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What is a Beta Level in Statistics? (Definition & Example)

In statistics, we use hypothesis tests to determine if some assumption about a population parameter is true.

A hypothesis test always has the following two hypotheses:

Null hypothesis (H 0 ): The sample data is consistent with the prevailing belief about the population parameter.

Alternative hypothesis (H A ): The sample data suggests that the assumption made in the null hypothesis is not true. In other words, there is some non-random cause influencing the data.

Whenever we conduct a hypothesis test, there are always four possible outcomes:

Beta vs. Alpha in hypothesis testing in statistics

There are two types of errors we can commit:

  • Type I Error: We reject the null hypothesis when it is actually true. The probability of committing this type of error is denoted as α .
  • Type II Error: We fail to reject the null hypothesis when it is actually false. The probability of committing this type of error is denoted as β .

The Relationship Between Alpha and Beta

Ideally researchers want both the probability of committing a type I error and the probability of committing a type II error to be low.

However, a tradeoff exists between these two probabilities. If we decrease the alpha level, we can decrease the probability of rejecting a null hypothesis when it’s actually true, but this actually increases the beta level – the probability that we fail to reject the null hypothesis when it actually is false.

The Relationship Between Power and Beta

The power of a hypothesis test refers to the probability of detecting an effect or difference when an effect or difference is actually present. In other words, it’s the probability of correctly rejecting a false null hypothesis.

It is calculated as:

Power = 1 – β

In general, researchers want the power of a test to be high so that if some effect or difference does exist, the test is able to detect it.

From the equation above, we can see that the best way to raise the power of a test is to reduce the beta level. And the best way to reduce the beta level is typically to increase the sample size.

The following examples shows how to calculate the beta level of a hypothesis test and demonstrate why increasing the sample size can lower the beta level.

Example 1: Calculate Beta for a Hypothesis Test

Suppose a researcher wants to test if the mean weight of widgets produced at a factory is less than 500 ounces. It is known that the standard deviation of the weights is 24 ounces and the researcher decides to collect a random sample of 40 widgets.

He will perform the following hypothesis at α = 0.05:

  • H 0 : μ = 500
  • H A : μ < 500

Now imagine that the mean weight of widgets being produced is actually 490 ounces. In other words, the null hypothesis should be rejected.

We can use the following steps to calculate the beta level – the probability of failing to reject the null hypothesis when it actually should be rejected:

Step 1: Find the non-rejection region.

According to the Critical Z Value Calculator , the left-tailed critical value at α = 0.05 is -1.645 .

Step 2: Find the minimum sample mean we will fail to reject.

The test statistic is calculated as z = ( x – μ) / (s/√ n )

Thus, we can solve this equation for the sample mean:

  • x = μ – z*(s/√ n )
  • x = 500 – 1.645*(24/√ 40 )
  • x = 493.758

Step 3: Find the probability of the minimum sample mean actually occurring.

We can calculate this probability as:

  • P(Z ≥ (493.758 – 490) / (24/√ 40 ))
  • P(Z ≥ 0.99)

According to the Normal CDF Calculator , the probability that Z ≥ 0.99 is  0.1611 .

beta analysis in research

Thus, the beta level for this test is β = 0.1611. This means there is a 16.11% chance of failing to detect the difference if the real mean is 490 ounces.

Example 2: Calculate Beta for a Test with a Larger Sample Size

Now suppose the researcher performs the exact same hypothesis test but instead uses a sample size of n = 100 widgets. We can repeat the same three steps to calculate the beta level for this test:

  • x = 500 – 1.645*(24/√ 100 )
  • P(Z ≥ (496.05 – 490) / (24/√ 100 ))
  • P(Z ≥ 2.52)

According to the Normal CDF Calculator , the probability that Z ≥ 2.52 is 0.0059.

Thus, the beta level for this test is β = 0.0059. This means there is only a .59% chance of failing to detect the difference if the real mean is 490 ounces.

Notice that by simply increasing the sample size from 40 to 100, the researcher was able to reduce the beta level from 0.1611 all the way down to .0059.

Bonus: Use this Type II Error Calculator to automatically calculate the beta level of a test.

Additional Resources

Introduction to Hypothesis Testing How to Write a Null Hypothesis (5 Examples) An Explanation of P-Values and Statistical Significance

Featured Posts

5 Tips for Interpreting P-Values Correctly in Hypothesis Testing

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

One Reply to “What is a Beta Level in Statistics? (Definition & Example)”

Your site is really great! Many things are explained clearly and to the point! Very helpful!

Nevertheless, as I’m not an expert I have difficulty in understanding why alpha and beta are not equal. Why does decreasing the alpha-level increase the beta-level? Is there an explanation why and how alpha and beta are connected to each other?

Thanks a lot in advance! Kind regards Peter Kleindienst

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How to conduct a meta-analysis in eight steps: a practical guide

  • Open access
  • Published: 30 November 2021
  • Volume 72 , pages 1–19, ( 2022 )

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beta analysis in research

  • Christopher Hansen 1 ,
  • Holger Steinmetz 2 &
  • Jörn Block 3 , 4 , 5  

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1 Introduction

“Scientists have known for centuries that a single study will not resolve a major issue. Indeed, a small sample study will not even resolve a minor issue. Thus, the foundation of science is the cumulation of knowledge from the results of many studies.” (Hunter et al. 1982 , p. 10)

Meta-analysis is a central method for knowledge accumulation in many scientific fields (Aguinis et al. 2011c ; Kepes et al. 2013 ). Similar to a narrative review, it serves as a synopsis of a research question or field. However, going beyond a narrative summary of key findings, a meta-analysis adds value in providing a quantitative assessment of the relationship between two target variables or the effectiveness of an intervention (Gurevitch et al. 2018 ). Also, it can be used to test competing theoretical assumptions against each other or to identify important moderators where the results of different primary studies differ from each other (Aguinis et al. 2011b ; Bergh et al. 2016 ). Rooted in the synthesis of the effectiveness of medical and psychological interventions in the 1970s (Glass 2015 ; Gurevitch et al. 2018 ), meta-analysis is nowadays also an established method in management research and related fields.

The increasing importance of meta-analysis in management research has resulted in the publication of guidelines in recent years that discuss the merits and best practices in various fields, such as general management (Bergh et al. 2016 ; Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ), international business (Steel et al. 2021 ), economics and finance (Geyer-Klingeberg et al. 2020 ; Havranek et al. 2020 ), marketing (Eisend 2017 ; Grewal et al. 2018 ), and organizational studies (DeSimone et al. 2020 ; Rudolph et al. 2020 ). These articles discuss existing and trending methods and propose solutions for often experienced problems. This editorial briefly summarizes the insights of these papers; provides a workflow of the essential steps in conducting a meta-analysis; suggests state-of-the art methodological procedures; and points to other articles for in-depth investigation. Thus, this article has two goals: (1) based on the findings of previous editorials and methodological articles, it defines methodological recommendations for meta-analyses submitted to Management Review Quarterly (MRQ); and (2) it serves as a practical guide for researchers who have little experience with meta-analysis as a method but plan to conduct one in the future.

2 Eight steps in conducting a meta-analysis

2.1 step 1: defining the research question.

The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed. When defining the research question, two hurdles might develop. First, when defining an adequate study scope, researchers must consider that the number of publications has grown exponentially in many fields of research in recent decades (Fortunato et al. 2018 ). On the one hand, a larger number of studies increases the potentially relevant literature basis and enables researchers to conduct meta-analyses. Conversely, scanning a large amount of studies that could be potentially relevant for the meta-analysis results in a perhaps unmanageable workload. Thus, Steel et al. ( 2021 ) highlight the importance of balancing manageability and relevance when defining the research question. Second, similar to the number of primary studies also the number of meta-analyses in management research has grown strongly in recent years (Geyer-Klingeberg et al. 2020 ; Rauch 2020 ; Schwab 2015 ). Therefore, it is likely that one or several meta-analyses for many topics of high scholarly interest already exist. However, this should not deter researchers from investigating their research questions. One possibility is to consider moderators or mediators of a relationship that have previously been ignored. For example, a meta-analysis about startup performance could investigate the impact of different ways to measure the performance construct (e.g., growth vs. profitability vs. survival time) or certain characteristics of the founders as moderators. Another possibility is to replicate previous meta-analyses and test whether their findings can be confirmed with an updated sample of primary studies or newly developed methods. Frequent replications and updates of meta-analyses are important contributions to cumulative science and are increasingly called for by the research community (Anderson & Kichkha 2017 ; Steel et al. 2021 ). Consistent with its focus on replication studies (Block and Kuckertz 2018 ), MRQ therefore also invites authors to submit replication meta-analyses.

2.2 Step 2: literature search

2.2.1 search strategies.

Similar to conducting a literature review, the search process of a meta-analysis should be systematic, reproducible, and transparent, resulting in a sample that includes all relevant studies (Fisch and Block 2018 ; Gusenbauer and Haddaway 2020 ). There are several identification strategies for relevant primary studies when compiling meta-analytical datasets (Harari et al. 2020 ). First, previous meta-analyses on the same or a related topic may provide lists of included studies that offer a good starting point to identify and become familiar with the relevant literature. This practice is also applicable to topic-related literature reviews, which often summarize the central findings of the reviewed articles in systematic tables. Both article types likely include the most prominent studies of a research field. The most common and important search strategy, however, is a keyword search in electronic databases (Harari et al. 2020 ). This strategy will probably yield the largest number of relevant studies, particularly so-called ‘grey literature’, which may not be considered by literature reviews. Gusenbauer and Haddaway ( 2020 ) provide a detailed overview of 34 scientific databases, of which 18 are multidisciplinary or have a focus on management sciences, along with their suitability for literature synthesis. To prevent biased results due to the scope or journal coverage of one database, researchers should use at least two different databases (DeSimone et al. 2020 ; Martín-Martín et al. 2021 ; Mongeon & Paul-Hus 2016 ). However, a database search can easily lead to an overload of potentially relevant studies. For example, key term searches in Google Scholar for “entrepreneurial intention” and “firm diversification” resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ). Addressing the challenge of identifying relevant articles in the growing number of database publications, (semi)automated approaches using text mining and machine learning (Bosco et al. 2017 ; O’Mara-Eves et al. 2015 ; Ouzzani et al. 2016 ; Thomas et al. 2017 ) can also be promising and time-saving search tools in the future. Also, some electronic databases offer the possibility to track forward citations of influential studies and thereby identify further relevant articles. Finally, collecting unpublished or undetected studies through conferences, personal contact with (leading) scholars, or listservs can be strategies to increase the study sample size (Grewal et al. 2018 ; Harari et al. 2020 ; Pigott and Polanin 2020 ).

2.2.2 Study inclusion criteria and sample composition

Next, researchers must decide which studies to include in the meta-analysis. Some guidelines for literature reviews recommend limiting the sample to studies published in renowned academic journals to ensure the quality of findings (e.g., Kraus et al. 2020 ). For meta-analysis, however, Steel et al. ( 2021 ) advocate for the inclusion of all available studies, including grey literature, to prevent selection biases based on availability, cost, familiarity, and language (Rothstein et al. 2005 ), or the “Matthew effect”, which denotes the phenomenon that highly cited articles are found faster than less cited articles (Merton 1968 ). Harrison et al. ( 2017 ) find that the effects of published studies in management are inflated on average by 30% compared to unpublished studies. This so-called publication bias or “file drawer problem” (Rosenthal 1979 ) results from the preference of academia to publish more statistically significant and less statistically insignificant study results. Owen and Li ( 2020 ) showed that publication bias is particularly severe when variables of interest are used as key variables rather than control variables. To consider the true effect size of a target variable or relationship, the inclusion of all types of research outputs is therefore recommended (Polanin et al. 2016 ). Different test procedures to identify publication bias are discussed subsequently in Step 7.

In addition to the decision of whether to include certain study types (i.e., published vs. unpublished studies), there can be other reasons to exclude studies that are identified in the search process. These reasons can be manifold and are primarily related to the specific research question and methodological peculiarities. For example, studies identified by keyword search might not qualify thematically after all, may use unsuitable variable measurements, or may not report usable effect sizes. Furthermore, there might be multiple studies by the same authors using similar datasets. If they do not differ sufficiently in terms of their sample characteristics or variables used, only one of these studies should be included to prevent bias from duplicates (Wood 2008 ; see this article for a detection heuristic).

In general, the screening process should be conducted stepwise, beginning with a removal of duplicate citations from different databases, followed by abstract screening to exclude clearly unsuitable studies and a final full-text screening of the remaining articles (Pigott and Polanin 2020 ). A graphical tool to systematically document the sample selection process is the PRISMA flow diagram (Moher et al. 2009 ). Page et al. ( 2021 ) recently presented an updated version of the PRISMA statement, including an extended item checklist and flow diagram to report the study process and findings.

2.3 Step 3: choice of the effect size measure

2.3.1 types of effect sizes.

The two most common meta-analytical effect size measures in management studies are (z-transformed) correlation coefficients and standardized mean differences (Aguinis et al. 2011a ; Geyskens et al. 2009 ). However, meta-analyses in management science and related fields may not be limited to those two effect size measures but rather depend on the subfield of investigation (Borenstein 2009 ; Stanley and Doucouliagos 2012 ). In economics and finance, researchers are more interested in the examination of elasticities and marginal effects extracted from regression models than in pure bivariate correlations (Stanley and Doucouliagos 2012 ). Regression coefficients can also be converted to partial correlation coefficients based on their t-statistics to make regression results comparable across studies (Stanley and Doucouliagos 2012 ). Although some meta-analyses in management research have combined bivariate and partial correlations in their study samples, Aloe ( 2015 ) and Combs et al. ( 2019 ) advise researchers not to use this practice. Most importantly, they argue that the effect size strength of partial correlations depends on the other variables included in the regression model and is therefore incomparable to bivariate correlations (Schmidt and Hunter 2015 ), resulting in a possible bias of the meta-analytic results (Roth et al. 2018 ). We endorse this opinion. If at all, we recommend separate analyses for each measure. In addition to these measures, survival rates, risk ratios or odds ratios, which are common measures in medical research (Borenstein 2009 ), can be suitable effect sizes for specific management research questions, such as understanding the determinants of the survival of startup companies. To summarize, the choice of a suitable effect size is often taken away from the researcher because it is typically dependent on the investigated research question as well as the conventions of the specific research field (Cheung and Vijayakumar 2016 ).

2.3.2 Conversion of effect sizes to a common measure

After having defined the primary effect size measure for the meta-analysis, it might become necessary in the later coding process to convert study findings that are reported in effect sizes that are different from the chosen primary effect size. For example, a study might report only descriptive statistics for two study groups but no correlation coefficient, which is used as the primary effect size measure in the meta-analysis. Different effect size measures can be harmonized using conversion formulae, which are provided by standard method books such as Borenstein et al. ( 2009 ) or Lipsey and Wilson ( 2001 ). There also exist online effect size calculators for meta-analysis. Footnote 2

2.4 Step 4: choice of the analytical method used

Choosing which meta-analytical method to use is directly connected to the research question of the meta-analysis. Research questions in meta-analyses can address a relationship between constructs or an effect of an intervention in a general manner, or they can focus on moderating or mediating effects. There are four meta-analytical methods that are primarily used in contemporary management research (Combs et al. 2019 ; Geyer-Klingeberg et al. 2020 ), which allow the investigation of these different types of research questions: traditional univariate meta-analysis, meta-regression, meta-analytic structural equation modeling, and qualitative meta-analysis (Hoon 2013 ). While the first three are quantitative, the latter summarizes qualitative findings. Table 1 summarizes the key characteristics of the three quantitative methods.

2.4.1 Univariate meta-analysis

In its traditional form, a meta-analysis reports a weighted mean effect size for the relationship or intervention of investigation and provides information on the magnitude of variance among primary studies (Aguinis et al. 2011c ; Borenstein et al. 2009 ). Accordingly, it serves as a quantitative synthesis of a research field (Borenstein et al. 2009 ; Geyskens et al. 2009 ). Prominent traditional approaches have been developed, for example, by Hedges and Olkin ( 1985 ) or Hunter and Schmidt ( 1990 , 2004 ). However, going beyond its simple summary function, the traditional approach has limitations in explaining the observed variance among findings (Gonzalez-Mulé and Aguinis 2018 ). To identify moderators (or boundary conditions) of the relationship of interest, meta-analysts can create subgroups and investigate differences between those groups (Borenstein and Higgins 2013 ; Hunter and Schmidt 2004 ). Potential moderators can be study characteristics (e.g., whether a study is published vs. unpublished), sample characteristics (e.g., study country, industry focus, or type of survey/experiment participants), or measurement artifacts (e.g., different types of variable measurements). The univariate approach is thus suitable to identify the overall direction of a relationship and can serve as a good starting point for additional analyses. However, due to its limitations in examining boundary conditions and developing theory, the univariate approach on its own is currently oftentimes viewed as not sufficient (Rauch 2020 ; Shaw and Ertug 2017 ).

2.4.2 Meta-regression analysis

Meta-regression analysis (Hedges and Olkin 1985 ; Lipsey and Wilson 2001 ; Stanley and Jarrell 1989 ) aims to investigate the heterogeneity among observed effect sizes by testing multiple potential moderators simultaneously. In meta-regression, the coded effect size is used as the dependent variable and is regressed on a list of moderator variables. These moderator variables can be categorical variables as described previously in the traditional univariate approach or (semi)continuous variables such as country scores that are merged with the meta-analytical data. Thus, meta-regression analysis overcomes the disadvantages of the traditional approach, which only allows us to investigate moderators singularly using dichotomized subgroups (Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ). These possibilities allow a more fine-grained analysis of research questions that are related to moderating effects. However, Schmidt ( 2017 ) critically notes that the number of effect sizes in the meta-analytical sample must be sufficiently large to produce reliable results when investigating multiple moderators simultaneously in a meta-regression. For further reading, Tipton et al. ( 2019 ) outline the technical, conceptual, and practical developments of meta-regression over the last decades. Gonzalez-Mulé and Aguinis ( 2018 ) provide an overview of methodological choices and develop evidence-based best practices for future meta-analyses in management using meta-regression.

2.4.3 Meta-analytic structural equation modeling (MASEM)

MASEM is a combination of meta-analysis and structural equation modeling and allows to simultaneously investigate the relationships among several constructs in a path model. Researchers can use MASEM to test several competing theoretical models against each other or to identify mediation mechanisms in a chain of relationships (Bergh et al. 2016 ). This method is typically performed in two steps (Cheung and Chan 2005 ): In Step 1, a pooled correlation matrix is derived, which includes the meta-analytical mean effect sizes for all variable combinations; Step 2 then uses this matrix to fit the path model. While MASEM was based primarily on traditional univariate meta-analysis to derive the pooled correlation matrix in its early years (Viswesvaran and Ones 1995 ), more advanced methods, such as the GLS approach (Becker 1992 , 1995 ) or the TSSEM approach (Cheung and Chan 2005 ), have been subsequently developed. Cheung ( 2015a ) and Jak ( 2015 ) provide an overview of these approaches in their books with exemplary code. For datasets with more complex data structures, Wilson et al. ( 2016 ) also developed a multilevel approach that is related to the TSSEM approach in the second step. Bergh et al. ( 2016 ) discuss nine decision points and develop best practices for MASEM studies.

2.4.4 Qualitative meta-analysis

While the approaches explained above focus on quantitative outcomes of empirical studies, qualitative meta-analysis aims to synthesize qualitative findings from case studies (Hoon 2013 ; Rauch et al. 2014 ). The distinctive feature of qualitative case studies is their potential to provide in-depth information about specific contextual factors or to shed light on reasons for certain phenomena that cannot usually be investigated by quantitative studies (Rauch 2020 ; Rauch et al. 2014 ). In a qualitative meta-analysis, the identified case studies are systematically coded in a meta-synthesis protocol, which is then used to identify influential variables or patterns and to derive a meta-causal network (Hoon 2013 ). Thus, the insights of contextualized and typically nongeneralizable single studies are aggregated to a larger, more generalizable picture (Habersang et al. 2019 ). Although still the exception, this method can thus provide important contributions for academics in terms of theory development (Combs et al., 2019 ; Hoon 2013 ) and for practitioners in terms of evidence-based management or entrepreneurship (Rauch et al. 2014 ). Levitt ( 2018 ) provides a guide and discusses conceptual issues for conducting qualitative meta-analysis in psychology, which is also useful for management researchers.

2.5 Step 5: choice of software

Software solutions to perform meta-analyses range from built-in functions or additional packages of statistical software to software purely focused on meta-analyses and from commercial to open-source solutions. However, in addition to personal preferences, the choice of the most suitable software depends on the complexity of the methods used and the dataset itself (Cheung and Vijayakumar 2016 ). Meta-analysts therefore must carefully check if their preferred software is capable of performing the intended analysis.

Among commercial software providers, Stata (from version 16 on) offers built-in functions to perform various meta-analytical analyses or to produce various plots (Palmer and Sterne 2016 ). For SPSS and SAS, there exist several macros for meta-analyses provided by scholars, such as David B. Wilson or Andy P. Field and Raphael Gillet (Field and Gillett 2010 ). Footnote 3 Footnote 4 For researchers using the open-source software R (R Core Team 2021 ), Polanin et al. ( 2017 ) provide an overview of 63 meta-analysis packages and their functionalities. For new users, they recommend the package metafor (Viechtbauer 2010 ), which includes most necessary functions and for which the author Wolfgang Viechtbauer provides tutorials on his project website. Footnote 5 Footnote 6 In addition to packages and macros for statistical software, templates for Microsoft Excel have also been developed to conduct simple meta-analyses, such as Meta-Essentials by Suurmond et al. ( 2017 ). Footnote 7 Finally, programs purely dedicated to meta-analysis also exist, such as Comprehensive Meta-Analysis (Borenstein et al. 2013 ) or RevMan by The Cochrane Collaboration ( 2020 ).

2.6 Step 6: coding of effect sizes

2.6.1 coding sheet.

The first step in the coding process is the design of the coding sheet. A universal template does not exist because the design of the coding sheet depends on the methods used, the respective software, and the complexity of the research design. For univariate meta-analysis or meta-regression, data are typically coded in wide format. In its simplest form, when investigating a correlational relationship between two variables using the univariate approach, the coding sheet would contain a column for the study name or identifier, the effect size coded from the primary study, and the study sample size. However, such simple relationships are unlikely in management research because the included studies are typically not identical but differ in several respects. With more complex data structures or moderator variables being investigated, additional columns are added to the coding sheet to reflect the data characteristics. These variables can be coded as dummy, factor, or (semi)continuous variables and later used to perform a subgroup analysis or meta regression. For MASEM, the required data input format can deviate depending on the method used (e.g., TSSEM requires a list of correlation matrices as data input). For qualitative meta-analysis, the coding scheme typically summarizes the key qualitative findings and important contextual and conceptual information (see Hoon ( 2013 ) for a coding scheme for qualitative meta-analysis). Figure  1 shows an exemplary coding scheme for a quantitative meta-analysis on the correlational relationship between top-management team diversity and profitability. In addition to effect and sample sizes, information about the study country, firm type, and variable operationalizations are coded. The list could be extended by further study and sample characteristics.

figure 1

Exemplary coding sheet for a meta-analysis on the relationship (correlation) between top-management team diversity and profitability

2.6.2 Inclusion of moderator or control variables

It is generally important to consider the intended research model and relevant nontarget variables before coding a meta-analytic dataset. For example, study characteristics can be important moderators or function as control variables in a meta-regression model. Similarly, control variables may be relevant in a MASEM approach to reduce confounding bias. Coding additional variables or constructs subsequently can be arduous if the sample of primary studies is large. However, the decision to include respective moderator or control variables, as in any empirical analysis, should always be based on strong (theoretical) rationales about how these variables can impact the investigated effect (Bernerth and Aguinis 2016 ; Bernerth et al. 2018 ; Thompson and Higgins 2002 ). While substantive moderators refer to theoretical constructs that act as buffers or enhancers of a supposed causal process, methodological moderators are features of the respective research designs that denote the methodological context of the observations and are important to control for systematic statistical particularities (Rudolph et al. 2020 ). Havranek et al. ( 2020 ) provide a list of recommended variables to code as potential moderators. While researchers may have clear expectations about the effects for some of these moderators, the concerns for other moderators may be tentative, and moderator analysis may be approached in a rather exploratory fashion. Thus, we argue that researchers should make full use of the meta-analytical design to obtain insights about potential context dependence that a primary study cannot achieve.

2.6.3 Treatment of multiple effect sizes in a study

A long-debated issue in conducting meta-analyses is whether to use only one or all available effect sizes for the same construct within a single primary study. For meta-analyses in management research, this question is fundamental because many empirical studies, particularly those relying on company databases, use multiple variables for the same construct to perform sensitivity analyses, resulting in multiple relevant effect sizes. In this case, researchers can either (randomly) select a single value, calculate a study average, or use the complete set of effect sizes (Bijmolt and Pieters 2001 ; López-López et al. 2018 ). Multiple effect sizes from the same study enrich the meta-analytic dataset and allow us to investigate the heterogeneity of the relationship of interest, such as different variable operationalizations (López-López et al. 2018 ; Moeyaert et al. 2017 ). However, including more than one effect size from the same study violates the independency assumption of observations (Cheung 2019 ; López-López et al. 2018 ), which can lead to biased results and erroneous conclusions (Gooty et al. 2021 ). We follow the recommendation of current best practice guides to take advantage of using all available effect size observations but to carefully consider interdependencies using appropriate methods such as multilevel models, panel regression models, or robust variance estimation (Cheung 2019 ; Geyer-Klingeberg et al. 2020 ; Gooty et al. 2021 ; López-López et al. 2018 ; Moeyaert et al. 2017 ).

2.7 Step 7: analysis

2.7.1 outlier analysis and tests for publication bias.

Before conducting the primary analysis, some preliminary sensitivity analyses might be necessary, which should ensure the robustness of the meta-analytical findings (Rudolph et al. 2020 ). First, influential outlier observations could potentially bias the observed results, particularly if the number of total effect sizes is small. Several statistical methods can be used to identify outliers in meta-analytical datasets (Aguinis et al. 2013 ; Viechtbauer and Cheung 2010 ). However, there is a debate about whether to keep or omit these observations. Anyhow, relevant studies should be closely inspected to infer an explanation about their deviating results. As in any other primary study, outliers can be a valid representation, albeit representing a different population, measure, construct, design or procedure. Thus, inferences about outliers can provide the basis to infer potential moderators (Aguinis et al. 2013 ; Steel et al. 2021 ). On the other hand, outliers can indicate invalid research, for instance, when unrealistically strong correlations are due to construct overlap (i.e., lack of a clear demarcation between independent and dependent variables), invalid measures, or simply typing errors when coding effect sizes. An advisable step is therefore to compare the results both with and without outliers and base the decision on whether to exclude outlier observations with careful consideration (Geyskens et al. 2009 ; Grewal et al. 2018 ; Kepes et al. 2013 ). However, instead of simply focusing on the size of the outlier, its leverage should be considered. Thus, Viechtbauer and Cheung ( 2010 ) propose considering a combination of standardized deviation and a study’s leverage.

Second, as mentioned in the context of a literature search, potential publication bias may be an issue. Publication bias can be examined in multiple ways (Rothstein et al. 2005 ). First, the funnel plot is a simple graphical tool that can provide an overview of the effect size distribution and help to detect publication bias (Stanley and Doucouliagos 2010 ). A funnel plot can also support in identifying potential outliers. As mentioned above, a graphical display of deviation (e.g., studentized residuals) and leverage (Cook’s distance) can help detect the presence of outliers and evaluate their influence (Viechtbauer and Cheung 2010 ). Moreover, several statistical procedures can be used to test for publication bias (Harrison et al. 2017 ; Kepes et al. 2012 ), including subgroup comparisons between published and unpublished studies, Begg and Mazumdar’s ( 1994 ) rank correlation test, cumulative meta-analysis (Borenstein et al. 2009 ), the trim and fill method (Duval and Tweedie 2000a , b ), Egger et al.’s ( 1997 ) regression test, failsafe N (Rosenthal 1979 ), or selection models (Hedges and Vevea 2005 ; Vevea and Woods 2005 ). In examining potential publication bias, Kepes et al. ( 2012 ) and Harrison et al. ( 2017 ) both recommend not relying only on a single test but rather using multiple conceptionally different test procedures (i.e., the so-called “triangulation approach”).

2.7.2 Model choice

After controlling and correcting for the potential presence of impactful outliers or publication bias, the next step in meta-analysis is the primary analysis, where meta-analysts must decide between two different types of models that are based on different assumptions: fixed-effects and random-effects (Borenstein et al. 2010 ). Fixed-effects models assume that all observations share a common mean effect size, which means that differences are only due to sampling error, while random-effects models assume heterogeneity and allow for a variation of the true effect sizes across studies (Borenstein et al. 2010 ; Cheung and Vijayakumar 2016 ; Hunter and Schmidt 2004 ). Both models are explained in detail in standard textbooks (e.g., Borenstein et al. 2009 ; Hunter and Schmidt 2004 ; Lipsey and Wilson 2001 ).

In general, the presence of heterogeneity is likely in management meta-analyses because most studies do not have identical empirical settings, which can yield different effect size strengths or directions for the same investigated phenomenon. For example, the identified studies have been conducted in different countries with different institutional settings, or the type of study participants varies (e.g., students vs. employees, blue-collar vs. white-collar workers, or manufacturing vs. service firms). Thus, the vast majority of meta-analyses in management research and related fields use random-effects models (Aguinis et al. 2011a ). In a meta-regression, the random-effects model turns into a so-called mixed-effects model because moderator variables are added as fixed effects to explain the impact of observed study characteristics on effect size variations (Raudenbush 2009 ).

2.8 Step 8: reporting results

2.8.1 reporting in the article.

The final step in performing a meta-analysis is reporting its results. Most importantly, all steps and methodological decisions should be comprehensible to the reader. DeSimone et al. ( 2020 ) provide an extensive checklist for journal reviewers of meta-analytical studies. This checklist can also be used by authors when performing their analyses and reporting their results to ensure that all important aspects have been addressed. Alternative checklists are provided, for example, by Appelbaum et al. ( 2018 ) or Page et al. ( 2021 ). Similarly, Levitt et al. ( 2018 ) provide a detailed guide for qualitative meta-analysis reporting standards.

For quantitative meta-analyses, tables reporting results should include all important information and test statistics, including mean effect sizes; standard errors and confidence intervals; the number of observations and study samples included; and heterogeneity measures. If the meta-analytic sample is rather small, a forest plot provides a good overview of the different findings and their accuracy. However, this figure will be less feasible for meta-analyses with several hundred effect sizes included. Also, results displayed in the tables and figures must be explained verbally in the results and discussion sections. Most importantly, authors must answer the primary research question, i.e., whether there is a positive, negative, or no relationship between the variables of interest, or whether the examined intervention has a certain effect. These results should be interpreted with regard to their magnitude (or significance), both economically and statistically. However, when discussing meta-analytical results, authors must describe the complexity of the results, including the identified heterogeneity and important moderators, future research directions, and theoretical relevance (DeSimone et al. 2019 ). In particular, the discussion of identified heterogeneity and underlying moderator effects is critical; not including this information can lead to false conclusions among readers, who interpret the reported mean effect size as universal for all included primary studies and ignore the variability of findings when citing the meta-analytic results in their research (Aytug et al. 2012 ; DeSimone et al. 2019 ).

2.8.2 Open-science practices

Another increasingly important topic is the public provision of meta-analytical datasets and statistical codes via open-source repositories. Open-science practices allow for results validation and for the use of coded data in subsequent meta-analyses ( Polanin et al. 2020 ), contributing to the development of cumulative science. Steel et al. ( 2021 ) refer to open science meta-analyses as a step towards “living systematic reviews” (Elliott et al. 2017 ) with continuous updates in real time. MRQ supports this development and encourages authors to make their datasets publicly available. Moreau and Gamble ( 2020 ), for example, provide various templates and video tutorials to conduct open science meta-analyses. There exist several open science repositories, such as the Open Science Foundation (OSF; for a tutorial, see Soderberg 2018 ), to preregister and make documents publicly available. Furthermore, several initiatives in the social sciences have been established to develop dynamic meta-analyses, such as metaBUS (Bosco et al. 2015 , 2017 ), MetaLab (Bergmann et al. 2018 ), or PsychOpen CAMA (Burgard et al. 2021 ).

3 Conclusion

This editorial provides a comprehensive overview of the essential steps in conducting and reporting a meta-analysis with references to more in-depth methodological articles. It also serves as a guide for meta-analyses submitted to MRQ and other management journals. MRQ welcomes all types of meta-analyses from all subfields and disciplines of management research.

Gusenbauer and Haddaway ( 2020 ), however, point out that Google Scholar is not appropriate as a primary search engine due to a lack of reproducibility of search results.

One effect size calculator by David B. Wilson is accessible via: .

The macros of David B. Wilson can be downloaded from: .

The macros of Field and Gillet ( 2010 ) can be downloaded from: .

The tutorials can be found via: .

Metafor does currently not provide functions to conduct MASEM. For MASEM, users can, for instance, use the package metaSEM (Cheung 2015b ).

The workbooks can be downloaded from: .

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Meta-analysis and the science of research synthesis

  • Jessica Gurevitch 1 ,
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Meta-analysis is the quantitative, scientific synthesis of research results. Since the term and modern approaches to research synthesis were first introduced in the 1970s, meta-analysis has had a revolutionary effect in many scientific fields, helping to establish evidence-based practice and to resolve seemingly contradictory research outcomes. At the same time, its implementation has engendered criticism and controversy, in some cases general and others specific to particular disciplines. Here we take the opportunity provided by the recent fortieth anniversary of meta-analysis to reflect on the accomplishments, limitations, recent advances and directions for future developments in the field of research synthesis.

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We dedicate this Review to the memory of Ingram Olkin and William Shadish, founding members of the Society for Research Synthesis Methodology who made tremendous contributions to the development of meta-analysis and research synthesis and to the supervision of generations of students. We thank L. Lagisz for help in preparing the figures. We are grateful to the Center for Open Science and the Laura and John Arnold Foundation for hosting and funding a workshop, which was the origination of this article. S.N. is supported by Australian Research Council Future Fellowship (FT130100268). J.G. acknowledges funding from the US National Science Foundation (ABI 1262402).

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Boosted Beta Regression

Matthias schmid.

1 Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany

Florian Wickler

2 Department of Statistics, University of Munich, Munich, Germany

Kelly O. Maloney

3 USGS - Leetown Science Center, Wellsboro, Pennsylvania, United States of America

Richard Mitchell

4 USEPA Office of Wetlands, Oceans, and Watersheds, Washington, DC, United States of America

Nora Fenske

Andreas mayr.

Conceived and designed the experiments: MS FW KOM RM NF AM. Analyzed the data: MS FW KOM RM NF AM. Wrote the paper: MS FW KOM RM NF AM.

Associated Data

Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.


The analysis of percentage data is a common issue in quantitative research. Percentage data arise in many scientific fields, for example in ecology [1] – [4] , in econometrics [5] , [6] , and in medical research [7] , [8] . A recent survey conducted by Warton & Hui [2] even found that nearly one third of papers published in Ecology in 2008/09 dealt with the analysis of percentage data.

From a statistical perspective, the analysis of percentage data is a challenging problem. This problem primarily concerns the development of regression models for percentage outcomes, which may be biased and inefficient if the specific nature of percentage outcomes is not taken into account. Although it would be convenient to use percentage responses as outcome variables in ordinary least squares (OLS) regression, this approach is problematic because OLS regression does not account for the fact that percentages are bounded by the interval c [9] . Hence, in order to avoid biased estimators and hypothesis tests, regression techniques that are tailored to the analysis of percentage outcomes are needed.

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In the statistical literature, beta regression has been established as a powerful technique to model percentages and proportions [10] . Also, the method has been used in a variety of research fields [3] , [8] , [12] . There are applications, however, where classical beta regression methodology still has a number of limitations:

1. Scientific databases often involve large numbers of potential predictor variables that could be included in a regression model. Consequently, if maximum likelihood estimation is used to fit a beta regression model, the model may become too complex and may thus overfit the data. This usually leads to a large variance and to a high uncertainty about the predictor-response relationships. As a consequence, techniques for variable selection in beta regression models are needed.

2. Statistical models often suffer from multicollinearity problems, meaning that predictor variables are highly correlated. Also, observations of the response variable may be affected by spatial correlation , which is, for example, a common problem in ecology [13] , [14] . To date, these issues have not been incorporated into beta regression methodology.

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To illustrate our method, we use data collected during the 2007 U.S.A. National Lakes Assessment (NLA) Survey [25] . The 2007 U.S.A. NLA is an example of ecological research that often involves the analysis of percentages: the assessment of aquatic biological health. In these studies, percentages of the biological community, often those deemed intolerant or tolerant to stressors, are used as indicators of stream or lake biological condition [26] and are often related to predictor variables such as water chemistry (temperature, dissolved oxygen, pH) and geographical information (site elevation, size of basin area, ecoregion). As response variable for our comparative analysis of modeling approaches we focus on the percentage of benthic macroinvertebrate taxa collected that are in the order Ephemeroptera (mayflies, here denoted as EPHEptax). Ephemeroptera are taxa sensitive to anthropogenic disturbance and are therefore often used to evaluate stream health [27] , [28] . As will be demonstrated in the results section of the paper, analyzing EPHEptax suggests that beta regression outperforms other approaches in terms of both model fit and prediction accuracy. Hence, by applying boosted beta regression to the 2007 NLA data, this paper builds directly on the modeling approaches of Warton & Hui [2] , who argued that the arcsine square root transformation should no longer be applied to analyze percentage outcomes in ecology.

The rest of the paper is organized as follows: In the next section, boosted beta regression is presented in detail, along with a description of the classical beta regression and gamboostLSS approaches. Additionally, we briefly review the arcsine square root and logit transformation approaches and discuss their limitations when used for modeling percentage outcomes. The characteristics of boosted beta regression are demonstrated in the results section of the paper, where the new method is benchmarked against a number of alternative regression models. Using the NLA data, we further show how to apply the new method to derive an easy-to-interpret regression model for the EPHEptax response. A summary and discussion of the main findings of the paper is given in the final section of the paper. Technical details on boosted beta regression are presented in the Supporting Information of the paper.

Transformation Models for Percentage Outcomes

In this subsection, we briefly review transformation models for percentage outcomes. This model class comprises both classical OLS regression and OLS regression with arcsine-square-root-transformed response. Transformation models are based on the model equation

equation image

Beta Regression for Percentage Outcomes

To overcome the problems and limitations discussed in the previous subsection, Ferrari & Cribari-Neto [6] introduced beta regression for proportions and percentage outcomes. In this subsection, we outline the main characteristics of the classical beta regression method.

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Following its introduction by Ferrari & Cribari-Neto [6] , beta regression has been used to model percentage outcomes in various fields of research. However, the classical version of the method still has several shortcomings. For example, scientific databases often contain a large number of possible predictor variables (relative to the sample size). It is well known that classical maximum likelihood estimators suffer from large variances in this case. This problem leads to overfitting and therefore to a decreased prediction accuracy of the classical beta regression model. To avoid overfitting (and also to improve the interpretability of the model), it is desirable to carry out variable selection, i.e., to include only the most “important” predictors in the model. Although there exist many “classical” techniques for variable selection (e.g., stepwise variable selection based on information criteria or hypothesis tests), these methods are known to be unreliable and require the model to be fitted multiple times [21] .

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The most important feature of gamboostLSS is its ability to carry out variable selection during the fitting process. This is accomplished by (a) assessing the individual fits of each predictor variable, and by (b) updating only the coefficient of the best-fitting predictor variable in each iteration. Also, when using gamboostLSS to fit a beta regression model, variable selection is carried out successively for both the mean model (3) and the precision model (4). After a finite number of iterations, the algorithm is stopped, so that the final model only contains the subset of best-fit predictor variables. A schematic overview of boosted beta regression is as follows:

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2. In each iteration….

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An important question is how to choose the stopping iteration of gamboostLSS. Usually, the stopping iteration of a boosting algorithm is chosen such that prediction accuracy of the model becomes highest [23] . For gamboostLSS, this is accomplished by using cross-validation techniques [18] . Note that it is possible to increase flexiblity of the algorithm by using two different stopping iterations for the mean and the precision models (see [32] for details). Because the benefits of a two-dimensional stopping strategy are usually small [18] , we will not consider this method in our numerical studies.

Nonlinear Predictor-Response Relationships

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In the following we will use boosted beta regression to model biological condition in lakes in the conterminous U.S. The outcome considered in our study is the percentage of benthic macroinvertebrate taxa in the order Ephemeroptera (EPHEptax). In addition to analyzing boosted beta regression, we compare the new method to conventional approaches such as OLS regression with transformed response. The first subsection starts with a description of the study design and the NLA database. Statistical analysis results are presented in the second subsection.

The NLA Database

Statistical analysis is based on data from the 2007 U.S. National Lakes Assessment program (NLA), during which 1,157 lakes were sampled in the summer from across the conterminous U.S. (see Figure 2 ).

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Distribution of lakes that were sampled for the 2007 U.S. National Lakes Assessment.

Littoral zone sampling consisted of ten randomly selected quadrates around each lake littoral zone that were combined into a single sample. In each sample, benthic macroinvertebrates were collected and physical habitat assessed. Habitat condition was measured by visual estimates of riparian vegetation condition, shoreline substrate (at the water’s edge), fish cover, aquatic macrophytes, and littoral bottom substrate in the samples. In addition, human disturbance or presence was estimated in each sample by identifying human activities (e.g., docks, roads, buildings, etc.) in the water or in adjacent riparian areas. Sampling at the deepest point of the lake (index site) included all other biological and chemical measures. Water column profiles of temperature, dissolved oxygen, conductivity, and pH were taken using a multi-probe sonde.

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Statistical analysis was based on a sample of 994 lakes that contained no missing values in any of the predictor variables. Altogether, 78 predictor variables were used for statistical analysis. Predictors with a highly right-skewed distribution were log transformed before fitting models for EPHEptax. The full list of predictor variables is given in the Supporting Information.

Statistical Analysis and Results

In a first step, we used graphical checks to analyze the response transformations discussed in the methods section of the paper. Figure 3 presents normal quantile-quantile plots for the arcsine-transformed, the logit-transformed and for the untransformed EPHEptax values (panels (a) to (c)). Neither transformation worked well, as the transformed EPHEptax values clearly do not follow a normal distribution. In addition, the inclusion of lakes with zero percentages seemed to be problematic because their values in the quantile-quantile plots did not match well with EPHEptax values that were larger than zero (see the horizontal accumulation of points in panels (a) to (c)). In contrast, EPHEptax was well approximated by a beta distributed random variable ( Figure 3(d) ). This result suggested that boosted beta regression is an adequate method for modeling EPHEptax.

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Normal quantile-quantile plots of arcsine-square-root-transformed (“arcsine”), logit-transformed (“logit”) and untransformed (“raw lm”) EPHEptax values (panels (a) - (c)). Panel (d) shows a beta quantile-quantile plot using the untransformed EPHEptax values. It is seen that EPHEptax is best approximated by a beta distributed random variable.

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Analysis of Model Performance

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Summarizing the results presented in Figure 4 , boosted beta regression outperformed the other modeling approaches for EPHEptax in terms of both goodness-of-fit and prediction accuracy.

Selection Rates of Modeling Approaches

Each modeling approach incorporated approximately 15 linear predictor effects and approximately 10 nonlinear predictor effects on average ( Figure 5 ). Moreover, the percentage of non-linear predictor effects was highest on average in the boosted beta regression model. This result further demonstrated the flexibility of the proposed algorithm.

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Analysis of the NLA Data. The two panels contain the number of selected predictor variables (averaged over 100 bootstrap samples) for various modeling approaches. Dark grey bars represent linear effects, light grey bars represent non-linear effects. In case of beta regression with fixed precision parameter (“beta fix”), the precision model contains only one predictor (namely, the intercept).

Analysis of Predictor-Response Relationships

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Figure 8 suggests that the effect of the chlorophyll- a concentration on EPHEptax is distinctly nonlinear, with large values leading to below-average values of EPHEptax. Chlorophyll- a is often used to indicate impairment of aquatic systems with high levels indicating eutrophication (e.g., [25] ). As such, species richness and diversity of littoral benthic macroinvertebrates declines with chloropyll- a [48] . Because Ephemeroptera are sensitive taxa, they may be disproportionately affected by higher chlorophyll- a levels. Additionally, high levels of Chlorophyll- a reduces mayfly secondary production [49] , which would possibly further reduce their presence.

The total nitrogen concentration had a pronounced negative effect on EPHEptax ( Figure 9 ). Total nitrogen is an important environmental factor related to littoral benthic macroinvertebrate community structure [50] and negatively relates to macroinvertebrate diversity [48] . Moreover, loss of Ephemeroptera has been reported in lakes where total nitrogen surpasses a threshold [51] .

Average depth at a sampling station had a negative linear effect on EPHEptax ( Figure 10 ). Littoral benthic macroinvertebrates often show a marked effect of depth (e.g., [52] , [53] ). The negative pattern in our study suggests Ephemeroptera prefer shallower habitats in lakes. Note that the variability of the estimates was large for all analyzed models, implying that the uncertainty about the association of the average depth at a sampling station with EPHEptax was large as well.

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Linear regression with normally distributed errors is arguably the most prominent analysis tool in applied statistics. The popularity of linear regression is based on the fact that random variations in observed data can often be approximated by a normal distribution with constant variance. If the response variable in a regression model is a rate or percentage, however, the normal approximation is no longer appropriate. For this reason, and because the analysis of percentages is an important issue in many fields of research, developing statistically valid analysis tools for percentage data is of high practical interest.

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In this paper, we have proposed boosted beta regression , which is a flexible alternative to logistic regression and response transformation models. Because beta regression is a generalization of logit regression to situations where the dependent variable is a proportion [29] , our modeling approach is appropriate in both the binomial and the non-binomial case. Moreover, if compared to classical estimation techniques for beta regression [17] , boosted beta regression has the advantage that nonlinear effects can be estimated without pre-specifying the functional forms of the predictor-response relationships. This implies that not only the mean but also the variance of a beta distributed response variable can be modeled in a highly flexible way. Specifically, our numerical results suggest that regressing the precision parameter of the model on the covariates leads to a notably better model fit than when the precision parameter is kept constant. In addition to incorporating nonlinear predictor-response relationships, boosted beta regression accounts for spatial correlation in both the mean and the variance structure of the model. Clearly, this issue is important if observation units have a neighborhood structure and may therefore influence each other.

A key aspect of boosted beta regression is its ability to carry out variable selection during the fitting process. This implies that only a small subset of the available predictor variables is included in the final model for the percentage response. Variable selection is of high practical interest in applications where large amounts of potentially important predictor variables are available. Consequently, one is often interested in determining the most informative predictor variables and in discarding those predictors that have a negligible effect on the response. In case of the NLA data, for example, boosted beta regression selected only 15 informative predictor variables out of the total set of 78 available predictors.

It is important to note that the variable selection mechanism in boosted beta regression is fundamentally different from earlier approaches to selecting predictor variables in statistical regression models. For example, because beta regression is a member of GAMLSS model class, one could alternatively fit the model using maximum likelihood techniques and apply AIC-based methods for selecting informative predictor variables (as implemented in the R add-on package gamlss , [31] ). This strategy, however, requires the model to be fitted multiple times. In contrast, our new method is based on boosting methodology and is therefore able to incorporate variable selection already into the model fitting process. Note that the sets of predictor variables selected for the mean and precision submodels do not have to be identical. For example, boosted beta regression allows for detecting factors that only manifest in the variance (but not in the mean) of the response (cf. [29] ).

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Supporting Information

This document provides technical details on boosted beta regression, as well as the full list of predictor variables used for the analysis of the NLA data.


We thank Charles Hawkins, Western Center for Monitoring and Assessment of Freshwater Ecosystems at Utah State University for providing several predictor variables. The views expressed in this article are those of the co-authors, and do not necessarily reflect the official views of the U.S. EPA or U.S. government. Use of trade, product or firm names does not imply endorsement by the U.S. Government.

Funding Statement

The work of Matthias Schmid and Andreas Mayr was supported by Deutsche Forschungsgemeinschaft (DFG) ( ), grant SCHM 2966/1-1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Using beta coefficients to impute missing correlations in meta-analysis research: Reasons for caution


  • 1 Department of Management, College of Business, Clemson University.
  • 2 Department of Management, University of Texas at San Antonio.
  • 3 Department of Human Resource Management, Fox School of Business, Temple University.
  • 4 Department of Management, Florida State University.
  • 5 Department of Management, Virginia Tech.
  • PMID: 29369653
  • DOI: 10.1037/apl0000293

Meta-analysis has become a well-accepted method for synthesizing empirical research about a given phenomenon. Many meta-analyses focus on synthesizing correlations across primary studies, but some primary studies do not report correlations. Peterson and Brown (2005) suggested that researchers could use standardized regression weights (i.e., beta coefficients) to impute missing correlations. Indeed, their beta estimation procedures (BEPs) have been used in meta-analyses in a wide variety of fields. In this study, the authors evaluated the accuracy of BEPs in meta-analysis. We first examined how use of BEPs might affect results from a published meta-analysis. We then developed a series of Monte Carlo simulations that systematically compared the use of existing correlations (that were not missing) to data sets that incorporated BEPs (that impute missing correlations from corresponding beta coefficients). These simulations estimated ρ̄ (mean population correlation) and SDρ (true standard deviation) across a variety of meta-analytic conditions. Results from both the existing meta-analysis and the Monte Carlo simulations revealed that BEPs were associated with potentially large biases when estimating ρ̄ and even larger biases when estimating SDρ. Using only existing correlations often substantially outperformed use of BEPs and virtually never performed worse than BEPs. Overall, the authors urge a return to the standard practice of using only existing correlations in meta-analysis. (PsycINFO Database Record

(c) 2018 APA, all rights reserved).

  • Data Interpretation, Statistical*
  • Meta-Analysis as Topic*
  • Models, Statistical*
  • How it works

researchprospect post subheader

Meta-Analysis – Guide with Definition, Steps & Examples

Published by Owen Ingram at April 26th, 2023 , Revised On April 26, 2023

“A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies. “

Meta-analysis and systematic review are the two most authentic strategies in research. When researchers start looking for the best available evidence concerning their research work, they are advised to begin from the top of the evidence pyramid. The evidence available in the form of meta-analysis or systematic reviews addressing important questions is significant in academics because it informs decision-making.

What is Meta-Analysis  

Meta-analysis estimates the absolute effect of individual independent research studies by systematically synthesising or merging the results. Meta-analysis isn’t only about achieving a wider population by combining several smaller studies. It involves systematic methods to evaluate the inconsistencies in participants, variability (also known as heterogeneity), and findings to check how sensitive their findings are to the selected systematic review protocol.   

When Should you Conduct a Meta-Analysis?

Meta-analysis has become a widely-used research method in medical sciences and other fields of work for several reasons. The technique involves summarising the results of independent systematic review studies. 

The Cochrane Handbook explains that “an important step in a systematic review is the thoughtful consideration of whether it is appropriate to combine the numerical results of all, or perhaps some, of the studies. Such a meta-analysis yields an overall statistic (together with its confidence interval) that summarizes the effectiveness of an experimental intervention compared with a comparator intervention” (section 10.2).

A researcher or a practitioner should choose meta-analysis when the following outcomes are desirable. 

For generating new hypotheses or ending controversies resulting from different research studies. Quantifying and evaluating the variable results and identifying the extent of conflict in literature through meta-analysis is possible. 

To find research gaps left unfilled and address questions not posed by individual studies. Primary research studies involve specific types of participants and interventions. A review of these studies with variable characteristics and methodologies can allow the researcher to gauge the consistency of findings across a wider range of participants and interventions. With the help of meta-analysis, the reasons for differences in the effect can also be explored. 

To provide convincing evidence. Estimating the effects with a larger sample size and interventions can provide convincing evidence. Many academic studies are based on a very small dataset, so the estimated intervention effects in isolation are not fully reliable.

Elements of a Meta-Analysis

Deeks et al. (2019), Haidilch (2010), and Grant & Booth (2009) explored the characteristics, strengths, and weaknesses of conducting the meta-analysis. They are briefly explained below. 


  • A systematic review must be completed before conducting the meta-analysis because it provides a summary of the findings of the individual studies synthesised. 
  • You can only conduct a meta-analysis by synthesising studies in a systematic review. 
  • The studies selected for statistical analysis for the purpose of meta-analysis should be similar in terms of comparison, intervention, and population. 


  • A meta-analysis takes place after the systematic review. The end product is a comprehensive quantitative analysis that is complicated but reliable. 
  • It gives more value and weightage to existing studies that do not hold practical value on their own. 
  • Policy-makers and academicians cannot base their decisions on individual research studies. Meta-analysis provides them with a complex and solid analysis of evidence to make informed decisions. 


  • The meta-analysis uses studies exploring similar topics. Finding similar studies for the meta-analysis can be challenging.
  • When and if biases in the individual studies or those related to reporting and specific research methodologies are involved, the meta-analysis results could be misleading.

Steps of Conducting the Meta-Analysis 

The process of conducting the meta-analysis has remained a topic of debate among researchers and scientists. However, the following 5-step process is widely accepted. 

Step 1: Research Question

The first step in conducting clinical research involves identifying a research question and proposing a hypothesis . The potential clinical significance of the research question is then explained, and the study design and analytical plan are justified.

Step 2: Systematic Review 

The purpose of a systematic review (SR) is to address a research question by identifying all relevant studies that meet the required quality standards for inclusion. While established journals typically serve as the primary source for identified studies, it is important to also consider unpublished data to avoid publication bias or the exclusion of studies with negative results.

While some meta-analyses may limit their focus to randomized controlled trials (RCTs) for the sake of obtaining the highest quality evidence, other experimental and quasi-experimental studies may be included if they meet the specific inclusion/exclusion criteria established for the review.

Step 3: Data Extraction

After selecting studies for the meta-analysis, researchers extract summary data or outcomes, as well as sample sizes and measures of data variability for both intervention and control groups. The choice of outcome measures depends on the research question and the type of study, and may include numerical or categorical measures.

For instance, numerical means may be used to report differences in scores on a questionnaire or changes in a measurement, such as blood pressure. In contrast, risk measures like odds ratios (OR) or relative risks (RR) are typically used to report differences in the probability of belonging to one category or another, such as vaginal birth versus cesarean birth.

Step 4: Standardisation and Weighting Studies

After gathering all the required data, the fourth step involves computing suitable summary measures from each study for further examination. These measures are typically referred to as Effect Sizes and indicate the difference in average scores between the control and intervention groups. For instance, it could be the variation in blood pressure changes between study participants who used drug X and those who used a placebo.

Since the units of measurement often differ across the included studies, standardization is necessary to create comparable effect size estimates. Standardization is accomplished by determining, for each study, the average score for the intervention group, subtracting the average score for the control group, and dividing the result by the relevant measure of variability in that dataset.

In some cases, the results of certain studies must carry more significance than others. Larger studies, as measured by their sample sizes, are deemed to produce more precise estimates of effect size than smaller studies. Additionally, studies with less variability in data, such as smaller standard deviation or narrower confidence intervals, are typically regarded as higher quality in study design. A weighting statistic that aims to incorporate both of these factors, known as inverse variance, is commonly employed.

Step 5: Absolute Effect Estimation

The ultimate step in conducting a meta-analysis is to choose and utilize an appropriate model for comparing Effect Sizes among diverse studies. Two popular models for this purpose are the Fixed Effects and Random Effects models. The Fixed Effects model relies on the premise that each study is evaluating a common treatment effect, implying that all studies would have estimated the same Effect Size if sample variability were equal across all studies.

Conversely, the Random Effects model posits that the true treatment effects in individual studies may vary from each other, and endeavors to consider this additional source of interstudy variation in Effect Sizes. The existence and magnitude of this latter variability is usually evaluated within the meta-analysis through a test for ‘heterogeneity.’

Forest Plot

The results of a meta-analysis are often visually presented using a “Forest Plot”. This type of plot displays, for each study, included in the analysis, a horizontal line that indicates the standardized Effect Size estimate and 95% confidence interval for the risk ratio used. Figure A provides an example of a hypothetical Forest Plot in which drug X reduces the risk of death in all three studies.

However, the first study was larger than the other two, and as a result, the estimates for the smaller studies were not statistically significant. This is indicated by the lines emanating from their boxes, including the value of 1. The size of the boxes represents the relative weights assigned to each study by the meta-analysis. The combined estimate of the drug’s effect, represented by the diamond, provides a more precise estimate of the drug’s effect, with the diamond indicating both the combined risk ratio estimate and the 95% confidence interval limits.

odds ratio

Figure-A: Hypothetical Forest Plot

Relevance to Practice and Research 

  Evidence Based Nursing commentaries often include recently published systematic reviews and meta-analyses, as they can provide new insights and strengthen recommendations for effective healthcare practices. Additionally, they can identify gaps or limitations in current evidence and guide future research directions.

The quality of the data available for synthesis is a critical factor in the strength of conclusions drawn from meta-analyses, and this is influenced by the quality of individual studies and the systematic review itself. However, meta-analysis cannot overcome issues related to underpowered or poorly designed studies.

Therefore, clinicians may still encounter situations where the evidence is weak or uncertain, and where higher-quality research is required to improve clinical decision-making. While such findings can be frustrating, they remain important for informing practice and highlighting the need for further research to fill gaps in the evidence base.

Methods and Assumptions in Meta-Analysis 

Ensuring the credibility of findings is imperative in all types of research, including meta-analyses. To validate the outcomes of a meta-analysis, the researcher must confirm that the research techniques used were accurate in measuring the intended variables. Typically, researchers establish the validity of a meta-analysis by testing the outcomes for homogeneity or the degree of similarity between the results of the combined studies.

Homogeneity is preferred in meta-analyses as it allows the data to be combined without needing adjustments to suit the study’s requirements. To determine homogeneity, researchers assess heterogeneity, the opposite of homogeneity. Two widely used statistical methods for evaluating heterogeneity in research results are Cochran’s-Q and I-Square, also known as I-2 Index.

Difference Between Meta-Analysis and Systematic Reviews

Meta-analysis and systematic reviews are both research methods used to synthesise evidence from multiple studies on a particular topic. However, there are some key differences between the two.

Systematic reviews involve a comprehensive and structured approach to identifying, selecting, and critically appraising all available evidence relevant to a specific research question. This process involves searching multiple databases, screening the identified studies for relevance and quality, and summarizing the findings in a narrative report.

Meta-analysis, on the other hand, involves using statistical methods to combine and analyze the data from multiple studies, with the aim of producing a quantitative summary of the overall effect size. Meta-analysis requires the studies to be similar enough in terms of their design, methodology, and outcome measures to allow for meaningful comparison and analysis.

Therefore, systematic reviews are broader in scope and summarize the findings of all studies on a topic, while meta-analyses are more focused on producing a quantitative estimate of the effect size of an intervention across multiple studies that meet certain criteria. In some cases, a systematic review may be conducted without a meta-analysis if the studies are too diverse or the quality of the data is not sufficient to allow for statistical pooling.

Software Packages For Meta-Analysis

Meta-analysis can be done through software packages, including free and paid options. One of the most commonly used software packages for meta-analysis is RevMan by the Cochrane Collaboration.

Assessing the Quality of Meta-Analysis 

Assessing the quality of a meta-analysis involves evaluating the methods used to conduct the analysis and the quality of the studies included. Here are some key factors to consider:

  • Study selection: The studies included in the meta-analysis should be relevant to the research question and meet predetermined criteria for quality.
  • Search strategy: The search strategy should be comprehensive and transparent, including databases and search terms used to identify relevant studies.
  • Study quality assessment: The quality of included studies should be assessed using appropriate tools, and this assessment should be reported in the meta-analysis.
  • Data extraction: The data extraction process should be systematic and clearly reported, including any discrepancies that arose.
  • Analysis methods: The meta-analysis should use appropriate statistical methods to combine the results of the included studies, and these methods should be transparently reported.
  • Publication bias: The potential for publication bias should be assessed and reported in the meta-analysis, including any efforts to identify and include unpublished studies.
  • Interpretation of results: The results should be interpreted in the context of the study limitations and the overall quality of the evidence.
  • Sensitivity analysis: Sensitivity analysis should be conducted to evaluate the impact of study quality, inclusion criteria, and other factors on the overall results.

Overall, a high-quality meta-analysis should be transparent in its methods and clearly report the included studies’ limitations and the evidence’s overall quality.

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Examples of Meta-Analysis

  • STANLEY T.D. et JARRELL S.B. (1989), « Meta-regression analysis : a quantitative method of literature surveys », Journal of Economics Surveys, vol. 3, n°2, pp. 161-170.
  • DATTA D.K., PINCHES G.E. et NARAYANAN V.K. (1992), « Factors influencing wealth creation from mergers and acquisitions : a meta-analysis », Strategic Management Journal, Vol. 13, pp. 67-84.
  • GLASS G. (1983), « Synthesising empirical research : Meta-analysis » in S.A. Ward and L.J. Reed (Eds), Knowledge structure and use : Implications for synthesis and interpretation, Philadelphia : Temple University Press.
  • WOLF F.M. (1986), Meta-analysis : Quantitative methods for research synthesis, Sage University Paper n°59.
  • HUNTER J.E., SCHMIDT F.L. et JACKSON G.B. (1982), « Meta-analysis : cumulating research findings across studies », Beverly Hills, CA : Sage.

Frequently Asked Questions

What is a meta-analysis in research.

Meta-analysis is a statistical method used to combine results from multiple studies on a specific topic. By pooling data from various sources, meta-analysis can provide a more precise estimate of the effect size of a treatment or intervention and identify areas for future research.

Why is meta-analysis important?

Meta-analysis is important because it combines and summarizes results from multiple studies to provide a more precise and reliable estimate of the effect of a treatment or intervention. This helps clinicians and policymakers make evidence-based decisions and identify areas for further research.

What is an example of a meta-analysis?

A meta-analysis of studies evaluating physical exercise’s effect on depression in adults is an example. Researchers gathered data from 49 studies involving a total of 2669 participants. The studies used different types of exercise and measures of depression, which made it difficult to compare the results.

Through meta-analysis, the researchers calculated an overall effect size and determined that exercise was associated with a statistically significant reduction in depression symptoms. The study also identified that moderate-intensity aerobic exercise, performed three to five times per week, was the most effective. The meta-analysis provided a more comprehensive understanding of the impact of exercise on depression than any single study could provide.

What is the definition of meta-analysis in clinical research?

Meta-analysis in clinical research is a statistical technique that combines data from multiple independent studies on a particular topic to generate a summary or “meta” estimate of the effect of a particular intervention or exposure.

This type of analysis allows researchers to synthesise the results of multiple studies, potentially increasing the statistical power and providing more precise estimates of treatment effects. Meta-analyses are commonly used in clinical research to evaluate the effectiveness and safety of medical interventions and to inform clinical practice guidelines.

Is meta-analysis qualitative or quantitative?

Meta-analysis is a quantitative method used to combine and analyze data from multiple studies. It involves the statistical synthesis of results from individual studies to obtain a pooled estimate of the effect size of a particular intervention or treatment. Therefore, meta-analysis is considered a quantitative approach to research synthesis.

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A case study is a detailed analysis of a situation concerning organizations, industries, and markets. The case study generally aims at identifying the weak areas.






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Finding Beta Research Guide

  • Introduction
  • Differences Between Beta
  • Calculating Beta
  • Fundamental Beta

What is Fundamental Beta?

Beta is the measurement of expected return between a stock and the market. Normally we assume that the past is going to represent the future and therefore historical betas are calculated using only historical returns volatility. This practice is widely used in calculating beta, often through a time-series regression analysis comparing the stock’s return with the market’s return.

Conversely, a fundamental beta (also know as predicted beta) is derived from current and predicted fundamentals of the company. Different models incorporate various risk factors, such as company size, volatility, momentum, and other value factors, in calculating a company’s fundamental beta. Northfield recalculates the fundamental beta monthly, thereby taking into account any changes in the company’s underlying risk structure during that time.

Adjusted or Fundamental Beta Using Bloomberg

Adjusted Beta in Bloomberg is based on historical data, but is an estimate of a security's future beta.  It is modified by the assumption that a security's beat moves toward the market average over time.

     However, its adjusted beta is .79

Should I use fundamental beta or historical beta in my analysis?

Professionals and academics alike argue that there are several problems with using historical betas. Two major complaints of a historical beta are that it:

· Does not factor in fundamental changes in a company’s operations. For example, selling off a large portion of a struggling business can significantly change a company’s risk characteristics and a historical beta would reflect this change only very slowly, over time. Whereas, a fundamental beta would account for this operational change right away.

· Is influenced by one-time events that are unlikely to affect a company again, thereby artificially depressing or raising a company’s beta.

The decision to use a historical or fundamental beta is up to the individual performing the analysis. However, many studies have demonstrated that fundamental betas significantly outperform historical betas as predictors of future stock behavior.

(Reference: Barra Beta Books for Companies, )

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What Is Beta?

Understanding beta in investing, how to read stock betas, how is beta calculated, high beta vs low beta: which is better, low beta stock example, high beta stock example, advantages of using beta as a proxy for risk, disadvantages of using beta as a proxy for risk, the bottom line.

  • Quantitative Analysis

What Beta Means When Considering a Stock's Risk

Daniel Liberto is a journalist with over 10 years of experience working with publications such as the Financial Times, The Independent, and Investors Chronicle.

beta analysis in research

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beta analysis in research

Yarilet Perez is an experienced multimedia journalist and fact-checker with a Master of Science in Journalism. She has worked in multiple cities covering breaking news, politics, education, and more. Her expertise is in personal finance and investing, and real estate.

beta analysis in research

Beta is a measure of a stock's volatility in relation to the overall market. By definition, the market, such as the S&P 500 Index, has a beta of 1.0, and individual stocks are ranked according to how much they deviate from the market. A stock that swings more than the market over time has a beta above 1.0. If a stock moves less than the market, the stock's beta is less than 1.0.

Key Takeaways

  • Beta is a concept that measures the expected move in a stock relative to movements in the overall market.
  • A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
  • Beta is a component of the Capital Asset Pricing Model, which calculates the cost of equity funding and can help determine the rate of return to expect relative to perceived risk.
  • Critics argue that beta does not give enough information about the fundamentals of a company and is of limited value when making stock selections.
  • Beta is probably a better indicator of short-term rather than long-term risk.

How should investors assess risk in the stocks that they buy or sell? While the concept of risk is hard to factor in stock analysis and valuation, one of the most popular indicators is a statistical measure called beta.

Beta measures risk in the form of volatility against a benchmark and is based on the principle that higher risk come with higher potential rewards. Analysts use beta when they want to determine a stock's risk profile. High-beta stocks , which generally means any stock with a beta higher than 1.0, are supposed to be riskier but provide higher return potential; low-beta stocks, those with a beta under 1.0, pose less risk but also usually lower returns.

Beta is a component of the capital asset pricing model (CAPM), which is widely used to determine the rate of return that shareholders might reasonably expect based on perceived investment risk.

Beta and CAPM

Beta is used in the capital asset pricing model  (CAPM), a widely used method for pricing risky securities and for generating estimates of the expected returns of assets, particularly stocks. The CAPM formula uses the total average market return and the beta value of the stock to determine the rate of return that shareholders might reasonably expect based on perceived investment risk. In this way, beta can impact a stock's expected rate of return and share valuation.

Beta is a numerical value. The overall market has a beta of 1.0, and individual stocks are ranked according to how much they deviate from the market. Market in this context means an index, such as the S&P 500 .

The S&P 500's 500 constituents will each have different betas based on how they moved in relation to the index over a set timeframe. Companies whose share prices were less volatile than the S&P 500 will have a beta value under 1.0. Conversely, share prices that were more volatile than the S&P 500 will have beta values over 1.0.

The higher the value, the more volatile the share price.

A negative beta is when an asset moves in the opposite direction of the stock market. An example of this could be gold during economic downturns.

Beta is calculated using  regression  analysis. Numerically, it represents the tendency for a security's returns to respond to swings in the market. 

To calculate the beta of a security, the  covariance  between the return of the security and the return of the market must be known as well as the  variance  of the market returns. The covariance of the return of an asset with the return of the  benchmark is divided by the variance of the return of the benchmark over a certain period.

Beta = Covariance Variance \text{Beta} = \frac{\text{Covariance}}{\text{Variance}} Beta = Variance Covariance ​

The higher the risk, the higher the potential reward is a common belief in investment circles. High-beta stocks are supposed to be riskier but provide higher return potential. Conversely, low-beta stocks pose less risk but also offer lower potential returns. Which is best depends on what type of investor you are.

More conservative investors or those that wish to soon tap into their funds will likely prefer low-beta stocks. These kinds of stocks historically tend to not fluctuate much in value. They are companies that consistently deliver steady revenues and profits in times of economic expansion and hardship. Positive or negative surprises are lacking and valuations are based on very realistic expectations that the company has a history of reaching.

Investors keen to bag big capital gains or day traders looking to make a quick buck from fluctuating share prices would be more interested in high-beta stocks. The share prices of these companies historically have a tendency to jump around quite a bit. Racy stocks, such as tech upstarts with the potential to revolutionize how certain things are done, fall into this category. Investing in one could make you a fortune or lead to big losses. Their future is unpredictable and that leads to lots of speculation and price movements.

Higher beta stocks also tend to outperform in bull markets when the economy is in expansion mode and confidence is high, whereas lower beta stocks tend to fare better during recessions.

A stock's beta will change over time because it compares the stock's return with the returns of the overall market.

Low beta stocks tend to be defensive companies. There is a constant demand for their products or services, regardless of where we are in the economic cycle , resulting in steady profits and revenues, which often translate into a steady share price and dividend payments.

A classic example of a low beta stock would be a company like Proctor & Gamble. The maker of household brands such as Pampers, Oral, Pantene, and Gillette, as of July 2023, has a five-year beta of 0.4. In other words, its share price fluctuates much less than the broader market. For every 1% move in the market, Proctor & Gamble's shares moved 0.4% on average. That's good in terms of protecting against losses but also means limited upside potential compared to other options.

High beta is generally associated with small companies or growth stocks . These are companies that are expected to grow revenues and profit fast and, as a result, experience lots of capital appreciation .

Many of the highest beta stocks are tech companies. A company behind the next big thing typically commands a high valuation. Investors buy the stock based on it living up to its potential, which requires lots of uncertain factors going its way. High hopes create volatility. A slip-up could result in the share price tumbling dramatically. Likewise, a small hint of good news can lead to another big rally.

Tesla falls into this category. There is a lot of hope baked into its share price, resulting in wild swings whenever it fails/exceeds expectations and a five-year beta of 2.08, as of July 2023.

To followers of CAPM, beta is useful. A stock's price variability is important to consider when assessing risk. If you think about risk as the possibility of a stock losing its value, beta has appeal as a proxy for risk. Intuitively, it makes plenty of sense. Think of an early-stage technology stock with a price that bounces up and down more than the market. It's hard not to think that stock will be riskier than, say, a safe-haven utility industry stock with a low beta.

Besides, beta offers a clear, quantifiable measure that is easy to work with. Sure, there are variations on beta depending on things such as the market index used and the time period measured. But broadly speaking, the notion of beta is fairly straightforward. It's a convenient measure that can be used to calculate the costs of equity used in a valuation method.

Beta is generally more useful as a risk metric for traders moving in and out of trades. For investors with long-term horizons, it's less useful.

The well-worn definition of risk is the possibility of suffering a loss. Of course, when investors consider risk, they are thinking about the chance that the stock they buy will decrease in value. The trouble is that beta, as a proxy for risk, doesn't distinguish between upside and downside price movements. For most investors, downside movements are a risk, while upside ones mean opportunity. Beta doesn't help investors tell the difference. For most investors, that doesn't make much sense.

Value investors scorn the idea of beta because it implies that a stock that has fallen sharply in value is riskier than it was before it fell. A value investor would argue that a company represents a lower-risk investment after it falls in value—investors can get the same stock at a lower price despite the rise in the stock's beta following its decline. Beta says nothing about the price paid for the stock in relation to fundamental factors like changes in company leadership, new product discoveries, or future cash flows.

Beta doesn't pay attention to a stock's fundamentals or incorporate new information. Consider a utility company: let's call it Company X. Company X has been considered a defensive stock with a low beta. When it entered the merchant energy business and assumed more debt, X's historic beta no longer captured the substantial risks the company took on.

At the same time, many technology stocks are relatively new to the market and thus have insufficient price history to establish a reliable beta.

Beta is based on past price movement and the past doesn't necessarily have a bearing on the future.

Another troubling factor is that past price movement is a poor predictor of the future. Betas are merely rear-view mirrors, reflecting very little of what lies ahead. Furthermore, the beta measure on a single stock tends to flip around over time, which makes it unreliable. Granted, for traders looking to buy and sell stocks within short time periods, beta is a fairly good risk metric. However, for investors with long-term horizons, it's less useful.

Does Beta Mean Alpha?

No, they are two different things. Beta is a measure of volatility relative to a benchmark. Alpha is excess return in relation to a benchmark and is commonly used to reveal how much active fund managers outperform the index they are trying to beat.

Is a Beta of 1.5 Good?

That depends on what kind of risk/return you’re looking for. A beta value of 1.5 implies that the stock is 50% more volatile than the broader market. That means higher than average risk and the potential for greater upside.

What Does a Beta of 1.0 Mean?

A beta of 1.0 means the stock over the allocated time frame moved similar to the rest of the market. This could be determined as an average level of risk.

Is Low Beta Bad?

Low beta generally means lower price volatiltiy than the average stock. That might suit some investors but not everyone.

Beta is the volatility of a security or portfolio against its benchmark. It's a numerical value that signifies how much a stock price jumps around. The higher the value, the more the company tends to fluctuate in value.

Ultimately, it's important for investors to make the distinction between short-term risk—where beta and price volatility are useful—and longer-term, fundamental risk, where big-picture risk factors are more telling. High betas may mean price volatility over the near term, but they don't always rule out long-term opportunities.

Nasdaq. " Beta ."

Harvard Business Review. " Does the Capital Asset Pricing Model Work? "

U.S. Department of Commerce, Commercial Law Development Program. “ Financial Modeling: CAPM & WACC .”

World Gold Council. " Gold Correlation to Stock ."

Sure Dividend. " 2023 High Beta Stocks List | The 100 Highest Beta S&P 500 Stocks ."

Yahoo Finance. " The Procter & Gamble Company ."

Yahoo Finance. " Tesla Inc ."

Graham Value. " Value Investing - Risk vs Beta ."

Fidelity. " All About Alpha, Beta, and Smart Beta ."

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The Top Stocks To Buy Now In June 2024

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Although recession worries are finally fading, the prevailing stock market outlook for the rest of 2024 isn't all that rosy. Analysts from Morningstar and JP Morgan expect volatility. And David Kostin, chief U.S. equity strategist at Goldman Sachs GS , says the S&P 500 has tapped its growth potential for the year.

A UBS report shares Kostin's view as a likely outcome, but also outlines factors that could drive the S&P 500 to 5,500 by year end. Those factors include continued earnings growth in tech stocks , ongoing investment in AI and falling interest rates.

Of those, the Fed's interest rate actions may be the most influential. Rate reductions, however, probably won't happen before September. That leaves investors with uncertainty heading into summer—when stock market returns tend to disappoint.

For those reasons, this list of best stocks to buy in June is defensive in nature. The stock picks have attractive valuations and competitive advantages that should lower downside risk in turbulent times. Let's dive in.

The brain trust at Forbes has run the numbers, conducted the research, and done the analysis to come up with some of the best places for you to make money in 2024. Download Forbes' most popular report, 12 Stocks To Buy Now.

Six Best Stocks To Buy In June 2024

The table below introduces six value stocks that can help prep your portfolio for uncertain times ahead. All are exchange-traded and available for purchase in a U.S.-based brokerage account .

Data source: Yahoo Finance.

1. Alphabet (GOOG)

Alphabet by the numbers:

  • Share price: $174.11
  • PEG ratio: 0.59
  • P/E ratio: 26.46
  • Price/book ratio: 7.3

Alphabet Overview

Alphabet operates the ubiquitous search engine Google GOOG , which generates advertising revenues. The company also runs a cloud-computing service, sells subscriptions and advertising on its video website YouTube and invests in young companies across a range of industries.

Why GOOG Stock Is A Top Choice

Google dominates the search engine market, fielding some 90% of global search queries. That leadership position insulates the company from competition and protects its advertising revenues. Alphabet also invests heavily in acquisitions and innovation to drive growth opportunities beyond its core search offering. Cloud computing, autonomous driving and AI are three potential sources of future growth for Alphabet.

In the first quarter of 2024, Alphabet reported 16% revenue growth versus the prior-year quarter on a constant-currency basis. Revenues from Google advertising, Google subscriptions and Google Cloud all increased from last year's first quarter. Diluted EPS of $1.89 grew 61%.

Alphabet also recently announced the initiation of its dividend program, with a quarterly payment of $0.20.

2. Wells Fargo WFC

Wells Fargo by the numbers:

  • Share price: $61.06
  • PEG ratio: 0.35
  • P/E ratio: 12.74
  • Price/book ratio: 1.3

Wells Fargo Overview

Wells Fargo is a financial services company. Key services include consumer and commercial banking, residential mortgages, investment management, credit cards and personal loans. The company primarily serves customers in the U.S.

WFC is managing through lingering regulatory actions dating back to the bank’s 2016 fake account scandal. The bank has satisfied six enforcement actions since 2019, but still operates under a federally imposed asset cap which prevents loan portfolio growth.

Insiders believe the feds could drop WFC's asset cap sometime next year, paving the way for the bank to grow its assets and earnings.

Why WFC Stock Is A Top Choice

Despite the history of scandal, Wells Fargo has a sticky customer base thanks to an extensive U.S. branch network and full-service offering. The bank maintains leading market share in several areas, including commercial banking, auto leasing and private banking. Once the asset cap is lifted, WFC can leverage its sizable customer base to drive significant growth.

In the first quarter of 2024, WFC reported modest revenue growth of 0.6% to $20.9 billion from $20.7 billion. Diluted EPS dipped by $0.03 to $1.20 versus last year's first quarter.

3. Comcast CMCSA

Comcast by the numbers:

  • Share price: $39.37
  • PEG ratio: 0.71
  • P/E ratio: 10.45
  • Price/book ratio: 1.9

Comcast Overview

Comcast has a diversified business model involving content creation and delivery. The company sells internet and television services through its broadband and cable network. It also creates and distributes entertainment content through NBCUniversal and provides immersive entertainment experiences at its Universal Studios theme parks.

Why CMCSA Stock Is A Top Choice

There are a few aspects of Comcast's business model that are difficult for competitors to replicate. First, the company's massive broadband infrastructure delivers internet access and bundled television and voice services to millions. Switching to new internet and television providers can be notoriously difficult, and that minimizes churn for Comcast.

Second, the vertically integrated NBCUniversal business provides cost synergies and added revenue opportunities. NBCUniversal creates content via owned movie studios, and then delivers that content through owned channels and networks. This business also licenses its content to broadcasters and streaming services internationally.

In the first quarter of this year, Comcast reported modest revenue growth of 1.2%. Adjusted EPS grew 7.6%. The company also spent $2.4 billion on share repurchases in the quarter.

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4. Charles Schwab (SCHW)

Charles Schwab by the numbers:

  • Share price: $78.04
  • PEG ratio: 1.23
  • P/E ratio: 32.66
  • Price/book ratio: 7.8

Charles Schwab Overview

Charles Schwab is a financial services company that provides brokerage, banking and wealth management services in the U.S. and United Kingdom.

Why SCHW Stock Is A Top Choice

Charles Schwab is solidly positioned in its space, due to the company's size, reputation, integrated service offering and low-cost focus. The company caters to value-conscious investors with features like commission-free trades, support for fractional investing and low-fee ETFs. Those investors tend to remain loyal to avoid the cost and hassle of switching to another provider. That loyalty combined with Schwab's extensive service offering creates cross-selling opportunities that improve revenue per customer.

In the first quarter of this year, Schwab reported 20% growth in total client assets to a record $9.1 trillion. Net revenues dipped 7% compared to last year's first quarter but rose 6% sequentially. Diluted EPS fell 18% versus the prior-year quarter to $0.68. Despite the quarter-over-quarter decline, the EPS result did beat analyst expectations.

5. Nike NKE

Nike by the numbers:

  • Share price: $91.77
  • PEG ratio: 1.69
  • P/E ratio: 26.99
  • Price/book ratio: 11.1

Nike Overview

Nike designs, manufactures, markets and sells sports apparel to customers around the world.

Why NKE Stock Is A Top Choice

Nike is one of the world's most recognized brands. Key aspects of the Nike brand are innovation and association with famous athletes from the world's most popular sports. The power of that branding fuels customer loyalty and supports premium pricing.

Nike also has demonstrated supply chain expertise that provides a cost advantage relative to competitors. The apparel brand outsources its manufacturing and uses automation and technology to keep product costs low.

For its third quarter of fiscal year 2024, Nike reported slight revenue growth to $12,429 million from $12,390 million. Diluted EPS fell slightly to $0.77 from $0.79. Restructuring charges were a factor in the EPS decline.

6. Estee Lauder (EL)

Estee Lauder by the numbers:

  • Share price: $138.24
  • PEG ratio: 1.74
  • P/E ratio: 76.4
  • Price/book ratio: 8.5

Estee Lauder Overview

Estee Lauder makes high-end skincare, hair care and cosmetics products and sells them to customers around the world. The company’s brand portfolio includes Estee Lauder, Clinique, La Mer, MAC, Bobbi Brown and Tom Ford Beauty.

Why EL Stock Is A Top Choice

Estee Lauder, like Nike, has brand power, an established global distribution network and proven skill in marketing and product innovation. Several brands in the portfolio have loyal customers and command premium prices.

Looking to enhance its innovation expertise, EL recently opened an AI innovation lab with a social listening component. The initiative should expedite product development and deepen insight into consumer needs and ingredient trends.

In its third quarter of fiscal year 2024, EL reported 5% sales growth to $3.9 billion. Adjusted diluted EPS doubled to $1.02 from $0.47 on a constant currency basis.

Methodology For These Stock Picks

These stocks are value plays with competitive advantages that protect them from deterioration. All six picks have reasonable valuation metrics, solid business fundamentals and are viewed positively by analysts.

As an added bonus, they're all dividend stocks too. With an average yield of 1.8%, these aren't big income-producers—but you'll appreciate those cash returns if the market turns sour. A dividend ETF is a better choice if income is your priority.

Bottom Line

Value stocks prove their worth during troubled economic times. With the U.S. economy in transition heading into summertime, a defensive strategy may be just what your portfolio needs.

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