Hello,

I hope this email finds you well I am currently constructing the results section of my report and I am running a moderation model, please may I have further advice on how I should run this model/ computation and a template on how I may be able to run write up results. Would appreciate it greatly, thanks always for your advice

Data Analysis

Overview

In this section, we present the data analysis conducted for our secondary analysis based on the model proposed by Freeston et al. (2020) on uncertainty distress in the context of Coronavirus (COVID-19). The analysis focused on examining the moderation effect using SPSS and the Hayes PROCESS macro.

Data Preparation

The dataset used for this analysis was obtained from the study by Freeston et al. (2020), which included measures of uncertainty distress and relevant moderator variables. Prior to analysis, the data were screened for missing values and outliers, and appropriate data cleaning procedures were applied.

Descriptive Statistics

Descriptive statistics, including means, standard deviations, and correlations, were computed to summarize the characteristics of the variables of interest. These statistics provided an initial understanding of the data and helped identify any potential issues or patterns.

Moderation Analysis

To examine the moderation effect, we conducted a moderation analysis using the PROCESS macro developed by Hayes (2013) for SPSS. The model proposed by Freeston et al. (2020) was tested with uncertainty distress as the outcome variable, a moderator variable, and relevant covariates.

Results

The results of the moderation analysis indicated [report the main findings of the moderation analysis here]. Specifically, [describe the nature and significance of the moderation effect, including any relevant statistics or effect sizes].

Hypothesis

H1: The relationship between actual uncertainty and uncertainty distress (UD) will be mediated by dispositional intolerance of uncertainty (DIU), such that higher levels of actual uncertainty will be associated with higher levels of DIU, which in turn will be associated with higher levels of UD.

Hypothesis

H1: The first hypothesis suggests that the relationship between actual uncertainty and uncertainty distress (UD) is mediated by dispositional intolerance of uncertainty (DIU). This implies that individuals who experience higher levels of actual uncertainty are expected to exhibit higher levels of DIU, which in turn contributes to greater levels of UD. In other words, individuals who are less tolerant of uncertainty are more likely to experience distress when faced with uncertain situations.

H2: Actual threat is positively related to uncertainty distress.Dispositional Intolerance of Uncertainty is going to positively moderate the relationship between actual threat and uncertainty distress.

Variables

Actual threat

Actual Uncertainty: This represents the objective level of uncertainty experienced by individuals in the context of the COVID-19 pandemic. It could include factors such as ambiguous information, unpredictability of events, or lack of clarity about the future.

Dispositional Intolerance of Uncertainty (DIU): DIU refers to an individual's tendency to perceive and react negatively to ambiguous or uncertain situations. It is a trait-like characteristic and reflects how comfortable or uncomfortable individuals are with uncertainty in general.

Uncertainty Distress (UD): UD represents the emotional distress or discomfort experienced by individuals in response to uncertainty. It encompasses feelings of anxiety, worry, or unease that arise when faced with uncertain situations.