The structure of the Statistics Anxiety Rating Scale: A confirmatory factor analysis using UK psychology students

This study aimed to examine the structure of the statistics anxiety rating scale. Responses from 650 undergraduate psychology students throughout the UK were collected through an on-line study. Based on previous research three different models were specified and estimated using confirmatory factor analysis. Fit indices were used to determine if the model fitted the data and a likelihood ratio difference test was used to determine the best fitting model. The original six factor model was the best explanation of the data. All six subscales were intercorrelated and internally consistent. It was concluded that the statistics anxiety rating scale was found to measure the six subscales it was designed to assess in a UK population.

The role of previous experience and attitudes toward statistics in statistics assessment outcomes among undergraduate psychology students

Previous research has demonstrated that students’ cognitions about statistics are related to their performance in statistics assessments. The purpose of this research is to examine the nature of the relationships between undergraduate psychology students’ previous experiences of maths, statistics and computing; their attitudes toward statistics; and assessment on a statistics course. Of the variables examined, the strongest predictor of assessment outcome was students’ attitude about their intellectual knowledge and skills in relation to statistics at the end of the statistics curriculum. This attitude was related to students’ perceptions of their maths ability at the beginning of the statistics curriculum. Interventions could be designed to change such attitudes with the aim of improving students’ learning of statistics.

The effects and side-effects of statistics education: Psychology students’ (mis-)conceptions of probability

In three studies we looked at two typical misconceptions of probability: the representativeness heuristic, and the equiprobability bias. The literature on statistics education predicts that some typical errors and biases (e.g., the equiprobability bias) increase with education, whereas others decrease. This is in contrast with reasoning theorists’ prediction who propose that education reduces misconceptions in general. They also predict that students with higher cognitive ability and higher need for cognition are less susceptible to biases. In Experiments 1 and 2 we found that the equiprobability bias increased with statistics education, and it was negatively correlated with students’ cognitive abilities. The representativeness heuristic was mostly unaffected by education, and it was also unrelated to cognitive abilities. In Experiment 3 we demonstrated through an instruction manipulation (by asking participants to think logically vs. rely on their intuitions) that the reason for these differences was that these biases originated in different cognitive processes.

The effect of statistics anxiety on students’ predicted and actual test scores

This study investigated the effect of statistics anxiety and attitudes on first year psychology students’ predicted and actual statistics class test scores. A total of 52 students completed the Statistics Anxiety Rating Scale and estimated their class test scores one week before their test at the end of first year. Regression models were conducted with the six attitude and anxiety subscales as predictors and the predicted and actual test scores as criterion variables. The results showed that computation self concept and fear of asking for help accounted for 37% of the variance in predicted test scores. However, when actual test scores were analysed the significant predictors were worth of statistics and interpretation anxiety, which accounted for 20% of the variance. These results suggested that while statistics anxiety does influence students’ perceptions of their competence it appears to have less effect on their actual performance. Results also suggested that students were unaware of their own statistical competence. Remedial action is required to address the level of statistics anxiety experienced by first year undergraduate psychology students, as it appears to result in unrealistic assessments of their ability and has detrimental effects on their statistics self-efficacy.

Psychology Statistics for Dummies

The quick, easy way to master all the statistics you'll ever need The bad news first: if you want a psychology degree you'll need to know statistics. Now for the good news: Psychology Statistics For Dummies. Featuring jargon-free explanations, step-by-step instructions and dozens of real-life examples, Psychology Statistics For Dummies makes the knotty world of statistics a lot less baffling. Rather than padding the text with concepts and procedures irrelevant to the task, the authors focus only on the statistics psychology students need to know. As an alternative to typical, lead-heavy statistics texts or supplements to assigned course reading, this is one book psychology students won't want to be without. Ease into statistics – start out with an introduction to how statistics are used by psychologists, including the types of variables they use and how they measure them Get your feet wet – quickly learn the basics of descriptive statistics, such as central tendency and measures of dispersion, along with common ways of graphically depicting information Meet your new best friend – learn the ins and outs of SPSS, the most popular statistics software package among psychology students, including how to input, manipulate and analyse data Analyse this – get up to speed on statistical analysis core concepts, such as probability and inference, hypothesis testing, distributions, Z-scores and effect sizes Correlate that – get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more Analyse by inference – master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs Open the book and find: Ways to describe statistical data How to use SPSS statistical software Probability theory and statistical inference Descriptive statistics basics How to test hypotheses Correlations and other relationships between variables Core concepts in statistical analysis for psychology Analysing research designs Learn to: Use SPSS to analyse data Master statistical methods and procedures using psychology-based explanations and examples Create better reports Identify key concepts and pass your course

Stress, Positive Psychology and the National Student Survey

The aim was to explore the predictive ability of sources of stress and a range of dispositional and coping behaviours on student satisfaction and motivation. Most research exploring sources of stress and coping in students construes stress as psychological distress, with little attempt to consider positive experiences of stress. A questionnaire was administered to 120 first-year UK psychology students. Questions were asked which measured sources of stress when rated as likely to contribute to distress (a hassle) and likely to help one achieve (an uplift). The sources of stress were amended from the UK National Student Survey (NSS, 2011). Support, control, self-efficacy, personality and coping style were also measured, along with their potential affect on
course satisfaction, motivation and feeling part of a learning community. The sources of stress likely to lead to distress were more often significant than sources of stress likely to lead to positive, eustress states. Ironically,
factors one would consider would help students, such as the university support facilities, only did so when rated as a hassle, not as an uplift. Published university league tables draw heavily on student course satisfaction but this negatively correlated with intellectual motivation and feeling part of a learning
community. This suggests course satisfaction alone reveals an incomplete picture of the student experience. Course educators need to consider how course experiences contribute not just to potential distress but to
potential eustress. Teaching quality, effective support and work-life balance are key to student satisfaction and motivation. How educators interact with their students and the opportunities they create in and outside the class to promote peer support are likely to enhance satisfaction and motivation.

Measuring critical thinking skills and dispositions in undergraduate students

This thesis investigates critical thinking with a particular focus on measurement in undergraduate students. A higher education context was chosen because many regard critical thinking development as a primary goal for third level education. Nine studies, both cross-sectional and longitudinal in design, were conducted with undergraduate psychology students (N=387), using the California Critical Thinking Disposition Inventory (CCTDI) and the California Critical Thinking Skills Test (CCTST). Studies 1-3 revealed psychometric weaknesses in the CCTDI and revised the scale with factor analysis and reliability analysis to form the CCTDI United Kingdom revision (CCTDI-UK). Study 4 investigated convergent validity and showed significant inter-correlation between the sub-scales of the CCTDI-UK, and significant correlations with the Openness scale of the NEO Personality Inventory (NEO PI-R). The study also provided evidence for improvement in scores on three of the six sub-scales in the CCTDI-UK (Truth-Seeking, Inquisitiveness, Open-Mindedness) during the course of an undergraduate degree. Study 5 explored a two factor structure for critical thinking dispositions. Study 6 used reliability analysis to revise the CCTST to produce the CCTST-UK. Study 7 showed that the CCTST-UK had a moderate correlation with degree attainment and a slightly higher correlation with a test of non-verbal intelligence (Raven’s Advanced Progressive Matrices short form); in addition, the study showed that scores on the CCTST-UK improved during the course of the degree. Studies 8 and 9 investigated the potential of critical thinking for predicting degree attainment. A-levels predicted approximately 10% of the variance of degree attainment while entry level scores on the CCTST-UK predicted an additional 5%. Exit level scores on the CCTST-UK and the Inquisitive sub-scale of the CCTDI-UK were found to be predictors of degree attainment The main conclusions of the thesis were that these tests had significant potential for predicting degree attainment and that they measured a substantial proportion of the theoretical constructs identified by the major authors in critical thinking.

Violent, caring, unpredictable: Public views on survivors of brain injury

The purpose of the present work was to investigate how members of the public perceived survivors of brain injury. A twenty-item list of attributes that could be used to describe characteristics of survivors of brain injury were given to three hundred and twenty-three participants. One hundred and sixty-nine psychology students and one hundred and fifty-four members of the public agreed to take part in the study. The effects of group (student and public), gender and socioeconomic status (low, moderate and high) on the attributes were assessed. Multivariate analysis of variance showed a statistically significant difference between the two groups with students holding more positive perceptions on 15 out of the 20 attributes. No effects of gender or socioeconomic status were found. The research suggests that members of the public hold less positive views on survivors of brain injury in respect to intellectual competency, ability to care and trustworthiness when compared to students.

The influence of aetiology and blame on prejudice towards survivors of brain injury.

The purpose of the present work was to investigate if a hierarchy of aetiology exists which would influence attitudes towards survivors of brain injury. An independent groups design utilised four independent variables; aetiology (measured at five levels: ‘Road Traffic Accident’ (RTA), ‘Alcohol’, ‘Drug Use’, ‘Aneurism’ and ‘Recreation’), blame (blame and no-blame), group (psychology students and members of the public) and gender to explore attitudes towards survivors of brain injury. The dependent variables were measured using the Prejudicial Evaluation Scale (PES) and Social Interaction Scale (SIS). Three hundred and twenty-five participants (173 students and 152 members of the public) were randomly allocated to one of ten possible conditions. Among individuals who contributed to receiving their injury greater prejudice was displayed towards those in the ‘Drugs’ condition followed by ‘Recreation’, ‘RTA’, ‘Alcohol’ and ‘Aneurism’. Findings suggest that a hierarchy of aetiology exists, which results in prejudicial attitudes, and is influenced by issues of blame. Key words: prejudice, blame, brain injury

Experiences and expectations: the real reason nobody likes stats

Within undergraduate psychology courses, students often have significant levels of anxiety and negative attitudes toward the statistical element. This has been attributed to poor interaction with teachers, fears about mathematical abilities, and simply being unaware of that portion of the course or its relevance to psychology. To address this, 196 undergraduate psychology students completed a survey on statistics anxiety and attitudes. Additionally, 27 different students in similar situations took part in focus group to share their experiences of introductory statistics courses. Survey results showed that fewer than half were aware of the statistics portion of their course and that the expectation was a key factor in their experiences. Qualitative feedback from the focus groups revealed much about how the teaching may or may not improve attitudes nor decrease anxiety. Findings support various broad strategies (i.e. increase awareness of statistics in psychology and confidence in success in the course) as opposed to skill-specific (better ways of teaching probability or using games to increase participation, for example) classroom interventions to improve statistics education.