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 discusses how to report the results of a split-plot ANOVA in APA format. It provides an example results section that indicates the main effects of gender and time were significant, but the interaction effect between gender and time was not significant. It then breaks down each part of the example to explain what each value represents, such as the F-ratio, degrees of freedom, mean square error, and p-values.
1) Participants were faster to detect a sad face among neutral or happy faces, showing an orientation towards sad stimuli.
2) For female faces only, those with higher BDI scores were slower to detect a neutral face among happy faces, interacting with disengagement from sad stimuli.
3) All participants were slower to detect a neutral face among sad faces compared to among happy faces, demonstrating disengagement from sad crowds.
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 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 discusses how to report the results of a split-plot ANOVA in APA format. It provides an example results section that indicates the main effects of gender and time were significant, but the interaction effect between gender and time was not significant. It then breaks down each part of the example to explain what each value represents, such as the F-ratio, degrees of freedom, mean square error, and p-values.
1) Participants were faster to detect a sad face among neutral or happy faces, showing an orientation towards sad stimuli.
2) For female faces only, those with higher BDI scores were slower to detect a neutral face among happy faces, interacting with disengagement from sad stimuli.
3) All participants were slower to detect a neutral face among sad faces compared to among happy faces, demonstrating disengagement from sad crowds.
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 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.
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.
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.
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 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.
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.
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.
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.
ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
Abdominal trauma in pediatrics refers to injuries or damage to the abdominal organs in children. It can occur due to various causes such as falls, motor vehicle accidents, sports-related injuries, and physical abuse. Children are more vulnerable to abdominal trauma due to their unique anatomical and physiological characteristics. Signs and symptoms include abdominal pain, tenderness, distension, vomiting, and signs of shock. Diagnosis involves physical examination, imaging studies, and laboratory tests. Management depends on the severity and may involve conservative treatment or surgical intervention. Prevention is crucial in reducing the incidence of abdominal trauma in children.
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
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.
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.
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.
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 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.
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.
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.
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.
ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
Abdominal trauma in pediatrics refers to injuries or damage to the abdominal organs in children. It can occur due to various causes such as falls, motor vehicle accidents, sports-related injuries, and physical abuse. Children are more vulnerable to abdominal trauma due to their unique anatomical and physiological characteristics. Signs and symptoms include abdominal pain, tenderness, distension, vomiting, and signs of shock. Diagnosis involves physical examination, imaging studies, and laboratory tests. Management depends on the severity and may involve conservative treatment or surgical intervention. Prevention is crucial in reducing the incidence of abdominal trauma in children.
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
Does Over-Masturbation Contribute to Chronic Prostatitis.pptxwalterHu5
In some case, your chronic prostatitis may be related to over-masturbation. Generally, natural medicine Diuretic and Anti-inflammatory Pill can help mee get a cure.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
1. Reporting a Split-Plot ANOVA in
SPSS
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
3. Note – the reporting format shown in this
learning module is for APA. For other formats,
consult specific format guides.
4. Note – 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.
6. A typical example of a split-plot analysis report
might be: “The main effect of Gender was
significant, F(1, 19) = 7.91, MSE = 23.20, p <
0.01, as was the main effect of Time, F(3, 19) =
12.70, MSE = 23.20, p < 0.01. The interaction of
these two factors was not significant, F(3, 19) =
2.71, MSE = 23.20, n.s.”
8. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
9. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the F ratio for the 1st
main effect. We compare
this value with the F critical.
If the F ratio is greater than
the F critical then we would
reject the null hypothesis.
10. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the degrees of freedom for gender
- 2 levels (female & male) - 1 = 1.
11. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the degrees of
freedom for error value.
12. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the F ratio for the 2nd
main effect
13. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the Mean Square
for the Error Value
14. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the p value indicating
that result was statistically
significant.
15. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
F ratio or value for the 2nd
main effect
16. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
Degrees of freedom for 4
levels of time (4-1 = 3)
17. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
Degrees of freedom for
the error value.
18. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the F ratio for the
2nd main effect
19. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the Mean Square for
the Error Value
20. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the p value indicating that
result of the 2nd main effect was
statistically significant.
21. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
F ratio or value for the
interaction effect
22. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
Degrees of freedom for (2-1=1)
levels of gender TIMES (4-1=3)
EQUALS 3 time X
23. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
Degrees of freedom for
the error value.
24. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the F ratio for the
interaction effect
25. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This is the Mean Square
for the Error Value
26. Let’s break this down: “The main effect of
Gender was significant, F(1, 19) = 7.91, MSE =
23.20, p < 0.01, as was the main effect of Time,
F(3, 19) = 12.70, MSE = 23.20, p < 0.01. The
interaction of these two factors was not
significant, F(3, 19) = 2.71, MSE = 23.20, n.s.”
This means that the
result is not significant.