Annotated Bibliography And Outline For PhD Doctorate

PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES

Fearless Dominance and the U.S. Presidency: Implications of Psychopathic Personality Traits for Successful and Unsuccessful Political Leadership

Scott O. Lilienfeld, Irwin D. Waldman, and Kristin Landfield

Emory University

Ashley L. Watts University of Georgia

Steven Rubenzer Houston, Texas

Thomas R. Faschingbauer Foundation for the Study of Personality in History, Houston,

Texas

Although psychopathic personality (psychopathy) is marked largely by maladaptive traits (e.g., poor impulse control, lack of guilt), some authors have conjectured that some features of this condition (e.g., fearlessness, interpersonal dominance) are adaptive in certain occupations, including leadership positions. We tested this hypothesis in the 42 U.S. presidents up to and including George W. Bush using (a) psychopathy trait estimates derived from personality data completed by historical experts on each president, (b) independent historical surveys of presidential leadership, and (c) largely or entirely objective indicators of presidential performance. Fearless Dominance, which reflects the boldness associated with psychopathy, was associated with better rated presidential performance, leadership, persuasiveness, crisis management, Congressional relations, and allied variables; it was also associated with several largely or entirely objective indicators of presidential perfor- mance, such as initiating new projects and being viewed as a world figure. Most of these associations survived statistical control for covariates, including intellectual brilliance, five factor model personality traits, and need for power. In contrast, Impulsive Antisociality and related traits of psychopathy were generally unassociated with rated presidential performance, although they were linked to some largely or entirely objective indicators of negative job performance, including Congressional impeachment resolutions, tolerating unethical behavior in subordinates, and negative character. These findings indicate that the boldness associated with psychopathy is an important but heretofore neglected predictor of presidential performance, and suggest that certain features of psychopathy are tied to successful interpersonal behavior.

Keywords: psychopathy, antisocial behavior, leadership, politics, personality

Psychopathic personality (psychopathy) is a constellation of personality traits encompassing superficial charm, egocentricity, dishonesty, guiltlessness, callousness, risk taking, poor impulse

control (Cleckley, 1941/1988; Hare, 2003), and, according to many authors (Fowles & Dindo, 2009; Lykken, 1995; Patrick, 2006), fearlessness, social dominance, and immunity to anxiety. In contrast to the Diagnostic and Statistical Manual of Mental Disor- ders, fourth edition, text revision (DSM–IV–TR; American Psychiatric Association, 2000), diagnosis of antisocial personality disorder (ASPD), which is primarily a behavioral condition that emphasizes a long-standing history of antisocial and criminal behavior, psychopa- thy is primarily a dispositional condition that emphasizes personality traits. Nevertheless, measures of these two conditions tend to be at least moderately correlated (Lilienfeld, 1994).

Factor analyses of the most extensively validated measure of psychopathy, the Psychopathy Checklist–Revised (PCL-R; Hare, 2003), have often revealed two broad and moderately correlated dimensions. The first dimension (Factor 1) assesses the core in- terpersonal and affective features of psychopathy (e.g., guiltless- ness, narcissism, glibness), whereas the second dimension (Factor 2) assesses an impulsive and antisocial lifestyle that is closely associated with ASPD (Harpur, Hare, & Hakstian, 1989; but see Cooke & Michie, 2001, and Hare, 2003, for alternative factor

This article was published Online First July 23, 2012. Scott O. Lilienfeld, Irwin D. Waldman, and Kristin Landfield, Depart-

ment of Psychology, Emory University; Ashley L. Watts, Department of Psychology, University of Georgia; Steven Rubenzer, Houston, Texas; Thomas R. Faschingbauer, Foundation for the Study of Personality in History, Houston, Texas.

We thank Joanna Berg, Rachel Ammirati, David Molho, Gabriella Rich, Zack Babin, Marie King, and Barbara Greenspan for their helpful com- ments on previous drafts of this manuscript; Joshua Miller for his statistical assistance; Alan Abramowitz for his helpful advice; and Caroline Hennigar and Alyssa Redmon for their valuable assistance with data entry and library research.

Correspondence concerning this article should be addressed to Scott O. Lilienfeld, Room 473, Psychology and Interdisciplinary Sciences Building, Emory University, 36 Eagle Row, Atlanta, GA 30322. E-mail: slilien@emory.edu

Journal of Personality and Social Psychology, 2012, Vol. 103, No. 3, 489–505 © 2012 American Psychological Association 0022-3514/12/$12.00 DOI: 10.1037/a0029392

489

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solutions). Although the PCL-R is a semistructured interview that incorporates file information, its two major dimensions can be closely approximated by scores on normal range personality di- mensions, such as those derived from the five-factor model (FFM) of personality. PCL-R Factor 1 is associated primarily with low scores on FFM Agreeableness, whereas PCL-R Factor 2 is asso- ciated primarily with low scores on both FFM Agreeableness and Conscientiousness (Miller, Lynam, Widiger, & Leukefeld, 2001). Most research demonstrates that psychopathy and its constituent traits are underpinned by dimensions rather than taxa (natural categories; see Edens, Marcus, Lilienfeld, & Poythress, 2006), offering empirical support for recent efforts to conceptualize and assess this condition within a general dimensional model of per- sonality structure.

Most research on the behavioral manifestations of psychopathy has focused on its relations with antisocial, criminal, and otherwise unsuccessful actions. Studies demonstrate that psychopathy is a risk factor for criminality and violent recidivism among prison inmates (Porter & Woodworth, 2006; Salekin, Rogers, & Sewell, 1996) as well as cheating among college students (Williams, Nathanson, & Paulhus, 2010). In addition, some authors have argued that psychopathy is associated with malignant workplace behavior. Babiak and Hare (2006) referred to psychopaths in business settings as “snakes in suits” and suggested that their propensity toward dishonesty and manipulativeness makes them destructive coworkers and bosses (see also Boddy, 2006; Heinze, Allen, Magai, & Ritzler, 2010).

Despite the lengthy research tradition linking psychopathy to unsuccessful behavior, a consistent strand of clinical lore has tied psychopathy, or at least certain features of it, to socially successful behavior across a variety of domains, including the business world, politics, and everyday life (Lilienfeld, 1998). In his classic writ- ings, Cleckley (1941/1988) referred to individuals with marked psychopathic traits whose “outward appearance may include busi- ness or professional careers that continue in a sense successful, and which are truly successful when measured by financial reward or even by the casual observer’s opinion of real accomplishment” (p. 191). Extending these observations, Lykken (1982) referred to psychopaths and heroes as “twigs from the same branch” (p. 22) and conjectured that the fearlessness associated with psychopathy can predispose to heroic behaviors. Other authors have raised the possibility of “subclinical” (Widom, 1977) or “successful” (Hall & Benning, 2006; Mullins-Sweatt, Glover, Miller, Derefinko, & Wi- diger, 2010) psychopaths, individuals with pronounced psycho- pathic traits who function effectively in circumscribed “adaptive niches” of society, such as politics, business, law enforcement, and high-risk sports. In one of the few studies to address this issue empirically, Babiak, Neumann, and Hare (2010) examined a sam- ple of 203 corporate professionals and found that scores on the PCL-R and its component factors were associated not only with a more problematic management style and with being a poor team player but also with superior communication skills, creativity, and strategic thinking. These important results raise the possibility that psychopathy, or at least some features of it, are associated with certain aspects of adaptive functioning in workplace settings, al- though they may also be associated with certain aspects of mal- adaptive functioning. Nevertheless, because the PCL-R ratings in this study were conducted by a single individual who was not blind to other information about participants, including information po-

tentially relevant to criterion ratings, these results should be viewed as preliminary.

Still others have speculated that some psychopathic traits, such as interpersonal dominance, persuasiveness, and venturesomeness, may be conducive to acquiring positions of political power and to successful leadership (Hogan, Raskin, & Fazzini, 1990; Lobac- weski, 2007). Indeed, Lykken (1995) speculated that British Prime Minister Winston Churchill and U.S. president Lyndon Baines Johnson possessed certain personality features of psychopathy: They started off life as “daring, adventurous, and unconventional youngsters who began playing by their own rules” (p. 116) but later managed to parlay these traits into political success.

Nevertheless, the successful manifestations of psychopathy re- main largely in the realm of clinical conjecture. Moreover, with the exception of the study by Babiak et al. (2010), the scattered research in this domain (e.g., Ishakawa, Raine, Lencz, Bihrle, & LaCasse, 2001; Widom, 1977) has focused almost exclusively on psychopathic individuals who have engaged in minimal antisocial behavior or managed to escape detection by the legal system, rather than those who are clearly successful from an interpersonal or societal standpoint (Hall & Benning, 2006).

Recent work on a widely used and well-validated self-report psychopathy measure, the Psychopathic Personality Inventory (PPI; Lilienfeld & Andrews, 1996), may shed light on this issue. Exploratory factor analyses of the PPI (Benning, Patrick, Hicks, Blonigen, & Krueger, 2003) in community samples have identified two largely uncorrelated higher order dimensions, Fearless Dom- inance (FD) and Impulsive Antisociality1 (IA; but see Neumann, Malterer, & Newman, 2008, for an alternative factor structure of the PPI). FD, which assesses what Patrick, Fowles, and Krueger (2009) term “boldness,” comprises such traits as social dominance, charm, physical fearlessness, and immunity to anxiety; IA com- prises such traits as egocentricity, manipulativeness, poor impulse control, rebelliousness, and tendency to externalize blame. Al- though these two factors bear some similarities to the two major PCL-R factors, they are not isomorphic with them empirically or conceptually. In particular, although IA and PCL-R Factor 2 are moderately to highly correlated, FD and PCL-R Factor 1 are only weakly correlated (Malterer, Lilienfeld, Newman, & Neumann, 2010), largely because FD assesses a more psychologically adap- tive set of traits than does PCL-R Factor 1 (Patrick, 2006).

Several studies have demonstrated that the boldness assessed by FD is associated with healthy psychological adjustment—and may reflect many of the traits commonly attributed to successful psy- chopathy—whereas IA is associated with psychological maladjust- ment. Offering provisional corroboration for Lykken’s (1982) con- jecture regarding fearlessness and heroism, Patrick, Edens, Poythress, Lilienfeld, and Benning (2006) found that in a sample of 96 prisoners, FD scores derived from the PPI were significantly and positively associated with self-reported heroic behaviors (e.g., breaking up fights in public, helping stranded motorists), whereas IA scores were significantly and negatively associated with these behaviors. In addition, PPI-derived FD is negatively correlated

1 In the revised version of the PPI (Lilienfeld & Widows, 2005), this dimension is termed Self-Centered Impulsivity. Nevertheless, we use the term Impulsive Antisociality here to retain continuity with most of the extant literature (e.g., Benning et al., 2003).

490 LILIENFELD ET AL.

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Test reliability determined by a correlation between scores from the same test taken at two different times is called

Complete the following quiz. Choose your response by highlighting your answer.

1.When we perform an experiment, we

a. measure independent variables,

b. produce dependent variables.

c. produce control variables.

d. produce a comparison.

e. hold independent variables constant.

2. The control group in an experiment

a. fixes the level of a variable across all experimental conditions.

b. is often untreated.

c. receives the same level of the independent variable as the experimental group.

d. refers to the manipulation of the independent variable.

3. In research on the decompression of pregnant rats, the independent variable is ______, a dependent variable is ________, and a control variable is _______________.

a. Reduced air pressure; behavioral tests; strain of the rat

b. Body weight; climbing ability; time of day

c. Atmospheric pressure; age of rat; climbing ability

d. Number of decompressions; body weight; home cage

e. Experimental group; control group; test performance

4. In experiments, independent variables are

a. the result of careful measurements.

b. extraneous to the experiment and held constant.

c. extraneous to the experiment and allowed to vary randomly.

d. independent of experimenter control.

e. varied by the researcher.

5. Dependent variables are

a. manipulated by the researcher.

b. potential independent variables that are held constant.

c. measured by the researcher.

d. probable behavioral causes.

6. One reason a valid experiment may produce null results is

a. the range of levels in the independent variable was insufficient to show an effect.

b. the dependent variable reflects a broad range of performance.

c. that the experiment is conducted in an environment that is too difficult.

d. that reactivity occurs in the participants (e.g., they adopt the role of “good behavior”).

7. In experiments, the independent variable should be _________, the dependent variable should be __________, and the control variable should be ________.

a. controlled; constant; randomized

b. constant; an effect; causal

c. free; restricted; elevated

d. balanced; unconfounded; an effect

e. manipulated; measured; held constant

8. An interaction occurs when

a. an independent variable effects a dependent variable.

b. one independent variable effects a second independent variable.

c. the effect one dependent variable has is not the same at each level of a second dependent variable.

d. the effect one independent variable has is not the same at each level of a second independent variable.

9. Which of the following is an example of the Hawthorne effect?

a. Experimenter bias

b. Reactivity in an experiment

c. Participant observation

d. Unobtrusive outcomes

10.  A variable that inadvertently causes an experimental result is

a.  confounded with the dependent variable.

b.  confounded with the independent variable.

c.  confounded with the control variables.

d.  unlikely to be important in experiments.

11.  Construct validity permits one to do which of the following?

a.  Generalize

b.  Attribute causality

c.  Have confidence in constructs

d.  Support hypothesis

12.  Which of the following is a source of construct invalidity?

a.  Bias

b.  Random error

c.  Carry-over effects

d.  Counterbalancing

13.  If a study has external validity, one is entitled to

a.  generalize.

b.  attribute causality.

c.  have confidence in constructs.

d.  support hypotheses.

14.  Internal validity allows one to do which of the following?

a.  Generalize

b.  Attribute causality

c.  Have confidence in constructs

d.  Support hypotheses

15.  Which of the following is the most likely to have the greatest internal validity?

a.  Surveys

b.  Case studies

c.  Relational research

d.  Experiments

16.  Test reliability determined by a correlation between scores from the same test taken at two different times is called

a.  test-retest reliability.

b.  parallel forms reliability.

c.  split-half reliability.

d.  predictive reliability.

17.  Statistical reliability determines whether results

a.  will occur five percent of the time.

b.  occur because of chance.

c.  are internally valid.

d.  are produced by bias.

18.  Which of the following is a major threat to internal validity?

a.  Confounding

b.  Deviant-case analysis

c.  Truncated range

d.  Dependent variables

19.  A type of validity that is specifically concerned with being able to make causal statements about relationships between variables is _______________ validity.

a.  External

b.  Internal

c.  Construct

d.  Predictive

20.  A replication of research helps to determine ______________ validity.

a.  Construct

b.  External

c.  Internal

d.  Predictive

Team B-Statistics Project Amal Andersen Jessica Bogunovich Jocelyn Cuff Zachary Ramoz PSYCH 625 Mary Sue Farmer April 13, 2015 1 Introduction Key Terms Degrees of Freedom Descriptive Statistics Interval ratio variables Pearson Product-Movement Correlation Positive correlation Significance Level 1. Degrees of Freedom is a value, which is different for different statistical tests, that approximates the sample size of number of individual cells in an experimental design. Descriptive statistics are values that organize and describe the characteristics of a collection of data, sometimes called a data set. Interval variables are those that measure a variable by giving a numerical value in steps Pearson Product-Movement correlations show the strength of a relationship using summations of values from each axis, the summation of the squares of the data points for each axis, and takes the sample number all into a neat equation. Positive correlations show a relationship between variables and a trend moving in the same direction be it small to great or great to small. Significance level is the risk set by the researcher for rejecting a null hypothesis when it is true. 3 Independent T-Test Another analysis we decided to run on the data set was an independent t-test comparing the means of reading, math, and total test scores between males and females. The independent t-test was used because this analysis deals with two groups and the participants were not being tested more than once (per topic over time). 4 Independent T-Test Results Degrees of freedom = 48 for all three tests (math, reading and total score) Math t value = -.487 Significance = .628 Reading t value = -1.250 Significance = .217 Total t value = -.956 Significance = .344 The SPSS output for the independent t-test on the previous slide demonstrates the t value of reading scores, math scores and total test scores, as well as the degrees of freedom (48 for all three computations). If one were to analyze the math scores only, they would find the t value to be -.487 and the significance to be .628. Analyzing the reading scores only we find the t value to be 1.250 and the significance to be .217. Analyzing the total score only one would find the obtained value, or t value, to be -.956 and find the significance to be .344 (p=.344). 5 Pearson Product-Movement Correlation Correlations TESTPREP MATHSCORE TESTPREP Pearson Correlation 1 .653** Sig. (1-tailed) .000 N 50 50 MATHSCORE Pearson Correlation .653** 1 Sig. (1-tailed) .000 N 50 50 **. Correlation is significant at the 0.01 level (1-tailed). The Pearson Product-Movement was run through SPSS to show a bivariate correlation between test preparation and how well the participants scored on the math portion of the test. The chart displays the numbers in a more readable, decipherable fashion with test prep as the x-axis and math score as the y-axis. The two varaibles, test prep and math score, are interval/ratio varaibles, thus the easy conversion to a correlation. 6 Pearson Product-Movement Correlation Results Positive Correlation = .653 As test prep number increases, so does the math score An up slope The visual representation shows the relationship Significance Shoes a relationship Not very strong Meaningful? The results of the correlation show a positive relationship. As the number of hours of test prep, the x-axis, increases so do does the score on the math test, y-axis. The direction of this positive relationship goes up. The scatterplot helps to being a visual representation to the chart for more discernable visuals. Although there is a decent correlation number, at .653, it seems the relationship is not very strong. This is due to the median time of 2 having many points. Also, the outliers also bring down the significance level as well. These results do show there is a relationship between the two varibles and one could argue that more test prep may yield a higher test score; However, it should be noted the realtionship is not high on significance thus making meaningfulness come to question. 7 Descriptive Statistics Descriptive statistics are used to describe the common data from a study (Salkind, 2014). These deliver summaries about the sample used in the study, as well as the types of measures that were used. The descriptive statistics combined with analytical visuals provide a quantitative analysis of the data. Descriptive statistics describe what the data is and illustrates. These are useful when trying to present and describe quantitative data descriptions in manageable pieces (Salkind, 2014). Researchers are able to simplify huge amounts of data in a meaningful way, as each descriptive statistics reduces the large amounts of data into a smaller summary. 8 Descriptive Statistics Summary 50 total participants 26 males 24 females Ages ranged from 25-40 Average age=32 Reading Test Scores ranged from 45-9 Average reading score=75.58 Math Test Scores ranged from 45 to 92 Average math score=75 Total Test Scores ranged from 95 to 186 Average total test score=150.78 Analytical data from a test group of 50 people was collected and studied. There were 26 males and 24 females in the test group. The participants were surveyed on age, sex, years of college experience, caffeine consumption, test prep, as well as math, reading and comprehensive test scores. This analysis focuses on descriptive statistics and uses the age, math score, reading score and total test score variables. The descriptive statistics demonstrate that the age of the participants ranges from 25 to 40 and the participants have an average age of 32. Math scores range from 45 to 92, and the average math score was 75. Reading scores range from 45 to 96 and the average reading score was 75.78. Total scores ranged from 95 to 186 and the average total score was 150.78. 9 Conclusion References Salkind, N. (2014). Statistics for people who think they hate statistics (5th ed.). Thousand Oaks, CA: Sage Publishing.

Please answer the following in 60 or more words per topic.

 

When would a researcher use a t test of independent means? Provide an example.

 

When would a researcher use a t test of dependent means? Provide an example.

 

What is an analysis of variance (ANOVA)? Describe the theory underlying it.

 

 

When would a researcher use ANOVA for data analysis? Provide an example

Psych 625 Week 6 Team Introduction And Summary

Team B-Statistics Project

Amal Andersen

Jessica Bogunovich

Jocelyn Cuff

Zachary Ramoz

PSYCH 625

Mary Sue Farmer

April 13, 2015

1

 

Introduction

 

Key Terms

Degrees of Freedom

Descriptive Statistics

Interval ratio variables

Pearson Product-Movement Correlation

Positive correlation

Significance Level

1. Degrees of Freedom is a value, which is different for different statistical tests, that approximates the sample size of number of individual cells in an experimental design.

Descriptive statistics are values that organize and describe the characteristics of a collection of data, sometimes called a data set.

Interval variables are those that measure a variable by giving a numerical value in steps

Pearson Product-Movement correlations show the strength of a relationship using summations of values from each axis, the summation of the squares of the data points for each axis, and takes the sample number all into a neat equation.

Positive correlations show a relationship between variables and a trend moving in the same direction be it small to great or great to small.

Significance level is the risk set by the researcher for rejecting a null hypothesis when it is true.

3

Independent T-Test

Another analysis we decided to run on the data set was an independent t-test comparing the means of reading, math, and total test scores between males and females. The independent t-test was used because this analysis deals with two groups and the participants were not being tested more than once (per topic over time).

4

Independent T-Test Results

Degrees of freedom = 48 for all three tests (math, reading and total score)

Math

t value = -.487

Significance = .628

Reading

t value = -1.250

Significance = .217

Total

t value = -.956

Significance = .344

 

The SPSS output for the independent t-test on the previous slide demonstrates the t value of reading scores, math scores and total test scores, as well as the degrees of freedom (48 for all three computations). If one were to analyze the math scores only, they would find the t value to be -.487 and the significance to be .628. Analyzing the reading scores only we find the t value to be 1.250 and the significance to be .217. Analyzing the total score only one would find the obtained value, or t value, to be -.956 and find the significance to be .344 (p=.344).

5

Pearson Product-Movement Correlation

 

Correlations

TESTPREP MATHSCORE

TESTPREP Pearson Correlation 1 .653**

Sig. (1-tailed) .000

N 50 50

MATHSCORE Pearson Correlation .653** 1

Sig. (1-tailed) .000

N 50 50

**. Correlation is significant at the 0.01 level (1-tailed).

 

The Pearson Product-Movement was run through SPSS to show a bivariate correlation between test preparation and how well the participants scored on the math portion of the test. The chart displays the numbers in a more readable, decipherable fashion with test prep as the x-axis and math score as the y-axis. The two varaibles, test prep and math score, are interval/ratio varaibles, thus the easy conversion to a correlation.

6

Pearson Product-Movement Correlation Results

Positive

Correlation = .653

As test prep number increases, so does the math score

An up slope

The visual representation shows the relationship

Significance

Shoes a relationship

Not very strong

Meaningful?

The results of the correlation show a positive relationship. As the number of hours of test prep, the x-axis, increases so do does the score on the math test, y-axis. The direction of this positive relationship goes up. The scatterplot helps to being a visual representation to the chart for more discernable visuals. Although there is a decent correlation number, at .653, it seems the relationship is not very strong. This is due to the median time of 2 having many points. Also, the outliers also bring down the significance level as well. These results do show there is a relationship between the two varibles and one could argue that more test prep may yield a higher test score; However, it should be noted the realtionship is not high on significance thus making meaningfulness come to question.

7

Descriptive Statistics

Descriptive statistics are used to describe the common data from a study (Salkind, 2014). These deliver summaries about the sample used in the study, as well as the types of measures that were used. The descriptive statistics combined with analytical visuals provide a quantitative analysis of the data. Descriptive statistics describe what the data is and illustrates. These are useful when trying to present and describe quantitative data descriptions in manageable pieces (Salkind, 2014). Researchers are able to simplify huge amounts of data in a meaningful way, as each descriptive statistics reduces the large amounts of data into a smaller summary.

 

8

Descriptive Statistics Summary

50 total participants

26 males

24 females

Ages ranged from 25-40

Average age=32

Reading Test Scores ranged from 45-9

Average reading score=75.58

Math Test Scores ranged from 45 to 92

Average math score=75

Total Test Scores ranged from 95 to 186

Average total test score=150.78

Analytical data from a test group of 50 people was collected and studied. There were 26 males and 24 females in the test group. The participants were surveyed on age, sex, years of college experience, caffeine consumption, test prep, as well as math, reading and comprehensive test scores. This analysis focuses on descriptive statistics and uses the age, math score, reading score and total test score variables. The descriptive statistics demonstrate that the age of the participants ranges from 25 to 40 and the participants have an average age of 32. Math scores range from 45 to 92, and the average math score was 75. Reading scores range from 45 to 96 and the average reading score was 75.78. Total scores ranged from 95 to 186 and the average total score was 150.78.

9

Conclusion

 

References

Salkind, N. (2014). Statistics for people who think they hate statistics (5th ed.). Thousand Oaks, CA: Sage Publishing.