نحوه گزارش آزمون تی نمونه های وابسته:
ابتدا یک جدول توصیفی و توضیح بسیار مختصر جدول و سپس گزارش نتایج استنباطی بدون جدول به صورت زیر👇

A paired-samples t-test was conducted to evaluate the impact of the intervention onstudents’ scores on the Fear of Statistics Test (FOST). There was a statistically significant decrease in FOST scores from Time 1 (M = 40.17, SD = 5.16) to Time 2 (M = 37.5, SD = 5.15), t(29) = 5.39, p < .001 (two-tailed). The mean decrease in FOST scores was 2.67, with a 95% confidence interval ranging from 1.66 to 3.68. The eta squared statistic (.50) indicated a large effect size.

نحوه گزارش آزمون MANOVA: ابتدا یک جدول توصیفی و توضیح بسیار مختصر جدول و سپس گزارش نتایج استنباطی بدون جدول به صورت زیر👇

A one-way between-groups multivariate analysis of variance was performed to investigate sex differences in psychological wellbeing. Three dependent variables were used: positive affect, negative affect and perceived stress. The independent variable was gender. Preliminary assumption testing was conducted to check for normality, linearity, univariate and multivariate outliers, homogeneity of variance-covariance matrices, and multicollinearity, with no serious violations noted. There was a statistically significant difference between males and females on the combined dependent variables, F (3, 428) = 3.57, p = .014, Wilks’ Lambda = .98, partial eta squared = .02. When the results for the dependent variables were considered separately, the only difference to reach statistical significance, using a Bonferroni adjusted alpha level of .017, was perceived stress, F (1, 430) = 8.34, p = .004, partial eta squared = .02. An inspection of the mean scores indicated that females reported slightly higher levels of perceived stress (M = 27.42, SD = 6.08) than males (M = 25.79, SD = 5.41).

PRESENTING THE RESULTS FROM CORRELATION: The relationship between perceived control of internal states (as measured by the
PCOISS) and perceived stress (as measured by the Perceived Stress Scale) was investigated using a Pearson product-moment correlation coefficient. Preliminary analyses were performed to ensure no violation of the assumptions of normality and linearity. There was a strong negative correlation between the two variables, r = –.58, n = 426, p < .001, with high levels of perceived control associated with lower levels of perceived stress.

PARTIAL CORRELATION: Partial correlation was used to explore the relationship between perceived control of internal states (as measured by the PCOISS) and perceived stress (measured by the Perceived Stress Scale) while controlling for scores on the Marlowe-Crowne Social Desirability Scale. Preliminary assessments were performed to ensure no violation of the assumptions of normality and linearity. There was a strong, negative partial correlation between perceived control of internal states and perceived stress,
controlling for social desirability, r = –.55, n = 425, p < .001, with high levels of perceived control being associated with lower levels of perceived stress. An inspection of the zero-order correlation coefficient (r = –.58) suggested that controlling for socially desirable responding had very little effect on the strength of the relationship between these two variables.

Hierarchical multiple regression: Hierarchical multiple regression was used to assess the ability of two control measures (Mastery Scale, Perceived Control of Internal States Scale: PCOISS) to predict levels of stress (Perceived Stress Scale) after controlling for the influence of social desirability and age. Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity and homoscedasticity. Age and social desirability were entered at Step 1, explaining 6% of the variance in perceived stress. After entry of the Mastery Scale and PCOISS at Step 2 the total variance explained by the model as a whole was 47%, F (4, 421) = 94.78, p < .001. The two control measures explained an additional 42% of the variance in stress after controlling for age and socially desirable responding, R squared change = .42, F change (2, 421) = 166.87, p < .001. In the final model, only the two control measures were statistically significant, with the Mastery Scale recording a higher semipartial correlation value (sr = –.36, p < .001) than the PCOISS Scale (sr = –.26, p < .001).

Logistic regression: Direct logistic regression was performed to assess the impact of a set of predictor variables on the odds that respondents would report that they had a problem with their sleep. The model contained five independent variables (sex, age, problems getting to sleep, problems staying asleep and hours of sleep per weeknight). The full model containing all predictors was statistically significant, χ2 (5, N = 241) = 76.02, p < .001, indicating that the model was able to distinguish between respondents who
reported versus did not report a sleep problem. The model as a whole correctly classified 75.1% of cases. As shown in Table 1, only three of the independent variables made a unique statistically significant contribution to the model (hours sleep per night, problems getting to sleep and problems staying asleep). The strongest predictor of reporting a sleep problem was difficulty staying asleep, recording an odds ratio of 7.27. This indicated that the odds are 7.27 times greater that respondents who had difficulty staying asleep would report a sleep problem than those who did not have difficulty staying asleep, controlling for all other factors in the model. The odds ratio of .64 for hours sleep per night was less than 1, indicating that for every additional hour of sleep per night the odds were .64 times lower that respondents would report having a sleep problem, controlling for other factors in the model.

Factor analysis: The 20 items of the Positive and Negative Affect Scale (PANAS) were subjected to principal components analysis (PCA) using IBM SPSS Statistics version 26. Prior to performing PCA, the suitability of data for factor analysis was assessed. Inspection of the correlation matrix revealed the presence of many coefficients of .3 and above. The Kaiser-Meyer-Olkin value was .87, exceeding the recommended value of .6 (Kaiser 1970, 1974), and Bartlett’s (1954) Test of Sphericity reached statistical significance, supporting the factorability of the correlation matrix. Principal components analysis revealed the presence of four components with eigenvalues exceeding 1, explaining 31.2%, 17.0%, 6.1% and 5.8% of the variance respectively. An inspection of the screeplot revealed a clear break after the second component. Using Catell’s (1966) scree test, it was decided to retain two components for further investigation. This was further supported by the results of parallel analysis, which showed only two components with eigenvalues exceeding the corresponding criterion values for a randomly generated data matrix of the same size (20 variables ×435respondents).
The two-component solution explained a total of 48.2% of the variance, with Component 1 contributing 31.25% and Component 2 contributing 17.0%. To aid in the interpretation of these two components, oblimin rotation was performed. The rotated solution revealed the presence of simple structure (Thurstone 1947), with both components showing several strong loadings and all variables loading substantially on only one component. The interpretation of the two components was consistent with previous research on the PANAS, with positive affect items loading strongly on Component 1 and negative affect items loading strongly on Component 2. There was a weak negative correlation between the two factors (r = –.28). The results of this analysis support the use of the positive affect items and the negative affect items as separate scales, as suggested by the scale authors (Watson, Clark & Tellegen 1988).

Chi-Square: A Chi-Square Goodness of Fit test indicates there was no significant difference in the proportion of smokers identified in the current sample (19.5%) as compared with the value of 20% that was obtained in a previous nationwide study, χ2 (1, n = 436) = .07, p = .79.

Chi-Square Test for Independence: A Chi-Square Test for Independence (with Yates’ Continuity Correction) indicated no significant association between gender and smoking status, c2 (1, n = 436) = .34, p = .56, phi = –.03.

Mann-Whitney U: A Mann-Whitney U Test revealed no signifi cant difference in the self-esteem levels of males (Md = 35, n = 184) and females (Md = 34.5, n = 252), U = 21594, z = –1.23, p = .22, r = .06.

همان طور که ملاحظه می کنید به راحتی می توان ده ها صفحه تحلیل فصل چهارم را در چند جمله با ذکر شاخص های مهم آماری در بخش یافته های چکیده جامع به زبان انگلیسی گزارش کرد. فقط توجه داشته باشید هنگام سابمیت مقاله هرگز چکیده جامع به زبان فارسی و انگلیسی را ننویسید. اجازه دهید مقاله داوری شود، اصلاح شود، تأیید نهایی شود، و سپس چکیده جامع فارسی بنویسید و بعد از تأیید چکیده جامع فارسی آنرا به زبان انگلیسی ترجمه کنید. شاید این سوال برایتان پیش بیاید که چرا از همان ابتدا به انگلیسی ننویسیم و دلیل ارائه این نمونه های انگلیسی چیست. وقتی می توانیم با الگوبرداری از نمونه های انگلیسی که در کانال گذاشته شد چکیده جامع انگلیسی را بنویسم چرا باید ابتدا به فارسی بنویسیم؟

در پاسخ باید به عرض برسانم که نویسنده باید محتوای مقاله خود را حتی در نسخه فارسی Paraphrase کند. برخی نویسندگان فکر می کنند که وقتی مطلبی را از یک مقاله یا کتابی منتشر شده با ذکر منبع در اثر علمی خودشان ارائه کنند، کافی است. مثلاً جمله ای را از کتاب یا مقاله خانزاده بردارند و در مقاله خود بیاورند و در داخل پرانتز بنویسند: خانزاده، 1398- در حالی که همین کار دقیقاً سرقت علمی به حساب می آید حتی اگر مطالبی را عیناً از مقالات منتشر شده قبلی خودتان هم بیاورید باز هم سرقت علمی به حساب می آید. (مگر اینکه نقل مستقیم باشد که بر حسب تعداد واژگان، قوانین خودش را دارد و واقعاً نمی شود در این بحث مختصر توضیح داد). حتی برخی فکر می کنندParaphrase به معنای تغییر دادن فعل و فاعل و مفعول و ... است، در حالی که این کار نیز سرقت علمی به حساب می آید. خیلی خوب پس Paraphrase به چه معنا است؟

اصطلاح Paraphrase بدین معنا است که نویسنده پارگراف یا جمله یا ... از یک مقاله یا ... را بخواند و پیام آن را به زبان خود بازنویسی کند و بعد در داخل پرانتز به نویسنده اثر ارجاع دهد. بنابراین صرف ارجاع دادن و یا تغییر فعل و فاعل یک جمله، کفایت نمی کند (باز هم تاکید می کنم ماجرای نقل قول مستقیم و غیرمستقیم قوانین و قواعد خود را دارد که توضیح آن در این مختصر نمی گنجد).

با سلام و احترام. با توجه به اینکه تلخیص بخش استنباطی تحلیل کوواریانس تک متغیری دوراهه (توجه داشته باشید یک متغیر وابسته و دو متغیر مستقل) در چکیده جامع انگلیسی بر اساس تقاضای برخی نویسندگان دشوار است. نمونه ای از نحوه گزارش آنکوای دوراهه به شرح زیر ارائه می شود. 👇

Presenting the results from two_way ANCOVA

A 2 × 2 between-groups analysis of covariance was conducted to assess the effectiveness of two programs in reducing fear of statistics for male and female participants. The independent variables were the type of program (maths skills, confidence building) and gender. The dependent variable was scores on the Fear of Statistics Test (FOST), administered following completion of the intervention programs (Time 2). Scores on the FOST administered prior to the commencement of the programs (Time 1) were used as a covariate to control for individual differences.
Preliminary checks were conducted to ensure that there was no violation of the assumptions of normality, linearity, homogeneity of variances, homogeneity of regression slopes or reliable measurement of the covariate. After adjusting for FOST scores at Time 1, there was a significant interaction effect. F (1, 25) = 31.7, p < .001, with a large effect size (partial eta squared = .56). Neither of the main effects was statistically significant, program: F (1, 25) = 1.43, p = .24; gender: F (1, 25) = 1.27, p= .27. These results suggest that males and females respond differently to the two types of interventions. Males showed a more substantial decrease in fear of statistics after participation in the maths skills program. Females, on the other hand, appeared to benefit more from the confidence-building program.

نحوه گزارش متنی انکوای دوراهه در بخش استنباطی 👆

به جملات زیر بر حسب تعداد متغیر مستقل و وابسته با دقت فراوان توجه کنید لطفاً:👇

Analysis of covariance can be used as part of one-way, two-way and multivariate ANOVA techniques. Designs:
one-way between-groups ANOVA (one independent variable, one dependent variable)
two-way between-groups ANOVA (two independent variables, one dependent variable).

حالا لطفا همین نکته بالا را به مانکوای دوراهه تعمیم دهید. یعنی: تحلیل کوواریانس چند متغیری (چند متغیر وابسته که زیر چتر یک سازه نظری هستند) دو راهه (دو متغیر متسقل)