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ANOVA (Analysis of variance) – Formulas, Types, and Examples

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Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means. It is similar to the t-test, but the t-test is generally used for comparing two means, while ANOVA is used when you have more than two means to compare.

ANOVA is based on comparing the variance (or variation) between the data samples to the variation within each particular sample. If the between-group variance is high and the within-group variance is low, this provides evidence that the means of the groups are significantly different.

ANOVA Terminology

When discussing ANOVA, there are several key terms to understand:

• Factor : This is another term for the independent variable in your analysis. In a one-way ANOVA, there is one factor, while in a two-way ANOVA, there are two factors.
• Levels : These are the different groups or categories within a factor. For example, if the factor is ‘diet’ the levels might be ‘low fat’, ‘medium fat’, and ‘high fat’.
• Response Variable : This is the dependent variable or the outcome that you are measuring.
• Within-group Variance : This is the variance or spread of scores within each level of your factor.
• Between-group Variance : This is the variance or spread of scores between the different levels of your factor.
• Grand Mean : This is the overall mean when you consider all the data together, regardless of the factor level.
• Treatment Sums of Squares (SS) : This represents the between-group variability. It is the sum of the squared differences between the group means and the grand mean.
• Error Sums of Squares (SS) : This represents the within-group variability. It’s the sum of the squared differences between each observation and its group mean.
• Total Sums of Squares (SS) : This is the sum of the Treatment SS and the Error SS. It represents the total variability in the data.
• Degrees of Freedom (df) : The degrees of freedom are the number of values that have the freedom to vary when computing a statistic. For example, if you have ‘n’ observations in one group, then the degrees of freedom for that group is ‘n-1’.
• Mean Square (MS) : Mean Square is the average squared deviation and is calculated by dividing the sum of squares by the corresponding degrees of freedom.
• F-Ratio : This is the test statistic for ANOVAs, and it’s the ratio of the between-group variance to the within-group variance. If the between-group variance is significantly larger than the within-group variance, the F-ratio will be large and likely significant.
• Null Hypothesis (H0) : This is the hypothesis that there is no difference between the group means.
• Alternative Hypothesis (H1) : This is the hypothesis that there is a difference between at least two of the group means.
• p-value : This is the probability of obtaining a test statistic as extreme as the one that was actually observed, assuming that the null hypothesis is true. If the p-value is less than the significance level (usually 0.05), then the null hypothesis is rejected in favor of the alternative hypothesis.
• Post-hoc tests : These are follow-up tests conducted after an ANOVA when the null hypothesis is rejected, to determine which specific groups’ means (levels) are different from each other. Examples include Tukey’s HSD, Scheffe, Bonferroni, among others.

Types of ANOVA

Types of ANOVA are as follows:

One-way (or one-factor) ANOVA

This is the simplest type of ANOVA, which involves one independent variable . For example, comparing the effect of different types of diet (vegetarian, pescatarian, omnivore) on cholesterol level.

Two-way (or two-factor) ANOVA

This involves two independent variables. This allows for testing the effect of each independent variable on the dependent variable , as well as testing if there’s an interaction effect between the independent variables on the dependent variable.

Repeated Measures ANOVA

This is used when the same subjects are measured multiple times under different conditions, or at different points in time. This type of ANOVA is often used in longitudinal studies.

Mixed Design ANOVA

This combines features of both between-subjects (independent groups) and within-subjects (repeated measures) designs. In this model, one factor is a between-subjects variable and the other is a within-subjects variable.

Multivariate Analysis of Variance (MANOVA)

This is used when there are two or more dependent variables. It tests whether changes in the independent variable(s) correspond to changes in the dependent variables.

Analysis of Covariance (ANCOVA)

This combines ANOVA and regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (interval variables) account. This allows the comparison of one variable outcome between groups, while statistically controlling for the effect of other continuous variables that are not of primary interest.

Nested ANOVA

This model is used when the groups can be clustered into categories. For example, if you were comparing students’ performance from different classrooms and different schools, “classroom” could be nested within “school.”

ANOVA Formulas

ANOVA Formulas are as follows:

Sum of Squares Total (SST)

This represents the total variability in the data. It is the sum of the squared differences between each observation and the overall mean.

• yi represents each individual data point
• y_mean represents the grand mean (mean of all observations)

Sum of Squares Within (SSW)

This represents the variability within each group or factor level. It is the sum of the squared differences between each observation and its group mean.

• yij represents each individual data point within a group
• y_meani represents the mean of the ith group

Sum of Squares Between (SSB)

This represents the variability between the groups. It is the sum of the squared differences between the group means and the grand mean, multiplied by the number of observations in each group.

• ni represents the number of observations in each group
• y_mean represents the grand mean

Degrees of Freedom

The degrees of freedom are the number of values that have the freedom to vary when calculating a statistic.

For within groups (dfW):

For between groups (dfB):

For total (dfT):

• N represents the total number of observations
• k represents the number of groups

Mean Squares

Mean squares are the sum of squares divided by the respective degrees of freedom.

Mean Squares Between (MSB):

Mean Squares Within (MSW):

F-Statistic

The F-statistic is used to test whether the variability between the groups is significantly greater than the variability within the groups.

If the F-statistic is significantly higher than what would be expected by chance, we reject the null hypothesis that all group means are equal.

Examples of ANOVA

Examples 1:

Suppose a psychologist wants to test the effect of three different types of exercise (yoga, aerobic exercise, and weight training) on stress reduction. The dependent variable is the stress level, which can be measured using a stress rating scale.

Here are hypothetical stress ratings for a group of participants after they followed each of the exercise regimes for a period:

• Yoga: [3, 2, 2, 1, 2, 2, 3, 2, 1, 2]
• Aerobic Exercise: [2, 3, 3, 2, 3, 2, 3, 3, 2, 2]
• Weight Training: [4, 4, 5, 5, 4, 5, 4, 5, 4, 5]

The psychologist wants to determine if there is a statistically significant difference in stress levels between these different types of exercise.

To conduct the ANOVA:

1. State the hypotheses:

• Null Hypothesis (H0): There is no difference in mean stress levels between the three types of exercise.
• Alternative Hypothesis (H1): There is a difference in mean stress levels between at least two of the types of exercise.

2. Calculate the ANOVA statistics:

• Compute the Sum of Squares Between (SSB), Sum of Squares Within (SSW), and Sum of Squares Total (SST).
• Calculate the Degrees of Freedom (dfB, dfW, dfT).
• Calculate the Mean Squares Between (MSB) and Mean Squares Within (MSW).
• Compute the F-statistic (F = MSB / MSW).

3. Check the p-value associated with the calculated F-statistic.

• If the p-value is less than the chosen significance level (often 0.05), then we reject the null hypothesis in favor of the alternative hypothesis. This suggests there is a statistically significant difference in mean stress levels between the three exercise types.

4. Post-hoc tests

• If we reject the null hypothesis, we conduct a post-hoc test to determine which specific groups’ means (exercise types) are different from each other.

Examples 2:

Suppose an agricultural scientist wants to compare the yield of three varieties of wheat. The scientist randomly selects four fields for each variety and plants them. After harvest, the yield from each field is measured in bushels. Here are the hypothetical yields:

The scientist wants to know if the differences in yields are due to the different varieties or just random variation.

Here’s how to apply the one-way ANOVA to this situation:

• Null Hypothesis (H0): The means of the three populations are equal.
• Alternative Hypothesis (H1): At least one population mean is different.
• Calculate the Degrees of Freedom (dfB for between groups, dfW for within groups, dfT for total).
• If the p-value is less than the chosen significance level (often 0.05), then we reject the null hypothesis in favor of the alternative hypothesis. This would suggest there is a statistically significant difference in mean yields among the three varieties.
• If we reject the null hypothesis, we conduct a post-hoc test to determine which specific groups’ means (wheat varieties) are different from each other.

How to Conduct ANOVA

Conducting an Analysis of Variance (ANOVA) involves several steps. Here’s a general guideline on how to perform it:

• Null Hypothesis (H0): The means of all groups are equal.
• Alternative Hypothesis (H1): At least one group mean is different from the others.
• The significance level (often denoted as α) is usually set at 0.05. This implies that you are willing to accept a 5% chance that you are wrong in rejecting the null hypothesis.
• Data should be collected for each group under study. Make sure that the data meet the assumptions of an ANOVA: normality, independence, and homogeneity of variances.
• Calculate the Degrees of Freedom (df) for each sum of squares (dfB, dfW, dfT).
• Compute the Mean Squares Between (MSB) and Mean Squares Within (MSW) by dividing the sum of squares by the corresponding degrees of freedom.
• Compute the F-statistic as the ratio of MSB to MSW.
• Determine the critical F-value from the F-distribution table using dfB and dfW.
• If the calculated F-statistic is greater than the critical F-value, reject the null hypothesis.
• If the p-value associated with the calculated F-statistic is smaller than the significance level (0.05 typically), you reject the null hypothesis.
• If you rejected the null hypothesis, you can conduct post-hoc tests (like Tukey’s HSD) to determine which specific groups’ means (if you have more than two groups) are different from each other.
• Regardless of the result, report your findings in a clear, understandable manner. This typically includes reporting the test statistic, p-value, and whether the null hypothesis was rejected.

When to use ANOVA

ANOVA (Analysis of Variance) is used when you have three or more groups and you want to compare their means to see if they are significantly different from each other. It is a statistical method that is used in a variety of research scenarios. Here are some examples of when you might use ANOVA:

• Comparing Groups : If you want to compare the performance of more than two groups, for example, testing the effectiveness of different teaching methods on student performance.
• Evaluating Interactions : In a two-way or factorial ANOVA, you can test for an interaction effect. This means you are not only interested in the effect of each individual factor, but also whether the effect of one factor depends on the level of another factor.
• Repeated Measures : If you have measured the same subjects under different conditions or at different time points, you can use repeated measures ANOVA to compare the means of these repeated measures while accounting for the correlation between measures from the same subject.
• Experimental Designs : ANOVA is often used in experimental research designs when subjects are randomly assigned to different conditions and the goal is to compare the means of the conditions.

Here are the assumptions that must be met to use ANOVA:

• Normality : The data should be approximately normally distributed.
• Homogeneity of Variances : The variances of the groups you are comparing should be roughly equal. This assumption can be tested using Levene’s test or Bartlett’s test.
• Independence : The observations should be independent of each other. This assumption is met if the data is collected appropriately with no related groups (e.g., twins, matched pairs, repeated measures).

Applications of ANOVA

The Analysis of Variance (ANOVA) is a powerful statistical technique that is used widely across various fields and industries. Here are some of its key applications:

Agriculture

ANOVA is commonly used in agricultural research to compare the effectiveness of different types of fertilizers, crop varieties, or farming methods. For example, an agricultural researcher could use ANOVA to determine if there are significant differences in the yields of several varieties of wheat under the same conditions.

Manufacturing and Quality Control

ANOVA is used to determine if different manufacturing processes or machines produce different levels of product quality. For instance, an engineer might use it to test whether there are differences in the strength of a product based on the machine that produced it.

Marketing Research

Marketers often use ANOVA to test the effectiveness of different advertising strategies. For example, a marketer could use ANOVA to determine whether different marketing messages have a significant impact on consumer purchase intentions.

Healthcare and Medicine

In medical research, ANOVA can be used to compare the effectiveness of different treatments or drugs. For example, a medical researcher could use ANOVA to test whether there are significant differences in recovery times for patients who receive different types of therapy.

ANOVA is used in educational research to compare the effectiveness of different teaching methods or educational interventions. For example, an educator could use it to test whether students perform significantly differently when taught with different teaching methods.

Psychology and Social Sciences

Psychologists and social scientists use ANOVA to compare group means on various psychological and social variables. For example, a psychologist could use it to determine if there are significant differences in stress levels among individuals in different occupations.

Biology and Environmental Sciences

Biologists and environmental scientists use ANOVA to compare different biological and environmental conditions. For example, an environmental scientist could use it to determine if there are significant differences in the levels of a pollutant in different bodies of water.

Advantages of ANOVA

Here are some advantages of using ANOVA:

Comparing Multiple Groups: One of the key advantages of ANOVA is the ability to compare the means of three or more groups. This makes it more powerful and flexible than the t-test, which is limited to comparing only two groups.

Control of Type I Error: When comparing multiple groups, the chances of making a Type I error (false positive) increases. One of the strengths of ANOVA is that it controls the Type I error rate across all comparisons. This is in contrast to performing multiple pairwise t-tests which can inflate the Type I error rate.

Testing Interactions: In factorial ANOVA, you can test not only the main effect of each factor, but also the interaction effect between factors. This can provide valuable insights into how different factors or variables interact with each other.

Handling Continuous and Categorical Variables: ANOVA can handle both continuous and categorical variables . The dependent variable is continuous and the independent variables are categorical.

Robustness: ANOVA is considered robust to violations of normality assumption when group sizes are equal. This means that even if your data do not perfectly meet the normality assumption, you might still get valid results.

Provides Detailed Analysis: ANOVA provides a detailed breakdown of variances and interactions between variables which can be useful in understanding the underlying factors affecting the outcome.

Capability to Handle Complex Experimental Designs: Advanced types of ANOVA (like repeated measures ANOVA, MANOVA, etc.) can handle more complex experimental designs, including those where measurements are taken on the same subjects over time, or when you want to analyze multiple dependent variables at once.

Disadvantages of ANOVA

Some limitations or disadvantages that are important to consider:

Assumptions: ANOVA relies on several assumptions including normality (the data follows a normal distribution), independence (the observations are independent of each other), and homogeneity of variances (the variances of the groups are roughly equal). If these assumptions are violated, the results of the ANOVA may not be valid.

Sensitivity to Outliers: ANOVA can be sensitive to outliers. A single extreme value in one group can affect the sum of squares and consequently influence the F-statistic and the overall result of the test.

Dichotomous Variables: ANOVA is not suitable for dichotomous variables (variables that can take only two values, like yes/no or male/female). It is used to compare the means of groups for a continuous dependent variable.

Lack of Specificity: Although ANOVA can tell you that there is a significant difference between groups, it doesn’t tell you which specific groups are significantly different from each other. You need to carry out further post-hoc tests (like Tukey’s HSD or Bonferroni) for these pairwise comparisons.

Complexity with Multiple Factors: When dealing with multiple factors and interactions in factorial ANOVA, interpretation can become complex. The presence of interaction effects can make main effects difficult to interpret.

Requires Larger Sample Sizes: To detect an effect of a certain size, ANOVA generally requires larger sample sizes than a t-test.

Equal Group Sizes: While not always a strict requirement, ANOVA is most powerful and its assumptions are most likely to be met when groups are of equal or similar sizes.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is ANOVA?

How does anova work, types of anova, anova assumptions, why use anova, benefits of anova for businesses, roi from anova, anova examples: when might you use it, how to conduct an anova test, anova analysis, what are the limitations of anova, additional considerations with anova, try qualtrics for free, what is anova (analysis of variance) testing.

19 min read ANOVA is a statistical test used to compare the means of multiple groups. Discover how it works, when to use it and why it’s a powerful tool for businesses.

ANOVA, or Analysis of Variance, is a test used to determine differences between research results from three or more unrelated samples or groups.

You might use ANOVA when you want to test a particular hypothesis between groups, determining – in using one-way ANOVA – the relationship between an independent variable and one quantitative dependent variable.

An example could be examining how the level of employee training impacts customer satisfaction ratings. Here the independent variable is the level of employee training; the quantitative dependent variable is customer satisfaction.

You would use ANOVA to help you understand how employees of different training levels – for example, beginner, intermediate and advanced – with the null hypothesis for the test being that they have the same customer satisfaction ratings. If there is a statistically significant result, it means the null hypothesis is rejected – meaning the employee groups performed differently.

The key word in ‘Analysis of Variance’ is the last one. ‘Variance’ represents the degree to which numerical values of a particular variable deviate from its overall mean. You could think of the dispersion of those values plotted on a graph, with the average being at the centre of that graph. The variance provides a measure of how scattered the data points are from this central value.

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While a one-way ANOVA is the most basic form, other variations exist that can be used in different situations:

Two-way ANOVA

Factorial anova, welch’s f-test anova, ranked anova, games-howell pairwise test.

ANOVA works by analysing the levels of variance within more than two groups through samples taken from each of them.

In an ANOVA test you first examine the variance within each group defined by the independent variable – this variance is calculated using the values of the dependent variable within each of these groups. Then, you compare the variance within each group to the overall variance of the group means.

In general terms, a large difference in means combined with small variances within the groups signifies a greater difference between the groups. Here the independent variable significantly varies by dependent variable, and the null hypothesis is rejected.

On the flip side, a small difference in means combined with large variances in the data suggests less variance between the groups. In this case, the independent variable does not significantly vary by the  dependent variable, and the null hypothesis is accepted.

There are various approaches to using ANOVA for your data analysis. Here’s an introduction to some of the most common ones.

One-way ANOVA

One-way ANOVA is its most simple form – testing differences between three or more groups based on one independent variable. For example, comparing the sales performance of different stores in a retail chain.

Used when there are two independent variables, two-way ANOVA allows for the evaluation of the individual and joint effects of the variables. For example, it could be used to understand the impact of both advertising spend and product placement on sales revenue.

This variant of ANOVA is used when there are more than two independent variables. For example, a business might use a factorial ANOVA to examine the combined effects of age, income and education level on consumer purchasing habits.

This type of ANOVA is used when the assumption of equal variances is not met. For example, a company might use Welch’s F-test to compare the job satisfaction levels of employees in different departments, where each department has a different variance in job satisfaction scores.

Stats iQ from Qualtrics recommends an unranked Welch’s F test if two assumptions about the data hold:

• The sample size is greater than 10 times the number of groups in the calculation (groups with only one value are excluded)
• There are few or no outliers in the continuous/discrete data

This version of ANOVA is used with ordinal data, or when the assumptions are violated. For instance, a business might use it to compare customer satisfaction ratings (e.g., from ‘very unsatisfied’ to ‘very satisfied’) across different product lines.

Stats iQ rank-transforms the data (replaces values with their rank ordering) and then runs a ranked ANOVA on that transformed data.

This is essentially a t-test but is used when the assumption of homogeneity of variances has been violated, which means different groups have different variances. For example, a company might use the Games-Howell test to compare the effectiveness of different training methods on employee performance, where the variances in performance are different between the methods.

Stats iQ runs Games-Howell tests regardless of the outcome of the ANOVA test (as per Zimmerman, 2010). Stats iQ shows unranked or ranked Games-Howell pairwise tests based on the same criteria as those used for ranked vs. unranked ANOVA, so if you see ‘Ranked ANOVA’ in the advanced output, the pairwise tests will also be ranked.

Additionally, while Stats iQ does not show results of pairwise tests for any group with less than four values, those groups are included in calculating the degrees of freedom for the other pairwise tests.

Like other types of statistical methods , ANOVA compares the means of different groups and shows you if there are any statistical differences between the means. ANOVA is classified as an omnibus test statistic. This means that it can’t tell you which specific groups were statistically significantly different from each other, only that at least two of the groups were.

ANOVA relies on three main assumptions that must be met for the test results to be valid.

The first assumption is that the groups each fall into what is called a normal distribution. This means that the groups should have a bell-curve distribution with few or no outliers.

Homogeneity of variance

Also known as homoscedasticity, this means that the variances between each group are the same.

Independence

The final assumption is that each value is independent from each other. This means, for example, that unlike a conjoint analysis the same person shouldn’t be measured multiple times.

ANOVA is a versatile and powerful statistical technique, and the essential tool when researching multiple groups or categories. The one-way ANOVA can help you know whether or not there are significant differences between the means of your independent variable.

Why is that useful? Because when you understand how the means of each group in your independent variable differ, you can begin to understand which of them has a connection to your dependent variable (such as landing page clicks) and begin to learn what is driving that behaviour.

You could also repeat this test multiple times to see whether or not a single independent variable (such as temperature) affects multiple dependent variables (such as purchase rates of suncream, attendance at outdoor venues and likelihood to hold a cook-out) and if so, which ones.

ANOVA has a wide range of applications in research across numerous fields, from social sciences to medicine, and industrial research to marketing.

Its unique benefits make ANOVA particularly valuable to businesses. Here are its three main use cases in the business world.

Informing decision making

Businesses can use ANOVA to inform decisions about product development, marketing strategies and more.

Using resources

By identifying which variables have the most significant impact on a particular outcome, businesses can better allocate resources to those areas.

Understanding different variables

ANOVA doesn’t just tell you that differences exist between groups – it can also reveal the interaction between different variables. This can help businesses better understand complex relationships and dynamics, leading to more effective interventions and strategies.

The Return on Investment (ROI) from using ANOVA can be significant.

It helps businesses to focus their resources on the most effective strategies by helping them make more informed decisions – potentially leading to increased efficiency, productivity and even profitability.

For instance, if ANOVA shows that one marketing strategy is significantly more effective than others, resources can be shifted to that strategy, potentially leading to increased sales and revenue.

Here’s how different types of ANOVA test can be used to solve different questions a business could face.

Does the geographical region have an effect on the sales performance of a retail chain?

A one-way ANOVA can be used to answer this question, as you have one independent variable (region) and one dependent variable (sales performance).

You’ll need to collect data for different geographical regions where your retail chain operates – for example, the USA’s Northeast, Southeast, Midwest, Southwest and West regions. A one-way ANOVA can then assess the effect of these regions on your dependent variable (sales performance) and determine whether there is a significant difference in sales performance across these regions.

Does the time of year and type of product have an effect on the sales of a company?

To answer this question, a two-way ANOVA can be used, as you have two independent variables (time of year and product type) and one dependent variable (sales).

You’ll need to collect data for different times of the year (such as Q1, Q2, Q3, Q4) and for the different types of products your company sells (like electronics, clothing, home goods, etc.). A two-way ANOVA can then simultaneously assess the effect of these variables on your dependent variable (sales) and determine whether there is an interaction effect between the time of the year and the type of product on the company’s sales.

Do age, sex or income have an effect on how much someone spends in your store per month?

To answer this question, a factorial ANOVA can be used, since you have three independent variables and one dependent variable.

You’ll need to collect data for different age groups (such as 0-20, 21-40, 41-70, 71+), different income brackets, and all relevant sexes. A factorial ANOVA can then simultaneously assess the effect of these variables on your dependent variable (spending) and determine whether they make a difference.

As with many of the older statistical tests, it’s possible to do ANOVA using a manual calculation based on formulas. However, you can run ANOVA tests much quicker using any number of popular stats software packages and systems, such as R, SPSS or Minitab.

A more recent development is to use automated tools such as Stats iQ from Qualtrics , which makes statistical analysis more accessible and straightforward than ever before.

Stats iQ and ANOVA

When you select one categorical variable with three or more groups and one continuous or discrete variable, Stats iQ runs a one-way ANOVA (Welch’s F test) and a series of pairwise ‘post hoc’ tests (Games-Howell tests).

The one-way ANOVA tests for an overall relationship between the two variables, and the pairwise tests test each possible pair of groups to see if one tends to have higher values than the other.

How to run an ANOVA test through Stats iQ

The Overall Stat Test of Averages in Stats iQ acts as an ANOVA, testing the relationship between a categorical and a numeric variable by testing the differences between two or more means. This test produces a p-value to determine whether the relationship is significant or not.

To run an ANOVA in Stats iQ , take the following steps:

• Select a variable with 3+ groups and one with numbers
• Select ‘Relate’
• You’ll then get an ANOVA, a related ‘effect size’ and a simple, easy to understand summary

Qualtrics Crosstabs and ANOVA

You can run an ANOVA test through the Qualtrics Crosstabs feature too. Here’s how:

• Ensure your ‘banner’ (column) variable has 3+ groups and your ‘stub’ (rows) variable has numbers (like Age) or numeric recodes (like ‘Very Satisfied’ = 7)
• Select ‘Overall stat test of averages’
• You’ll see a basic ANOVA p-value

Once you’ve performed your ANOVA test, you now need to analyse the data and capture your findings. There are two main steps to focus on here.

Interpret the output

The F-value, degrees of freedom and the p-value collectively form the backbone of hypothesis testing in ANOVA. They work together to provide a complete picture of your data and allow you to make an informed decision about your research question.

The F-value and degrees of freedom are used together to compute the p-value; the p-value is used to determine whether or not differences between your groups are due to chance or not. Generally, if this p-value is less than 0.05 we say that the results are statistically significant, meaning that it is unlikely that they are due to chance.

This measures the ratio of the variability between groups to the variability within groups. It’s the fundamental statistic in ANOVA that quantifies the relative extent to which the group means differ.

Degrees of freedom (df)

This is necessary to adjust the F-value for the number of groups and the number of observations. It helps to take into account the sample size and the number of groups in the analysis, which influences the reliability and accuracy of the F-value.

This translates the F-value (and its degrees of freedom) into a probability that helps you make a decision about the null hypothesis. It provides the statistical significance of the analysis and allows for a more intuitive understanding of the results.

Post-hoc testing

If you find a significant effect using ANOVA, it means that there is a significant difference between at least two of the groups. But it doesn’t specify which groups are significantly different from each other. For this, you’ll need to perform post-hoc tests.

Here are some of the most common types of post hoc test.

Tukey’s Honestly Significant Difference (HSD)

This test compares all possible pairs of means and controls for the familywise error rate. It is most appropriate when all groups have equal sample sizes.

This is a very conservative test that also compares all possible pairs of means. It adjusts the significance level by dividing it by the number of comparisons. It’s highly robust to type I errors, but increases the chance of type II errors.

Scheffe’s Test

This is a very flexible test that allows for any type of comparison, not just pairwise comparisons. It is also very conservative.

Fisher’s Least Significant Difference (LSD)

This test does not control for familywise error rate, so it tends to be liberal in detecting significant differences.

While ANOVA will help you to analyse the difference in means between two independent variables, it won’t tell you which statistical groups were different from each other.

If your test returns a significant F-value (the value you get when you run an ANOVA test), you may need to run an ad hoc test (like the Least Significant Difference test) to tell you exactly which groups had a difference in means.

Furthermore, ANOVA doesn’t provide information on the direction of the relationship between the independent and dependent variables – it only indicates if there is a statistically significant difference between group means.

ANOVA can be a very useful tool for analysing data, but there are some considerations you should keep in mind before deciding to use it.

Sample size

With smaller sample sizes , data can be visually inspected to determine if it is in fact normally distributed; if it is, unranked t-test results are still valid even for small samples. In practice, this assessment can be difficult to make, so Stats iQ recommends ranked t-tests by default for small samples.

With larger sample sizes, outliers are less likely to negatively affect results. Stats iQ uses Tukey’s ‘outer fence’ to define outliers as points more than three times the interquartile range above the 75th or below the 25th percentile point.

It’s worth highlighting that ANOVA is most reliable when the sample sizes for all groups are equal.

The type of data

ANOVA requires the dependent variable to be continuous (interval/ratio), and the independent variable to be categorical (nominal/ordinal). If your variables do not meet these requirements, then ANOVA may not be the best choice.

Unambiguously ordinal data

Data like ‘Highest level of education completed’ or ‘Finishing order in marathon’ are unambiguously ordinal. While Likert scales (like a 1 to 7 scale, where 1 is ‘very dissatisfied’ and 7 is ‘very satisfied’) are technically ordinal, it is common practice in social sciences to treat them as though they are continuous (i.e., with an unranked t-test).

Read more about additional statistical analysis types:

• Conjoint Analysis
• CrossTab Analysis
• Cluster Analysis 
• Factor Analysis 

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What Is An ANOVA Test In Statistics: Analysis Of Variance

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An ANOVA test is a statistical test used to determine if there is a statistically significant difference between two or more categorical groups by testing for differences of means using a variance.

Another key part of ANOVA is that it splits the independent variable into two or more groups.

For example, one or more groups might be expected to influence the dependent variable, while the other group is used as a control group and is not expected to influence the dependent variable.

Assumptions of ANOVA

The assumptions of the ANOVA test are the same as the general assumptions for any parametric test:

• An ANOVA can only be conducted if there is no relationship between the subjects in each sample. This means that subjects in the first group cannot also be in the second group (e.g., independent samples/between groups).
• The different groups/levels must have equal sample sizes .
• An ANOVA can only be conducted if the dependent variable is normally distributed so that the middle scores are the most frequent and the extreme scores are the least frequent.
• Population variances must be equal (i.e., homoscedastic). Homogeneity of variance means that the deviation of scores (measured by the range or standard deviation, for example) is similar between populations.

Types of ANOVA Tests

There are different types of ANOVA tests. The two most common are a “One-Way” and a “Two-Way.”

The difference between these two types depends on the number of independent variables in your test.

One-way ANOVA

A one-way ANOVA (analysis of variance) has one categorical independent variable (also known as a factor) and a normally distributed continuous (i.e., interval or ratio level) dependent variable.

The independent variable divides cases into two or more mutually exclusive levels, categories, or groups.

The one-way ANOVA test for differences in the means of the dependent variable is broken down by the levels of the independent variable.

An example of a one-way ANOVA includes testing a therapeutic intervention (CBT, medication, placebo) on the incidence of depression in a clinical sample.

Note : Both the One-Way ANOVA and the Independent Samples t-Test can compare the means for two groups. However, only the One-Way ANOVA can compare the means across three or more groups.

Two-way (factorial) ANOVA

A two-way ANOVA (analysis of variance) has two or more categorical independent variables (also known as a factor) and a normally distributed continuous (i.e., interval or ratio level) dependent variable.

The independent variables divide cases into two or more mutually exclusive levels, categories, or groups. A two-way ANOVA is also called a factorial ANOVA.

An example of factorial ANOVAs include testing the effects of social contact (high, medium, low), job status (employed, self-employed, unemployed, retired), and family history (no family history, some family history) on the incidence of depression in a population.

What are “Groups” or “Levels”?

In ANOVA, “groups” or “levels” refer to the different categories of the independent variable being compared.

For example, if the independent variable is “eggs,” the levels might be Non-Organic, Organic, and Free Range Organic. The dependent variable could then be the price per dozen eggs.

ANOVA F -value

The test statistic for an ANOVA is denoted as F . The formula for ANOVA is F = variance caused by treatment/variance due to random chance.

The ANOVA F value can tell you if there is a significant difference between the levels of the independent variable, when p < .05. So, a higher F value indicates that the treatment variables are significant.

Note that the ANOVA alone does not tell us specifically which means were different from one another. To determine that, we would need to follow up with multiple comparisons (or post-hoc) tests.

When the initial F test indicates that significant differences exist between group means, post hoc tests are useful for determining which specific means are significantly different when you do not have specific hypotheses that you wish to test.

Post hoc tests compare each pair of means (like t-tests), but unlike t-tests, they correct the significance estimate to account for the multiple comparisons.

What Does “Replication” Mean?

Replication requires a study to be repeated with different subjects and experimenters. This would enable a statistical analyzer to confirm a prior study by testing the same hypothesis with a new sample.

How to run an ANOVA?

For large datasets, it is best to run an ANOVA in statistical software such as R or Stata. Let’s refer to our Egg example above.

Non-Organic, Organic, and Free-Range Organic Eggs would be assigned quantitative values (1,2,3). They would serve as our independent treatment variable, while the price per dozen eggs would serve as the dependent variable. Other erroneous variables may include “Brand Name” or “Laid Egg Date.”

Using data and the aov() command in R, we could then determine the impact Egg Type has on the price per dozen eggs.

ANOVA vs. t-test?

T-tests and ANOVA tests are both statistical techniques used to compare differences in means and spreads of the distributions across populations.

The t-test determines whether two populations are statistically different from each other, whereas ANOVA tests are used when an individual wants to test more than two levels within an independent variable.

Referring back to our egg example, testing Non-Organic vs. Organic would require a t-test while adding in Free Range as a third option demands ANOVA.

Rather than generate a t-statistic, ANOVA results in an f-statistic to determine statistical significance.

What does anova stand for?

ANOVA stands for Analysis of Variance. It’s a statistical method to analyze differences among group means in a sample. ANOVA tests the hypothesis that the means of two or more populations are equal, generalizing the t-test to more than two groups.

It’s commonly used in experiments where various factors’ effects are compared. It can also handle complex experiments with factors that have different numbers of levels.

When to use anova?

ANOVA should be used when one independent variable has three or more levels (categories or groups). It’s designed to compare the means of these multiple groups.

What does an anova test tell you?

An ANOVA test tells you if there are significant differences between the means of three or more groups. If the test result is significant, it suggests that at least one group’s mean differs from the others. It does not, however, specify which groups are different from each other.

Why do you use chi-square instead of ANOVA?

You use the chi-square test instead of ANOVA when dealing with categorical data to test associations or independence between two categorical variables. In contrast, ANOVA is used for continuous data to compare the means of three or more groups.

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15.1: Introduction to ANOVA

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Learning Objectives

• What null hypothesis is tested by ANOVA
• Describe the uses of ANOVA

Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means. It may seem odd that the technique is called "Analysis of Variance" rather than "Analysis of Means." As you will see, the name is appropriate because inferences about means are made by analyzing variance.

ANOVA is used to test general rather than specific differences among means. This can be seen best by example. In the case study "Smiles and Leniency," the effect of different types of smiles on the leniency shown to a person was investigated. Four different types of smiles (neutral, false, felt, miserable) were investigated. The chapter "All Pairwise Comparisons among Means" showed how to test differences among means. The results from the Tukey HSD test are shown in Table \(\PageIndex{1}\).

Notice that the only significant difference is between the False and Neutral conditions.

ANOVA tests the non-specific null hypothesis that all four population means are equal. That is,

\[\mu _{false} = \mu _{felt} = \mu _{miserable} = \mu _{neutral}\]

This non-specific null hypothesis is sometimes called the omnibus null hypothesis. When the omnibus null hypothesis is rejected, the conclusion is that at least one population mean is different from at least one other mean. However, since the ANOVA does not reveal which means are different from which, it offers less specific information than the Tukey HSD test . The Tukey HSD is therefore preferable to ANOVA in this situation. Some textbooks introduce the Tukey test only as a follow-up to an ANOVA. However, there is no logical or statistical reason why you should not use the Tukey test even if you do not compute an ANOVA.

You might be wondering why you should learn about ANOVA when the Tukey test is better. One reason is that there are complex types of analysis that can be done with ANOVA and not with the Tukey test. A second is that ANOVA is by far the most commonly-used technique for comparing means, and it is important to understand ANOVA in order to understand research reports.

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Lesson 10: introduction to anova, overview section  .

In the previous lessons, we learned how to perform inference for a population mean from one sample and also how to compare population means from two samples (independent and paired). In this Lesson, we introduce Analysis of Variance or ANOVA. ANOVA is a statistical method that analyzes variances to determine if the means from more than two populations are the same. In other words, we have a quantitative response variable and a categorical explanatory variable with more than two levels. In ANOVA, the categorical explanatory is typically referred to as the factor.

• Describe the logic behind analysis of variance.
• Set up and perform one-way ANOVA.
• Identify the information in the ANOVA table.
• Interpret the results from ANOVA output.
• Perform multiple comparisons and interpret the results, when appropriate.

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Application of Student's t -test, Analysis of Variance, and Covariance

Prabhaker mishra.

Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Uttam Singh

Chandra m pandey, priyadarshni mishra.

1 Department of Ophthalmology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Gaurav Pandey

2 Department of Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Student's t test ( t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value. A significant P value of the ANOVA test indicates for at least one pair, between which the mean difference was statistically significant. To identify that significant pair(s), we use multiple comparisons. In ANOVA, when using one categorical independent variable, it is called one-way ANOVA, whereas for two categorical independent variables, it is called two-way ANOVA. When using at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA. When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.

Introduction

Student's t test ( t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. For these methods, testing variable (dependent variable) should be in continuous scale and approximate normally distributed. Mean is the representative measure for normally distributed continuous variable and statistical methods used to compare between the means are called parametric methods. For non-normal continuous variable, median is representative measure, and in this situation, comparison between the groups is performed using non-parametric methods. Most parametric test has an alternative nonparametric test.[ 1 , 2 , 3 ]

There are many statistical tests within Student's t test ( t test), ANOVA and ANCOVA, and each test has its own assumptions. Although not every method is popular, some of them can be managed from other available methods. The aim of the present article is to discuss the assumptions, application, and interpretation of the some popular T, ANOVA, and ANCOVA methods i.e., one sample t test, independent samples t test, paired samples t test, one-way ANOVA, two-ways ANOVA, one-way repeated measures ANOVA, two-ways repeated measures ANOVA, one-way ANCOVA, and One-way repeated measures ANCOVA. To understand the above statistical methods, an example [ Table 1 ] with a data set of 20 patients whose age groups, gender, body mass index (BMI), and diastolic blood pressure (DBP) measured at baseline (B/L), 30 min and 60 min are given below. Further, examples related to the above statistical methods are discussed from the given data.

Data of the 20 patients

Age groups: 1 (<30 years), 2 (30-50 years), 3 (>50 years). Gender: M=Male, F=Female, BMI=Body mass index, DBP=Diastolic blood pressure, B/L=Baseline, min=Minute

T test, ANOVA, and ANCOVA

Basic concepts.

The Student's t test (also called T test) is used to compare the means between two groups and there is no need of multiple comparisons as unique P value is observed, whereas ANOVA is used to compare the means among three or more groups.[ 4 , 5 ] In ANOVA, the first gets a common P value. A significant P value of ANOVA test indicates for at least one pair, between which the mean difference was statistically significant.[ 6 ] To identify that significant pair(s), post-hoc test (multiple comparisons) is used. In ANOVA test, when at least one covariate (continuous variable) is adjusted to remove the confounding effect from the result called ANCOVA. ANOVA test (F test) is called “Analysis of Variance” rather than “Analysis of Means” because inferences about means are made by analyzing variance.[ 7 , 8 , 9 ]

Steps in hypothesis testing

Hypothesis building.

Like other tests, there are two kinds of hypotheses; null hypothesis and alternative hypothesis. The alternative hypothesis assumes that there is a statistically significant difference exists between the means, whereas the null hypothesis assumes that there is no statistically significant difference exists between the means.

Computation of test statistics

In these test, first step is to calculate test statistics (called t value in student's t test and F value in ANOVA test) also called calculated value. It is calculated after putting inputs (from the samples) in statistical test formula. In student's t test, calculated t value is ratio of mean difference and standard error, whereas in the ANOVA test, calculated F value is ratio of the variability between groups with the variability of the observations within the groups.[ 1 , 4 ]

Tabulated value

At degree of freedom of the given observations and desired level of the confidence (usually at two-sided test, which is more powerful than one-sided test), corresponding tabulated value of the T test or F test is selected (from the statistical table).[ 1 , 4 ]

Comparison of calculated value with tabulated value and null hypothesis

If the calculated value is greater than the tabulated value, then reject the null hypothesis where null hypothesis states that means are statistically same between the groups.[ 1 , 4 ] As the sample size increases corresponding degree of freedom also increases. For a given level of confidence, higher degree of freedom has lower tabulated value. That's the reason, when the sample size increases, its significance level also improves (i.e., P value is decreasing).

It is one of the most popular statistical techniques used to test whether mean difference between two groups is statistically significant. Null hypothesis stated that both means are statistically equal, whereas alternative hypothesis stated that both means are not statistically equal i.e., they are statistically different to each other.[ 1 , 3 , 7 ] T test are three types i.e., one sample t test, independent samples t test, and paired samples t test.

One-sample t test

The one sample t test is a statistical procedure used to determine whether mean value of a sample is statistically same or different with mean value of its parent population from which sample was drawn. To apply this test, mean, standard deviation (SD), size of the sample (Test variable), and population mean or hypothetical mean value (Test value) are used. Sample should be continuous variable and normally distributed.[ 1 , 9 , 10 , 11 ] One-sample t test is used when sample size is <30. In case sample size is ≥30 used to prefer one sample z test over one sample t test although for one sample z test, population SD must be known. If population SD is not known, one sample t test can be used at any sample size. In one sample Z test, tabulated value is z value (instead of t value in one sample t test). To apply this test through popular statistical software i.e., statistical package for social sciences (SPSS), option can be found in the following menu [Analyze – compare means – one-sample t test].

Example : From Table 1 , BMI (mean ± SD) was given 24.45 ± 2.19, whereas population mean was assumed to be 25.5. One sample t test indicated that mean difference between sample mean and population mean was statistically significantly different to each other ( P = 0.045).

Independent samples t test

The independent t test, also called unpaired t test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated (independent) groups?

To apply this test, a continuous normally distributed variable (Test variable) and a categorical variable with two categories (Grouping variable) are used. Further mean, SD, and number of observations of the group 1 and group 2 would be used to compute significance level. In this procedure, first significance level of Levene's test is computed and when it is insignificant ( P > 0.05), equal variances otherwise ( P < 0.05), unequal variances are assumed between the groups and according P value is selected for independent samples t test.[ 1 , 10 , 11 , 12 ] In SPSS [Analyze – compare means – independent samples t test].

Example : From Table 1 , mean BMI of the male ( n = 10) and female ( n = 10) were 24.80 ± 2.20 and 24.10 ± 2.23, respectively. Levene's test ( p = 0.832) indicated that variances between the groups were statistically equal. At equal variances assumed, independent samples t test ( p = 0.489) indicated that mean BMI of the male and female was statistically equal.

Paired samples t test

The paired samples t test, sometimes called the dependent samples t -test, is used to determine whether the change in means between two paired observations is statistically significant? In this test, same subjects are measured at two time points or observed by two different methods.[ 4 ] To apply this test, paired variables (pre-post observations of same subjects) are used where paired variables should be continuous and normally distributed. Further mean and SD of the paired differences and sample size (i.e., no. of pairs) would be used to calculate significance level.[ 1 , 11 , 13 ] In SPSS [Analyze – compare means – paired samples t test].

Example : From Table 1 , DBP of the 20 patients (mean ± SD); at baseline, 30 min and paired differences (difference between baselines and 30 min) were 79.55 ± 4.87, 83.90 ± 5.58, and 4.35 ± 4.16. Paired samples t test indicated that mean difference of paired observations of DBP between baseline and 30 min was statistically significant ( P < 0.001).

ANOVA test (F test)

A statistical technique used to compare the means between three or more groups is known as ANOVA or F test. It is important that ANOVA is an omnibus test statistic. Its significant P value indicates that there is at least one pair in which the mean difference is statistically significant. To determine the specific pair's, post hoc tests (multiple comparisons) are used. There are various ANOVAs test, and their objectives are varying from one test to another. There are two main types of ANOVA i.e., one-way ANOVA and one-way repeated measures ANOVA. First is used for independent observations and later for dependent observations. When used one categorical independent variable called one-way ANOVA, whereas for two categorical independent variables called two-way ANOVA. When used at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA.[ 1 , 11 , 14 ]

Post-hoc test (multiple comparisons): Post hoc tests (pair-wise multiple comparisons) used to determine the significant pair(s) after ANOVA was found significant. Before applying post-hoc test (in between subjects factors), first need to test the homogeneity of the variances among the groups (Levene's test). If variances are homogeneous ( P ≥ 0.05), select any multiple comparison methods from least significant difference (LSD), Bonferroni, Tukey's, etc.[ 15 , 16 ] If variances are not homogeneous ( P < 0.05), used to select any multiple comparison methods from Games-Howell, Tamhane's T2, etc.[ 15 , 16 ] Bonferroni is a good method for equal variances, whereas Tamhane's T2 for unequal variances as both calculate significance level by controlling error rate. Similarly, for repeated measures ANOVA (RMA) (in within subjects factors), select any method from LSD, Boneferroni, Sidak although Bonferroni might be a better choice. The significance level of each of the multiple comparison method is varying from other methods as each used for a particular situation.

One-way ANOVA

The One-way ANOVA is extension of independent samples t test (In independent samples t test used to compare the means between two independent groups, whereas in one-way ANOVA, means are compared among three or more independent groups). A significant P value of this test refers to multiple comparisons test to identify the significant pair(s).[ 17 ] In this test, one continuous dependent variable and one categorical independent variable are used, where categorical variable has at least three categories. In SPSS [Analyze–compare means–one-way ANOVA].

Example : From Table 1 , 20 patient's DBP (at 30 min) are given. One-way ANOVA test was used to compare the mean DBP in three age groups (independent variable), which was found statistically significant ( p = 0.002). Levene test for homogeneity was insignificant ( p = 0.231), as a result Bonferroni test was used for multiple comparisons, which showed that DBP was significantly different between two pairs i.e., age group of <30 to 30–50 and <30 to >50 ( P < 0.05) but insignificant between one pair i.e., 30–50 to >50 ( P > 0.05).

Two-way ANOVA

The two-way ANOVA is extension of one-way ANOVA [In one-way ANOVA, only one independent variable, whereas in two-way ANOVA, two independent variables are used]. The primary purpose of a two-way ANOVA is to understand whether there is any interrelationship between two independent variables on a dependent variable.[ 18 ] In this test, a continuous dependent variable (approximately normally distributed) and two categorical independent variables are used. In SPSS [Analyze –General Linear Model –Univariate].

Example : From Table 1 , 20 patient's DBP (at 30 min) are given. Two-way ANOVA test was used to compare the mean DBP between age groups (independent variable_1) and gender (independent variable_2), which indicated that there was no significant interaction of DBP with age groups and gender (tests of Between-Subjects effects in age groups*gender; P = 0.626) with effect size (Partial Eta Squared) of 0.065. The result also showed that there was significant difference in estimated marginal means (adjusted mean) of DBP between age groups ( P = 0.005) but insignificant in gender ( P = 0.662), where sex and age groups was adjusted.

One-way repeated measures ANOVA

Repeated Measures ANOVA (RMA) is the extension of the paired t test. RMA is also referred to as within-subjects ANOVA or ANOVA for paired samples. Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or more than two time periods. (In paired samples t test, compared the means between two dependent groups, whereas in RMA, compared the means between three or more dependent groups). Before calculating the significance level, Mauchly's test is used to assess the homogeneity of the variance (also called sphericity) within all possible pairs. When P value of Mauchly's test is insignificant ( P ≥ 0.05), equal variances are assumed and P value for RMA would be taken from sphericity assumed test (Tests of Within-Subjects effects). In case variances are not homogeneous (Mauchly's test: P < 0.05), epsilon (ε) value (which shows the departure of the sphericity, 1 shows perfect sphericity) decides the statistical method to calculate P value for RMA. When ε≥0.75 Huynh-Feldt while for ε< 0.75, Greenhouse-Geisser method (univariate method) or Wilks' lambda (multivariate method) is used to calculate P value for the RMA.[ 19 ] When the RMA is significant, pair-wise comparison contains multiple paired t tests with a Bonferroni correction is used.[ 20 ] In SPSS [Analyze –General Linear Model – Repeated Measures ANOVA].

Example : From Table 1 , 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity indicated that variances were equal ( P = 0.099) between the pairs. RMA tests (i.e., Within-Subjects effects) was assessed using sphericity assumed test ( P value = 0.001), which indicated that change in DBP over the time was statistically significant. Bonferroni multiple comparisons indicated that mean difference was statistically significant between DBP_B/l to DBP_30 min and DBP_30 min to DBP_60 min ( P < 0.05) but insignificant between DBP_B/l to DBP_60 min ( P > 0.05).

Two-way repeated measures ANOVA

Two-way Repeated Measures ANOVA is combination of between-subject and within-subject factors. A two-way RMA (also known as a two-factor RMA or a two-way “Mixed ANOVA”) is extension of one-way RMA [In one-way RMA, use one dependent variable under repeated observations (normally distributed continuous variable) and one categorical independent variable (i.e., time points), whereas in two-way RMA; one additional categorical independent variable is used]. The primary purpose of two-way RMA is to understand if there is an interaction between these two categorical independent variables on the dependent variable (continuous variable). The distribution of the dependent variable in each combination of the related groups should be approximately normally distributed.[ 21 ] In SPSS [Analyze–General Linear Model – Repeated Measures], where second independent variable will be included as between subjects factor.

Example : From Table 1 , 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity ( P = 0.138) indicated that variances were equal between the pairs. Two-way RMA tests for interaction (i.e., Within-Subjects effects) were assessed using sphericity assumed test (DBP*gender: P value = 0.214), which indicated that there was no interaction of gender with time and associated change in DBP over the time was statistically insignificant.

One-way ANCOVA

One-way ANCOVA is extension of one-way ANOVA [In one-way ANOVA, do not adjust the covariate, whereas in the one-way ANCOVA; adjust at least one covariate]. Thus, the one-way ANCOVA tests find out whether the independent variable still influences the dependent variable after the influence of the covariate(s) has been removed (i.e., adjusted). In this test, one continuous dependent variable, one categorical independent variable, and at least one continuous covariate for removing its effect/adjustment are used.[ 8 , 22 ] In SPSS [Analyze - General Linear Model – Univariate].

Example : From Table 1 , 20 patient's DBP at 30 min are given. One-way ANCOVA test was used to compare the mean DBP in three age groups (independent variable) after adjusting the effect of baseline DBP, which was found to be statistically significant ( P = 0.021). As Levene test for homogeneity was insignificant ( P = 0.601), resultant Bonferroni test was used for multiple comparisons, which showed that DBP was significantly different between one pair i.e., age group of <30 to >50 ( P = 0.031) and insignificant between rest two pairs i.e., <30 to 30–50 and 30–50 to >50 ( P > 0.05).

One-way repeated measures ANOCOVA

One-way repeated measures ANCOVA is the extension of the One-way RMA. [In one-way RMA, we do not adjust the covariate, whereas in the one-way repeated measures ANCOVA, we adjust at least one covariate]. Thus, the One-way repeated Measures ANCOVA is used to test whether means are still statistically equal or different after adjusting the effect of the covariate(s).[ 23 , 24 ] In SPSS [Analyze –General Linear Model – Repeated Measures ANOVA].

Example : From Table 1 , 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity indicated that variances were equal ( P = 0.093) between the pairs. RMA tests (i.e., Within-Subjects effects) were assessed using sphericity assumed test (DBP*BMI: P value = 0.011), which indicated that change in DBP over the time was statistically significant after adjusting BMI. Bonferroni multiple comparisons indicated that mean difference was statistically significant between DBP_B/l to DBP_30 min and DBP_30 min to DBP_60 min but insignificant between DBP_B/l to DBP_60 min after adjusting BMI.

Conclusions

Student's t test, ANOVA, and ANCOVA are the statistical methods frequently used to analyze the data. Two common things among these methods are dependent variable must be in continuous scale and normally distributed, and comparisons are made between the means. All above methods are parametric method.[ 2 ] When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgments

Authors would like to express their deep and sincere gratitude to Dr. Prabhat Tiwari, Professor, Department of Anaesthesiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, for his encouragement to write this article. His critical reviews and suggestions were very useful for improvement in the article.

Quantitative Research Methods

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One of the most commonly used tests of difference is the ANOVA ( An alysis O f Va riance) test. While t-tests are used to compare the means of two groups, ANOVA compares the means of three or more. There are multiple types of ANOVA tests. You may have heard of One-Way ANOVA, Two-Way ANOVA, or Three-Way ANOVA.  

• Introduction to ANOVA Penn State University tutorial
• One-Way ANOVA Chapter of Experimental Design and Analysis by Howard Seltman.
• Two-Way ANOVA Penn State University tutorial
• Statistics 101: ANOVA, A Visual Introduction YouTube video by Brandon Foltz.

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Understanding the Core of ANOVA Test in Research Methodology

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Introduction

What is research methodology.

A research methodology can be defined as the section of a research paper that outlines the methods, techniques, and procedures employed in the study for data collection and data analysis purposes. Research papers have a section dedicated to the research methodology employed in the study. This section allows readers to assess the reliability and validity of the procedures employed. 

What is an ANOVA Test?

The analysis of variance test, abbreviated as ANOVA test, is a statistical tool used in research to analyze the means between groups of data to identify whether there are any statistically significant differences. The ANOVA test involves splitting an observed aggregate variability that is found in a data set into two parts: the systematic factors and the random factors. The systematic factors are the factors that have a statistical influence on the data set while the random factors are the factors that do not have a statistical influence on the data set. The ANOVA test is used to determine the influence of different independent variables on the dependent variable in a regression study. 

Exploratory Research Guide

Conducting exploratory research seems tricky but an effective guide can help.

ANOVA Test in Research Methodology

The ANOVA technique allows researchers to examine a range of factors that are thought to influence the dependent variable in the study. It is used in research to help determine whether the null hypothesis should be accepted or rejected. 

In order to gain an understanding of ANOVA tests, one must have a clear understanding of the following terms: 

• Groups/Levels : When we talk about ‘groups’ or ‘levels’ in regard to the ANOVA test, we are talking about different groups with the same independent variable.
• One-Way and Two-Way : ‘One-way’ and ‘two-way’ are used to denote the number of independent variables in the ANOVA test. In a one-way ANOVA test, there is one independent variable with two levels. In a two-way ANOVA test, there are two variables and they can have multiple levels. 
• Replication : In ANOVA testing, replication refers to the duplication of your test(s) with multiple groups.

Types of ANOVA Tests

There are two key types of ANOVA tests: 

• One-Way ANOVA : The one-way ANOVA test is used when testing two groups to identify whether there is a difference between their means.
• Two-Way ANOVA : The two-way ANOVA test can be divided further into two groups; the two-way ANOVA with replication and the  two-way ANOVA without replication. 
• The two-way ANOVA without replication is used when testing the same group twice (double-testing). 
• The two-way ANOVA with replication is used when testing two  different groups that are doing more than one thing. 

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Calculating anova.

The formula for ANOVA is as follows: 

F = ANOVA coefficient

MST = Mean Sum of Squares due to Treatment

MSE = Mean Sum of Squares due to Error

FAQs on ANOVA Test in Research Methodology

In a research paper, the section dedicated to the research methodology provides an outline of the procedures, techniques, and methods used in the study for the analysis and collection of data.

An ANOVA test, or the analysis of variance test, is a statistical tool used to analyze the difference among the means of different data sets in order to determine whether there are any significant statistical differences present.

The t-test is used when researchers want to compare the means of two groups of data. However, when there are three or more groups of data, the ANOVA test must be used to compare their means.

There are two main types of ANOVA tests; the one-way ANOVA test and the two-way ANOVA test. The two-way ANOVA test can be further classified into two groups; the two-way ANOVA with replication and the two-way ANOVA without replication.

The two-way ANOVA without replication is used when testing the same group twice (double-testing) while the two-way ANOVA with replication is used when testing two different groups that are doing more than one thing.

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What Is Analysis of Variance (ANOVA)?

Using anova, what anova reveals, one-way vs. two-way anova, the bottom line.

• Fundamental Analysis

Learn how to use this statistical analysis tool

Erika Rasure is globally-recognized as a leading consumer economics subject matter expert, researcher, and educator. She is a financial therapist and transformational coach, with a special interest in helping women learn how to invest.

Analysis of variance (ANOVA) is a statistical test used to evaluate the difference between the means of more than two groups. This statistical analysis tool separates the total variability within a data set into two components: random and systematic factors.

A one-way ANOVA uses one independent variable. A two-way ANOVA uses two independent variables. Analysts use the ANOVA test to determine independent variables' influence on the dependent variable in a regression study.

Key Takeaways

• Analysis of variance (ANOVA) is a statistical test used to evaluate the difference between the means of more than two groups.
• A one-way ANOVA uses one independent variable. A two-way ANOVA uses two independent variables.
• If no true variance exists between the groups, the ANOVA's F-ratio should equal close to 1.

An ANOVA test can be applied when data needs to be experimental. Analysis of variance is employed if there is no access to statistical software and ANOVA must be calculated by hand. It is simple to use and best suited for small samples. It is employed with subjects, test groups, and between and among groups.

ANOVA is similar to multiple two-sample t-tests . However, it results in fewer type I errors . ANOVA groups differences by comparing each group's means and includes spreading the variance into diverse sources. Analysts use a one-way ANOVA with collected data about one independent variable and one dependent variable. A two-way ANOVA uses two independent variables . The independent variable should have at least three different groups or categories. ANOVA determines if the dependent variable changes according to the level of the independent variable. 

A researcher might test students from multiple colleges to see if students from one of the colleges consistently outperform students from the other schools. In a business application, an R&D researcher might test two different processes of creating a product to see if one is better than the other in terms of cost efficiency.

F = MST MSE where: F = ANOVA coefficient MST = Mean sum of squares due to treatment MSE = Mean sum of squares due to error \begin{aligned} &\text{F} = \frac{ \text{MST} }{ \text{MSE} } \\ &\textbf{where:} \\ &\text{F} = \text{ANOVA coefficient} \\ &\text{MST} = \text{Mean sum of squares due to treatment} \\ &\text{MSE} = \text{Mean sum of squares due to error} \\ \end{aligned} ​ F = MSE MST ​ where: F = ANOVA coefficient MST = Mean sum of squares due to treatment MSE = Mean sum of squares due to error ​

History of ANOVA

The t- and z-test methods developed in the 20th century were used for statistical analysis until 1918 when Ronald Fisher created the analysis of variance method. ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. The term became well-known in 1925, after appearing in Fisher's book, "Statistical Methods for Research Workers." It was employed in experimental psychology and later expanded to more complex subjects.

The ANOVA test is the initial step in analyzing factors that affect a given data set. Once the test is finished, an analyst performs additional testing on the methodical factors that measurably contribute to the data set's inconsistency. The analyst utilizes the ANOVA test results in an f-test to generate additional data that aligns with the proposed regression models.

ANOVA splits an observed aggregate variability inside a data set into two parts: systematic factors and random factors. The systematic factors influence the given data set, while the random factors do not.

The ANOVA test allows a comparison of more than two groups simultaneously to determine whether a relationship exists between them. The result of the ANOVA formula, the F statistic or F-ratio, allows for the analysis of multiple data groups to determine the variability between samples and within samples.

If no real difference exists between the tested groups, called the null hypothesis , the result of the ANOVA's F-ratio statistic will be close to 1. The distribution of all possible values of the F statistic is the F-distribution. This is a group of distribution functions, with two characteristic numbers, called the numerator degrees of freedom and the denominator degrees of freedom.

A one-way ANOVA evaluates the impact of a sole factor on a sole response variable. It determines whether all the samples are the same. The one-way ANOVA is used to determine whether there are any statistically significant differences between the means of three or more independent groups.

A two-way ANOVA is an extension of the one-way ANOVA. With a one-way, you have one independent variable affecting a dependent variable. With a two-way ANOVA, there are two independents. For example, a two-way ANOVA allows a company to compare worker productivity based on two independent variables, such as salary and skill set. It is utilized to observe the interaction between the two factors and test the effect of two factors simultaneously.

MANOVA (multivariate ANOVA), differs from ANOVA as it tests for multiple dependent variables simultaneously while the ANOVA assesses only one dependent variable at a time.

How Does ANOVA Differ From a T Test?

ANOVA differs from T tests in that ANOVA can compare three or more groups while T tests are only useful for comparing two groups at one time.

What Is Analysis of Covariance (ANCOVA)?

Analysis of Covariance combines ANOVA and regression. It can be useful for understanding within-group variance that ANOVA tests do not explain.

Does ANOVA Rely on Any Assumptions?

Yes, ANOVA tests assume that the data is normally distributed and that variance levels in each group are roughly equal. Finally, it assumes that all observations are made independently. If these assumptions are inaccurate, ANOVA may not be useful for comparing groups.

ANOVA can compare more than two groups to identify relationships between them. The technique can be used in scholarly settings to analyze research or finance to predict future movements in stock prices.

Genetic Epidemiology, Translational Neurogenomics, Psychiatric Genetics and Statistical Genetics-QIMR Berghofer Medical Research Institute. " The Correlation Between Relatives on the Supposition of Mendelian Inheritance ."

Ronald Fisher. " Statistical Methods for Research Workers ." Springer-Verlag New York, 1992.

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Teach yourself statistics

Experimental Design for ANOVA

There is a close relationship between experimental design and statistical analysis. The way that an experiment is designed determines the types of analyses that can be appropriately conducted.

In this lesson, we review aspects of experimental design that a researcher must understand in order to properly interpret experimental data with analysis of variance.

What Is an Experiment?

An experiment is a procedure carried out to investigate cause-and-effect relationships. For example, the experimenter may manipulate one or more variables (independent variables) to assess the effect on another variable (the dependent variable).

Conclusions are reached on the basis of data. If the dependent variable is unaffected by changes in independent variables, we conclude that there is no causal relationship between the dependent variable and the independent variables. On the other hand, if the dependent variable is affected, we conclude that a causal relationship exists.

Experimenter Control

One of the features that distinguish a true experiment from other types of studies is experimenter control of the independent variable(s).

In a true experiment, an experimenter controls the level of the independent variable administered to each subject. For example, dosage level could be an independent variable in a true experiment; because an experimenter can manipulate the dosage administered to any subject.

What is a Quasi-Experiment?

A quasi-experiment is a study that lacks a critical feature of a true experiment. Quasi-experiments can provide insights into cause-and-effect relationships; but evidence from a quasi-experiment is not as persuasive as evidence from a true experiment. True experiments are the gold standard for causal analysis.

A study that used gender or IQ as an independent variable would be an example of a quasi-experiment, because the study lacks experimenter control over the independent variable; that is, an experimenter cannot manipulate the gender or IQ of a subject.

As we discuss experimental design in the context of a tutorial on analysis of variance, it is important to point out that experimenter control is a requirement for a true experiment; but it is not a requirement for analysis of variance. Analysis of variance can be used with true experiments and with quasi-experiments that lack only experimenter control over the independent variable.

Note: Henceforth in this tutorial, when we refer to an experiment, we will be referring to a true experiment or to a quasi-experiment that is almost a true experiment, in the sense that it lacks only experimenter control over the independent variable.

What Is Experimental Design?

The term experimental design refers to a plan for conducting an experiment in such a way that research results will be valid and easy to interpret. This plan includes three interrelated activities:

• Write statistical hypotheses.
• Collect data.
• Analyze data.

Let's look in a little more detail at these three activities.

Statistical Hypotheses

A statistical hypothesis is an assumption about the value of a population parameter . There are two types of statistical hypotheses:

H 0: μ i = μ j

Here, μ i is the population mean for group i , and μ j is the population mean for group j . This hypothesis makes the assumption that population means in groups i and j are equal.

H 1: μ i ≠ μ j

This hypothesis makes the assumption that population means in groups i and j are not equal.

The null hypothesis and the alternative hypothesis are written to be mutually exclusive. If one is true, the other is not.

Experiments rely on sample data to test the null hypothesis. If experimental results, based on sample statistics , are consistent with the null hypothesis, the null hypothesis cannot be rejected; otherwise, the null hypothesis is rejected in favor of the alternative hypothesis.

Data Collection

The data collection phase of experimental design is all about methodology - how to run the experiment to produce valid, relevant statistics that can be used to test a null hypothesis.

Identify Variables

Every experiment exists to examine a cause-and-effect relationship. With respect to the relationship under investigation, an experimental design needs to account for three types of variables:

• Dependent variable. The dependent variable is the outcome being measured, the effect in a cause-and-effect relationship.
• Independent variables. An independent variable is a variable that is thought to be a possible cause in a cause-and-effect relationship.
• Extraneous variables. An extraneous variable is any other variable that could affect the dependent variable, but is not explicitly included in the experiment.

Note: The independent variables that are explicitly included in an experiment are also called factors .

Define Treatment Groups

In an experiment, treatment groups are built around factors, each group defined by a unique combination of factor levels.

For example, suppose that a drug company wants to test a new cholesterol medication. The dependent variable is total cholesterol level. One independent variable is dosage. And, since some drugs affect men and women differently, the researchers include an second independent variable - gender.

This experiment has two factors - dosage and gender. The dosage factor has three levels (0 mg, 50 mg, and 100 mg), and the gender factor has two levels (male and female). Given this combination of factors and levels, we can define six unique treatment groups, as shown below:

Note: The experiment described above is an example of a quasi-experiment, because the gender factor cannot be manipulated by the experimenter.

Select Factor Levels

A factor in an experiment can be described by the way in which factor levels are chosen for inclusion in the experiment:

• Fixed factor. The experiment includes all factor levels about which inferences are to be made.
• Random factor. The experiment includes a random sample of levels from a much bigger population of factor levels.

Experiments can be described by the presence or absence of fixed or random factors:

• Fixed-effects model. All of the factors in the experiment are fixed.
• Random-effects model. All of the factors in the experiment are random.
• Mixed model. At least one factor in the experiment is fixed, and at least one factor is random.

The use of fixed factors versus random factors has implications for how experimental results are interpreted. With a fixed factor, results apply only to factor levels that are explicitly included in the experiment. With a random factor, results apply to every factor level from the population.

For example, consider the blood pressure experiment described above. Suppose the experimenter only wanted to test the effect of three particular dosage levels - 0 mg, 50 mg, and 100 mg. He would include those dosage levels in the experiment, and any research conclusions would apply to only those particular dosage levels. This would be an example of a fixed-effects model.

On the other hand, suppose the experimenter wanted to test the effect of any dosage level. Since it is not practical to test every dosage level, the experimenter might choose three dosage levels at random from the population of possible dosage levels. Any research conclusions would apply not only to the selected dosage levels, but also to other dosage levels that were not included explicitly in the experiment. This would be an example of a random-effects model.

Select Experimental Units

The experimental unit is the entity that provides values for the dependent variable. Depending on the needs of the study, an experimental unit may be a person, animal, plant, product - anything. For example, in the cholesterol study described above, researchers measured cholesterol level (the dependent variable) of people; so the experimental units were people.

Note: When the experimental units are people, they are often referred to as subjects . Some researchers prefer the term participant , because subject has a connotation that the person is subservient.

If time and money were no object, you would include the entire population of experimental units in your experiment. In the real world, where there is never enough time or money, you will usually select a sample of experimental units from the population.

Ultimately, you want to use sample data to make inferences about population parameters. With that in mind, it is best practice to draw a random sample of experimental units from the population. This provides a defensible, statistical basis for generalizing from sample findings to the larger population.

Finally, it is important to consider sample size. The larger the sample, the greater the statistical power ; and the more confidence you can have in your results.

Assign Experimental Units to Treatments

Having selected a sample of experimental units, we need to assign each unit to one or more treatment groups. Here are two ways that you might assign experimental units to groups:

• Independent groups design. Each experimental unit is randomly assigned to one, and only one, treatment group. This is also known as a between-subjects design .
• Repeated measures design. Experimental units are assigned to more than one treatment group. This is also known as a within-subjects design .

Control for Extraneous Variables

Extraneous variables can mask effects of independent variables. Therefore, a good experimental design controls potential effects of extraneous variables. Here are a few strategies for controlling extraneous variables:

• Randomization Assign subjects randomly to treatment groups. This tends to distribute effects of extraneous variables evenly across groups.
• Repeated measures design. To control for individual differences between subjects (age, attitude, religion, etc.), assign each subject to multiple treatments. This strategy is called using subjects as their own control.
• Counterbalancing. In repeated measures designs, randomize or reverse the order of treatments among subjects to control for order effects (e.g., fatigue, practice).

As we describe specific experimental designs in upcoming lessons, we will point out the strategies that are used with each design to control the confounding effects of extraneous variables.

Data Analysis

Researchers follow a formal process to determine whether to reject a null hypothesis, based on sample data. This process, called hypothesis testing, consists of five steps:

• Formulate hypotheses. This involves stating the null and alternative hypotheses. Because the hypotheses are mutually exclusive, if one is true, the other must be false.
• Choose the test statistic. This involves specifying the statistic that will be used to assess the validity of the null hypothesis. Typically, in analysis of variance studies, researchers compute a F ratio to test hypotheses.
• Compute a P-value, based on sample data. Suppose the observed test statistic is equal to S . The P-value is the probability that the experiment would yield a test statistic as extreme as S , assuming the null hypothesis is true.
• Choose a significance level. The significance level, denoted by α, is the probability of rejecting the null hypothesis when it is really true. Researchers often choose a significance level of 0.05 or 0.01.
• Test the null hypothesis. If the P-value is smaller than the significance level, we reject the null hypothesis; if it is larger, we fail to reject.

A good experimental design includes a precise plan for data analysis. Before the first data point is collected, a researcher should know how experimental data will be processed to accept or reject the null hypotheses.

Test Your Understanding

In a well-designed experiment, which of the following statements is true?

I. The null hypothesis and the alternative hypothesis are mutually exclusive. II. The null hypothesis is subjected to statistical test. III. The alternative hypothesis is subjected to statistical test.

(A) I only (B) II only (C) III only (D) I and II (E) I and III

The correct answer is (D). The null hypothesis and the alternative hypothesis are mutually exclusive; if one is true, the other must be false. Only the null hypothesis is subjected to statistical test. When the null hypothesis is accepted, the alternative hypothesis is rejected. The alternative hypothesis is not tested explicitly.

In a true experiment, each subject is assigned to only one treatment group. What type of design is this?

(A) Independent groups design (B) Repeated measures design (C) Within-subjects design (D) None of the above (E) All of the above

The correct answer is (A). In an independent groups design, each experimental unit is assigned to one treatment group. In the other two designs, each experimental unit is assigned to more than one treatment group.

In a true experiment, which of the following does the experimenter control?

(A) How to manipulate independent variables. (B) How to assign subjects to treatment conditions. (C) How to control for extraneous variables. (D) None of the above (E) All of the above

The correct answer is (E). The experimenter chooses factors and factor levels for the experiment, assigns experimental units to treatment groups (often through a random process), and implements strategies (randomization, counterbalancing, etc.) to control the influence of extraneous variables.

• Open access
• Published: 27 April 2024

The feasibility of integrating a home telehealth model for older persons living with hemodialysis

• Wanicha Pungchompoo 1 ,
• Saowaros Parinyachitta 2 ,
• Sirirat Pungchompoo 3 ,
• Warawan Udomkhwamsuk 4 &
• Panadda Suwan 5  

BMC Geriatrics volume  24 , Article number:  378 ( 2024 ) Cite this article

Metrics details

In Thailand, there is a rapidly increasing population of older persons living with hemodialysis (OPLWH) for whom quality of life and clinical outcomes are their main focus. This study aims to assess the feasibility of an integrated home telehealth model on quality of life and laboratory parameters of OPLWH.

In this study, the second phase of a mixed methods exploratory sequential design was conducted using a repeated measures experimental design. Participants met the inclusion criteria, which included being an OPLWH at a single hemodialysis center of one hospital in Chiang Mai province, Thailand, during the experimental period between 1 April and 30 September 2018, and willing to participate in the study. The 54 participants were purposively selected and randomly assigned to receive either an intervention ( n  = 24) consisting of health education and health monitoring using a telehealth device (an iPad) and a web application, or usual care ( n  = 30). The instruments included a demographic data form, which was analyzed using the chi-square test. The health-related quality of life questionnaire (the 9 - item Thai Health Status Assessment questionnaire) and blood chemistry (BUN, Cr, Hb, Hct, Alb, K, Kt/V, and nPCR) were compared and measured at baseline, and at 3 and 6 months after enrolment using independent t-test and one-way repeated measures ANOVA.

The comparison of quality of life between the two groups at the two points of repeated measurement (after 3 months) showed a statistically significant difference in mental health scores at P  <  0.05 . Six months after the intervention, mean scores for health outcomes and patients’ quality of life improved; however, this change did not reach statistical significance.

Conclusions

An integrated home telehealth model implemented by a hemodialysis nurse is a feasible holistic care approach for OPLWH. However, the absence of statistical significance may partly be associated with the clinical characteristics of frailty and risk factors such as increased age, hypertension, diabetes, heart disease, longer dialysis time, and inadequacy of Kt/V. Large-scale multi-centre trials are warranted to fully examine the acceptability of the model. The duration and long-term effects of the telehealth model are also recommended for further investigation.

Patient or public contribution

The development of a home telehealth model was a collaborative process between patients, caregivers, healthcare professionals from the hemodialysis unit, and the research team.

Peer Review reports

What is already known?

In the wake of the COVID-19 pandemic, we are living in a new normal wherein many telehealth systems have been developed—for example, the use of telemedicine to prevent COVID-19.

However, little is known about whether an integrated home telehealth model might be able to promote quality of life and prevent complications for older persons undergoing hemodialysis.

What does this paper contribute to the wider global clinical community?

This paper outlines the development of a home telehealth model specifically focused on holistic care for OPLWH.

The study demonstrates increases in health-related quality of life across both physical and mental scores as a result of receiving the telehealth model. Six months after the experimental intervention, there was no statistical difference between the two groups, but the evidence revealed positive changes in mean scores for quality of life and some laboratory results compared with participants who received usual care.

An integrated holistic home telehealth model delivered by renal nurses and with interprofessional management is recommended as a feasible new clinical care model for the management of OPLWH at home. This is the first model of its kind to be implemented in Thailand.

Introduction

The structure of the world’s population is changing, with an increase in the proportion of older persons. The proportion of the global population aged 65 years or above is projected to rise from 10% in 2022 to 16% in 2050. By 2050, the number of persons aged 65 years or over, worldwide, is projected to be more than twice the number of children under age 5, and about the same as the number of children under age 12 [ 1 ]. The number of Thai older people has increased seven-fold in recent years, and by 2035, is forecast to reach 20 million [ 2 ].

More than 3 million Thais aged 60 and over have been living with chronic illness during the past five years, a statistic that illustrates the increase in chronic illness among the elderly population [ 3 ]. Chronic kidney disease (CKD), a progressive chronic disease common in older persons, is estimated to affect approximately 44% of individuals aged 65 and older [ 4 ] and more than 10% of the population worldwide [ 5 , 6 ]. End-stage renal disease (ESRD) refers to patients with an estimated glomerular filtration rate lower than 15 ml per minute per 1.73 m 2 body surface area [ 7 , 8 ]. ESRD treatment involves renal replacement therapy (RRT), which includes peritoneal dialysis (PD), hemodialysis (HD), and kidney transplantation (KT) [ 9 ]. New cases of Thais living with end-stage renal disease (ESRD) and receiving hemodialysis reached 39,398 during the period from 2017 to 2019 [ 10 ]. Patients receiving hemodialysis include those aged 45 to 64 years (41%), 65 to 74 years (24%), and 75 years and over (20%) [ 10 ]. Older persons undergoing dialysis with ESRD represent a major health and social care burden in the context of aging populations [ 4 ].

Considering this situation, nurse-led telehealth could contribute to managing some of these problems by using information and communication technologies [ 11 ]. Telehealth is a broad term encompassing the use of electronic communication to provide clinical care by replacing face-to-face visits; however, the focus here is on interactive videoconferencing, remote monitoring, mobile phones, web applications, and the internet of things [ 12 ]. Telehealth, integrating holistic and interprofessional management, is a new model of care for chronic disease management. The use of telehealth to facilitate the assessment of symptoms reported by patients themselves may also enhance effective symptom management for persons with end-stage renal disease and could provide a means to overcome identified barriers to home care, thereby improving patients’ care experiences [ 13 , 14 ].

Home telehealth can be used to deliver a range of interventions, from providing information to supporting therapeutic procedures. More timely visits may also result in earlier interventions, preventing complications and reducing unnecessary use of health services while assisting patients to manage their symptoms at home [ 15 ]. Patients who are ill or who find it difficult to travel to attend appointments with physicians can benefit from reduced travel costs; hemodialysis treatment often requires time off work and time away from family, in addition to the financial burden of transportation [ 12 ]. Remote monitoring of patients through information and communication technologies, termed “telecare” or “telehealth”, is increasingly being evidenced as a means of addressing the interest in and demands on health services, alongside more patient-focused care [ 16 ].

The conceptual framework of this study applies the continuum of home telehealth technologies, developed by Hebert, Korabek, and Scott, to examine the feasibility of using a home telehealth program for older persons living with hemodialysis (OPLWH) in Thailand [ 15 ]. These technologies can be categorized following the 3 steps of the home telehealth technologies’ continuum [ 17 ], from 1) patients initiating contact, through 2) automatic monitoring via technology, to 3) healthcare professionals initiating contact (see Fig.  1 ). Categorizing telehealth services may also be based on desirable patient outcomes and the need for services [ 15 ]. The combination of effective telehealth and a continuum of healthcare will increase understanding of health, patient outcomes, and the cost of care. Previous studies have suggested that interprofessional care management or home monitoring could improve intermediate outcomes of CKD patients [ 13 ].

Continuum of home telehealth technologies (adapted from Hebert, Korabek, and Scott, 2006, p. 788) [ 15 ]

The effectiveness of telehealth lies in its potential for improving symptom management and quality of life for patients with long-term chronic conditions, as well as potentially reducing healthcare costs; these benefits have been demonstrated in a number of recent studies [ 9 , 12 , 14 , 18 ]. Telehealth has also been successfully implemented to encourage meaningful engagement with the care system, especially for primary outcomes among both rural and urban patients with chronic kidney disease (estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m 2 ). Telehealth and the remote monitoring of older patients living with chronic conditions may also provide a means to address geographical barriers to continuum care, thereby improving access to home dialysis, patient quality of life, and outcomes [ 12 ]. Furthermore, the impact of telehealth interventions on clinical outcomes can be reported through lower hospitalization rates and fewer clinical visits. In addition, it has been found that patients who receive telehealth were less likely to miss HD treatment sessions compared with patients who received standard care [ 19 ]. A one-year randomization control trial study, with baseline characteristics of participants including a mean age of 75.1 ± 8.1 (SD) years, found that the telehealth implementation of the Patient Aligned Care Team (PACT) was independently related to reductions in emergency department visits and hospitalization, while improving the ability to control hypertension [ 14 ]. In addition, a longitudinal cohort study confirmed that telehealth videoconferencing for kidney transplant and CKD patients with a mean age of 63.9 years (SD = 12.3 years) provided a feasible intervention for 1 year and sustainable outcomes [ 20 ]. Another qualitative study explored the perceptions of telehealth of older adults with advanced CKD, their partners, and kidney clinicians, demonstrating that telehealth may improve convenience and care partner engagement while still presenting concerns about clinical effectiveness, limitations of virtual physical examination, reduction of the patient-clinician relationship, and patient trust [ 21 ].

During the last decade, the number of OPLWH in Thailand has been increasing steadily. While improving patient outcomes and achieving higher quality care at a lower cost are two goals of adopting telehealth technology, it is necessary to better understand the underlying rationale for how and why home telehealth is effective for improving health outcomes and reducing care costs.

The overarching mixed-methods research project of which this study is a part was conducted to explore and examine the feasibility of using home telehealth for older patients living with end-stage renal disease. We conducted this study as phase 2 of a project entitled Integrating home telehealth into the holistic care: Pilot study in older persons with ESRD to examine whether integrated home telehealth with nurse oversight could be effectively implemented and whether it could improve the quality of life and clinical outcomes in OPLWH when compared with usual care.

A mixed-methods exploratory sequential design was conducted for the project entitled Integrating home telehealth into the holistic care:

Pilot study in older persons with ESRD. Mixed-method research is the combining and integrating of two research types, namely qualitative and quantitative research [ 22 , 23 ]. A description of the study carried out as phase 1 of the project has been published previously [ 24 ]. In this manuscript, we report on phase 2 of the study.

Building on the exploratory results from phase 1, this second phase focuses on gathering quantitative data in order to test or generalize the initial findings. Interaction between the qualitative and quantitative strands occurs as the researcher develops a telehealth model as an intermediate step between the two phases, building on the qualitative results and using them in the subsequent qualitative data collection. The exploratory sequential design in two phases which has been used in this study is set out below, and the scope of the research is presented (see Fig.  2 ).

The scope of the study design

Study design and setting

The first phase of the wider mixed-methods study aimed to describe and explore symptom experiences and needs related to the integration of home telehealth into holistic care with nurse oversight for OPLWH. A survey study design with a nonprobability sampling method was employed, and OPLWH were surveyed over a six-month period (between 1 January and 30 June 2017). Participants were recruited from two hemodialysis units at two hospitals in Chiang Mai province. Purposive sampling was used to identify and recruit the participants. The instruments included the VOICES (View of Informal Carers Evaluation of Service-ESRD/Thai patients’ version) questionnaire. Descriptive statistics were used to analyse the data.

Most participants had comorbid conditions, such as hypertension or diabetes mellitus and three main physical symptoms were found (shortness of breath, and pain, and edema) while two psychological symptoms were also present (anxiety and moderate stress). Participants had experienced readmission to hospital at least twice per month. Furthermore, the majority were unable to access home care.

Next, the qualitative section of the study was conducted via in-depth interviews with 20 participants to explore the needs of participants using thematic analysis to analyze the data. The findings revealed the needs of participants related to telehealth including four dimensions: 1) symptom management at home; 2) activity and role management; 3) emotional management; and 4) spiritual support [ 24 ]. The results of the first phase were then utilized to inform and develop an effective home telehealth program for OPLWH.

Participants

In the second phase of the project, a repeated measures experimental design was implemented. Potential participants included 141 OPLWH aged 60 or over who were recruited from a single hemodialysis center at Maharaj Nakorn Chiang Mai hospital in Chiang Mai province, Thailand, during the period between 1 January 2018 and 30 March 2018. Finally, 54 participants met the inclusion criteria and were willing and able to participate in the study, as well as being capable of providing informed consent.

Inclusion and exclusion criteria

Participants were eligible to participate in the study if they were aged 60 years or over, and if they: 1) were being managed for hemodialysis and 2) had been diagnosed with end-stage renal disease stage 5 by a nephrologist. Eligible participants gave their informed consent to participate. Those who were hospitalized with acute illnesses, who had psychological or cognitive disorders, or who had physical limitations which affected their self-care were excluded. Those who were unable to meet the inclusion criteria or were not willing to participate were also excluded.

The sample calculation and randomization procedure

A simple randomization method using a 1:1 ratio was used to assign participants to either the experimental group or the control group, using the random allocation concealment method. Randomization was carried out over the telephone by the researcher who was blinded to patient identity. Although the participants could not be masked in relation to their assignment, the renal nurses caring for them were blind to the participants’ study assignment. Based on repeated measures analysis of variance, an estimated sample size of 60 (30 per group) was considered adequate to demonstrate the effects of the experimental intervention, according to previous studies [ 25 , 26 ]. However, 6 participants in the experimental group were removed because they either withdrew (3), were admitted to the intensive care unit due to co-morbid conditions (2), or died from critical renal failure (1). Thus, 54 participants remained in the study: 24 in the experimental group, and 30 in the control group. G*Power software was used to determine the sample size, and the 8 study factors were analyzed using multivariate statistics. In general, 5–20 samples per factor are preferred. However, in this study, T-test and ANOVA statistics were used. In this determination, the degree of freedom of the factor levels was 2 (3 levels of a changing time period) with two groups (a control group and an experimental group), and the patrial eta square was 0.35 according to the ANOVA results. The sample size which was calculated varied from 24 to 27 samples when we changed the number of covariance from 1 to 12 covariances and used a power of test value of 0.85. Therefore, the results showed that the sample sizes were sufficient for analysis with the ANOVA method [ 27 ]. The attrition rate for the study was 14.58% [ 25 ] (Fig.  3 ).

Recruitment flowchart

Intervention measures

Experimental group.

• Home telehealth model

The home telehealth model for OPLWH integrates two concepts. The first focuses on the requirement for health service provision for older persons related to their need for: the control of physical health symptoms, such as breathlessness, pain or edema; mental support and alleviating factors, such as anxiety and stress; spiritual support; clinical care, such as symptom management at home; psychological and spiritual support; regular home visits; an effective referral system; health education; and financial support [ 7 , 24 ]. The second relates to the continuum of home telehealth technologies [ 15 ] (see Fig.  4 ). The framework for our holistic home telehealth model for OPLWH operates across four dimensions: 1) health education related to chronic kidney disease and treatment; 2) the referral system; 3) online home visits; and 4) telephone/live counselling.

Framework of the holistic home telehealth model for OPLWH

The home telehealth model was designed and developed based on results from the first phase of the study [ 24 ]. The model was focused on video visiting, telephone counselling, web-based education and monitoring, and preparing for the referral system.

Five educational videos and a booklet were developed for participants, containing information and instructions about renal disease and treatment, food and water management, exercise and stress management, and medication management. A panel of experts made up of two renal nurse specialists, a nephrologist, and two experts in computer science evaluated the knowledge that was provided in the videos and the booklet for content validity and calibrated the web application system by testing the function of the model application’s data architecture (Fig.  5 ). This was combined with the website, which included a home page, a user panel, and an administration panel, and the mobile app, which included a user area and an expert area. The panel of experts made comments and suggestions, and the model was revised twice and returned to the panel of experts for evaluation until they were satisfied.

Data architecture of the model application

Finally, 3 research assistants, including an advanced practitioner nurse in hemodialysis and two hemodialysis nurses, were trained as “supporters” for assisting participants in the experimental group and also helping the researcher in implementing the intervention. The training session lasted for seven hours (a full day of training at the dialysis unit) and included a study information overview, information about the objectives and conceptual framework of the study, the instruments for recruiting potential participants, and the strategies applied to implement the model. Additionally, time was dedicated to sharing ideas, demonstrations, repeat demonstrations, and practice.

Participants and their caregivers in the experimental group received the home telehealth model in addition to standard care and were provided with a set of 1 manual and a tablet with internet connection for 6 months, containing the downloaded web application for the home telehealth model. They underwent a day of group teaching sessions, consisting of answering questions and addressing concerns as well as training in self-management skills. During each session the participants were educated about renal disease and treatment, food and water management, exercise and stress management, and medication management at home. Additionally, the participants and their caregivers were taught how to use the application, such as how to log in to the system, fill in their illness health history, record their body weight, make an online appointment, connect to Facebook, access Line to have a conversation with a hemodialysis nurse, and create a group chat with other patients. They also learned how to use video visiting, online monitoring, and telephone counselling.

The first week of group training sessions were followed by their six-month in-person participation in the home telehealth model, as well as routine online visits and telephone consulting, resulting in a total of one day per month for meeting and discussion with a hemodialysis nurse at the dialysis unit, and six months of monitoring and consultation online.

Application of the home telehealth model for OPLWH: web application

The development of the home telehealth model resulted in an application that could be used on a website or on a mobile phone and is referred to here as the web application. The website and mobile versions are connected by an application programming interface. The website creates an application programming interface and receives orders from the application. The application will then send a request for interpretation and a summary.

At the same time, the home telehealth model website is a content management system that is divided into three parts including a home page, a user panel, and an administration panel. The patient’s data from the administration panel and the web page are recalled when the patient logs in to the website linked to the user panel. The mobile application is designed to be used on a mobile phone, tablet, or iPad by connecting data from the website with the application programming interface. The mobile application site consists of two sections (user and expert). The user section is for patients to log in to make appointments and view their health history. The expert section is connected to the application programming interface, Facebook Messenger, and is controlled by a nurse or physician who checks the online appointment requests made by patients. In this section, the nurse or physician can also provide online individual counselling or group discussions (Fig.  5 ).

Control group

The control group received standard care from the nursing staff at the renal unit. Standard care for persons receiving hemodialysis in government hospital in Thailand includes routine laboratory investigations, physical examinations, health education, consultations by a renal nurse and/or a general nurse, and monthly follow-ups. After finishing the study, the participants in the control group were invited to join the home telehealth model and received copies of the set of manuals on how to manage their hemodialysis at home.

Measurement and data collection

Instruments and outcomes, the 9-item thai health status assessment instrument.

was used to evaluate the quality of life and health status of the dialysis patients [ 28 ] and is separated into two scale scores measuring physical and mental health. The instrument is composed of seven domains and two global health ratings [ 28 , 29 ]. The seven domains encourage subjects to rate their experiences with health conditions during the past month. Response choices for the seven domains are interpreted according to the perceived severity of the problems on a 5-point scale, where 1 = “very severe”, 2 = “severe”, 3 = “moderate”, 4 = “mild”, and 5 = “not at all”.

The first global question is used to compare participants’ health at the present time with their health during the preceding year. The second question compares their health with the health of others of similar age, gender, social/economic status, employment type, and lifestyle. Response choices for the two global questions included 1 = “much worse”, 2 = “a little bit worse”, 3 = “the same as”, 4 = “a little bit better”, and finally 5 = “much better”. The calculated score values range between 20 and 80 (± 3SDs); if the physical or mental health score is above 20, the scores are interpreted as equal to the general healthy Thai population. Higher calculated scores reflect better health than the general population.

The 9-item Thai Health Status Assessment Instrument is a valid and reliable quality of life measure for Thai renal replacement therapy patients. Its convergent and divergent validity have been demonstrated using the SF-36 as a concurrent measure. Its concurrent validity has also been assessed using clinical variables. Its test–retest reliability is satisfactory as the inter-class correlation coefficients are 0.79 (physical health score) and 0.78 (mental health score) [ 25 , 29 ].

After obtaining permission, we assessed the appropriateness and applicability of these instruments for our participant population. All the instruments were given to the participants in Thai language.

Demographic data

was collected at baseline. The participants were asked to provide information about their age, gender, marital status, educational level, occupation, and household income by completing the demographic data form. Also, the participants were asked about the duration of their hemodialysis treatment, while blood samples, and data related to their health status and health-related quality of life were collected at baseline (the firs week), 3 months (the last week), and 6 months (the last week).

Laboratory parameters

The laboratory parameters measured the effectiveness of the home telehealth model for older persons living with hemodialysis by comparing the significant differences between the experimental and control groups before the experiment, 3 months after, and 6 months after. The blood chemistry analyses included Blood Uria Nitrogen (BUN), Creatinine (Cr), Hemoglobin (Hb), Hematocrit (Hct), Albumin (Alb), and Potassium (K) while the adequacy of hemodialysis measurement included weekly Kt/V and Normalized protein catabolic rate (nPCR).

Data collection procedures

Purposive sampling was used to obtain a sample of 54 OPLWH, who participated in the intervention phase of the study over a period of 6 months (between 1 April 2018 to 30 September 2018). Simple randomization was used to assign participants to either the experimental or the control group. The researcher sent information letters and copies of the study protocol to the administrators of the relevant medical units to ask them for permission to collect data. The medical units had a register of OPLWH which included patients’ telephone numbers; using this register, the researcher was able to invite potential participants to take part in the study by telephone. An advanced practitioner nurse in hemodialysis and two hemodialysis nurses who were trained as “supporters” helped to recruit participants, participated in monthly meetings and discussions with researcher and participants at the hemodialysis unit, and provided monitoring and consultation online. They also helped the researcher collect laboratory parameters from medical records at the hemodialysis unit. Participants were asked to provide information about demographic data and health status assessment by completing forms and undergoing routine blood testing at baseline, 3 and 6 months. Questionnaires and measurements were used to collect data from the participants at the hemodialysis unit by the researcher. The instruments included a demographic data form, the 9 - item Thai Health Status Assessment Instrument, and blood chemistry analyses (BUN, Cr, Hb, Hct, K, Alb, Kt/V, and nPCR).

Data analysis

Data were analyzed using SPSS programme version 17. Descriptive statistics were used to analyze participants’ demographic data and clinical characteristics. Chi-square test was used to test the difference in demographic data between the two groups at baseline. The independent t-test and one-way repeated measures ANOVA were applied to compare differences in mean scores for quality of life between the two groups. One-way repeated measures ANOVA was also used to test the interaction of the Home Telehealth Model within the two groups. ANOVA was used to detect the interaction of the model and statistically significant differences in laboratory results (BUN, Cr, Hb, Hct, K, Alb, Kt/V, nPCR) between the experimental and control groups at baseline and at 3 and 6 months.

Information of participants

From Table  1 , participants’ demographic data were analyzed at baseline, and there were no differences between the experimental and control groups in terms of gender, age, marital status, religion, education level, current occupation, people living together, regular contact person, and payment of medical expenses. Of all general characteristics, only income, duration of receiving hemodialysis, and illness with other disease showed a statistical difference between the intervention and control groups at baseline.

In both groups, most participants were between 60–69 years old, and their education level was primary school. In addition, most of the participants were Buddhist. Most participants had insufficient income and received financial support from their children. However, both groups received payments from the health care coverage scheme for medical expenses.

Table 2 shows that most participants in both the experimental and control groups had received kidney replacement therapy or dialysis during the past 1–5 years. Moreover, both groups also had underlying diseases, for example hypertension, diabetes mellitus and heart disease, especially hypertension which was the highest percentage (70–80%) of the chronic diseases found in participants from each group. There were no differences in terms of demographic characteristics of the participants in receiving treatment when comparing the two groups.

Comparison of quality of life and laboratory parameters between and within the two groups

As shown in Table  3 , before the intervention, the independent t-test results showed no statistically significant difference between the two groups in mean scores of laboratory parameters ( P  > 0.05). However, after 3 months, the independent t-test revealed a statistically significant difference between the two groups in the mean score of Kt/V ( P  < 0.05). In addition, 6 months after the intervention, the comparison of laboratory results between the two groups found a statistically significant difference in the mean hemoglobin (Hb) score ( P  < 0.05).

Table 4 displays changes in the means of patient outcomes across three time periods. The repeated measured one-way ANOVA using the General linear model and Sphericity assumed for within-subjects and between-subjects effect were performed for the multiple comparison test. The comparison between the two groups at the three points of measurement showed no statistically significant difference in either quality of life or laboratory parameters. However, after 3 months, the quality-of-life scores for the mental health dimension showed statistically significant differences between the two groups ( P  < 0.05).

This study evaluated whether a hemodialysis nursing team integrated with a home telehealth model was a feasible method of holistic care delivery to maintain quality of life and improve health outcomes among OPLWH. The results confirm that the integrated intervention is feasible for improving the participants health outcomes. Telehealth and inter-professional care can be successfully implemented with meaningful engagement as part of the care system while telehealth delivered and supported by an inter-professional team has been shown to be a feasible care delivery strategy for CKD patients [ 14 ]. Advances in health technology over recent decades have kindled interest in the possibility of using home telehealth programs to extend human resources, improve access to services, and minimize the costs of care [ 15 ].

Before the COVID-19 pandemic, there were limitations related to remote care that created barriers to patient access to continuum care via telehealth, and these limitations disincentivized renal nurses to offer telehealth as an option. However, providing and receiving tele-visiting became the norm for nurses, healthcare teams, and patients when social distancing, stay-at-home, and other hospital policies were introduced in 2020 due to the COVID-19 pandemic. The utilization of the home telehealth model should be promoted and expanded for its ability to promote equal access to telehealth and the opportunity for CKD patients living with hemodialysis to receive continuing care outside hospitals. Both inpatient and outpatient practices had to quickly adapt to telehealth [ 30 ], and the development of home telehealth services has since become an essential and valuable aspect of care, allowing patients with advanced conditions to remain at home and in their own communities, although it is still the case that when they experience problems, they may feel unsure about who they can contact, or how. Nevertheless, the use of telehealth services can help to empower individuals in terms of their experiences with life-limiting illness, and the experiences of their caretakers, by facilitating the provision of real-time communication between patients and healthcare providers [ 31 ]. It can also be used to complement transitions from acute services based on patients’ needs [ 32 ].

In this study, the participants who received the home telehealth model showed no significant effects observed in terms of their quality of life or clinical outcomes over 6 months ( P  > 0.05). Comparison within the intervention group between two time measurements (at baseline vs after 3 months) found statistical evidence of improvement in the mean scores of health outcomes (BUN: 53.86 ± 21.61 vs 45.34 ± 21.10; Cr: 9.67 ± 14.04 vs 6.53 ± 2.87; Hb: 10.39 ± 1.42 vs 10.95 ± 1.69; Hct: 33.02 ± 4.55 vs 34.44 ± 4.78; Alb: 4.36 ± 1.87 vs 3.91 ± 0.60; and K: 4.42 ± 0.75 vs 4.35 ± 0.79) and patients’ quality of life (physical health scores: 44.85 ± 8.09 vs 48.42 ± 8.92 and mental health scores: 45.86 ± 7.05 vs 49.89 ± 8.97). After 6 months, the comparison within the intervention group showed increases in the mean scores of Hct (34.44 ± 4.78vs 35.14 ± 8.43), Kt/V (1.59 ± 0.37 vs 1.66 ± 0.31), and nPCR (0.99 ± 0.26 vs 1.17 ± 0.73). According to the three-time measurements of quality of life and laboratory parameters, there was no statistical difference between the two groups ( P  > 0.05). However, regarding the comparison using the independent t-test, after 3 months and 6 months, some of the laboratory (Kt/V and Hb) results showed statistically significant differences between the two groups ( P  < 0.05) while some of the tests were not significant (BUN, Cr, Hct, K, Alb, and nPCR) ( P  > 0.05). In addition, after 3 months repeated measurement, quality of life, in terms of the mental health scores, showed statistically significant differences between the two groups ( P  < 0.05).

Further investigation might be needed in terms of the relationships between each laboratory result and the intervention. This finding is contradictory to the findings of previous studies. According to Minatodani, Chao, and Berman, home telehealth for high-risk dialysis patients has demonstrated improvements in the cost-effectiveness of health outcomes over 21 months [ 33 ]. In addition, a randomized controlled study on the use of home telehealth in promoting illness self-management demonstrated an improvement in health outcomes and cost-effectiveness for high-risk dialysis patients [ 34 ]. Home telehealth has also demonstrated improved health outcomes and cost-effectiveness for high-risk patients with ESRD, using remote technology for home health monitoring with support from remote care nurses [ 34 ]. Another study reported a statistically significant reduction in depression, anxiety, and stress due to telehealth interventions [ 35 ]. In contrast, the biomarkers predicted patients’ clinical outcomes. Standardized Kt/V was a significant indicator for the adequacy of the dialysis and was related to a reduction in clinical complications of dialysis patients, such as hypocalcemia, hyperphosphatemia, and anemia [ 36 ]. It was also suggested that poorer laboratory indicators demonstrated an inability to control mineral bone density and anemia [ 12 ]. The participants received hemodialysis treatment 2–3 times per week, and they needed to evaluate their hemodialysis adequacy following the KDOQI Clinical Practice Guidelines for Hemodialysis Adequacy: 2015 update [ 36 ]. Dialysis dose has been reported to have great significance for the outcome of hemodialysis treatment. Many studies have shown a relationship between dialysis dose, measured as Kt/V, and morbidity and mortality among hemodialysis patients [ 36 ]. In the case of inadequate Kt/V, patients needed to increase the dose of dialysis. In addition, the lower nPCR indicated that patients needed to increase their protein intake, or they would be at a risk for malnutrition. Patients who had low serum levels of albumin and creatinine clearance may report low quality of life and inadequacy of dialysis [ 17 , 36 ].

These results suggest that at 3 months after the intervention, the experimental group achieved a higher quality of life than the control group along both physical and mental dimensions. In the experimental group, the mean quality of life from the mental health scores showed a more positive increase than for the control group at three points of measurement. However, the success of home telehealth depends strongly on patient adherence to the prescribed program [ 6 , 17 ]. Home telehealth with remote care nurse (RCN) support can promote health behavioral change, resulting in better outcomes. Moreover, home telehealth self-monitoring with RCN support can effectively empower patients to take a more active role in their healthcare, indirectly improving quality of life for people living with chronic illness [ 18 ]. Unfortunately, we did not detect a statistically significant difference in quality of life between the two groups using repeated independent t-test and one-way repeated ANOVA. This might be because the six-month duration was not sufficient, or because the sample group was simply too small to observe a significant difference. In addition, 50% of participants in both groups were between 60–69 years old, had a low education level, needed financial support, and depended on their family members. These demographic characteristics could be indirect factors in reducing patients’ ability for maintaining adherence behavior in management with hemodialysis complications at home. Moreover, longtime management with hemodialysis and having comorbidities such as hypertension, diabetes mellitus, and heart disease can have negative impacts by increasing morbidity and mortality rates, reducing quality of life, and resulting in inadequate symptom management at home [ 24 ]. According to Table  2 , the difference in duration of receiving hemodialysis treatment, especially for patients with CKD stage 3–5, could result in poorer clinical outcomes and higher risk of emergency department visit, hospitalization, and death. This is related to the negative impacts of prolonged hemodialysis cycles for people living with ESRD, and fluctuations in terms of their cognitive and physical wellbeing, as well as in their emotional state [ 14 ]. Increased age, history of diabetes, longer dialysis vintage, and lower Kt/V were independently associated with frailty [ 37 ]. Frailty is also extremely common and influences serious clinical outcomes among older hemodialysis patients [ 37 ]. Integrating a home telehealth model with nurse oversight into holistic care for OPLWH can help them maintain their quality of life and improve their health outcomes. Moreover, the literature related to telehealth and kidney disease care has strongly reflected high patient satisfaction [ 30 ]. The results of this study show that integrating home telehealth with nurse oversight into holistic care for OPLWH has acceptable feasibility in providing a higher quality of life, as well as in terms of relative improvements in some biometric outcomes and the quality of dialysis. Therefore, it will be highly beneficial to establish such a model in renal units. However, the effectiveness of different telehealth models may need to be evaluated before implementation.

Strengths and limitations

Like any research, our study has both limitations and strengths. This is the first study of its kind in Thailand. Its main strength is the study’s implementation site, namely a hemodialysis center in an urban setting where the prevalence of OPLWH is relatively high.

On the other hand, our study had two limitations. Our relatively small sample size is likely to limit the external validity of the study. We should also recognize that the participants were OPLWH who might be unable to access particular technologies, or who may be unfamiliar with using technologies such as web applications. To solve this problem, we asked caregivers to stay with participants during the training and to be present during consultations in the 6-month intervention period (monthly routine online visits and telephone consulting), in order to help participants to access the web application and to support them when they needed help. Other studies have also found that more comprehensive interventions can require a caregiver or other person to assist patients [ 30 ].

However, those who live alone and have inadequate social support might have poorer self-management behaviors [ 17 , 38 ] and might not gain as much benefit from a telehealth model in terms of disparities in accessing telehealth for patients having lower socio-economic status. Moveover, patients with limited health literacy or geriatric symptoms, such as hearing impairment, visual problems, and fingers or bodily shaking, may not be able to operate the video calls or may not have the technical capability. In addition, it is possible that these factors affect the ability of some to have a serious discussion with clinicians during the time that they communicate via video telehealth or phone. In addition, older persons who live alone may not be able to maintain their engagement in management with self-care, medical regimens, and self-monitoring, affecting their ability to effectively report about their clinical progression [ 21 ].

Furthermore, the study was conducted at a time when third generation (3G) internet coverage was offered in Thailand. This was suitable for participants who owned modern smartphones and lived in urban areas with constant high-speed internet connection but may have presented limitations for participants in more rural areas or those who did not have access to the most up-to-date technology. To mitigate these problems, the researcher provided all participants with a mini-iPad and free Wi-Fi for 6 months so that they could regularly access the home telehealth program. Currently, fifth generation (5G) internet coverage is standard in Thailand, and Thai health policy is focusing on the digital transformation concept to ensure the vast majority of users, including the older population, are familiar with accessing internet communication in both urban and rural areas.

Implications for clinical practice

Due to limitations on renal nurses visiting patients at home and the transforming of the digital health system after the COVID-19 pandemic, older persons undergoing hemodialysis may suffer with clinical complications at home and require clinical nurse specialists to provide them with continuing renal care. Integrating a home telehealth model for this population is a feasible response, enhancing their quality of life and improving their health outcomes. The value of a home telehealth self-monitoring model with nurse oversight has been demonstrated with positive results in terms of reducing clinical complications. Despite less contact with homecare facilities due to shortages of clinical nurse specialists visiting patients at home, OPLWH experienced sustained quality of life and improved clinical outcomes after receiving the integrated home telehealth model.

In the future , renal nurses in dialysis units should emphasize the use of a telehealth model to promote the physical and mental health of their patients. Effective digital health innovations for virtual care, such as the home telehealth model implemented in this study, can provide effective solutions to the problem of a shortage of renal nurse specialists, and possibly decrease care costs for patients. Despite the study limitations, however, we recommend the use of a home telehealth model for OPLWH, based on its potential for increasing quality of life and reducing the risk of clinical complications by improving patients’ health outcomes. Further studies within different settings, populations, and cultural contexts are recommended in order to confirm the effectiveness of the home telehealth model. In addition, improving the cost effectiveness of future studies should focus on larger sample sizes, 1-year interventions, and intensive retraining sessions for patients, caregivers, and renal clinicians. An expansion of the criteria of reimbursement and regulation for patients who receive telehealth services is needed. Telephone calls may be appropriate for patients of lower socio-economic status, or those having poor internet access and low levels of technological literacy.

Our findings confirm that the implementation of a home telehealth model can yield positive changes in all aspects, including quality of life and health outcomes, for OPLWH. However, our findings are based on a short-term intervention over a six-month period. The long-term benefits of such a program should also be evaluated, and its economic impact on the delivery of healthcare services should be assessed. Our integrated holistic home telehealth model was an effective strategy to increase health-related quality of life and health outcomes over a 6-month implementation period. Our study provides evidence in favour of the feasibility of a home telehealth model for OPLWH after they have received training alongside their standard care. Such a model may potentially reduce workloads for renal nurses and improve routine care, enhancing health-related quality of life and reducing complications among OPLWH.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The statistics were checked prior to submission by an expert statistician and state their name and email address.

Abbreviations

Chronic kidney disease

Renal replacement therapy

Peritoneal dialysis

• Hemodialysis

Kidney transplantation

End-stage renal disease

Older persons living with hemodialysis

Estimated glomerular filtration rate

Blood Uria Nitrogen

Normalized protein catabolicrate

A measurement of dialysis adequacy

Urea reduction ratio

United Nations Department of Economic and Social Affairs Population Division. World population prospects 2022. 2022. https://population.un.org/wpp/ . Accessed 20 Nov 2022.

Google Scholar  

Knodel J, Teerawichitchainan B, Prachuabmoh V, Pothisiri W. The situation of Thailand’s older population: an update based on the 2014 survey of older persons in Thailand. Chiang Mai: HelpAge International; 2015.

National Statistics Office. Ministry of information and communication technology older persons in Thailand. 2014.  http://web.nso.go.th/en/survey/age/tables_older_2014.pdf . Accessed 13 Jan 2021.

Feng L, Yap KB, Yeoh LY, Ng TP. Kidney function and cognitive and functional decline in elderly adults: findings from the Singapore longitudinal aging study. J Am Geriatr Soc. 2012;60(7):1208–14. https://doi.org/10.1111/j.1532-5415.2012.04043.x .

Article   PubMed   Google Scholar  

Bello AK, Alrukhaimi M, Ashuntantang GE, Basnet S, Rotter RC, Douthat WG, et al. Complications of chronic kidney disease: current state, knowledge gaps, and strategy for action. Kidney Int Suppl (2011). 2017;7(2):122–9. https://doi.org/10.1016/j.kisu.2017.07.007 .

Kelly JT, Warner MM, Conley M, Reidlinger DP, Hoffmann T, Craig J, et al. Feasibility and acceptability of telehealth coaching to promote healthy eating in chronic kidney disease: a mixed-methods process evaluation. BMJ Open. 2019;9(1): e024551. https://doi.org/10.1136/bmjopen-2018-024551 .

Article   PubMed   PubMed Central   Google Scholar  

Pungchompoo W, Richardson A, Brindle LA. Experiences and needs of older people with end stage renal disease: bereaved carers perspective. Int J Palliat Nurs. 2016;22(10):490–9. https://doi.org/10.12968/ijpn.2016.22.10.490 .

Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med. 2003;139(2):137–47. https://doi.org/10.7326/0003-4819-139-2-200307150-00013 .

Malkina A, Tuot DS. Role of telehealth in renal replacement therapy education. Semin Dial. 2018;31(2):129–34. https://doi.org/10.1111/sdi.12663 .

Nephrology Society of Thailand. Annual reports on Thailand’s renal replacement therapy figures for the years 2016 to 2019. 2021. https://www.nephrothai.org/annual-report-thailand-renal-replacement-therapy-2007-2019-th/ . Accessed 1 May 2021.

Finet P, Le Bouquin Jeannès R, Dameron O, Gibaud B. Review of current telemedicine applications for chronic diseases. Toward a more integrated system? IRBM. 2015;36(3):133–57. https://doi.org/10.1016/j.irbm.2015.01.009 .

Article   Google Scholar  

Krishna VN, Managadi K, Smith M, Wallace E. Telehealth in the delivery of home dialysis care: catching up with technology. Adv Chronic Kidney Dis. 2017;24(1):12–6. https://doi.org/10.1053/j.ackd.2016.11.014 .

Margolis KL, Asche SE, Bergdall AR, Dehmer SP, Groen SE, Kadrmas HM, et al. Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial. JAMA. 2013;310(1):46–56. https://doi.org/10.1001/jama.2013.6549 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ishani A, Christopher J, Palmer D, Otterness S, Clothier B, Nugent S, et al. Telehealth by an interprofessional team in patients with CKD: a randomized controlled trial. Am J Kidney Dis. 2016;68(1):41–9. https://doi.org/10.1053/j.ajkd.2016.01.018 .

Hebert MA, Korabek B, Scott RE. Moving research into practice: a decision framework for integrating home telehealth into chronic illness care. Int J Med Inform. 2006;75(12):786–94. https://doi.org/10.1016/j.ijmedinf.2006.05.041 .

Suter P, Yueng C, Johnston D, Suter WN. Telehealth infection control: a movement toward best practice. Home Healthc Nurse. 2009;27(5):319–23. https://doi.org/10.1097/01.nhh.0000356785.85750.32 .

Arad M, Goli R, Parizad N, Vahabzadeh D, Baghaei R. Do the patient education program and nurse-led telephone follow-up improve treatment adherence in hemodialysis patients? A randomized controlled trial. BMC Nephrol. 2021;22(1):119. https://doi.org/10.1186/s12882-021-02319-9 .

Suwanrada W, Pothisiri W, Siriboon S, Bangkaew B, Milintangul C. Evaluation of the replication project of the elderly home care volunteers. Bangkok: College of Population Studies, Chulalongkorn University; 2016.

Lunney M, Lee R, Tang K, Wiebe N, Bello AK, Thomas C, et al. Impact of telehealth interventions on processes and quality of care for patients with ESRD. Am J Kidney Dis. 2018;72(4):592–600. https://doi.org/10.1053/j.ajkd.2018.02.353 .

Lambooy S, Krishnasamy R, Pollock A, Hilder G, Gray NA. Telemedicine for outpatient care of kidney transplant and CKD patients. Kidney Int Rep. 2021;6(5):1265–72. https://doi.org/10.1016/j.ekir.2021.02.016 .

Ladin K, Porteny T, Perugini JM, Gonzales KM, Aufort KE, Levine SK, et al. Perceptions of telehealth vs in-person visits among older adults with advanced kidney disease, care partners, and clinicians. JAMA Netw Open 2021;4(12). https://doi.org/10.1001/jamanetworkopen.2021.37193 .

Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. 2nd ed. California: SAGE; 2011.

Creswell JW. Research Design. 4th ed. California: SAGE; 2014.

Pungchompoo W, Udomkhwamsuk W. Symptom experiences and needs of older persons living with hemodialysis in relation to integrating home telehealth into holistic end of life care: Phase I. CMU J Nat Sci. 2021;20(4):e2021081. https://doi.org/10.12982/CMUJNS.2021.081 .

Pungchompoo W, Parinyajittha S, Pungchompoo S, Kumtan P. Effectiveness of a self-management retraining program improving the quality of life of people receiving continuous ambulatory peritoneal dialysis. Nurs Health Sci. 2020;22(2):406–15. https://doi.org/10.1111/nhs.12672 .

Bowling A. Research methods in health: investigating health and health services. Buckingham, UK: Open University Press; 2002.

Kang H. Sample size determination for repeated measures design using G Power software. Anesth Pain Med. 2015;10(1):6–15. https://doi.org/10.17085/apm.2015.10.1.6 .

Cheawchanwattana A, Limwattananon C, Gross C, Limwattananon S, Tangcharoensathien V, Pongskul C, Sirivongs D. The validity of a new practical quality of life measure in patients on renal replacement therapy. J Med Assoc Thai. 2006;89(Suppl 2):S207–17.

PubMed   Google Scholar  

Saisunantararom W, Cheawchanwattana A, Kanjanabuch T, Buranapatana M, Chanthapasa K. Associations among spirituality, health-related quality of life, and depression in pre-dialysis chronic kidney disease patients: an exploratory analysis in Thai buddhist patients. Religions. 2015;6(4):1249–62.

Lew SQ, Sikka N. Telehealth and kidney disease care: role after the public health emergency? Clin J Am Soc Nephrol. 2021;16(12):1784–6. https://doi.org/10.2215/cjn.13651021 .

Johnston MJ, King D, Arora S, Behar N, Athanasiou T, Sevdalis N, Darzi A. Smartphones let surgeons know WhatsApp: an analysis of communication in emergency surgical teams. Am J Surg. 2015;209(1):45–51. https://doi.org/10.1016/j.amjsurg.2014.08.030 .

Roberts D, Tayler C, MacCormack D, Barwich D. Telenursing in hospice palliative care. Can Nurse. 2007;103(5):24–7.

Minatodani DE, Chao PJ, Berman SJ. Home telehealth: facilitators, barriers, and impact of nurse support among high-risk dialysis patients. Telemed J E Health. 2013;19(8):573–8. https://doi.org/10.1089/tmj.2012.0201 .

Berman SJ, Wada C, Minatodani D, Halliday T, Miyamoto R, Lindo J, Jordan PJ. Home-based preventative care in high-risk dialysis patients: a pilot study. Telemed J E Health. 2011;17(4):283–7. https://doi.org/10.1089/tmj.2010.0169 .

Kargar Jahromi M, Javadpour S, Taheri L, Poorgholami F. Effect of nurse-led telephone follow ups (tele-nursing) on depression, anxiety and stress in hemodialysis patients. Glob J Health Sci. 2015;8(3):168–73. https://doi.org/10.5539/gjhs.v8n3p168 .

Daugirdas JT, Depner TA, Inrig J, Mehrotra R, Rocco MV, Suri RS, Weiner DE. KDOQI Clinical Practice Guideline for Hemodialysis Adequacy: 2015 update. Am J Kidney Dis. 2015;66(5):884–930. https://doi.org/10.1053/j.ajkd.2015.07.015 .

Guo Y, Tian R, Ye P, Luo Y. Frailty in older patients undergoing hemodialysis and its association with all-cause mortality: a prospective cohort study. Clin Interv Aging. 2022;17:265–75.

Chen YC, Chang LC, Liu CY, Ho YF, Weng SC, Tsai TI. The roles of social support and health literacy in self-management among patients with chronic kidney disease. J Nurs Scholarsh. 2018;50(3):265–75. https://doi.org/10.1111/jnu.12377 .

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Acknowledgements

This study was supported by a grant from the National Research Council of Thailand. This research was also supported by the Office of Research Administration, Chiang Mai University, for the Health Promotion, Technology and Innovation Research Group. Regarding the 9-item Thai Health Status Assessment Instrument (9-Thai), permission for its use in this study was given by Assistant Professor Dr. Areewan Cheawchanwattana. The authors would like to give special thanks to their participants who graciously gave their time to take part in this study.

Authors’ statement

The authors would like to confirm that:

1. The manuscript has not been published or submitted for publication elsewhere.

2. All authors approved the content of the manuscript and have contributed significantly to the research involved in the process of protocol development, data collection and analysis, discussion and writing for manuscript publication and process of manuscript’s revision.

3. The protocol for the research project has been approved by the Research Ethics Committees of the Institutional Review Board of Faculty of Nursing (IRB) and,

4. by the Research Ethics Committees of the Institutional Review Board of the Faculty of Medicine, Chiang Mai University, Thailand.

5. All participants gave informed consent for the research, and that their anonymity was preserved according to the Declaration of Helsinki (as revised in Brazil 2013).

6. We declare not to have any financial support or relationships that may pose a conflict of interest by disclosing any financial arrangements they have with a company whose product figures prominently in the submitted manuscript or with a company making a competing product, or any conflict relating to technology or methodology.

7. This research was funded by the National Research Council of Thailand (NRCT) and Chiang Mai University, Thailand.

8. The researchers have received permission for using the instruments utilized in the study.

This study is supported by funding from the National Research Council of Thailand (NRCT) (Grant No. 270392) and the Chiang Mai University (Grant No. R000016567, Grant No. R000029878).

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Department of Medical Nursing, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand

Wanicha Pungchompoo

Department of Nephrology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand

Saowaros Parinyachitta

Department of Industrial and Manufacturing Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand

Sirirat Pungchompoo

Department of Surgical Nursing, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand

Warawan Udomkhwamsuk

Department of Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand

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Contributions

W.P. contributed to the concept and the design of the study, as well as provided supervision. W.P. and Sa.P. were also involved in the development of the Telehealth model and in the web-application design and development. W.P., S.P. and P.S. were involved with the data materials and data collection processing. W.P. and Si.P. provided analysis and/or interpretation for the study. W.P. and W.U. conducted a search of the literature. W.P. wrote the manuscript, and W.P. provided a critical review of the manuscript, revisions for important intellectual content, and other work. All authors read and approved of the final manuscript.

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Correspondence to Wanicha Pungchompoo .

Ethics declarations

Ethics approval and consent to participate.

The study protocol was approved by the Research Ethics Committee of the Institutional Review Board of the Faculty of Nursing and the Faculty of Medicine, Chiang Mai University, Thailand, Institutional Review Board No. 003–2559 (date of approval: November 30, 2016; date of expiry: November 29, 2017, and date of extension: September 2018), before the process of data collection began.

After approval was granted, the researchers informed the potential research subjects about the purpose of the research, its possible benefits, and the research process. Potential subjects were told that their participation was voluntary, that they had the right to choose whether to participate or not, and that their decision would not affect the treatment they were receiving in the hospital. They were given clear information, and they expressed their clear understanding and decision to participate before signing an informed consent form. The researchers considered each patient’s confidentiality and reassured them the data from the research results would be presented as a whole, not broken down by each individual. The researchers were available to the participants if they had any further questions.

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Pungchompoo, W., Parinyachitta, S., Pungchompoo, S. et al. The feasibility of integrating a home telehealth model for older persons living with hemodialysis. BMC Geriatr 24 , 378 (2024). https://doi.org/10.1186/s12877-024-04981-8

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Received : 24 October 2023

Accepted : 16 April 2024

Published : 27 April 2024

DOI : https://doi.org/10.1186/s12877-024-04981-8

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