Statistical Analyses

A sample size calculation was conducted to identify the necessary sample size for each statistical test. Therefore an a priori sample size calculation was carried out using G Power version 3 software package (Paul et a l, 2007) to ascertain how many participants would be required to achieve the desired level o f power. The statistical power is the ability o f a test to detect an effect (Field, 2006). Cohen (1992) recommended power o f .8 (an 80% chance o f detecting an effect if it truly exists). The sample size calculation in this study used a medium effect size because it was considered more clinically valuable to be able to explain a larger rather than a smaller proportion of the variance. A sample size calculation based on using correlation indicated that with an expected effect size o f 0.3 (medium effect), alpha at 0.05 and power at 80%, the sample size needed was 82. A sample size calculation based on using regression analysis indicated that with an expected effect size of 0.15 (medium effect), alpha at 0.05 and power at 80%, the sample size needed with six predictor variables was 98. Given that the sample size calculation indicated a necessary sample ranging from 82 to 98 participants, a minimum o f 98 was needed to achieve power on all statistical tests to be used.

It was expected that the data would yield two distinct groups of participants; those who were experiencing emotional distress and those who were not. Therefore it was anticipated that the complete dataset would not meet the assumption o f normal

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distribution required for parametric statistical tests. With this in mind, the planned non parametric statistical tests, unless otherwise stated, are described under each hypothesis.

2.7.1 Hypothesis 1: Nurses will experience bullying and harassment in the

workplace.

Descriptive statistics were used to calculate frequencies.

2.7.2 Hypothesis 2: Nurses will experience bullying and harassment in the workplace

as a traumatic event and report post-traumatic stress symptoms as a consequence.

It would be expected that, if examined separately, the variables in this subset would meet the assumptions for parametric statistics. Thus, descriptive statistics were used to calculate frequencies and a multiple regression used to examine factors that would contribute to post-traumatic stress symptoms following bullying and harassment at work.

2.7.3 Hypothesis 3: There will be a difference in levels o f anxiety and depression

between participants who experience bullying and harassment in the workplace and

those who do not.

The Mann-Whitney test, U, is a non-parametric test that explores the difference between two conditions when different participants have been allocated to different groups according to status. It is used when one variable is categorical (i.e. those who have experienced bullying and those who have not). The test works on the principle o f ranking the data. The analysis is then performed on the ranked data rather than the actual data, thus allowing the data to be noncompliant with the assumptions o f parametric statistics

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(Field, 2006). This test was used to explore the differences between:

a) Participants who subjectively reported bullying and those who did not and anxiety and depression.

b) Participants who met the objective bullying criteria and those who did not and anxiety and depression.

Spearman’s correlation coefficient, rg, is a non-parametric statistic that can be used to explore whether a relationship exists between two variables when the data have violated parametric assumptions. Again, it works by ranking the data and then applying the Pearson’s equation to those ranks (Field, 2006). Spearman’s correlation was used to explore whether a relationship existed between the types o f negative behaviour (person orientated or task orientated negative behaviours) that constitute bullying or harassment and anxiety and depression. Spearman’s correlation was used to explore whether a relationship existed between the number o f negative acts experienced and anxiety and depression in those who reported bullying and those who did not. It is important to note that a correlation does not infer direction o f causality, as it cannot determine which variable causes the other to change; it merely highlights an association between two variables (Field, 2006).

2.7.4 Hypothesis 4: There will be a difference in levels o f anxiety and depression

between those participants who witness bullying and harassment in the workplace and

those who do not.

A Mann-Whitney Test was used to explore differences between participants who reported

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depression.

2.7.5 Hypothesis 5: Experience o f previous traumas, work setting, age o f victim,

anxiety, task orientated negative behaviours and person orientated negative behaviours

may contribute towards bullying and harassment in the workplace.

The relationship between each o f the variables specified (predictor variables) and subjective bullying or objective bullying (outcome variables) was explored in turn. Mann-Whitney tests were if the predictor variable was continuous (i.e. number of traumas experienced, age, task orientated and person orientated negative behaviours). Pearson’s chi-squared test was used if the predictor variable was categorical (i.e. previous trauma and work setting).

Pearson’s chi-squared test, %^, is a non-parametric test that explores the relationship between two categorical variables. It is based on the idea of comparing the frequencies observed in certain categories to the frequencies expected in those categories by chance (Field, 2006). The chi-squared test assumes that each person contributes to only one cell of the contingency table and that expected frequencies should be greater than five otherwise statistical power is lost (Field, 2006).

Any predictor variables in which there was an association with the outcome variable were then entered into a logistic regression to examine possible predictors of bullying. Logistic regression allows the prediction of a discrete outcome, such as group membership, when predictors are continuous, discrete or a combination o f the two (Tabachnik & Fidell, 2007). Logistic regression has no assumptions about the distribution o f predictor variables. Cautions about causal inference apply. To highlight the probability o f