Certainly! When writing the results section of a research paper, it's important to present your findings clearly and logically. Starting with preliminary analyses, including checking assumptions, is a good approach. Here’s a structured way to present your findings:

Results

Preliminary Analyses

Assumptions Check

Before presenting the main findings, it is crucial to ensure that the assumptions underlying the statistical analyses are met. The following assumptions were checked:

• Normality:


• The normality of the data was assessed using the Shapiro-Wilk test and visual inspection of Q-Q plots. The results indicated that the data were approximately normally distributed, with no significant deviations from normality (Shapiro-Wilk test, p > 0.05).


• Homogeneity of Variance:


• Levene's test was conducted to check for homogeneity of variance. The results showed that the variances were homogeneous across groups (Levene's test, p > 0.05), satisfying the assumption of equal variances.


• Independence of Observations:


• The independence of observations was ensured through the study design, where participants were randomly assigned to different groups, and each participant's data was collected independently.


• Linearity:


• For regression analyses, the linearity assumption was checked using scatter plots and residual plots. The plots indicated a linear relationship between the predictor and outcome variables.


• Multicollinearity:


• Variance Inflation Factor (VIF) values were calculated to check for multicollinearity among predictor variables. All VIF values were below 5, indicating no significant multicollinearity issues.

Descriptive statistics were calculated to summarize the main characteristics of the sample. The mean, standard deviation, and range for each variable are presented in Table 1.

Main Analyses

Hypothesis Testing

• Hypothesis 1: There is a significant difference in [outcome variable] between [group 1] and [group 2].


• An independent samples t-test was conducted to compare the means of [outcome variable] between [group 1] and [group 2]. The results indicated a significant difference (t(df) = value, p < 0.05), with [group 1] having a higher mean [outcome variable] than [group 2].


• Hypothesis 2: There is a significant relationship between [predictor variable] and [outcome variable].


• A linear regression analysis was performed to examine the relationship between [predictor variable] and [outcome variable]. The results showed a significant positive relationship (β = value, p < 0.05), explaining [R² value]% of the variance in [outcome variable].

Further analyses were conducted to explore potential moderators and mediators of the relationships observed.

• Moderation Analysis:


• A moderation analysis was performed to examine whether [moderator variable] influenced the relationship between [predictor variable] and [outcome variable]. The results indicated that [moderator variable] significantly moderated the relationship (interaction term, p < 0.05).


• Mediation Analysis:


• A mediation analysis was conducted to test whether [mediator variable] mediated the relationship between [predictor variable] and [outcome variable]. The results showed a significant indirect effect (indirect effect = value, p < 0.05), suggesting that [mediator variable] partially mediated the relationship.

Summary of Findings

In summary, the preliminary analyses confirmed that the assumptions for the statistical tests were met. The main analyses revealed significant differences between groups and a significant relationship between the predictor and outcome variables. Additional analyses identified significant moderation and mediation effects, providing a more nuanced understanding of the relationships observed.

By following this structure, you can present your findings clearly and logically, ensuring that readers can easily follow your results and understand the significance of your findings.