determined parameters for conducting searches by first examining already published literature reviews or meta-analyses relating to early childhood literacy or mathematics and relating to issues of school readiness. Four criteria emerged for articles to be included: (1) publication after 1995; (2) publication in a reputable peer-reviewed journal; (3) grounding in empirical research; and (4) use of rigorous research methods. These criteria are similar to those used in examples found in the preliminary review of the literature (Justice, 2003; La Paro & Pianta, 2000); however, many previous analyses were limited to large scale quantitative studies. During the preliminary review, meta-analyses of this literature published in 1995 or before were identified (Bus, Ijzendoorn, & Pellegrini, 1995). Therefore, this literature review focused on research following those publications to determine if any changes have occurred.

Having established parameters, the research team searched the literature to compile articles relevant to the research question. Both criterion and snowball sampling methods were used to identify literature. For criterion sampling, the research team conducted electronic searches of a variety of databases and search engines to identify articles that met the established parameters. Snowball sampling involved examining reference sections from theoretical articles related to school readiness, school achievement, early childhood mathematics, and early childhood literacy to find empirical research relevant to the research question. Snowball sampling was also conducted on the reference sections for each empirical study identified in the review to determine if additional sources could be included.

Finally, the research team conducted preliminary readings of the articles to obtain an overall understanding of the data. Following this analysis, articles were clustered based on similarity of findings. After clustering, articles underwent a secondary analysis to establish predictors of school readiness in mathematics and literacy.

Results

In general, literature relating to predictors of success in early childhood literacy was more prevalent than literature relating to early childhood mathematics. Therefore, more predictors of success relating to literacy were identified in this review. It is likely then that the findings do not encompass all potential predictors of school readiness in mathematics.

Seven themes emerged from the literature review regarding factors associated with school readiness in mathematics and literacy: (1) child care experience; (2) family structure and parenting; (3) home environment; (4) learning-related skills; (5) social behavior; (6) mathematical and literacy-based tasks; and (7) health and socioeconomic status. The sections that follow describe findings relating to each of these themes.

Child care experience. Several studies reviewed noted correlations between children’s exposure to high-quality child care and their performance on measures of school readiness in literacy and mathematics. In a longitudinal study conducted by the NICHD Early Child Care Research Network (2002), participation in high quality, center-based child care was associated with higher language performance (NICHD, 2002). However, increased time spent in child care did not increase language performance, and a higher number of hours spent in child care was associated with increased behavior problems, as reported by caregivers (NICHD, 2002).

Ramey and Ramey (2004) reported the results of multiple randomized controlled trials investigating experiences in preschool education and their connection to school readiness. The authors identified seven types of experiences that are “essential to ensure normal brain and behavioral development and school readiness” (2004, p. 474). These experiences should: “(1) encourage exploration, (2) mentor in basic skills, (3) celebrate developmental advances, (4) rehearse and extend new skills, (5) protect from inappropriate disapproval, teasing, and punishment, (6) communicate richly and responsively, and (7) guide and limit behavior” (Ramey & Ramey, 2004, p. 474).

The authors indicate that children’s exposure to high-quality child care built around these types of experiences can better prepare children for school. Magnuson and colleagues (2004) also examined the relationship between quality of care and school readiness and, in particular, how different types of preschool experiences may affect children of economically advantaged and disadvantaged families. Child care was categorized as parental care, center-based care, Head Start, or other non-parental care. Using a sample from the Early Childhood Longitudinal Study, Kindergarten Class (ECLS-K), the authors found that children who attended center-based programs before kindergarten performed better in math and reading than children who experienced only parental care. Having attended center-based programs was associated with greater benefits for children from “disadvantaged” families than for those with higher economic status, including enhancement of mathematics performance (Magnuson, Meyers, Ruhm, & Waldfogel, 2004).

High quality child care was not always defined in the literature reviewed for this study, but some of the literature did examine aspects of high-quality care. Klein, Starkey, Clements, Sarama, and Iyer (2008) examined the effects of a preschool mathematics curriculum on children’s levels of school readiness. Their findings suggest that use of high-quality curricula implemented with fidelity can lead to higher levels of school readiness in mathematics (Klein, Starkey, Clements, Sarama, & Iyer, 2008). Bracken and Fischel (2007) examined the impact of a supplementary literacy-based curriculum on Head Start preschoolers’ mathematics and literacy achievement and social and behavior skills. More students displayed positive behavior and social skills when engaging with the supplementary curriculum; these skills were associated with higher levels of performance on literacy tasks (Bracken & Fischel, 2007). Characteristics of instruction have also been considered in determining child care quality. Chien and colleagues (2010) investigated the types of engagement young children could encounter in child care settings (free play, group or individual instruction, and scaffolded learning). Children in settings with more free play showed smaller gains than their peers on literacy and mathematics indicators at the preschool level. Individual instruction tended to be a stronger predictor of success on preschool assessments (Chien, Howes, Burchinal, Pianta, Ritchie, Bryant, Clifford, Early, & Barbarin, 2010). However, that study focused only on the types of engagement as predictors of achievement success and did not discuss the potential positive implications of free play and group instruction on other domains of development.

Parenting Style and Family Structure. Parenting styles, parent and child relationships, and family structure were considered as factors potentially related to school readiness in some of the studies in this literature review. Hill (2001) examined the relationship between parenting styles and kindergarten children’s school readiness in African-American and Euro-American families with comparable socioeconomic status. Maternal warmth or acceptance was found to be positively related to children’s performance on a pre-reading measure, while “short temper” and lack of patience were associated with lower scores. Also positively related to children’s performance were teachers’ perception of the extent to which parents valued education, and the quality of parent involvement (high quality was characterized by primarily parent-initiated involvement; lower quality by primarily teacher-initiated parent involvement). Mothers’ expectations for grades were positively related to children’s performance on the pre-reading measure. Hill (2001) also compared parenting styles to kindergarten children’s performance on a measure of quantitative concepts. Again, maternal warmth and high expectations for good grades were associated with higher scores on the pre-mathematics measure, while lack of patience was connected to lower scores. However, no significant relationship was found between children’s performance and teacher-parent contact; the teacher-parent relationship alone did not predict better performance.

Wu and Qi (2006) examined the relationship between parenting styles and African American children’s achievement in the areas of reading, math, and science. They found that parents’ perceptions of children’s abilities and expectations for good grades were strong predictors of success for students at all grade levels. These predictors were just as strong as parents’ socioeconomic status (Wu & Qi, 2006). While parental involvement is commonly cited in the literature as a strong predictor of success, in this study, parental involvement was not shown to have a large impact on student achievement. Wu and Qi (2006) reported that their study “found limited positive effects of school-based parental involvement and, in addition, some negative effects of home-based parental involvement on achievement test scores” (p. 426). Lahaie (2008) found parental involvement to be a predictor of success for children of immigrants; that study’s analysis of data from the ECLS-K indicated a correlation between higher levels of parental involvement and young children’s higher proficiency in English and mathematics.

Family structure has also been cited as an important predictive factor relating to school readiness in mathematics and literacy. For example, Entwisle and Alexander (1996) investigated the relationship between children’s literacy and mathematics school readiness and parent configuration, or family type, in a random sample of Baltimore children. Mothers who were single parents were found to have lower expectations for their children’s grades in both reading and mathematics than mothers in two-parent families. However, regardless of family type, children in families with greater economic resources and who had a parent or parents with high expectations for success “consistently outperformed other children in reading and math” (Entwisle & Alexander, 1996, p. 341).

Home Environment. The research literature on school readiness includes several studies of the relationship between daily home activities and school readiness. Clarke and Kurtz-Costes (1997) examined the educational quality of the home environment and the influence of television-watching on readiness. They interviewed children and caregivers of low-income, African-American families and compared these data to school readiness assessments. Negative correlations were found between the amount of time spent watching television and number of books in the home, and between television viewing time and amount of parent-child instructional interactions. More television viewing time also predicted lower scores on readiness assessments (Clarke & Kurtz-Costes, 1997). Wright and colleagues (2001) investigated the relations between young children’s television viewing experiences and their performance on tests of school readiness and vocabulary. Television programming was divided into 4 categories: (1) child-audience, informative or educational; (2) child-audience, fully animated cartoons with no informative purpose; (3) child-audience, other programs (neither of the above); and (4) general-audience programs. According to the authors, “for very young children [2-3], viewing informative programming designed for children was associated with subsequent letter-word skills, number skills, receptive vocabulary and school readiness” (Wright, Huston, Murphy, St. Peters, Pinon, Scantlin, & Kotler, 2001, p. 1361). The authors found this difference to be stable across the study; young children who frequently watched educational television at ages 2 and 3 performed better on a battery of tests at age 3 than did infrequent viewers. However, children who were frequent viewers of non-educational cartoons or general-audience programs at ages 2 and 3 had lower scores than infrequent viewers.

A longitudinal study of children’s reading abilities and the literacy environment in the home (Burgess & Hecht, 2002) found that the home literacy environment (HLE) was significantly related to young children’s oral language ability, word decoding ability, and phonological sensitivity. The authors define the home literacy environment in two ways: (1) Passive HLE, or “those parental activities that expose children to models of literacy usage (e.g., seeing a parent read a newspaper)” (2002, p. 413), and (2) Active HLE, or, “those parental efforts that directly engage the child in activities designed to foster literacy or language development (e.g., rhyming games, shared readings)” (2002, p. 413). In a study in the Netherlands, Leseman and de Jong (1998) examined three issues related to home literacy: the potential influence of affective factors, such as cooperation, co-construction, or social-emotional constructs; the influence of contextuality, or cultural or social background factors; and causality, the impact of home literacy on language development. They found that home literacy environment factors determined children’s school literacy achievement when controlling for confounding factors. Their findings suggest that combining exposure to literacy in the home with co-construction opportunities increased the predictive value of home literacy in relation to early literacy achievement.

Learning-related characteristics. “Learning-related characteristics” include children’s behaviors and dispositions related to engaging in tasks as well as their strategies for completing tasks. McClelland, Morrison, and Holmes (2000) studied the relationship between work-related social skills and student performance in kindergarten classrooms and again in second grade. Examples of children’s work-related social skills included the ability to follow directions, take turns in group activities, and stay on task. When child demographic information (e.g., IQ, entrance age, ethnicity, parental education level, and home literacy environment) was controlled, findings showed that work-related skills contributed to children’s academic success in mathematics. Children with poor work-related skills performed significantly worse in mathematics upon school entry and at the end of second grade (McClelland, Morrison, & Holmes, 2000). McClelland, Acock, and Morrison (2006) later examined the influence of learning-related skills in kindergarten on academic math and reading success in elementary school. In this study, the math and reading abilities of children rated as having poor learning-related skills were compared to children rated as having high learning-related skills. Findings suggested that learning-related skills such as self-regulation and social competence predicted math and reading achievement between kindergarten and sixth grade. These effects were strongest between kindergarten and second grade but were still significant through sixth grade (McClelland, Acock, & Morrison, 2006).

Social behavior. Connections between school readiness and children’s temperament, or the innate aspects of their personality, have been addressed in the research literature. A child’s tendency to display characteristics such as being active or sociable may be correlated with school readiness; Chang and Burns (2005) examined the connection between temperament and attention skills for children attending Head Start. Findings from their multiple regression analysis indicate that temperament and motivational development are related to levels of attention in young children, similar to findings from research conducted with older children (Chang & Burns, 2005).

Konold and Pianta (2005) examined the predictive value of particular cognitive processes and social behaviors related to self-regulation on typically-developing children’s kindergarten and first grade achievement. The authors developed six normative profiles of patterns of school readiness: (1) attention problems; (2) low cognitive ability; (3) low-to-average social and cognitive skills; (4) social and externalizing problems; (5) high social competence; and (6) high cognitive ability and mild externalizing (Konold & Pianta, 2005). Findings suggest that cognitive ability and social skills should be considered predictors of school readiness, and that although these factors are interrelated, they can operate independently of each other in terms of their predictive value. For example, children with high cognitive abilities performed better on achievement measures, regardless of social skills, while students with average cognitive ability and higher social competence also tended to perform at higher levels than did those with average to low cognitive ability and average social competence (Konold & Pianta, 2005).

Normandeau and Guay (1998) investigated the relationship between cognitive self-control and prosocial behaviors such as collaboration and effective communication in kindergarten-age children. Cognitive self-control was correlated with increased student achievement, which was evidenced when following these children to the end of first grade. Aggressive behaviors were negatively correlated to cognitive self-control while prosocial behaviors had a positive correlation. Children who displayed more aggressive behaviors tended to have less self-control when attempting to complete school tasks, which led to poorer student achievement (Normandeau & Guay, 1998). Dobbs and colleagues (2006) examined the relationship of prosocial behaviors to mathematics skills in preschoolers. The authors found that when students participated in an early math intervention, which consisted of over 85 mathematical tasks that their teachers could select to implement, they were less likely to display negative behaviors such as aggression or a lack of attention.

Performance on mathematical and literacy-based tasks. Correlations between young children’s readiness-related literacy and mathematics skills and their experience with mathematics- and literacy-based tasks were explored in some of the literature reviewed during this study. Tasks might include such activities as examining concepts about print (literacy) and playing number games or block building (mathematics). Siegler and Ramani (2008) examined the role that playing numerical board games could play in preparing children in low-income families for school. They found that the numerical ability of children from affluent families was significantly higher than the numerical ability of children from impoverished families; however, the gap between groups in terms of their understanding of numerical magnitude was closed as a result of the intervention (Siegler & Ramani, 2008). Following this study, Ramani and Siegler (2008) sought to determine if playing linear numerical board games had an impact on a broader range of mathematical topics and whether this impact was stable over time by exploring informal board game play in the home environment (Ramani & Siegler, 2008). They reported the positive connection between informal board game play in the home environment and numerical ability. Playing card games and video games did not have the same results (Ramani & Siegler, 2008).

Building spatial sense through block play has also been considered as a potential predictor of success in terms of school achievement in the elementary years and beyond. Hanline, Milton, and Phelps (2009) examined the relationship between block play at the preschool level and later school success in math and reading. Although no significant relationships were identified in this study between block play and later math achievement, a significant relationship was identified between block play and later reading ability. Higher levels of sophistication in young children’s representations through block construction correlated with greater success in reading during the early elementary years (Hanline, Milton, & Phelps, 2009). While block play may not be a predictor of mathematics success at the early elementary level, it has been found to be a predictor of success for later school achievement in mathematics. Wolfgang, Stannard, and Jones (2001) reported the positive predictive relationship of levels of preschool block play (as determined by the Lunzer Five Point Play Scale) and mathematics achievement during middle and high school. Similar findings were reported regarding construction-type play with LEGOs and later school achievement (Wolfgang, Stannard, & Jones, 2001).

Health and socioeconomic status. Characteristics of child and parent health have long been cited in the literature as possible correlates of children’s school readiness, and are sometimes included as confounding variables when authors are attempting to identify alternative predictors (such as child care or parent-child interactions). Janus and Duku (2007) examined five constructs they identified as having a potential impact on school readiness: (1) socioeconomic status, (2) family structure, (3) parent health, (4) child health, and (5) parent involvement. Their Early Development Instrument, an assessment of school readiness, was built around these five factors in an effort to determine which of the five factors would be most relevant in predicting school readiness. Based on this assessment, health (including current health and low-birth weight) and gender of the child (boys are twice as likely to struggle with school readiness compared to girls) were the strongest predictors. In addition, children from low-income families were twice as likely to have difficulty with school readiness as children from middle- or high-income families.

Patrianakos-Hoobler and colleagues (2009) also examined risk factors related to health of premature infants in relation to the children’s eventual school readiness. They found that boys born premature were twice as likely as girls to display lower school readiness levels. Lower readiness was also identified for premature “infants born to black mothers” as compared to “infants born to nonblack mothers” (Patrianakos-Hoobler, Msall, Marks, Huo, & Schreiber, 2009, p. 4). Socioeconomic status emerged as the “strongest barrier to achieving school readiness” (Patrianakos-Hoobler, et al., 2009, p. 5).

Low socioeconomic status has been consistently negatively correlated to school readiness in the research literature. In 1997, Stipek and Ryan studied the cognitive differences and motivation of economically advantaged and disadvantaged children at school entry. Significant cognitive differences were found relative to number skills, problem solving, and memory. Economically disadvantaged children had as much motivation for learning as economically advantaged children. However, economically advantaged children showed higher levels of concern regarding performance and decreased levels of enjoyment as the study progressed (Stipek & Ryan, 1997).

Discussion and Recommendations

Definitions of school readiness have long been under contention, and it is unclear whether the view that students should be ready for school rather than schools being ready for children is developmentally appropriate. This systematic review of empirical research literature published after 1995 and before 2013 identified seven themes for which correlates of school readiness could be categorized. Table 1 describes the 24 predictors that were categorized under each of these themes.

Table 1

Predictors of school readiness in literacy and mathematics

While the above table describes factors that hold potential for predicting young children’s school readiness, risk factors were also identified in the literature. These factors include health risks such as low birth weight, prematurity, or general health issues, as well as demographic criteria such as gender (some studies have indicated that boys are more likely to struggle than girls), family structure (single mothers tend to have lower grade expectations for their children), maternal education level (not finishing high school) or the occupation of the head of household (due to the level of income associated with this occupation). In addition, low parental income or socioeconomic status and belonging to a minority group (including African American and Hispanic ethnicities) have often been identified as risk factors for school success.

Parents, caregivers, and teachers of young children as well as the children themselves are the primary stakeholders who would benefit from early interventions designed to enhance school readiness for young children. Initiatives focusing on building positive parent-child relationships and enhancing readiness-related aspects of the home environment have the potential to influence students’ readiness and later school achievement. Future research is needed on the roles parents play in children’s academic success. Specifically, little research can be found regarding parent involvement at the early childhood and primary levels and the influence of role models on children’s positive behavior and dispositions relative to school readiness. Research on the home environment is also necessary, including the increasing role of digital technologies and how they may influence family dynamics and, in turn, children’s future school success. Further investigations of the effects of implementing literacy- and mathematics-based tasks in the home, such as increasing math talk or encouraging children to build structures in a variety of shapes, may enhance what is currently understood about how home environments affect readiness.

Given the apparent correlation between child care quality and children’s school readiness, initiatives to improve early childhood teacher quality and the overall quality of public and private child care programs could have the potential to promote children’s school readiness in literacy and mathematics. The research identified in this review did not yield consistent definitions for high-quality child care. Future studies comparing types of child care settings could help to clarify what is high-quality care, considering such components as teacher quality and instructional practices, classroom environments, and curricula (e.g., presentation of mathematical and literacy-based tasks), and the longitudinal effects of such factors on student success.

The research included in this review was not consistent regarding types of assessments used to measure children’s school readiness. Many studies indicated that an effective tool for measuring school readiness has yet to be developed (Kilday & Kinzie, 2009). Recommendations for future research include the development and validation of a school readiness assessment that measures constructs across domains of development. Once such an assessment has been validated, it could be used for further investigation of the factors identified in this review.

Finally, initiatives specifically focused on young children could include interventions that enhance prosocial behaviors, motivation toward learning, and academic skills. Such interventions could occur in the context of the home, community, or child care setting. Some factors in school readiness and success that are specifically related to demographics (e.g., socioeconomic status, entrance age, belonging to a minority group) or health (e.g., birth weight) are difficult to isolate for the purposes of interventions that might enhance or reduce their influence on children’s school readiness and success. Often these factors occur simultaneously with additional factors within the child, parent, or teacher as described above; thus, future research on ways that some predictors may mediate the effects of demographic or health-related risk factors could be particularly helpful to the field.

Given the importance attached to children’s school readiness, any research that sheds further light on its components and processes is likely to enable adult stakeholders to better discern what constitutes the best possible environments and experiences for children. These environments and experiences can provide young children with the foundation for success on whatever paths they choose in the future.

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Author Information

Dr. Sandra M. Linder is an assistant professor of early childhood mathematics education at Clemson University. Her research centers on improving early childhood teacher quality and student understandings in mathematics.

Sandra M. Linder, Ph.D.

Assistant Professor of Early Childhood Mathematics Education

Coordinator of Early Childhood Education

Clemson University, School of Education

407F Tillman Hall

Clemson, SC 29634-0705

864-656-5102

sandram@clemson.edu

M. Deanna Ramey is a Ph.D. student in curriculum and instruction at Clemson University, studying literacy and early childhood education. Her research interests include young children's experiences with informational text, motivation and engagement, and self-regulation.

V. Serbay Zambak is a Ph.D. student in curriculum and instruction at Clemson University with an emphasis on mathematics education. He has taught mathematics in İstanbul and Amsterdam and has supported practicing teachers' instructional technology skills for mathematics education.

Volume 15 Number 1

©The Author(s) 2013

Predictors of School Readiness in Literacy and Mathematics: A Selective Review of the Literature

Sandra M. Linder, Ph.D.

M. Deanna Ramey

Serbay Zambak

Clemson University, School of Education

Abstract

This paper presents findings from a selective review of the literature related to predictors of school readiness in literacy and mathematics. School readiness was defined as what children are expected to know and do in a variety of academic domains and processes of learning prior to entering a formal classroom setting. Seven themes emerged, based on a review of selected empirical research published over a sixteen-year period. Twenty-four predictors of success for school readiness were categorized under these themes. Implications for practice and recommendations for future research are presented.

Introduction

Young children are increasingly entering academically rigorous school settings where an emphasis on accountability and standards has replaced an emphasis on child development. However, many young children enter school unprepared for both academic and social expectations. Research suggests (Aunola, Leskinen, Lerkkanen, & Nurmi, 2004) that if students enter kindergarten at a disadvantage, early gaps in understandings of literacy or mathematics tend to be sustained or widened over time; this appears to be particularly true for children of poverty (McLoyd & Purtell, 2008). It is imperative for the field to identify strategies that move young children toward becoming independent and reflective learners, to increase the likelihood of their school success in later years.

In order to achieve this vision, we must first identify the specific characteristics or factors that enable certain children to enter formal schooling at an advantage while others enter at a disadvantage. Since the 1950s, researchers have investigated how external factors can influence or predict student success in school, and particularly school readiness (Milner, 1951), but a comprehensive list of factors that may affect cognitive, social, emotional, or language development in the school-age years has yet to be compiled. This literature review focuses on school readiness in the areas of literacy and mathematics. Its purposes are to provide stakeholders such as parents, caregivers, and teachers with insight into factors that research has identified as possibly contributing to children’s successful entry into formal schooling and to enable them to identify whether particular children are affected by these factors.

Many definitions of school readiness can be found in the research literature. For some, school readiness relates to students’ cognitive abilities (Nobel, Tottenham, & Casey, 2005). For others, readiness is more related to maturational, social, and emotional domains of development (Ray & Smith, 2010) or to whether or not students have the tools necessary to work effectively in a classroom setting (Carlton & Winsler, 1999). For the purposes of this study, school readiness was defined as children’s preparedness for what they are expected to know and do in academic domains and processes of learning when they enter a formal classroom setting. Rather than focusing on specific activities such as counting to ten or saying the alphabet, this definition considers such components as children’s social-emotional characteristics, cognitive processes related to conceptual understanding, and their ability to communicate about their understandings.

Methods

A systematic review of the literature was conducted over three months during the spring of 2011. The question guiding the literature review was: What predictors of school readiness in mathematics and literacy have been identified by empirical research in education?

Data Collection and Analysis

The research team determined parameters for conducting searches by first examining already published literature reviews or meta-analyses relating to early childhood literacy or mathematics and relating to issues of school readiness. Four criteria emerged for articles to be included: (1) publication after 1995; (2) publication in a reputable peer-reviewed journal; (3) grounding in empirical research; and (4) use of rigorous research methods. These criteria are similar to those used in examples found in the preliminary review of the literature (Justice, 2003; La Paro & Pianta, 2000); however, many previous analyses were limited to large scale quantitative studies. During the preliminary review, meta-analyses of this literature published in 1995 or before were identified (Bus, Ijzendoorn, & Pellegrini, 1995). Therefore, this literature review focused on research following those publications to determine if any changes have occurred.

Having established parameters, the research team searched the literature to compile articles relevant to the research question. Both criterion and snowball sampling methods were used to identify literature. For criterion sampling, the research team conducted electronic searches of a variety of databases and search engines to identify articles that met the established parameters. Snowball sampling involved examining reference sections from theoretical articles related to school readiness, school achievement, early childhood mathematics, and early childhood literacy to find empirical research relevant to the research question. Snowball sampling was also conducted on the reference sections for each empirical study identified in the review to determine if additional sources could be included.

Finally, the research team conducted preliminary readings of the articles to obtain an overall understanding of the data. Following this analysis, articles were clustered based on similarity of findings. After clustering, articles underwent a secondary analysis to establish predictors of school readiness in mathematics and literacy.

Results

In general, literature relating to predictors of success in early childhood literacy was more prevalent than literature relating to early childhood mathematics. Therefore, more predictors of success relating to literacy were identified in this review. It is likely then that the findings do not encompass all potential predictors of school readiness in mathematics.

Seven themes emerged from the literature review regarding factors associated with school readiness in mathematics and literacy: (1) child care experience; (2) family structure and parenting; (3) home environment; (4) learning-related skills; (5) social behavior; (6) mathematical and literacy-based tasks; and (7) health and socioeconomic status. The sections that follow describe findings relating to each of these themes.

Child care experience. Several studies reviewed noted correlations between children’s exposure to high-quality child care and their performance on measures of school readiness in literacy and mathematics. In a longitudinal study conducted by the NICHD Early Child Care Research Network (2002), participation in high quality, center-based child care was associated with higher language performance (NICHD, 2002). However, increased time spent in child care did not increase language performance, and a higher number of hours spent in child care was associated with increased behavior problems, as reported by caregivers (NICHD, 2002).

Ramey and Ramey (2004) reported the results of multiple randomized controlled trials investigating experiences in preschool education and their connection to school readiness. The authors identified seven types of experiences that are “essential to ensure normal brain and behavioral development and school readiness” (2004, p. 474). These experiences should: “(1) encourage exploration, (2) mentor in basic skills, (3) celebrate developmental advances, (4) rehearse and extend new skills, (5) protect from inappropriate disapproval, teasing, and punishment, (6) communicate richly and responsively, and (7) guide and limit behavior” (Ramey & Ramey, 2004, p. 474).

The authors indicate that children’s exposure to high-quality child care built around these types of experiences can better prepare children for school. Magnuson and colleagues (2004) also examined the relationship between quality of care and school readiness and, in particular, how different types of preschool experiences may affect children of economically advantaged and disadvantaged families. Child care was categorized as parental care, center-based care, Head Start, or other non-parental care. Using a sample from the Early Childhood Longitudinal Study, Kindergarten Class (ECLS-K), the authors found that children who attended center-based programs before kindergarten performed better in math and reading than children who experienced only parental care. Having attended center-based programs was associated with greater benefits for children from “disadvantaged” families than for those with higher economic status, including enhancement of mathematics performance (Magnuson, Meyers, Ruhm, & Waldfogel, 2004).

High quality child care was not always defined in the literature reviewed for this study, but some of the literature did examine aspects of high-quality care. Klein, Starkey, Clements, Sarama, and Iyer (2008) examined the effects of a preschool mathematics curriculum on children’s levels of school readiness. Their findings suggest that use of high-quality curricula implemented with fidelity can lead to higher levels of school readiness in mathematics (Klein, Starkey, Clements, Sarama, & Iyer, 2008). Bracken and Fischel (2007) examined the impact of a supplementary literacy-based curriculum on Head Start preschoolers’ mathematics and literacy achievement and social and behavior skills. More students displayed positive behavior and social skills when engaging with the supplementary curriculum; these skills were associated with higher levels of performance on literacy tasks (Bracken & Fischel, 2007). Characteristics of instruction have also been considered in determining child care quality. Chien and colleagues (2010) investigated the types of engagement young children could encounter in child care settings (free play, group or individual instruction, and scaffolded learning). Children in settings with more free play showed smaller gains than their peers on literacy and mathematics indicators at the preschool level. Individual instruction tended to be a stronger predictor of success on preschool assessments (Chien, Howes, Burchinal, Pianta, Ritchie, Bryant, Clifford, Early, & Barbarin, 2010). However, that study focused only on the types of engagement as predictors of achievement success and did not discuss the potential positive implications of free play and group instruction on other domains of development.

Parenting Style and Family Structure. Parenting styles, parent and child relationships, and family structure were considered as factors potentially related to school readiness in some of the studies in this literature review. Hill (2001) examined the relationship between parenting styles and kindergarten children’s school readiness in African-American and Euro-American families with comparable socioeconomic status. Maternal warmth or acceptance was found to be positively related to children’s performance on a pre-reading measure, while “short temper” and lack of patience were associated with lower scores. Also positively related to children’s performance were teachers’ perception of the extent to which parents valued education, and the quality of parent involvement (high quality was characterized by primarily parent-initiated involvement; lower quality by primarily teacher-initiated parent involvement). Mothers’ expectations for grades were positively related to children’s performance on the pre-reading measure. Hill (2001) also compared parenting styles to kindergarten children’s performance on a measure of quantitative concepts. Again, maternal warmth and high expectations for good grades were associated with higher scores on the pre-mathematics measure, while lack of patience was connected to lower scores. However, no significant relationship was found between children’s performance and teacher-parent contact; the teacher-parent relationship alone did not predict better performance.

Wu and Qi (2006) examined the relationship between parenting styles and African American children’s achievement in the areas of reading, math, and science. They found that parents’ perceptions of children’s abilities and expectations for good grades were strong predictors of success for students at all grade levels. These predictors were just as strong as parents’ socioeconomic status (Wu & Qi, 2006). While parental involvement is commonly cited in the literature as a strong predictor of success, in this study, parental involvement was not shown to have a large impact on student achievement. Wu and Qi (2006) reported that their study “found limited positive effects of school-based parental involvement and, in addition, some negative effects of home-based parental involvement on achievement test scores” (p. 426). Lahaie (2008) found parental involvement to be a predictor of success for children of immigrants; that study’s analysis of data from the ECLS-K indicated a correlation between higher levels of parental involvement and young children’s higher proficiency in English and mathematics.

Family structure has also been cited as an important predictive factor relating to school readiness in mathematics and literacy. For example, Entwisle and Alexander (1996) investigated the relationship between children’s literacy and mathematics school readiness and parent configuration, or family type, in a random sample of Baltimore children. Mothers who were single parents were found to have lower expectations for their children’s grades in both reading and mathematics than mothers in two-parent families. However, regardless of family type, children in families with greater economic resources and who had a parent or parents with high expectations for success “consistently outperformed other children in reading and math” (Entwisle & Alexander, 1996, p. 341).

Home Environment. The research literature on school readiness includes several studies of the relationship between daily home activities and school readiness. Clarke and Kurtz-Costes (1997) examined the educational quality of the home environment and the influence of television-watching on readiness. They interviewed children and caregivers of low-income, African-American families and compared these data to school readiness assessments. Negative correlations were found between the amount of time spent watching television and number of books in the home, and between television viewing time and amount of parent-child instructional interactions. More television viewing time also predicted lower scores on readiness assessments (Clarke & Kurtz-Costes, 1997). Wright and colleagues (2001) investigated the relations between young children’s television viewing experiences and their performance on tests of school readiness and vocabulary. Television programming was divided into 4 categories: (1) child-audience, informative or educational; (2) child-audience, fully animated cartoons with no informative purpose; (3) child-audience, other programs (neither of the above); and (4) general-audience programs. According to the authors, “for very young children [2-3], viewing informative programming designed for children was associated with subsequent letter-word skills, number skills, receptive vocabulary and school readiness” (Wright, Huston, Murphy, St. Peters, Pinon, Scantlin, & Kotler, 2001, p. 1361). The authors found this difference to be stable across the study; young children who frequently watched educational television at ages 2 and 3 performed better on a battery of tests at age 3 than did infrequent viewers. However, children who were frequent viewers of non-educational cartoons or general-audience programs at ages 2 and 3 had lower scores than infrequent viewers.

A longitudinal study of children’s reading abilities and the literacy environment in the home (Burgess & Hecht, 2002) found that the home literacy environment (HLE) was significantly related to young children’s oral language ability, word decoding ability, and phonological sensitivity. The authors define the home literacy environment in two ways: (1) Passive HLE, or “those parental activities that expose children to models of literacy usage (e.g., seeing a parent read a newspaper)” (2002, p. 413), and (2) Active HLE, or, “those parental efforts that directly engage the child in activities designed to foster literacy or language development (e.g., rhyming games, shared readings)” (2002, p. 413). In a study in the Netherlands, Leseman and de Jong (1998) examined three issues related to home literacy: the potential influence of affective factors, such as cooperation, co-construction, or social-emotional constructs; the influence of contextuality, or cultural or social background factors; and causality, the impact of home literacy on language development. They found that home literacy environment factors determined children’s school literacy achievement when controlling for confounding factors. Their findings suggest that combining exposure to literacy in the home with co-construction opportunities increased the predictive value of home literacy in relation to early literacy achievement.

Learning-related characteristics. “Learning-related characteristics” include children’s behaviors and dispositions related to engaging in tasks as well as their strategies for completing tasks. McClelland, Morrison, and Holmes (2000) studied the relationship between work-related social skills and student performance in kindergarten classrooms and again in second grade. Examples of children’s work-related social skills included the ability to follow directions, take turns in group activities, and stay on task. When child demographic information (e.g., IQ, entrance age, ethnicity, parental education level, and home literacy environment) was controlled, findings showed that work-related skills contributed to children’s academic success in mathematics. Children with poor work-related skills performed significantly worse in mathematics upon school entry and at the end of second grade (McClelland, Morrison, & Holmes, 2000). McClelland, Acock, and Morrison (2006) later examined the influence of learning-related skills in kindergarten on academic math and reading success in elementary school. In this study, the math and reading abilities of children rated as having poor learning-related skills were compared to children rated as having high learning-related skills. Findings suggested that learning-related skills such as self-regulation and social competence predicted math and reading achievement between kindergarten and sixth grade. These effects were strongest between kindergarten and second grade but were still significant through sixth grade (McClelland, Acock, & Morrison, 2006).

Social behavior. Connections between school readiness and children’s temperament, or the innate aspects of their personality, have been addressed in the research literature. A child’s tendency to display characteristics such as being active or sociable may be correlated with school readiness; Chang and Burns (2005) examined the connection between temperament and attention skills for children attending Head Start. Findings from their multiple regression analysis indicate that temperament and motivational development are related to levels of attention in young children, similar to findings from research conducted with older children (Chang & Burns, 2005).

Konold and Pianta (2005) examined the predictive value of particular cognitive processes and social behaviors related to self-regulation on typically-developing children’s kindergarten and first grade achievement. The authors developed six normative profiles of patterns of school readiness: (1) attention problems; (2) low cognitive ability; (3) low-to-average social and cognitive skills; (4) social and externalizing problems; (5) high social competence; and (6) high cognitive ability and mild externalizing (Konold & Pianta, 2005). Findings suggest that cognitive ability and social skills should be considered predictors of school readiness, and that although these factors are interrelated, they can operate independently of each other in terms of their predictive value. For example, children with high cognitive abilities performed better on achievement measures, regardless of social skills, while students with average cognitive ability and higher social competence also tended to perform at higher levels than did those with average to low cognitive ability and average social competence (Konold & Pianta, 2005).

Normandeau and Guay (1998) investigated the relationship between cognitive self-control and prosocial behaviors such as collaboration and effective communication in kindergarten-age children. Cognitive self-control was correlated with increased student achievement, which was evidenced when following these children to the end of first grade. Aggressive behaviors were negatively correlated to cognitive self-control while prosocial behaviors had a positive correlation. Children who displayed more aggressive behaviors tended to have less self-control when attempting to complete school tasks, which led to poorer student achievement (Normandeau & Guay, 1998). Dobbs and colleagues (2006) examined the relationship of prosocial behaviors to mathematics skills in preschoolers. The authors found that when students participated in an early math intervention, which consisted of over 85 mathematical tasks that their teachers could select to implement, they were less likely to display negative behaviors such as aggression or a lack of attention.

Performance on mathematical and literacy-based tasks. Correlations between young children’s readiness-related literacy and mathematics skills and their experience with mathematics- and literacy-based tasks were explored in some of the literature reviewed during this study. Tasks might include such activities as examining concepts about print (literacy) and playing number games or block building (mathematics). Siegler and Ramani (2008) examined the role that playing numerical board games could play in preparing children in low-income families for school. They found that the numerical ability of children from affluent families was significantly higher than the numerical ability of children from impoverished families; however, the gap between groups in terms of their understanding of numerical magnitude was closed as a result of the intervention (Siegler & Ramani, 2008). Following this study, Ramani and Siegler (2008) sought to determine if playing linear numerical board games had an impact on a broader range of mathematical topics and whether this impact was stable over time by exploring informal board game play in the home environment (Ramani & Siegler, 2008). They reported the positive connection between informal board game play in the home environment and numerical ability. Playing card games and video games did not have the same results (Ramani & Siegler, 2008).

Building spatial sense through block play has also been considered as a potential predictor of success in terms of school achievement in the elementary years and beyond. Hanline, Milton, and Phelps (2009) examined the relationship between block play at the preschool level and later school success in math and reading. Although no significant relationships were identified in this study between block play and later math achievement, a significant relationship was identified between block play and later reading ability. Higher levels of sophistication in young children’s representations through block construction correlated with greater success in reading during the early elementary years (Hanline, Milton, & Phelps, 2009). While block play may not be a predictor of mathematics success at the early elementary level, it has been found to be a predictor of success for later school achievement in mathematics. Wolfgang, Stannard, and Jones (2001) reported the positive predictive relationship of levels of preschool block play (as determined by the Lunzer Five Point Play Scale) and mathematics achievement during middle and high school. Similar findings were reported regarding construction-type play with LEGOs and later school achievement (Wolfgang, Stannard, & Jones, 2001).

Health and socioeconomic status. Characteristics of child and parent health have long been cited in the literature as possible correlates of children’s school readiness, and are sometimes included as confounding variables when authors are attempting to identify alternative predictors (such as child care or parent-child interactions). Janus and Duku (2007) examined five constructs they identified as having a potential impact on school readiness: (1) socioeconomic status, (2) family structure, (3) parent health, (4) child health, and (5) parent involvement. Their Early Development Instrument, an assessment of school readiness, was built around these five factors in an effort to determine which of the five factors would be most relevant in predicting school readiness. Based on this assessment, health (including current health and low-birth weight) and gender of the child (boys are twice as likely to struggle with school readiness compared to girls) were the strongest predictors. In addition, children from low-income families were twice as likely to have difficulty with school readiness as children from middle- or high-income families.

Patrianakos-Hoobler and colleagues (2009) also examined risk factors related to health of premature infants in relation to the children’s eventual school readiness. They found that boys born premature were twice as likely as girls to display lower school readiness levels. Lower readiness was also identified for premature “infants born to black mothers” as compared to “infants born to nonblack mothers” (Patrianakos-Hoobler, Msall, Marks, Huo, & Schreiber, 2009, p. 4). Socioeconomic status emerged as the “strongest barrier to achieving school readiness” (Patrianakos-Hoobler, et al., 2009, p. 5).

Low socioeconomic status has been consistently negatively correlated to school readiness in the research literature. In 1997, Stipek and Ryan studied the cognitive differences and motivation of economically advantaged and disadvantaged children at school entry. Significant cognitive differences were found relative to number skills, problem solving, and memory. Economically disadvantaged children had as much motivation for learning as economically advantaged children. However, economically advantaged children showed higher levels of concern regarding performance and decreased levels of enjoyment as the study progressed (Stipek & Ryan, 1997).

Discussion and Recommendations

Definitions of school readiness have long been under contention, and it is unclear whether the view that students should be ready for school rather than schools being ready for children is developmentally appropriate. This systematic review of empirical research literature published after 1995 and before 2013 identified seven themes for which correlates of school readiness could be categorized. Table 1 describes the 24 predictors that were categorized under each of these themes.

Table 1

Predictors of school readiness in literacy and mathematics

While the above table describes factors that hold potential for predicting young children’s school readiness, risk factors were also identified in the literature. These factors include health risks such as low birth weight, prematurity, or general health issues, as well as demographic criteria such as gender (some studies have indicated that boys are more likely to struggle than girls), family structure (single mothers tend to have lower grade expectations for their children), maternal education level (not finishing high school) or the occupation of the head of household (due to the level of income associated with this occupation). In addition, low parental income or socioeconomic status and belonging to a minority group (including African American and Hispanic ethnicities) have often been identified as risk factors for school success.

Parents, caregivers, and teachers of young children as well as the children themselves are the primary stakeholders who would benefit from early interventions designed to enhance school readiness for young children. Initiatives focusing on building positive parent-child relationships and enhancing readiness-related aspects of the home environment have the potential to influence students’ readiness and later school achievement. Future research is needed on the roles parents play in children’s academic success. Specifically, little research can be found regarding parent involvement at the early childhood and primary levels and the influence of role models on children’s positive behavior and dispositions relative to school readiness. Research on the home environment is also necessary, including the increasing role of digital technologies and how they may influence family dynamics and, in turn, children’s future school success. Further investigations of the effects of implementing literacy- and mathematics-based tasks in the home, such as increasing math talk or encouraging children to build structures in a variety of shapes, may enhance what is currently understood about how home environments affect readiness.

Given the apparent correlation between child care quality and children’s school readiness, initiatives to improve early childhood teacher quality and the overall quality of public and private child care programs could have the potential to promote children’s school readiness in literacy and mathematics. The research identified in this review did not yield consistent definitions for high-quality child care. Future studies comparing types of child care settings could help to clarify what is high-quality care, considering such components as teacher quality and instructional practices, classroom environments, and curricula (e.g., presentation of mathematical and literacy-based tasks), and the longitudinal effects of such factors on student success.

The research included in this review was not consistent regarding types of assessments used to measure children’s school readiness. Many studies indicated that an effective tool for measuring school readiness has yet to be developed (Kilday & Kinzie, 2009). Recommendations for future research include the development and validation of a school readiness assessment that measures constructs across domains of development. Once such an assessment has been validated, it could be used for further investigation of the factors identified in this review.

Finally, initiatives specifically focused on young children could include interventions that enhance prosocial behaviors, motivation toward learning, and academic skills. Such interventions could occur in the context of the home, community, or child care setting. Some factors in school readiness and success that are specifically related to demographics (e.g., socioeconomic status, entrance age, belonging to a minority group) or health (e.g., birth weight) are difficult to isolate for the purposes of interventions that might enhance or reduce their influence on children’s school readiness and success. Often these factors occur simultaneously with additional factors within the child, parent, or teacher as described above; thus, future research on ways that some predictors may mediate the effects of demographic or health-related risk factors could be particularly helpful to the field.

Given the importance attached to children’s school readiness, any research that sheds further light on its components and processes is likely to enable adult stakeholders to better discern what constitutes the best possible environments and experiences for children. These environments and experiences can provide young children with the foundation for success on whatever paths they choose in the future.

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Author Information

Dr. Sandra M. Linder is an assistant professor of early childhood mathematics education at Clemson University. Her research centers on improving early childhood teacher quality and student understandings in mathematics.

Sandra M. Linder, Ph.D.

Assistant Professor of Early Childhood Mathematics Education

Coordinator of Early Childhood Education

Clemson University, School of Education

407F Tillman Hall

Clemson, SC 29634-0705

864-656-5102

sandram@clemson.edu

M. Deanna Ramey is a Ph.D. student in curriculum and instruction at Clemson University, studying literacy and early childhood education. Her research interests include young children's experiences with informational text, motivation and engagement, and self-regulation.

V. Serbay Zambak is a Ph.D. student in curriculum and instruction at Clemson University with an emphasis on mathematics education. He has taught mathematics in İstanbul and Amsterdam and has supported practicing teachers' instructional technology skills for mathematics education.