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Introduction to Research Statistical Analysis: An Overview of the Basics

Christian vandever.

1 HCA Healthcare Graduate Medical Education

Description

This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

Introduction

Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Some of the information is more applicable to retrospective projects, where analysis is performed on data that has already been collected, but most of it will be suitable to any type of research. This primer will help the reader understand research results in coordination with a statistician, not to perform the actual analysis. Analysis is commonly performed using statistical programming software such as R, SAS or SPSS. These allow for analysis to be replicated while minimizing the risk for an error. Resources are listed later for those working on analysis without a statistician.

After coming up with a hypothesis for a study, including any variables to be used, one of the first steps is to think about the patient population to apply the question. Results are only relevant to the population that the underlying data represents. Since it is impractical to include everyone with a certain condition, a subset of the population of interest should be taken. This subset should be large enough to have power, which means there is enough data to deliver significant results and accurately reflect the study’s population.

The first statistics of interest are related to significance level and power, alpha and beta. Alpha (α) is the significance level and probability of a type I error, the rejection of the null hypothesis when it is true. The null hypothesis is generally that there is no difference between the groups compared. A type I error is also known as a false positive. An example would be an analysis that finds one medication statistically better than another, when in reality there is no difference in efficacy between the two. Beta (β) is the probability of a type II error, the failure to reject the null hypothesis when it is actually false. A type II error is also known as a false negative. This occurs when the analysis finds there is no difference in two medications when in reality one works better than the other. Power is defined as 1-β and should be calculated prior to running any sort of statistical testing. Ideally, alpha should be as small as possible while power should be as large as possible. Power generally increases with a larger sample size, but so does cost and the effect of any bias in the study design. Additionally, as the sample size gets bigger, the chance for a statistically significant result goes up even though these results can be small differences that do not matter practically. Power calculators include the magnitude of the effect in order to combat the potential for exaggeration and only give significant results that have an actual impact. The calculators take inputs like the mean, effect size and desired power, and output the required minimum sample size for analysis. Effect size is calculated using statistical information on the variables of interest. If that information is not available, most tests have commonly used values for small, medium or large effect sizes.

When the desired patient population is decided, the next step is to define the variables previously chosen to be included. Variables come in different types that determine which statistical methods are appropriate and useful. One way variables can be split is into categorical and quantitative variables. ( Table 1 ) Categorical variables place patients into groups, such as gender, race and smoking status. Quantitative variables measure or count some quantity of interest. Common quantitative variables in research include age and weight. An important note is that there can often be a choice for whether to treat a variable as quantitative or categorical. For example, in a study looking at body mass index (BMI), BMI could be defined as a quantitative variable or as a categorical variable, with each patient’s BMI listed as a category (underweight, normal, overweight, and obese) rather than the discrete value. The decision whether a variable is quantitative or categorical will affect what conclusions can be made when interpreting results from statistical tests. Keep in mind that since quantitative variables are treated on a continuous scale it would be inappropriate to transform a variable like which medication was given into a quantitative variable with values 1, 2 and 3.

Categorical vs. Quantitative Variables

Both of these types of variables can also be split into response and predictor variables. ( Table 2 ) Predictor variables are explanatory, or independent, variables that help explain changes in a response variable. Conversely, response variables are outcome, or dependent, variables whose changes can be partially explained by the predictor variables.

Response vs. Predictor Variables

Choosing the correct statistical test depends on the types of variables defined and the question being answered. The appropriate test is determined by the variables being compared. Some common statistical tests include t-tests, ANOVA and chi-square tests.

T-tests compare whether there are differences in a quantitative variable between two values of a categorical variable. For example, a t-test could be useful to compare the length of stay for knee replacement surgery patients between those that took apixaban and those that took rivaroxaban. A t-test could examine whether there is a statistically significant difference in the length of stay between the two groups. The t-test will output a p-value, a number between zero and one, which represents the probability that the two groups could be as different as they are in the data, if they were actually the same. A value closer to zero suggests that the difference, in this case for length of stay, is more statistically significant than a number closer to one. Prior to collecting the data, set a significance level, the previously defined alpha. Alpha is typically set at 0.05, but is commonly reduced in order to limit the chance of a type I error, or false positive. Going back to the example above, if alpha is set at 0.05 and the analysis gives a p-value of 0.039, then a statistically significant difference in length of stay is observed between apixaban and rivaroxaban patients. If the analysis gives a p-value of 0.91, then there was no statistical evidence of a difference in length of stay between the two medications. Other statistical summaries or methods examine how big of a difference that might be. These other summaries are known as post-hoc analysis since they are performed after the original test to provide additional context to the results.

Analysis of variance, or ANOVA, tests can observe mean differences in a quantitative variable between values of a categorical variable, typically with three or more values to distinguish from a t-test. ANOVA could add patients given dabigatran to the previous population and evaluate whether the length of stay was significantly different across the three medications. If the p-value is lower than the designated significance level then the hypothesis that length of stay was the same across the three medications is rejected. Summaries and post-hoc tests also could be performed to look at the differences between length of stay and which individual medications may have observed statistically significant differences in length of stay from the other medications. A chi-square test examines the association between two categorical variables. An example would be to consider whether the rate of having a post-operative bleed is the same across patients provided with apixaban, rivaroxaban and dabigatran. A chi-square test can compute a p-value determining whether the bleeding rates were significantly different or not. Post-hoc tests could then give the bleeding rate for each medication, as well as a breakdown as to which specific medications may have a significantly different bleeding rate from each other.

A slightly more advanced way of examining a question can come through multiple regression. Regression allows more predictor variables to be analyzed and can act as a control when looking at associations between variables. Common control variables are age, sex and any comorbidities likely to affect the outcome variable that are not closely related to the other explanatory variables. Control variables can be especially important in reducing the effect of bias in a retrospective population. Since retrospective data was not built with the research question in mind, it is important to eliminate threats to the validity of the analysis. Testing that controls for confounding variables, such as regression, is often more valuable with retrospective data because it can ease these concerns. The two main types of regression are linear and logistic. Linear regression is used to predict differences in a quantitative, continuous response variable, such as length of stay. Logistic regression predicts differences in a dichotomous, categorical response variable, such as 90-day readmission. So whether the outcome variable is categorical or quantitative, regression can be appropriate. An example for each of these types could be found in two similar cases. For both examples define the predictor variables as age, gender and anticoagulant usage. In the first, use the predictor variables in a linear regression to evaluate their individual effects on length of stay, a quantitative variable. For the second, use the same predictor variables in a logistic regression to evaluate their individual effects on whether the patient had a 90-day readmission, a dichotomous categorical variable. Analysis can compute a p-value for each included predictor variable to determine whether they are significantly associated. The statistical tests in this article generate an associated test statistic which determines the probability the results could be acquired given that there is no association between the compared variables. These results often come with coefficients which can give the degree of the association and the degree to which one variable changes with another. Most tests, including all listed in this article, also have confidence intervals, which give a range for the correlation with a specified level of confidence. Even if these tests do not give statistically significant results, the results are still important. Not reporting statistically insignificant findings creates a bias in research. Ideas can be repeated enough times that eventually statistically significant results are reached, even though there is no true significance. In some cases with very large sample sizes, p-values will almost always be significant. In this case the effect size is critical as even the smallest, meaningless differences can be found to be statistically significant.

These variables and tests are just some things to keep in mind before, during and after the analysis process in order to make sure that the statistical reports are supporting the questions being answered. The patient population, types of variables and statistical tests are all important things to consider in the process of statistical analysis. Any results are only as useful as the process used to obtain them. This primer can be used as a reference to help ensure appropriate statistical analysis.

Funding Statement

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity.

Conflicts of Interest

The author declares he has no conflicts of interest.

Christian Vandever is an employee of HCA Healthcare Graduate Medical Education, an organization affiliated with the journal’s publisher.

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.

Introduction: Statistics as a Research Tool

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• David Weisburd 5 , 6 ,
• Chester Britt 7 ,
• David B. Wilson 5 &
• Alese Wooditch 8  

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Statistics seem intimidating because they are associated with complex mathematical formulas and computations. Although some knowledge of math is required, an understanding of the concepts is much more important than an in-depth understanding of the computations. The researcher’s aim in using statistics is to communicate findings in a clear and simple form. As a result, the researcher should always choose the simplest statistic appropriate for answering the research question. Statistics offer commonsense solutions to research problems. The following principles apply to all types of statistics: (1) in developing statistics, we seek to reduce the level of error as much as possible; (2) statistics based on more information are generally preferred over those based on less information; (3) outliers present a significant problem in choosing and interpreting statistics; and (4) the researcher must strive to systematize the procedures used in data collection and analysis. There are two principal uses of statistics discussed in this book. In descriptive statistics, the researcher summarizes large amounts of information in an efficient manner. Two types of descriptive statistics that go hand in hand are measures of central tendency, which describe the characteristics of the average case, and measures of dispersion, which tell us just how typical this average case is. We use inferential statistics to make statements about a population on the basis of a sample drawn from that population.

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Reiss, A. J., Jr. (1971). Systematic Observation of Natural Social Phenomena. Sociological Methodology, 3,  3–33. doi:10.2307/270816

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Weisburd, D., Britt, C., Wilson, D.B., Wooditch, A. (2020). Introduction: Statistics as a Research Tool. In: Basic Statistics in Criminology and Criminal Justice. Springer, Cham. https://doi.org/10.1007/978-3-030-47967-1_1

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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Research Papers / Publications

• DOI: 10.2139/ssrn.4667836
• Corpus ID: 269962311

An Exploration of Some Special Functions and Their Applications

• Mfonobong Ukweso Peter
• Published in Social Science Research… 30 May 2024
• Mathematics, Economics

16 References

Special functions: an introduction to the classical functions of mathematical physics, on the results of coffy and moli, new extension of beta function and its applications, some completely monotonic properties for the (p;q)-gamma function, on the fundamental theorem of $(p,q)$-calculus and some $(p,q)$-taylor formulas, special functions, derivation and implementation of a fifth stage fourth order explicit runge-kutta formula using 𝑓(𝑥,𝑦) functional derivatives, interchanging a limit and an integral: necessary and sufficient conditions, introduction to probability, statistics, and random processes, a statistic method for the prediction of the succession of bear and bull stock market, related papers.

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Examining the response to covid-19 in logistics and supply chain processes: insights from a state-of-the-art literature review and case study analysis.

1. Introduction

• RQ1 (scientific): How have researchers studied the impact of COVID-19 on logistics and supply chain processes? Which industrial sectors were mostly studied and why? Which additional topics can be related to COVID-19 and logistics/supply chain?
• RQ2 (practical): What effects of COVID-19 on logistics and supply chain processes were experienced by companies?

2. Materials and Methods

2.1. systematic literature review, 2.1.1. sample creation, 2.1.2. descriptive analyses, 2.1.3. paper classification.

• Macro theme: sustainability, resilience, risk, information technology, economics, performance, planning and food security. This classification represents paper’s core topic.
• Industrial sector: aerospace, agri-food, apparel, automotive, construction, e-commerce, electronic, energy, fast-moving consumer goods, food, healthcare, logistics, manufacturing and service.
• Data collection method: questionnaire/interview, third-party sources or case study. This classification represents the method used by the authors to collect the data useful to their study.
• Research method: statistical, decision-making, simulation, empirical, literature review or economic. This category describes the tool used by the authors to conduct the study and reach the related goals.
• Specific method, e.g., descriptive statistics, structural equation modeling (SEM), multi-criteria decision making (MCDM), etc.; this feature describes more accurately the type of work carried out by the authors and the tools used.
• Country: it reflects the geographical area in which the study was carried out, in terms, for instance, of the country in which a sample of people has been interviewed or where empirical data were collected, or where the simulation was set. This method of classification, although more elaborated, was preferred over traditional approaches, in which the country of the study is defined based merely on the affiliation of the first author of the paper, because the exact knowledge of the country in which the study was carried out is, for sure, a more representative source of information about the research. This is true in general, but it is even more important for this subject matter, as the management of the COVID-19 pandemic was made on a country or regional basis, with significant differences from country to country; knowing the exact location of the study helps in better interpreting the research outcomes. Possible entries in this field also include “multiple countries” and “not specified”, with the obvious meanings of the terms.

2.1.4. Cross-Analyses

2.1.5. interrelated aspects, 2.2. case study, 2.2.1. data collection.

• Economic data: some key economic data were retrieved from the company’s balance sheet, from 2019 up to the latest available document, which refers to 2022.
• Organizational data: these data describe changes in the operational, decision-making and business structure of the company in terms, e.g., of number of employees hired, number of drivers, etc.
• The related data were collected and elaborated between July and September 2023.

2.2.2. Survey Phase

2.2.3. analysis and summary, 3. results—systematic literature review, 3.1. descriptive statistics, 3.2. common classification fields, 3.2.1. macro theme, 3.2.2. industrial sector, 3.2.3. data collection method, 3.2.4. research method, 3.2.5. country, 3.3. cross-analyses, 3.3.1. macro theme vs. industrial sector, 3.3.2. research method vs. macro theme, 3.4. interrelated aspects, 4. results—case study, 4.1. company overview, 4.2. pre-covid-19 period, 4.3. covid-19 period, 4.4. post-covid-19 period, 4.5. analysis and summary.

• Strengths : at present, Company A benefits from a robust network of relationships with customers and suppliers (e.g., drivers), which was leveraged during the pandemic period to provide a rapid response to the increased request by the consumers. The company has also leveraged the usage of digital technologies, which made logistics activities more efficient and, again, allowed the company to respond to consumer demand in the pandemic period.
• Weaknesses : Company A has suffered from low economic results, in particular in the post-COVID-19 period, mainly due to the high production costs. Efforts must be made by the company to reduce expenses. At the same time, however, the service level, in terms of delivery lead time or on-time delivery, should be safeguarded.
• Opportunities : the growth of e-commerce, experienced in the COVID-19 period but expected to last over time, creates opportunities for increasing the volume of items handled by Company A. Indeed, the survey phase demonstrated that the company’s consumers have shifted towards the usage of online sales; hence, the company could consider investing in this area to increase its market share. By leveraging the e-commerce logistics and diversifying service, expansions could also be possible at an international level. Even if the company has already embraced the implementation of digital technologies, some emerging technologies (e.g., drones or advanced traceability systems) could also be introduced for further improving the logistics efficiency. Finally, sustainability is another opportunity to be leveraged, because of the current push towards the adoption of environmental-friendly logistics solutions. Examples of those solutions include a reduction in CO 2 emissions, and the usage of electric vehicles or zero-impact materials.
• Threats : the growth of e-commerce can be seen as an opportunity, but because many logistics companies have already entered this field, the sector is characterized by very high competition, which could limit the market share of Company A; this could instead be seen as a threat needing to be properly managed. Another threat comes from the increased cost of fuel, which, for sure, for a logistics company plays an important role in determining the cost of the transport activities (also, having previously observed that the company suffered from a limited revenue in recent years). This factor could further push towards the adoption of environmentally friendly transport modes (e.g., electric vehicles), which have been previously mentioned as an opportunity for leveraging in the logistics sector.

5. Conclusions

5.1. answer to the research questions, 5.2. scientific and practical implications, 5.3. suggestions for future research directions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Christopher, M. Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service Financial Times ; Pitman Publishing: London, UK, 1998; ISBN 0 273 63049 0. [ Google Scholar ]
• Gechevski, D.; Kochov, A.; Popovska-Vasilevska, S.; Polenakovik, R.; Donev, V. Reverse Logistics and Green Logistics Way to Improving the Environmental Sustainability. Acta Tech. Corviniensis-Bull. Eng. 2016 , 9 , 63. [ Google Scholar ]
• Mbang, A. A New Introduction to Supply Chains and Supply Chain Management: Definitions and Theories Perspective. Int. Bus. Res. 2011 , 5 , 194–207. [ Google Scholar ] [ CrossRef ]
• Jones, T.C.; Riley, D.W. Using Inventory for Competitive Advantage through Supply Chain Management. Int. J. Phys. Distrib. Mater. Manag. 1985 , 15 , 16–26. [ Google Scholar ] [ CrossRef ]
• Monczka, R.M.; Trent, R.J.; Handfield, R.B. Purchasing and Supply Chain Management ; South Western Educational Publishing: Natorp Blvd Cincinnati, OH, USA, 2002; ISBN 0-324-02315-4. [ Google Scholar ]
• Belhadi, D.; Peiffer-Smadja, N.; Lescure, F.-X.; Yazdanpanah, Y.; Mentré, F.; Laouénan, C. A Brief Review of Antiviral Drugs Evaluated in Registered Clinical Trials for COVID-19. MedRxiv 2020 . [ Google Scholar ] [ CrossRef ]
• Shen, Z.; Sun, Y. Strengthening Supply Chain Resilience during COVID-19: A Case Study of JD.Com. J. Oper. Manag. 2021 , 69 , 359–383. [ Google Scholar ] [ CrossRef ]
• Dohale, V.; Verma, P.; Gunasekaran, A.; Ambilkar, P. COVID-19 and Supply Chain Risk Mitigation: A Case Study from India. Int. J. Logist. Manag. 2023 , 34 , 417–442. [ Google Scholar ] [ CrossRef ]
• Mishra, P.K. COVID-19, Black Swan Events and the Future of Disaster Risk Management in India. Prog. Disaster Sci. 2020 , 8 , 100137. [ Google Scholar ] [ CrossRef ]
• Badhotiya, G.K.; Soni, G.; Jain, V.; Joshi, R.; Mittal, S. Assessing Supply Chain Resilience to the Outbreak of COVID-19 in Indian Manufacturing Firms. Oper. Manag. Res. 2022 , 15 , 1161–1180. [ Google Scholar ] [ CrossRef ]
• Mahmoud, M.A.; Mahmoud, A.; Abubakar, S.L.; Garba, A.S.; Daneji, B.A. COVID-19 Operational Disruption and SMEs’ Performance: The Moderating Role of Disruption Orientation and Government Support. Benchmarking Int. J. 2022 , 29 , 2641–2664. [ Google Scholar ] [ CrossRef ]
• Aldrighetti, R.; Battini, D.; Ivanov, D. Increasing Supply Chain Resilience through Efficient Redundancy Allocation: A Risk-Averse Mathematical Model. Ifac-papersonline 2021 , 54 , 1011–1016. [ Google Scholar ]
• Tang, O.; Nurmaya Musa, S. Identifying Risk Issues and Research Advancements in Supply Chain Risk Management. Int. J. Prod. Econ. 2011 , 133 , 25–34. [ Google Scholar ] [ CrossRef ]
• Rinaldi, M.; Murino, T.; Gebennini, E.; Morea, D.; Bottani, E. A Literature Review on Quantitative Models for Supply Chain Risk Management: Can They Be Applied to Pandemic Disruptions? Comput. Ind. Eng. 2022 , 170 , 108329. [ Google Scholar ] [ CrossRef ]
• Corallo, A.; Lazoi, M.; Lezzi, M.; Luperto, A. Cybersecurity Awareness in the Context of the Industrial Internet of Things: A Systematic Literature Review. Comput. Ind. 2022 , 137 , 103614. [ Google Scholar ] [ CrossRef ]
• Rinaldi, M.; Bottani, E. How Did COVID-19 Affect Logistics and Supply Chain Processes? Immediate, Short and Medium-Term Evidence from Some Industrial Fields of Italy. Int. J. Prod. Econ. 2023 , 262 , 108915. [ Google Scholar ] [ CrossRef ]
• Chowdhury, P.; Paul, S.K.; Kaisar, S.; Moktadir, M.A. COVID-19 pandemic related supply chain studies: A systematic review. Transp. Res. Part E—Logist. Transp. Rev. 2021 , 148 , 102271. [ Google Scholar ] [ CrossRef ]
• Nandi, S.; Sarkis, J.; Hervani, A.A.; Helms, M.M. Redesigning supply chains using blockchain-enabled circular economy and COVID-19 experiences. Sustain. Prod. Consum. 2021 , 27 , 10–22. [ Google Scholar ] [ CrossRef ]
• Kraus, S.; Clauss, T.; Breier, M.; Gast, J.; Zardini, A.; Tiberius, V. The economics of COVID-19: Initial empirical evidence on how family firms in five European countries cope with the corona crisis. Int. J. Entrep. Behav. Res. 2020 , 26 , 1067–1092. [ Google Scholar ] [ CrossRef ]
• De Vet, J.M.; Nigohosyan, D.; Ferrer, J.N.; Gross, A.K.; Kuehl, S.; Flickenschild, M. Impacts of the COVID-19 Pandemic on EU Industries. 2021. Available online: https://www.europarl.europa.eu/RegData/etudes/STUD/2021/662903/IPOL_STU(2021)662903_EN.pdf (accessed on 29 April 2024).
• Manuj, I.; Mentzer, J.T. Global Supply Chain Risk Management. J. Bus. Logist. 2008 , 29 , 133–155. [ Google Scholar ] [ CrossRef ]
• Ali, I.; Golgeci, I.; Arslan, A. Achieving Resilience through Knowledge Management Practices and Risk Management Culture in Agri-Food Supply Chains. Supply Chain. Manag. Int. J. 2023 , 28 , 284–299. [ Google Scholar ] [ CrossRef ]
• Madhavika, N.; Jayasinghe, N.; Ehalapitiya, S.; Wickramage, T.; Fernando, D.; Jayasinghe, V. Operationalizing Resilience through Collaboration: The Case of Sri Lankan Tea Supply Chain during Covid-19. Qual. Quant. 2023 , 57 , 2981–3018. [ Google Scholar ] [ CrossRef ]
• Aman, S.; Seuring, S. Analysing Developing Countries Approaches of Supply Chain Resilience to COVID-19. Int. J. Logist. Manag. 2023 , 34 , 909–934. [ Google Scholar ] [ CrossRef ]
• Maharjan, R.; Kato, H. Resilient Supply Chain Network Design: A Systematic Literature Review. Transp. Rev. 2022 , 42 , 739–761. [ Google Scholar ] [ CrossRef ]
• Zidi, S.; Hamani, N.; Kermad, L. Antecedents and Enablers of Supply Chain Reconfigurability and Their Effects on Performance. Int. J. Adv. Manuf. Technol. 2022 , 120 , 3027–3043. [ Google Scholar ] [ CrossRef ]
• Nabipour, M.; Ülkü, M.A. On Deploying Blockchain Technologies in Supply Chain Strategies and the COVID-19 Pandemic: A Systematic Literature Review and Research Outlook. Sustainability 2021 , 13 , 10566. [ Google Scholar ] [ CrossRef ]
• Rokicki, T.; Bórawski, P.; Bełdycka-Bórawska, A.; Szeberényi, A.; Perkowska, A. Changes in Logistics Activities in Poland as a Result of the COVID-19 Pandemic. Sustainability 2022 , 14 , 10303. [ Google Scholar ] [ CrossRef ]
• Figura, J.; Gądek-Hawlena, T. The Impact of the COVID-19 Pandemic on the Development of Electromobility in Poland. The Perspective of Companies in the Transport-Shipping-Logistics Sector: A Case Study. Energies 2022 , 15 , 1461. [ Google Scholar ] [ CrossRef ]
• Baldrighi, E.; Monferdini, L.; Bottani, E. The Response to COVID-19 in Logistics and Supply Chain Processes: Evidence from a Review of the Literature. In Proceedings of the International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation, Athens, Greece, 18–20 September 2023. [ Google Scholar ]
• Bottani, E.; Monferdini, L. Studies related to Covid-19 in logistics and supply chain processes (2021–2023). Mendeley Data 2024 , V1. [ Google Scholar ] [ CrossRef ]
• Kesmodel, U.S. Cross-Sectional Studies—What Are They Good For? Acta Obstet. Gynecol. Scand. 2018 , 97 , 388–393. [ Google Scholar ] [ CrossRef ]
• Thompson, M.L.; Myers, J.; Kriebel, D. Prevalence Odds Ratio or Prevalence Ratio in the Analysis of Cross Sectional Data: What Is to Be Done? Occup. Environ. Med. 1998 , 55 , 272–277. [ Google Scholar ] [ CrossRef ]
• Robson, C. Real World Research: A Resource for Social Scientists and Practitioner-Researchers ; Blackwell Publishing: Hoboken, NJ, USA, 1993; ISBN 978-0-631-17689-3. [ Google Scholar ]
• Yin, R. Case Studies. Int. Encycl. Soc. Behav. Sci. 2015 , 2 , 194–201. [ Google Scholar ] [ CrossRef ]
• Wohlin, C. Case Study Research in Software Engineering—It Is a Case, and It Is a Study, but Is It a Case Study? Inf. Softw. Technol. 2021 , 133 , 106514. [ Google Scholar ] [ CrossRef ]
• Grabot, B.; Vallespir, B.; Samuel, G.; Bouras, A.; Kiritsis, D. Advances in Production Management Systems: Innovative and Knowledge-Based Production Management in a Global-Local World: IFIP WG 5.7 International Conference, APMS 2014, Ajaccio, France, 20–24 September 2014, Proceedings, Part II ; Springer: Berlin/Heidelberg, Germany, 2014; Volume 439, ISBN 3-662-44736-3. [ Google Scholar ]
• Alicke, K.; Azcue, X.; Barriball, E.; Supply-Chain Recovery in Coronavirus Times—Plan for Now and the Future. McKinsey & Company. 2020. Available online: https://www.mckinsey.com (accessed on 29 April 2024).
• Ivanov, D. Supply Chain Viability and the COVID-19 Pandemic: A Conceptual and Formal Generalisation of Four Major Adaptation Strategies. Int. J. Prod. Res. 2021 , 59 , 3535–3552. [ Google Scholar ] [ CrossRef ]
• Adobor, H.; McMullen, R.S. Supply Chain Resilience: A Dynamic and Multidimensional Approach. Int. J. Logist. Manag. 2018 , 29 , 1451–1471. [ Google Scholar ] [ CrossRef ]
• Singh, C.S.; Soni, G.; Badhotiya, G.K. Performance Indicators for Supply Chain Resilience: Review and Conceptual Framework. J. Ind. Eng. Int. 2019 , 15 , 105–117. [ Google Scholar ] [ CrossRef ]
• Mohammed, A.; Jabbour, A.B.L.D.S.; Diabat, A. COVID-19 Pandemic Disruption: A Matter of Building Companies’ Internal and External Resilience. Int. J. Prod. Res. 2023 , 61 , 2716–2737. [ Google Scholar ] [ CrossRef ]
• El Khoury, R.; Nasrallah, N.; Atayah, O.F.; Dhiaf, M.M.; Frederico, G.F. The Impact of Green Supply Chain Management Practices on Environmental Performance during COVID-19 Period: The Case of Discretionary Companies in the G-20 Countries. Benchmarking Int. J. 2022 . ahead of print . [ Google Scholar ] [ CrossRef ]
• Sharma, V.; Singh, A.; Rai, S.S. Disruptions in Sourcing and Distribution Practices of Supply Chains Due to COVID-19 Pandemic: A Sustainability Paradigm. J. Glob. Oper. Strateg. Sourc. 2022 , 15 , 235–261. [ Google Scholar ] [ CrossRef ]
• Karmaker, C.L.; Bari, A.B.M.M.; Anam, M.Z.; Ahmed, T.; Ali, S.M.; de Jesus Pacheco, D.A.; Moktadir, M.A. Industry 5.0 Challenges for Post-Pandemic Supply Chain Sustainability in an Emerging Economy. Int. J. Prod. Econ. 2023 , 258 , 108806. [ Google Scholar ] [ CrossRef ]
• Paul, A.; Shukla, N.; Trianni, A. Modelling Supply Chain Sustainability Challenges in the Food Processing Sector amid the COVID-19 Outbreak. Socio-Econ. Plan. Sci. 2023 , 87 , 101535. [ Google Scholar ] [ CrossRef ]
• Siuta, K.; Kaszyński, D. The Principal-Agent Problem in Supply Chain Management—The Simulation Based Framework. Control. Cybern. 2021 , 50 , 195–221. [ Google Scholar ] [ CrossRef ]
• Kumar, P.; Singh, S.S.; Pandey, A.K.; Singh, R.K.; Srivastava, P.K.; Kumar, M.; Dubey, S.K.; Sah, U.; Nandan, R.; Singh, S.K.; et al. Multi-Level Impacts of the COVID-19 Lockdown on Agricultural Systems in India: The Case of Uttar Pradesh. Agric. Syst. 2021 , 187 , 103027. [ Google Scholar ] [ CrossRef ]
• Atayah, O.F.; Dhiaf, M.M.; Najaf, K.; Frederico, G.F. Impact of COVID-19 on Financial Performance of Logistics Firms: Evidence from G-20 Countries. J. Glob. Oper. Strateg. Sourc. 2022 , 15 , 172–196. [ Google Scholar ] [ CrossRef ]
• Israfilov, N.; Druzyanova, V.; Ermakova, M.; Sinitsyna, A. Key Directions for Transforming Supply Chain Management in Emerging Markets during the PostCOVID-19 Pandemic Period. Oper. Supply Chain. Manag. 2023 , 16 , 498–508. [ Google Scholar ] [ CrossRef ]
• Dwivedi, A.; Chowdhury, P.; Paul, S.K.; Agrawal, D. Sustaining Circular Economy Practices in Supply Chains during a Global Disruption. Int. J. Logist. Manag. 2023 , 34 , 644–673. [ Google Scholar ] [ CrossRef ]
• Ghadge, D.A.; Er, M.; Ivanov, D.; Chaudhuri, A. Visualisation of Ripple Effect in Supply Chains under Long-Term, Simultaneous Disruptions: A System Dynamics Approach. Int. J. Prod. Res. 2021 , 60 , 1987547. [ Google Scholar ] [ CrossRef ]
• Eldem, B.; Kluczek, A.; Bagiński, J. The COVID-19 Impact on Supply Chain Operations of Automotive Industry: A Case Study of Sustainability 4.0 Based on Sense–Adapt–Transform Framework. Sustainability 2022 , 14 , 5855. [ Google Scholar ] [ CrossRef ]
• Gui, D.; Wang, H.; Yu, M. Risk Assessment of Port Congestion Risk during the COVID-19 Pandemic. J. Mar. Sci. Eng. 2022 , 10 , 150. [ Google Scholar ] [ CrossRef ]
• Brdulak, H.; Brdulak, A. Challenges and Threats Faced in 2020 by International Logistics Companies Operating on the Polish Market. Sustainability 2021 , 13 , 359. [ Google Scholar ] [ CrossRef ]
• Paul, S.K.; Chowdhury, P.; Chowdhury, M.T.; Chakrabortty, R.K.; Moktadir, M.A. Operational Challenges during a Pandemic: An Investigation in the Electronics Industry. Int. J. Logist. Manag. 2023 , 34 , 336–362. [ Google Scholar ] [ CrossRef ]
• Klein, M.; Gutowska, E.; Gutowski, P. Innovations in the T&L (Transport and Logistics) Sector during the COVID-19 Pandemic in Sweden, Germany and Poland. Sustainability 2022 , 14 , 3323. [ Google Scholar ] [ CrossRef ]
• Mishrif, A.; Khan, A. Technology Adoption as Survival Strategy for Small and Medium Enterprises during COVID-19. J. Innov. Entrep. 2023 , 12 , 53. [ Google Scholar ] [ CrossRef ]
• Ishak, S.; Shaharudin, M.R.; Salim, N.A.M.; Zainoddin, A.I.; Deng, Z. The Effect of Supply Chain Adaptive Strategies During the COVID-19 Pandemic on Firm Performance in Malaysia’s Semiconductor Industries. Glob. J. Flex. Syst. Manag. 2023 , 24 , 439–458. [ Google Scholar ] [ CrossRef ]
• Hendijani, R.; Norouzi, M. Supply Chain Integration and Firm Performance in the COVID-19 Era: The Mediating Role of Resilience and Robustness. J. Glob. Oper. Strateg. Sourc. 2023 , 16 , 337–367. [ Google Scholar ] [ CrossRef ]
• Vilko, J.; Hallikas, J. Impact of COVID-19 on Logistics Sector Companies. Int. J. Ind. Eng. Oper. Manag. 2024 , 6 , 25–42. [ Google Scholar ] [ CrossRef ]
• Palouj, M.; Lavaei Adaryani, R.; Alambeigi, A.; Movarej, M.; Safi Sis, Y. Surveying the Impact of the Coronavirus (COVID-19) on the Poultry Supply Chain: A Mixed Methods Study. Food Control 2021 , 126 , 108084. [ Google Scholar ] [ CrossRef ]
• Ali, I.; Arslan, A.; Khan, Z.; Tarba, S. The Role of Industry 4.0 Technologies in Mitigating Supply Chain Disruption: Empirical Evidence From the Australian Food Processing Industry. IEEE Trans. Eng. Manag. 2021 , 71 , 10600–10610. [ Google Scholar ] [ CrossRef ]
• Grigorescu, I.; Popovici, E.-A.; Damian, N.; Dumitraşcu, M.; Sima, M.; Mitrică, B.; Mocanu, I. The Resilience of Sub-Urban Small Farming in Bucharest Metropolitan Area in Response to the COVID-19 Pandemic. Land Use Policy 2022 , 122 , 106351. [ Google Scholar ] [ CrossRef ]
• Perrin, A.; Martin, G. Resilience of French Organic Dairy Cattle Farms and Supply Chains to the Covid-19 Pandemic. Agric. Syst. 2021 , 190 , 103082. [ Google Scholar ] [ CrossRef ]
• Coopmans, I.; Bijttebier, J.; Marchand, F.; Mathijs, E.; Messely, L.; Rogge, E.; Sanders, A.; Wauters, E. COVID-19 Impacts on Flemish Food Supply Chains and Lessons for Agri-Food System Resilience. Agric. Syst. 2021 , 190 , 103136. [ Google Scholar ] [ CrossRef ]
• Ababulgu, N.; Abajobir, N.; Wana, H. The Embarking of COVID-19 and the Perishable Products’ Value Chain in Ethiopia. J. Innov. Entrep. 2022 , 11 , 34. [ Google Scholar ] [ CrossRef ]
• Eileen Bogweh, N.; Lutomia, C. COVID-19 Challenges to Sustainable Food Production and Consumption: Future Lessons for Food Systems in Eastern and Southern Africa from a Gender Lens. Sustain. Prod. Consum. 2021 , 27 , 2208–2220. [ Google Scholar ] [ CrossRef ]
• Igberi, C.; Omenyi, L.; Osuji, E.; Egwu, P.; Ibrahim-olesin, S. Comparative Analysis of the Sustainable Dimensions of Food Security with COVID-19 and Climate Change: A Case Study. Int. J. Adv. Appl. Sci. 2022 , 9 , 9–15. [ Google Scholar ] [ CrossRef ]
• Mugabe, P.A.; Renkamp, T.M.; Rybak, C.; Mbwana, H.; Gordon, C.; Sieber, S.; Löhr, K. Governing COVID-19: Analyzing the Effects of Policy Responses on Food Systems in Tanzania. Agric. Food Secur. 2022 , 11 , 47. [ Google Scholar ] [ CrossRef ]
• Nunes, M.; Abreu, A.; Bagnjuk, J.; Nunes, E.; Saraiva, C. A Strategic Process to Manage Collaborative Risks in Supply Chain Networks (SCN) to Improve Resilience and Sustainability. Sustainability 2022 , 14 , 5237. [ Google Scholar ] [ CrossRef ]
• World Economic Forum (WEF). How to Rebound Stronger from COVID-19—Resilience in Manufacturing and Supply Systems ; World Economic Forum: Cologny, Switzerland, 2020. [ Google Scholar ]
• Zulkiffli, S.N.; Zaidi, N.F.; Padlee, S.F.; Sukri, N.K. Eco-Innovation Capabilities and Sustainable Business Performance during the COVID-19 Pandemic. Sustainability 2022 , 14 , 7525. [ Google Scholar ] [ CrossRef ]
• Moosavi, J.; Hosseini, S. Simulation-Based Assessment of Supply Chain Resilience with Consideration of Recovery Strategies in the COVID-19 Pandemic Context. Comput. Ind. Eng. 2021 , 160 , 107593. [ Google Scholar ] [ CrossRef ]
• Ho, W.; Zheng, T.; Yildiz, H.; Talluri, S. Supply Chain Risk Management: A Literature Review. Int. J. Prod. Res. 2015 , 53 , 1030467. [ Google Scholar ] [ CrossRef ]
• Dogbe, C.S.K.; Iddris, F.; Duah, E.; Boateng, P.A.; Kparl, E.M. Analyzing the Health Supply Chain Risks during COVID-19 Pandemic: The Moderating Role of Risk Management. Cogent Bus. Manag. 2023 , 10 , 2281716. [ Google Scholar ] [ CrossRef ]
• Shenoi, V.; Dath, S.; Rajendran, C. Supply Chain Risk Management in Indian Manufacturing Industries: An Empirical Study and a Fuzzy Approach ; Springer Nature Switzerland: Cham, Switzerland, 2021; pp. 107–145. ISBN 978-3-030-69264-3. [ Google Scholar ]
• Jifar, W.; Geneti, G.; Dubale, S. The Impact of COVID-19 on Pharmaceutical Shortages and Supply Disruptions for Non-Communicable Diseases Among Public Hospitals of South West, Oromia, Ethiopia. J. Multidiscip. Healthc. 2022 , 15 , 1933–1943. [ Google Scholar ] [ CrossRef ]
• Goodarzian, F.; Taleizadeh, A.; Ghasemi, P.; Abraham, A. An Integrated Sustainable Medical Supply Chain Network during COVID-19 ; Elsevier: Amsterdam, The Netherlands, 2021; Volume 100. [ Google Scholar ]
• Bump, J.B.; Friberg, P.; Harper, D.R. International Collaboration and Covid-19: What Are We Doing and Where Are We Going? BMJ 2021 , 372 , n180. [ Google Scholar ] [ CrossRef ]
• Abu-Elmagd, K.; Fung, J.; Bueno, J.; Martin, D.; Madariaga, J.R.; Mazariegos, G.; Bond, G.; Molmenti, E.; Corry, R.J.; Starzl, T.E. Logistics and Technique for Procurement of Intestinal, Pancreatic, and Hepatic Grafts from the Same Donor. Ann. Surg. 2000 , 232 , 680–687. [ Google Scholar ] [ CrossRef ]
• Fu, H.; Ke, G.Y.; Lian, Z.; Zhang, L. 3PL Firm’s Equity Financing for Technology Innovation in a Platform Supply Chain. Transp. Res. Part E Logist. Transp. Rev. 2021 , 147 , 102239. [ Google Scholar ] [ CrossRef ]
• Ferrari, P. The Reasons for the Success of Freight Rail Transport through the Swiss Alps|Le Ragioni Del Successo Del Trasporto Ferroviario Delle Merci Attraverso Le Alpi Svizzere. Ing. Ferrov. 2019 , 74 , 9–26. [ Google Scholar ]
• Hess, A.-K.; Schubert, I. Functional Perceptions, Barriers, and Demographics Concerning e-Cargo Bike Sharing in Switzerland. Transp. Res. Part D Transp. Environ. 2019 , 71 , 153–168. [ Google Scholar ] [ CrossRef ]
• Eurostat Sustainable Development in the European Union: Monitoring Report on Progress towards the SDGS in an EU Context ; Publications office of the European Union: Maastricht, The Netherlands, 2017; ISBN 92-79-72288-3.
• European Union. European Union Horizon 2020 in Brief. In The EU Framework Programme for Research and Innovation ; European Union: Maastricht, The Netherlands, 2014. [ Google Scholar ]
• Stoll, J.; Harrison, H.; De Sousa, E.; Callaway, D.; Collier, M.; Harrell, K.; Jones, B.; Kastlunger, J.; Kramer, E.; Kurian, S.; et al. Alternative Seafood Networks During COVID-19: Implications for Resilience and Sustainability. Front. Sustain. Food Syst. 2021 , 5 , 614368. [ Google Scholar ] [ CrossRef ]
• Tendall, D.; Joerin, J.; Kopainsky, B.; Edwards, P.; Shreck, A.; Le, Q.B.; Krütli, P.; Grant, M.; Six, J. Food System Resilience: Defining the Concept. Glob. Food Secur. 2015 , 6 , 17–23. [ Google Scholar ] [ CrossRef ]
• Li, D.; Wang, X.; Chan, H.K.; Manzini, R. Sustainable Food Supply Chain Management. Int. J. Prod. Econ. 2014 , 152 , 1–8. [ Google Scholar ] [ CrossRef ]
• Neven, D. Developing Sustainable Food Value Chains ; FAO: Rome, Italy, 2014; ISBN 92-5-108481-5. [ Google Scholar ]
• Mishra, D.; Gunasekaran, A.; Papadopoulos, T.; Dubey, R. Supply Chain Performance Measures and Metrics: A Bibliometric Study. Benchmarking Int. J. 2018 , 25 , 932–967. [ Google Scholar ] [ CrossRef ]
• Guersola, M.; Pinheiro de Lima, E.; Steiner, M. Supply Chain Performance Measurement: A Systematic Literature Review. Int. J. Logist. Syst. Manag. 2018 , 31 , 109. [ Google Scholar ] [ CrossRef ]
• Perdana, T.; Onggo, B.; Sadeli, A.; Chaerani, D.; Achmad, A.; Rahayu, F.; Gong, Y. Food Supply Chain Management in Disaster Events: A Systematic Literature Review. Int. J. Disaster Risk Reduct. 2022 , 79 , 103183. [ Google Scholar ] [ CrossRef ]
• Rahbari, M.; Arshadi Khamseh, A.; Mohammadi, M. Robust Optimization and Strategic Analysis for Agri-Food Supply Chain under Pandemic Crisis: Case Study from an Emerging Economy. Expert Syst. Appl. 2023 , 225 , 120081. [ Google Scholar ] [ CrossRef ]
• Singh, R.; Sinha, V.; Joshi, P.; Kumar, M. Modelling Agriculture, Forestry and Other Land Use (AFOLU) in Response to Climate Change Scenarios for the SAARC Nations. Environ. Monit. Assess. 2020 , 192 , 1–18. [ Google Scholar ] [ CrossRef ]
• Divergences, N.G. World Economic Outlook. 2023. Available online: https://www.imf.org/en/Publications/WEO/Issues/2023/10/10/world-economic-outlook-october-2023 (accessed on 29 April 2024).
• Ponomarov, S.; Holcomb, M. Understanding the Concept of Supply Chain Resilience. Int. J. Logist. Manag. 2009 , 20 , 124–143. [ Google Scholar ] [ CrossRef ]
• Tarigan, Z.J.H.; Siagian, H.; Jie, F. Impact of Internal Integration, Supply Chain Partnership, Supply Chain Agility, and Supply Chain Resilience on Sustainable Advantage. Sustainability 2021 , 13 , 5460. [ Google Scholar ] [ CrossRef ]
• Zidi, S.; Hamani, N.; Kermad, L. New Metrics for Measuring Supply Chain Reconfigurability. J. Intell. Manuf. 2022 , 33 , 2371–2392. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Wang, H.; Li, G.; Wang, J. Modeling of the Resilient Supply Chain System from a Perspective of Production Design Changes. Front. Eng. Manag. 2023 , 10 , 96–106. [ Google Scholar ] [ CrossRef ]
• Brundtland, G.H. World Commission on Environment and Development. Environ. Policy Law 1985 , 14 , 26–30. [ Google Scholar ]
• Yao, Q.; Liu, J.; Sheng, S.; Fang, H. Does Eco-Innovation Lift Firm Value? The Contingent Role of Institutions in Emerging Markets. J. Bus. Ind. Mark. 2019 . ahead of print . [ Google Scholar ] [ CrossRef ]
• Althaf, S.; Babbitt, C.W. Disruption Risks to Material Supply Chains in the Electronics Sector. Resour. Conserv. Recycl. 2021 , 167 , 105248. [ Google Scholar ] [ CrossRef ]
• Zeng, X.; Mathews, J.A.; Li, J. Urban Mining of E-Waste Is Becoming More Cost-Effective than Virgin Mining. Environ. Sci. Technol. 2018 , 52 , 4835–4841. [ Google Scholar ] [ CrossRef ]
• Nosalska, K.; Piątek, Z.; Mazurek, G.; Rządca, R. Industry 4.0: Coherent Definition Framework with Technological and Organizational Interdependencies. J. Manuf. Technol. Manag. [ CrossRef ]
• Munongo, S.; Pooe, D. Small and Medium Enterprises’ Adoption of 4IR Technologies for Supply Chain Resilience during the COVID-19 Pandemic. J. Transp. Supply Chain. Manag. 2022 , 14 , 747. [ Google Scholar ] [ CrossRef ]
• Kumar, A.; Singh, R.K. Supply Chain Management Practices, Retail Outlets Attributes and Organisational Performance: A Case of Organised Food Retailers in India. J. Glob. Oper. Strateg. Sourc. 2023 , 16 , 568–589. [ Google Scholar ] [ CrossRef ]
• Nisar, Q.A.; Haider, S.; Ameer, I.; Hussain, M.S.; Gill, S.S.; Usama, A. Sustainable Supply Chain Management Performance in Post COVID-19 Era in an Emerging Economy: A Big Data Perspective. Int. J. Emerg. Mark. 2022; ahead of print . [ Google Scholar ] [ CrossRef ]
• Miljenović, D.; Beriša, B. Pandemics Trends in E-Commerce: Drop Shipping Entrepreneurship during COVID-19 Pandemic. Pomorstvo 2022 , 36 , 31–43. [ Google Scholar ] [ CrossRef ]
• Laborde, D.; Martin, W.; Swinnen, J.; Vos, R. COVID-19 Risks to Global Food Security. Science 2020 , 369 , 500–502. [ Google Scholar ] [ CrossRef ]
• Yearbook, F.S. World Food and Agriculture ; Food and Agriculture Organization of the United Nations: Rome, Italy, 2013; Volume 15. [ Google Scholar ]
• Kim, B.; Kim, G.; Kang, M. Study on Comparing the Performance of Fully Automated Container Terminals during the COVID-19 Pandemic. Sustainability 2022 , 14 , 9415. [ Google Scholar ] [ CrossRef ]
• ISO 9001:2015 ; Quality Management Systems-Requirements. International Organization for Standardization: Geneva, Switzerland, 2015. Available online: https://committee.iso.org/standard/62085.html (accessed on 13 June 2024).

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Monferdini, L.; Bottani, E. Examining the Response to COVID-19 in Logistics and Supply Chain Processes: Insights from a State-of-the-Art Literature Review and Case Study Analysis. Appl. Sci. 2024 , 14 , 5317. https://doi.org/10.3390/app14125317

Monferdini L, Bottani E. Examining the Response to COVID-19 in Logistics and Supply Chain Processes: Insights from a State-of-the-Art Literature Review and Case Study Analysis. Applied Sciences . 2024; 14(12):5317. https://doi.org/10.3390/app14125317

Monferdini, Laura, and Eleonora Bottani. 2024. "Examining the Response to COVID-19 in Logistics and Supply Chain Processes: Insights from a State-of-the-Art Literature Review and Case Study Analysis" Applied Sciences 14, no. 12: 5317. https://doi.org/10.3390/app14125317

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Oral Health in America: Advances and Challenges

Oral Health in America: Advances and Challenges is a culmination of two years of research and writing by over 400 contributors. As a follow up to the Surgeon General's Report on Oral Health in America, this report explores the nation's oral health over the last 20 years.

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Emerging Science and Promising Technologies to Transform Oral Health

Scientific and technological advances present opportunities to improve the oral health of individuals and communities. These discoveries can drive new approaches for person-centered oral health care and help guide decision making by researchers, policy makers, clinicians, and individuals.

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NIDCR Director Dr. Rena D’Souza and colleagues announce release of the landmark report Oral Health in America: Advances and Challenges on December 21, 2021. Panelists included senior editors Dr. Judith Albino and Dr. Bruce A. Dye, as well as Dr. Renee Joskow, former senior advisor to the NIDCR director.