A One-way ANOVA was conducted to compare the effect of type of athlete on the number of pizza slices eaten. The ANOVA results showed that the effect of type of athlete on number of pizza slices eaten was significant, F(2,66) = 99.82, p = .000.
Reporting a one way repeated measures anovaKen Plummer
The document provides guidance on reporting the results of a one-way repeated measures ANOVA in APA style. It includes templates for reporting the main ANOVA results and any post-hoc pairwise comparisons between conditions. Key sections are highlighted to fill in values from an example SPSS output to generate a complete APA-style results section reporting a significant effect of time of season on pizza consumption.
This document discusses how to report the results of a Pearson correlation analysis in APA style. It provides an example of a problem investigating the relationship between broccoli extract consumption and well-being scores. The template shown reports that a strong positive correlation was found between broccoli extract consumption and well-being (r = .88, p < .05).
The document provides guidance on reporting the results of a one-way ANOVA in APA format. It recommends including that a one-way ANOVA was conducted to examine the effect of an independent variable on a dependent variable. It provides a template for reporting the F-statistic, degrees of freedom, and significance level based on the ANOVA output. Filling in the specifics of the independent variable, dependent variable, and ANOVA results completes the report.
This document provides guidelines for writing up results sections based on APA style. It discusses reporting statistical tests, including describing test statistics, significance levels, means, standard deviations, and directions of effects. Examples are provided for how to report results from t-tests, ANOVAs, post hoc tests, chi-square tests, correlations, and regressions. Tables and figures can help report complex results. The guidelines emphasize identifying analyses and their relation to hypotheses, and assuming reader knowledge of statistics.
Reporting an independent sample t- testAmit Sharma
An independent samples t-test was conducted to compare truck driver drowsiness scores for country music listening and no country music listening conditions. There was a significant difference in scores for country music listening (M=4.2, SD=1.3) and no country music listening (M=2.2, SD=0.84); t(8)=2.89, p=0.02.
Reporting Chi Square Test of Independence in APAKen Plummer
This document provides guidance on reporting the results of a chi-square test of independence in APA style. It presents an example problem investigating the relationship between heart disease and gender. It then shows the general template for how to report a chi-square test, including reporting the chi-square value, degrees of freedom, and statistical significance. The template example finds a significant relationship between heart disease and gender, with men more likely to have heart disease than women.
The document discusses how to report the results of a Pearson correlation analysis in APA style. It provides an example of a problem investigating the relationship between the amount of broccoli extract consumed and scores of well-being. It then shows the template for reporting the Pearson correlation, stating the correlation coefficient r and the p-value.
The document provides guidance on reporting the results of an ANCOVA analysis in APA format. It recommends including that a one-way ANCOVA was conducted to determine differences between levels of an independent variable on a dependent variable while controlling for a covariate. An example is given using athlete type as the independent variable, slices of pizza eaten as the dependent variable, and weight as the covariate. The document also provides a template for reporting the F-ratio, degrees of freedom, and significance level.
Reporting a one way repeated measures anovaKen Plummer
The document provides guidance on reporting the results of a one-way repeated measures ANOVA in APA style. It includes templates for reporting the main ANOVA results and any post-hoc pairwise comparisons between conditions. Key sections are highlighted to fill in values from an example SPSS output to generate a complete APA-style results section reporting a significant effect of time of season on pizza consumption.
This document discusses how to report the results of a Pearson correlation analysis in APA style. It provides an example of a problem investigating the relationship between broccoli extract consumption and well-being scores. The template shown reports that a strong positive correlation was found between broccoli extract consumption and well-being (r = .88, p < .05).
The document provides guidance on reporting the results of a one-way ANOVA in APA format. It recommends including that a one-way ANOVA was conducted to examine the effect of an independent variable on a dependent variable. It provides a template for reporting the F-statistic, degrees of freedom, and significance level based on the ANOVA output. Filling in the specifics of the independent variable, dependent variable, and ANOVA results completes the report.
This document provides guidelines for writing up results sections based on APA style. It discusses reporting statistical tests, including describing test statistics, significance levels, means, standard deviations, and directions of effects. Examples are provided for how to report results from t-tests, ANOVAs, post hoc tests, chi-square tests, correlations, and regressions. Tables and figures can help report complex results. The guidelines emphasize identifying analyses and their relation to hypotheses, and assuming reader knowledge of statistics.
Reporting an independent sample t- testAmit Sharma
An independent samples t-test was conducted to compare truck driver drowsiness scores for country music listening and no country music listening conditions. There was a significant difference in scores for country music listening (M=4.2, SD=1.3) and no country music listening (M=2.2, SD=0.84); t(8)=2.89, p=0.02.
Reporting Chi Square Test of Independence in APAKen Plummer
This document provides guidance on reporting the results of a chi-square test of independence in APA style. It presents an example problem investigating the relationship between heart disease and gender. It then shows the general template for how to report a chi-square test, including reporting the chi-square value, degrees of freedom, and statistical significance. The template example finds a significant relationship between heart disease and gender, with men more likely to have heart disease than women.
The document discusses how to report the results of a Pearson correlation analysis in APA style. It provides an example of a problem investigating the relationship between the amount of broccoli extract consumed and scores of well-being. It then shows the template for reporting the Pearson correlation, stating the correlation coefficient r and the p-value.
The document provides guidance on reporting the results of an ANCOVA analysis in APA format. It recommends including that a one-way ANCOVA was conducted to determine differences between levels of an independent variable on a dependent variable while controlling for a covariate. An example is given using athlete type as the independent variable, slices of pizza eaten as the dependent variable, and weight as the covariate. The document also provides a template for reporting the F-ratio, degrees of freedom, and significance level.
A two-way ANOVA was conducted to examine the effects of athlete type (football, basketball, soccer) and age (younger, older) on slices of pizza eaten. There were significant main effects of athlete type and an interaction between athlete type and age, but no main effect of age. Football players ate the most pizza, followed by basketball players and then soccer players.
This document discusses the null hypothesis for a one-way analysis of covariance (ANCOVA). It explains that a one-way ANCOVA compares the influence of an independent variable with at least two levels on a dependent variable, while controlling for the effect of a covariate. The document provides a template for writing the null hypothesis, which states that there is no significant effect of the independent variable on the dependent variable when controlling for the covariate. It gives two examples applying this template.
The document provides guidance on reporting the results of a paired sample t-test in APA format. It includes templates for reporting the study design, results, and statistical analysis. Key details include reporting the means, standard deviations, and standard errors for each condition. It also notes reporting the t-statistic, degrees of freedom, and significance level based on the t-test output.
Parametric tests assume variables are normally distributed but this is sometimes untrue. Non-parametric tests like the Mann-Whitney U test can be used instead as they do not require normal distributions. The Mann-Whitney U test is analogous to the independent samples t-test but uses medians instead of means, making it not sensitive to outliers. It operates on subjects' rank positions rather than differences from the mean.
Reporting a multiple linear regression in apaKen Plummer
A multiple linear regression was calculated to predict weight based on height and sex. A significant regression equation was found (F(2,13)=981.202, p<.000), with an R2 of .993. Participants' predicted weight is equal to 47.138 + 2.101(height) - 39.133(sex), where height is measured in inches and sex is coded as 0 for male and 1 for female. Both height and sex were significant predictors of weight.
The document provides guidance on reporting paired sample t-test results in APA format. It includes an example of how to write the results in a sentence, explaining that there was a significant/not significant difference between the scores for condition 1 (providing the mean and standard deviation) and condition 2 (providing the mean and standard deviation). It also demonstrates how to fill in the t-statistic, degrees of freedom, and p-value using output from SPSS.
Reporting Statistics in Psychology
This document provides guidelines for reporting statistics in psychology research. It outlines how to round numbers and report means, standard deviations, p-values, effect sizes, and results from t-tests, ANOVAs, and other statistical analyses. Key recommendations include reporting exact p-values to two or three decimal places, using abbreviations like M and SD consistently, and noting any violations of statistical assumptions.
The document provides a template for reporting the results of an independent samples t-test in APA format. It demonstrates how to write a sentence summarizing that there was a significant/non-significant difference between two groups by including the group means, standard deviations, t-statistic, and p-value filled in from a sample SPSS output.
Reporting a single linear regression in apaKen Plummer
The document provides a template for reporting the results of a simple linear regression analysis in APA format. It explains that a linear regression was conducted to predict weight based on height. The regression equation was found to be significant, F(1,14)=25.925, p<.000, with an R2 of .649. The predicted weight is equal to -234.681 + 5.434 (height in inches) pounds.
The document describes how to report a partial correlation in APA format. It provides a template for reporting that when controlling for a covariate, the partial correlation between two variables is r = ___, p = ___. As an example, it states that when controlling for age, the partial correlation between intense fanaticism for a professional sports team and proximity to the city the team resides is r = .82, p = .000.
The document describes a study conducted by a pizza café owner to determine which type of high school athlete to market to. The owner measured the ounces of pizza consumed by 12 football players, 12 basketball players, and 12 soccer players. The owner also surveyed each athlete's preference for pizza prior to the study. The document discusses that an analysis of covariance (ANCOVA) is needed to control for the covariate of pizza preference when comparing pizza consumption between the athlete groups.
Reporting point biserial correlation in apaKen Plummer
This document provides guidance on reporting point-biserial correlations in APA style. It describes analyzing the relationship between preference for taking a fencing class on a scale of 1-10 and gender, coded as 1 for male and 2 for female. It recommends reporting the point-biserial correlation coefficient rpb, the statistical significance level p, and an interpretation of the relationship, such as "Females tend to prefer taking a fencing class more than males."
Reporting Pearson Correlation Test of Independence in APAKen Plummer
A Pearson correlation test of independence was conducted to determine if student height and GPA were related. A weak correlation was found between height and GPA (r = .217, p > .05), indicating that student height and GPA are independent of each other.
Reporting a multiple linear regression in APAAmit Sharma
A multiple linear regression was calculated to predict weight based on height and sex. The regression equation was significant and height and sex were significant predictors of weight, explaining 99.3% of the variance. Participants' predicted weight is equal to 47.138 - 39.133 (sex) + 2.101 (height), where height is measured in inches and sex is coded as 0 for female and 1 for male.
This document provides guidance on reporting the results of a Phi-Coefficient test in APA style. It describes analyzing whether there is a non-random pattern between on-time graduation (no=1 and yes=2) and gender (male=1 and female=2). The general template is to state the main finding, include the Phi-Coefficient value (f), and report the p-value. For example, "Based on the results of the study, males are less likely to graduate on time than females f = .82, p < .05."
The document discusses factorial analysis of variance (ANOVA). It explains how total sums of squares can be partitioned into explained and unexplained components. An example shows an F ratio of 5.0 for one data set, indicating variation between groups is rare. This allows rejecting the null hypothesis with a low probability of Type I error. Finally, it describes how factorial ANOVA can analyze the effects of multiple independent variables on a single dependent variable.
A pizza café owner wants to determine how much inventory is needed during football and basketball seasons based on how many slices of pizza each group eats. After collecting data showing outliers among basketball players, a Mann Whitney U test will be used. The null hypothesis would state that there is no statistically significant difference between the median slices of pizza eaten by football players and basketball players.
A pizza café owner conducted a study to determine which sport players (football, basketball, soccer) ate the most slices of pizza on average. After collecting the data, an analysis using the Kruskal-Wallis test was performed due to outliers among basketball players. The results showed a statistically significant difference between the number of slices eaten by different player types, with football players eating the most on average.
This document provides guidance on reporting the results of a single sample t-test in APA format. It includes templates for describing the test and population in the introduction and reporting the mean, standard deviation, t-value and significance in the results. An example is given of a hypothetical single sample t-test comparing IQ scores of people who eat broccoli regularly to the general population.
A mixed between-within subjects ANOVA was conducted to examine the impact of different instruction methods (lecture, slides, instruction with student presentation, pair work) on linguistics test scores over two time periods (pretest and posttest) among 32 students randomly assigned to four groups. There was a significant interaction between time and instruction method, and time had a significant main effect. The different instruction methods also had a significant main effect on test scores. Post hoc tests revealed significant differences in scores between the lecture and student presentation groups, and the student presentation and pair work groups.
This document provides an overview of key concepts in hypothesis testing including:
- The null and alternative hypotheses, where the null hypothesis is what we aim to reject or fail to reject.
- The level of significance and critical region, which define the threshold for rejecting the null hypothesis.
- Type I and type II errors, where we aim to minimize both by choosing an appropriate significance level and critical region.
- Common test statistics like z, t, and chi-squared that are used to evaluate hypotheses based on samples.
- The process of hypothesis testing, which involves defining hypotheses, choosing a test statistic and significance level, and making a decision to reject or fail to reject the null based on the critical region.
This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
A two-way ANOVA was conducted to examine the effects of athlete type (football, basketball, soccer) and age (younger, older) on slices of pizza eaten. There were significant main effects of athlete type and an interaction between athlete type and age, but no main effect of age. Football players ate the most pizza, followed by basketball players and then soccer players.
This document discusses the null hypothesis for a one-way analysis of covariance (ANCOVA). It explains that a one-way ANCOVA compares the influence of an independent variable with at least two levels on a dependent variable, while controlling for the effect of a covariate. The document provides a template for writing the null hypothesis, which states that there is no significant effect of the independent variable on the dependent variable when controlling for the covariate. It gives two examples applying this template.
The document provides guidance on reporting the results of a paired sample t-test in APA format. It includes templates for reporting the study design, results, and statistical analysis. Key details include reporting the means, standard deviations, and standard errors for each condition. It also notes reporting the t-statistic, degrees of freedom, and significance level based on the t-test output.
Parametric tests assume variables are normally distributed but this is sometimes untrue. Non-parametric tests like the Mann-Whitney U test can be used instead as they do not require normal distributions. The Mann-Whitney U test is analogous to the independent samples t-test but uses medians instead of means, making it not sensitive to outliers. It operates on subjects' rank positions rather than differences from the mean.
Reporting a multiple linear regression in apaKen Plummer
A multiple linear regression was calculated to predict weight based on height and sex. A significant regression equation was found (F(2,13)=981.202, p<.000), with an R2 of .993. Participants' predicted weight is equal to 47.138 + 2.101(height) - 39.133(sex), where height is measured in inches and sex is coded as 0 for male and 1 for female. Both height and sex were significant predictors of weight.
The document provides guidance on reporting paired sample t-test results in APA format. It includes an example of how to write the results in a sentence, explaining that there was a significant/not significant difference between the scores for condition 1 (providing the mean and standard deviation) and condition 2 (providing the mean and standard deviation). It also demonstrates how to fill in the t-statistic, degrees of freedom, and p-value using output from SPSS.
Reporting Statistics in Psychology
This document provides guidelines for reporting statistics in psychology research. It outlines how to round numbers and report means, standard deviations, p-values, effect sizes, and results from t-tests, ANOVAs, and other statistical analyses. Key recommendations include reporting exact p-values to two or three decimal places, using abbreviations like M and SD consistently, and noting any violations of statistical assumptions.
The document provides a template for reporting the results of an independent samples t-test in APA format. It demonstrates how to write a sentence summarizing that there was a significant/non-significant difference between two groups by including the group means, standard deviations, t-statistic, and p-value filled in from a sample SPSS output.
Reporting a single linear regression in apaKen Plummer
The document provides a template for reporting the results of a simple linear regression analysis in APA format. It explains that a linear regression was conducted to predict weight based on height. The regression equation was found to be significant, F(1,14)=25.925, p<.000, with an R2 of .649. The predicted weight is equal to -234.681 + 5.434 (height in inches) pounds.
The document describes how to report a partial correlation in APA format. It provides a template for reporting that when controlling for a covariate, the partial correlation between two variables is r = ___, p = ___. As an example, it states that when controlling for age, the partial correlation between intense fanaticism for a professional sports team and proximity to the city the team resides is r = .82, p = .000.
The document describes a study conducted by a pizza café owner to determine which type of high school athlete to market to. The owner measured the ounces of pizza consumed by 12 football players, 12 basketball players, and 12 soccer players. The owner also surveyed each athlete's preference for pizza prior to the study. The document discusses that an analysis of covariance (ANCOVA) is needed to control for the covariate of pizza preference when comparing pizza consumption between the athlete groups.
Reporting point biserial correlation in apaKen Plummer
This document provides guidance on reporting point-biserial correlations in APA style. It describes analyzing the relationship between preference for taking a fencing class on a scale of 1-10 and gender, coded as 1 for male and 2 for female. It recommends reporting the point-biserial correlation coefficient rpb, the statistical significance level p, and an interpretation of the relationship, such as "Females tend to prefer taking a fencing class more than males."
Reporting Pearson Correlation Test of Independence in APAKen Plummer
A Pearson correlation test of independence was conducted to determine if student height and GPA were related. A weak correlation was found between height and GPA (r = .217, p > .05), indicating that student height and GPA are independent of each other.
Reporting a multiple linear regression in APAAmit Sharma
A multiple linear regression was calculated to predict weight based on height and sex. The regression equation was significant and height and sex were significant predictors of weight, explaining 99.3% of the variance. Participants' predicted weight is equal to 47.138 - 39.133 (sex) + 2.101 (height), where height is measured in inches and sex is coded as 0 for female and 1 for male.
This document provides guidance on reporting the results of a Phi-Coefficient test in APA style. It describes analyzing whether there is a non-random pattern between on-time graduation (no=1 and yes=2) and gender (male=1 and female=2). The general template is to state the main finding, include the Phi-Coefficient value (f), and report the p-value. For example, "Based on the results of the study, males are less likely to graduate on time than females f = .82, p < .05."
The document discusses factorial analysis of variance (ANOVA). It explains how total sums of squares can be partitioned into explained and unexplained components. An example shows an F ratio of 5.0 for one data set, indicating variation between groups is rare. This allows rejecting the null hypothesis with a low probability of Type I error. Finally, it describes how factorial ANOVA can analyze the effects of multiple independent variables on a single dependent variable.
A pizza café owner wants to determine how much inventory is needed during football and basketball seasons based on how many slices of pizza each group eats. After collecting data showing outliers among basketball players, a Mann Whitney U test will be used. The null hypothesis would state that there is no statistically significant difference between the median slices of pizza eaten by football players and basketball players.
A pizza café owner conducted a study to determine which sport players (football, basketball, soccer) ate the most slices of pizza on average. After collecting the data, an analysis using the Kruskal-Wallis test was performed due to outliers among basketball players. The results showed a statistically significant difference between the number of slices eaten by different player types, with football players eating the most on average.
This document provides guidance on reporting the results of a single sample t-test in APA format. It includes templates for describing the test and population in the introduction and reporting the mean, standard deviation, t-value and significance in the results. An example is given of a hypothetical single sample t-test comparing IQ scores of people who eat broccoli regularly to the general population.
A mixed between-within subjects ANOVA was conducted to examine the impact of different instruction methods (lecture, slides, instruction with student presentation, pair work) on linguistics test scores over two time periods (pretest and posttest) among 32 students randomly assigned to four groups. There was a significant interaction between time and instruction method, and time had a significant main effect. The different instruction methods also had a significant main effect on test scores. Post hoc tests revealed significant differences in scores between the lecture and student presentation groups, and the student presentation and pair work groups.
This document provides an overview of key concepts in hypothesis testing including:
- The null and alternative hypotheses, where the null hypothesis is what we aim to reject or fail to reject.
- The level of significance and critical region, which define the threshold for rejecting the null hypothesis.
- Type I and type II errors, where we aim to minimize both by choosing an appropriate significance level and critical region.
- Common test statistics like z, t, and chi-squared that are used to evaluate hypotheses based on samples.
- The process of hypothesis testing, which involves defining hypotheses, choosing a test statistic and significance level, and making a decision to reject or fail to reject the null based on the critical region.
This document introduces the concept of data classification and levels of measurement in statistics. It explains that data can be either qualitative or quantitative. Qualitative data consists of attributes and labels while quantitative data involves numerical measurements. The document also outlines the four levels of measurement - nominal, ordinal, interval, and ratio - from lowest to highest. Each level allows for different types of statistical calculations, with the ratio level permitting the most complex calculations like ratios of two values.
- A hypothesis is a tentative statement about the relationship between two or more variables that is tested through collecting sample data. The null hypothesis states there is no relationship and the alternative hypothesis proposes an alternative relationship.
- Type I error occurs when a true null hypothesis is rejected. Type II error is failing to reject a false null hypothesis. Choosing a significance level balances these two errors, with a higher level increasing Type I errors and a lower level increasing Type II errors.
- In medical testing, it is better to make a Type II error and accept a null hypothesis of no drug difference when there actually is a difference, to avoid releasing an ineffective drug. So a lower significance level that increases Type II errors would be chosen.
This document discusses analyzing research data through descriptive and analytical statistics. Descriptive statistics summarize variables one by one through measures like frequency, percentage, mean, median and standard deviation depending on the variable level. Analytical statistics examine relationships between two or more variables. The document demonstrates analyzing a hypertension study dataset in SPSS, including checking normality distribution through histograms, Shapiro-Wilk test and Q-Q plots to determine appropriate tests. Frequency is used to describe categorical gender variable while numerical age is described through mean, standard deviation and histogram with normal curve fitting.
This document provides guidance on writing and reporting clinical case studies. It discusses the key components of a clinical case study such as structure, data collection, variables, and analytical tools. Clinical case studies should analyze a real patient situation to identify problems, suggest solutions, and recommend the best solution. The document also differentiates between a clinical case study and clinical case report, noting that reports are shorter summaries of an individual patient case. It emphasizes writing for the target journal and audience when composing a case study.
The document discusses reporting the results of a split-plot ANOVA in APA style. It provides an example results section that reports the main effects of gender and time as significant but the interaction effect as not significant. It then breaks down each part of the example, explaining what each value represents, such as the F-ratio, degrees of freedom, mean square error, and p-values.
The document provides instructions for conducting an independent samples t-test in SPSS. It explains how to specify the grouping and test variables, define the groups being compared, and set options. It also demonstrates running a t-test to compare mile times between athletes and non-athletes using sample data, and interpreting the output, which includes Levene's test for equal variances and the t-test results.
The document provides instructions for conducting an independent samples t-test in SPSS. It explains how to specify the grouping and test variables, define the groups being compared, and set options. It also demonstrates running a t-test to compare mile times between athletes and non-athletes, checking assumptions, and interpreting the output, including Levene's test for equal variances and the t-test results.
The document describes how to conduct and interpret a paired samples t-test in SPSS. It explains that a paired samples t-test is used to compare the means of two related variables measured on the same subjects. It provides an example using reaction time data collected from participants before and after drinking a beer. It outlines the steps to check assumptions, run the t-test in SPSS, and interpret the output, finding that participants had significantly slower reaction times after consuming alcohol.
Reporting a multiple linear regression in apa Amit Sharma
A multiple linear regression was calculated to predict weight based on height and sex. The regression equation was significant and height and sex were significant predictors of weight, explaining 99.3% of the variance. Participants' predicted weight is equal to 47.138 - 39.133 (sex) + 2.101 (height), where height is measured in inches and sex is coded as 0 for female and 1 for male.
Reporting a single sample t- test revisedAmit Sharma
The document provides instructions for reporting the results of a single sample t-test in APA format. It includes an example result comparing the mean IQ scores of persons who eat broccoli regularly (M=120, SD=12.2) to the general population. The t-test found a statistically significant difference between the samples, t(22)=7.86, p=0.000.
Null hypothesis for single linear regressionAmit Sharma
The document discusses the null hypothesis for a single linear regression model. It explains that a null hypothesis states that there is no effect or relationship between the independent and dependent variables. For a regression predicting ACT scores from hours of sleep, the null hypothesis would be: "There will be no significant prediction of ACT scores by hours of sleep." The document provides a template for writing the null hypothesis and works through an example applying the template to the relationship between hours of sleep and ACT scores.
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1. Reporting a One-Way ANOVA
Amit Sharma
Associate Professor
Dept. of Pharmacy Practice
ISF COLLEGE OF PHARMACY
Ghal Kalan, Ferozpur GT Road, MOGA, 142001, Punjab
Mobile: 09646755140, 09418783145
Phone: No. 01636-650150, 650151
Website: - www.isfcp.org
2. Reporting the Study using APA
• Note – that the reporting format shown in this
learning module is for APA. For other formats
consult specific format guides.
3. Reporting the Study using APA
• Note – that the reporting format shown in this
learning module is for APA. For other formats
consult specific format guides.
• It is also recommended to consult the latest APA
manual to compare what is described in this
learning module with the most updated formats for
APA
4. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
5. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
• “A One-way ANOVA was conducted to compare
effect of [name the effect (IV)] on the (dependent
variable).”
6. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
• “A One-way ANOVA was conducted to compare
effect of [name the effect (IV)] on the (dependent
variable).”
• Here is an example:
7. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
• “A One-way ANOVA was conducted to compare
effect of [name the effect (IV)] on the (dependent
variable).”
• Here is an example:
“A One-way ANOVA was conducted to compare the
effect of [type of athlete] on the (the number of slices
of Pizza eaten in one sitting).”
8. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
• “A One-way ANOVA was conducted to compare
effect of [name the effect (IV)] on the (dependent
variable).”
• Here is an example:
“A One-way ANOVA was conducted to compare the
effect of [type of athlete] on the (the number of slices
of Pizza eaten in one sitting).”
9. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
• “A One-way ANOVA was conducted to compare
effect of [name the effect (IV)] on the (dependent
variable).”
• Here is an example:
“A One-way ANOVA was conducted to compare the
effect of [type of athlete] on the (the number of slices
of Pizza eaten in one sitting).”
10. Reporting the Study using APA
• You can report that you conducted a One-way
ANOVA by using the template below.
• “A One-way ANOVA was conducted to compare
effect of [name the effect (IV)] on the (dependent
variable).”
• Here is an example:
“A One-way ANOVA was conducted to compare the
effect of [type of athlete] on the (the number of slices
of Pizza eaten in one sitting).”
Note - Type of Athlete could
be football, basketball,
volleyball, soccer, etc. players
12. Reporting Results using APA
• You can report data from your own experiments by
using the template below.
13. Reporting Results using APA
• You can report data from your own experiments by
using the template below.
• An analysis of variance showed that the effect of
_________ on _________ was significant, F (__,__) =
____, p = ____
14. Reporting Results using APA
• You can report data from your own experiments by
using the template below.
• An analysis of variance showed that the effect of
_________ on _________ was significant, F (__,__) =
____, p = ____
• Just fill in the blanks by using the SPSS output
15. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (__,__) = ____, p = ____
16. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (__,__) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
17. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F ( 2,__) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
18. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F ( 2,__) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
19. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F ( 2,__) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
Degrees of freedom
Beween Groups
20. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F ( 2,__) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
21. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
22. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
23. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
Degrees of freedom
Within Groups
24. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = ____, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
25. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = 99.82, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
26. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = 99.82, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
27. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = 99.82, p = ____
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
F ratio
276.053 / 2.765 = 99.818
28. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = 99.82, p = .000.
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
29. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = 99.82, p = .000.
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
30. Reporting Results using APA
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2,66 ) = 99.82, p = .000.
ANOVA
Pizza_Slices
Sum of Squares df Mean Square F Sig.
Between Groups 552.087 2 276.043 99.818 .000
Within Groups 182.522 66 2.765
Total 734.609 68
P value or
significance
32. Reporting Results using APA
• Once the blanks are full you have your report
• An analysis of variance showed that the effect of
type of athlete on number of pizza slices eaten was
significant, F (2, 66) = 99.82, p = .000.