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Stereotype threat is the threat of confirming a negative stereotype about a group with which a person identifies. Researchers have found that stereotype threat can result in underperformance in multiple domains, shifts in social behavior, and shifts in assessed implicit attitudes, the likelihood of which increases as an individual's concern about the domain of interest increases. According to theory, this threat can be "alleviated",thereby diminishing or eliminating its impact. In this project, over the course of two experiments, the impact of stereotype threat and stereotype threat-alleviation on explicit self-report measures are examined. In experiment one, white college student participants were exposed (or not) to an on-line task intended to elicit race-based stereotype threat. Differences in reporting style (i.e., bias) between the two groups on self-reported measures of race-related attitudes were examined. It was hypothesized that the group exposed to stereotype threat would endorse lower racism and lower stereotypicality (i.e., stereotypic "White" behaviors, attitudes, adjectives, and beliefs). The data provided only partial support for the hypothesis - the threat group reported significantly less stereotypicality than the non threat group. However, the groups were not statistically different on measures of racism or race and social policy. In experiment two, again examining white college students who participated on-line, a stereotype threat-alleviation task was added, and whether this diminished or removed bias was examined. It was hypothesized the threat group would endorse lower stereotypicality and racism than the non threat group and the group receiving the threat alleviation task. The findings from study one did not replicate in study two. Instead, contrary to predictions, across measures of racism and stereotypicality, it was the non threat group that consistently showed the lowest scores. Potential explanations for these findings are offered, including the possibility of having eliciting stereotype threat, cognitive dissonance, or both for the threat and non threat groups via their filler task. Finally, implications for assessing, broaching, and reducing stereotype threat in clinical and research applications are also discussed. / Ph. D.

It is critical for researchers to be confident of the quality of survey data. Problems with data quality often relate to measurement method design, through choices made by researchers in their creation of standardised measurement instruments. This is known to affect the way respondents interpret and respond to these instruments, and can result in substantial measurement error. Current methods for removing measurement error are post-hoc and have been shown to be problematic. This research proposes that innovations can be made through the creation of measurement methods that take respondents' individual cognitions into consideration, to reduce measurement error in survey data. Specifically, the aim of the study was to develop and test a measurement instrument capable of having respondents individualise their own rating-scales. A mixed methodology was employed. The qualitative phase provided insights that led to the development of the Individualised Rating-Scale Procedure (IRSP). This electronic measurement method was then tested in a large multi-group experimental study, where its measurement properties were compared to those of Likert-Type Rating-Scales (LTRSs). The survey included pre-validated psychometric constructs which provided a baseline for comparing the methods, as well as to explore whether certain individual characteristics are linked to respondent choices. Structural equation modelling was used to analyse the survey data. Whilst no strong associations were found between individual characteristics and respondent choices, the results demonstrated that the IRSP is reliable and valid. This study has produced a dynamic measurement instrument that accommodates individual-level differences, not addressed by typical fixed rating-scales.

Noncontact, the failure of a postal survey sample member to receive a survey request, is a potential source of nonresponse bias that has largely been ignored. This is due to the difficulty of separating the components of nonresponse in postal surveys when nothing is heard from potential respondents. Yet, the need to understand postal nonresponse is increasing as more studies move to mixed mode designs incorporating a postal element, and technological, resource and societal changes increase the attractiveness of self-administered surveys. Thus, this research sought to estimate the level of noncontact in postal surveys, to identify the direction and magnitude of bias due to it, and to investigate targeted in-field mechanisms for reducing this bias. A series of empirical studies involving New Zealand postal surveys fielded between 2001 and 2006 were undertaken to meet these aims. Noncontact was found to relate to survey-independent demographic variables (e.g., age, household composition). Furthermore, its incidence was estimated to be as much as 400% higher than indicated by ‘gone, no address’ (GNA) returns, although an envelope message tested as part of the research was able to increase levels of GNA reporting significantly. Thus, noncontact was established as a nontrivial source of nonresponse in the surveys examined. As far as bias is concerned, noncontacts had a different profile compared to refusers and ineligibiles, and were estimated to account for up to 40% of net nonresponse error for some of the variables in the surveys examined. Accordingly, there appears to be a clear opportunity for methods targeted at reducing noncontact bias to improve final survey estimates for a range of items. A number of potential methods for reducing noncontact bias were explored, but only one had both a compelling theoretical foundation and potential for wide applicability; the noncontact propensity sampling (NPS) scheme. In a resampling simulation study a prototype of the scheme, which increases the selection probabilities for sample units with a higher predicted propensity for noncontact, consistently improved the demographic profile of valid postal survey returns compared to a simple random sample (SRS). Furthermore, the scheme reduced nonresponse bias by an average of 28% as measured against a range of frame variables (e.g., age, gender) and 17% as measured against survey variables for which census parameters were known (e.g., religiosity, household size, qualifications, income and marital status). Although the prototype NPS procedure increased the standard deviation of simulated point estimates for a given sample size (1,500 in this research), the effect was small; an average of 4% for frame variables and 2% for survey variables. Furthermore, the scheme had almost no impact on reported cooperation rates and is likely to be cost effective compared to other potential targeted in-field mechanisms, particularly in situations where researchers regularly survey a specific population. Pairing the scheme with three common post-survey adjustment methods (frame or census age/sex cell weighing, and response wave extrapolation) did not lead to consistently better estimates than an unweighted SRS. But this was largely due to the shortcomings of these methods because in many cases combining them with either sampling scheme (SRS or NPS) actually degraded estimates. This reinforces the idea that researchers should expend effort minimising bias during the field period rather than relying on post-survey weighting to deal with the issue. Finally, since the NPS scheme aims to reduce error due to noncontact but is not expected to affect error due to other components (e.g., refusal, ineligibility), it presents an opportunity for researchers to begin decomposing the various facets of postal survey nonresponse bias, an important precursor to the development of other targeted bias reduction interventions. Thus, as a methodological tool, the NPS scheme may serve a dual role as both a bias reduction and decomposition mechanism. In addition to their implications for postal survey research, the methods developed and insights into noncontact established in this research are likely to have applications in other domains. In particular, they will inform activities such as research into online survey nonresponse, organisational database management cost reduction and list procurement.

Faces provide cues for judgments regarding the emotional state of individuals. Using signal-detection methodology and a standardized stimulus set, the overall aim of the present dissertation was to investigate the detection of emotional facial expressions (i.e., angry and happy faces) with neutral expressions as the nontarget stimuli. Study I showed a happy-superiority effect and a bias towards reporting happiness in female faces. As work progressed, questions arose regarding whether the emotional stimuli were equal with regard to perceived strength of emotion, and whether the neutral faces were perceived as neutral. To further investigate the effect of stimulus quality on the obtained findings, Study II was designed such that the facial stimuli were rated on scales of happy-sad, angry-friendly, and emotionality. Results showed that ‘neutral’ facial expressions were not rated as neutral, and that there was a greater perceived distance between happy and neutral faces than between angry and neutral faces. These results were used to adjust the detectability measures to compensate for the varying distances of the angry and happy stimuli from the neutral stimuli in the emotional space. The happy-superiority effect was weakened, while an angry-female disadvantage remained. However, as these results were based upon different participant groups for detection and emotional rating, Study III was designed to investigate whether the results from Studies I and II could be replicated in a design where the same participants performed both tasks. Again, the results showed the non-neutrality of ‘neutral’ expressions and that happiness was more easily detected than anger, as shown in general emotion as well as specific emotion detection. Taken together, the overall results of the present dissertation demonstrate a happy-superiority effect that was greater for female than male faces, that angry-female faces were the most difficult to detect, and a bias to report female faces as happy. / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: In press. Paper 2: Manuscript. Paper 3: Manuscript.

It is critical for researchers to be confident of the quality of survey data. Problems with data quality often relate to measurement method design, through choices made by researchers in their creation of standardised measurement instruments. This is known to affect the way respondents interpret and respond to these instruments, and can result in substantial measurement error. Current methods for removing measurement error are post-hoc and have been shown to be problematic. This research proposes that innovations can be made through the creation of measurement methods that take respondents¿ individual cognitions into consideration, to reduce measurement error in survey data. Specifically, the aim of the study was to develop and test a measurement instrument capable of having respondents individualise their own rating-scales. A mixed methodology was employed. The qualitative phase provided insights that led to the development of the Individualised Rating-Scale Procedure (IRSP). This electronic measurement method was then tested in a large multi-group experimental study, where its measurement properties were compared to those of Likert-Type Rating-Scales (LTRSs). The survey included pre-validated psychometric constructs which provided a baseline for comparing the methods, as well as to explore whether certain individual characteristics are linked to respondent choices. Structural equation modelling was used to analyse the survey data. Whilst no strong associations were found between individual characteristics and respondent choices, the results demonstrated that the IRSP is reliable and valid. This study has produced a dynamic measurement instrument that accommodates individual-level differences, not addressed by typical fixed rating-scales.

The aim of this dissertation was to address a theoretical debate on belief bias. Belief bias is the tendency for people to be influenced by their prior beliefs when engaged in deductive reasoning. Deduction is the act of drawing necessary conclusions from premises which are meant to be assumed as true. Given that the logical validity of an argument is independent of its content, being influenced by your prior beliefs in such content is considered a bias. Traditional theories posit there are two belief bias components. Motivated reasoning is the tendency to reason better for arguments with unbelievable conclusions relative to arguments with believable conclusions. Response bias is the tendency to accept believable arguments and to reject unbelievable arguments. Dube et al. (2010) pointed out critical methodological problems that undermine evidence for traditional theories. Using signal detection theory (SDT), they found evidence for response bias only. We adopted the SDT method to compare the viability of the traditional and the response bias accounts. In Chapter 1 the relevant literature is reviewed. In Chapter 2 four experiments which employed a novel SDT-based forced choice reasoning method are presented, showing evidence compatible with motivated reasoning. In Chapter 3 four experiments which used the receiver operating characteristic (ROC) method are presented. Crucially, cognitive ability turned out to be linked to motivated reasoning. In Chapter 4 three experiments are presented in which we investigated the impact of cognitive ability and analytic cognitive style on belief bias, concluding that cognitive style mediated the effects of cognitive ability on motivated reasoning. In Chapter 5 we discuss our findings in light of a novel individual differences account of belief bias. We conclude that using the appropriate measurement method and taking individual differences into account are two key elements to furthering our understanding of belief bias, human reasoning, and cognitive psychology in general.