quantitative result and discussion in research example

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Results and Discussion

• In book: The Social Psychology of English as a Global Language (pp.95-138)

• Northumbria University

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• Open access
• Published: 17 June 2024

Web-based occupational stress prevention in German micro- and small-sized enterprises – process evaluation results of an implementation study

• Miriam Engels 1   na1 ,
• Louisa Scheepers 2 , 3   na1 ,
• Judith Engels 1 , 4 ,
• Leif Boß 5 ,
• Rebekka Kuhlmann 4 ,
• Johanna Kuske 6 ,
• Lutz Lesener 7 ,
• Valeria Pavlista 3 ,
• Kira Schmidt-Stiedenroth 3 ,
• Mathias Diebig 11 ,
• Sascha A. Ruhle 5 , 8 ,
• Florian B. Zapkau 9 ,
• Peter Angerer 3 ,
• Jörg Hoewner 7 ,
• Dirk Lehr 5 ,
• Christian Schwens 6 ,
• Stefan Süß 4 ,
• Ines C. Wulf 2 , 10 &
• Nico Dragano 2  

BMC Public Health volume  24 , Article number:  1618 ( 2024 ) Cite this article

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Metrics details

Structural and behavioral interventions to manage work-related stress are effective in employees. Nonetheless, they have been implemented insufficiently, particularly in micro- and small-sized enterprises (MSE). Main barriers include a lack of knowledge and limited resources, which could potentially be overcome with simplified web-based alternatives for occupational stress prevention. However, there is a lack of implementation research about web-based prevention in realistic settings of MSE.

The aim of this study is to evaluate the implementation process and success of an integrated web-based platform for occupational stress prevention (“System P”) and to identify potential barriers for its uptake and use in MSE in Germany.

This study with a mixed-methods approach investigates eight process-related outcomes in a quantitative part I (adoption, reach, penetration, fidelity/dose, costs, acceptability) and a qualitative part II (acceptability, appropriateness and feasibility). Part I has a pre-post design with two measurements (6 months apart) with 98 individual participants and part II consists of 12 semi-structured interviews with managers and intercorporate stakeholders.

Part I revealed shortcomings in the implementation process. Adoption/Reach: Despite extensive marketing efforts, less than 1% of the contacted MSE responded to the offer of System P. A total of 40 MSE registered, 24 of which, characterized by good psychosocial safety climate, adopted System P. Penetration: Within these 24 MSE, 15% of the employees used the system. Fidelity/Dose: 11 MSE started a psychosocial risk-assessment (PRA), and no MSE finished it. The stress-management training (SMT) was started by 25 users and completed by 8. Costs: The use of System P was free of charge, but the time required to engage with was an indirect cost. Part II added insights on the perception of the web-based intervention: Acceptance of System P by users and stakeholders was good and it was assessed as appropriate for MSE. Results for feasibility were mixed.

Conclusions

Although System P was generally perceived as useful and appropriate, only a small number of contacted MSE implemented it as intended. Prior experience and sensitivity for occupational (stress) prevention were mentioned as key facilitators, while (perceived) indirect costs were a key barrier. Enabling MSE to independently manage stress prevention online did not result in successful implementation. Increasing external support could be a solution.

⁺ Full project name

“PragmatiKK – Pragmatische Lösungen für die Implementation von Maßnahmen zur Stressprävention in Kleinst- und Kleinbetrieben” (= Pragmatic solutions for the implementation of stress prevention interventions in micro and small-sized enterprises).

Trial registration

German Register of Clinical Studies (DRKS) DRKS00026154, date of registration 2021-09-16.

Peer Review reports

Introduction

Work-related stress is associated with an increased risk for several severe health problems like cardiovascular diseases and depression in employees [ 1 ]. Due to the frequency of its occurrence, it is also a major economic challenge, e.g. regarding the costs of healthcare, sick leave, early retirement or disability [ 2 ]. These costs can be especially detrimental to smaller businesses because they lack staff to compensate longer absences. In 2021, the vast majority of all enterprises in Europe were micro- (under ten employees), small- (under 50 employees) and medium (under 250 employees) sized enterprises making up 64% of total employment in Europe. Micro enterprises account for more than 90% of all enterprises in Europe [ 3 , 4 ]. In Germany, 38% of all employees work in micro- and small-sized enterprises (MSE) with less than 50 employees [ 5 ]. Yet, studies on occupational stress prevention in the setting of MSE are very rare.

Occupational stress prevention

There are two types of interventions intended to reduce work-related stress and thus to prevent (mental) illnesses: (1) structural interventions at an organizational level and (2) behavioral interventions at an individual level. Structural interventions aim to improve working conditions. One of the most important measures at the organizational level is the psychosocial risk-assessment (PRA) at the workplace [ 6 , 7 ]. Behavioral interventions aim to improve the coping strategies and resilience of employees and include stress-management training (SMT) and other measures at an individual level. Experts recommend a combination of both approaches to prevention in order to sustain mental health at the workplace [ 8 , 9 , 10 ].

Reviews of workplace-based interventions to reduce work-related stress and improve (mental) health show clear benefits [ 11 ]. General evidence for the effectiveness of workplace-based interventions at organizational level is strong, especially for interventions that aim to increase employee control (as does PRA) [ 12 , 13 ]. The PRA is a process for assessing and reducing psychosocial risks at work (work processes or work populations), such as high workloads, unfavorable working environments or conflictual interpersonal relationships [ 14 , 15 ]. In Germany, the process is specified by the Joint German Occupational Safety and Health Strategy [ 8 , 15 ] and comprises seven steps: (1) preparation of the overall process by defining the area of investigation, (2) measurement of psychosocial stress at work, (3) analysis of psychosocial stress at work, (4) development and implementation of measures, (5) effectiveness monitoring, (6) updating and maintenance of the process and (7) documentation. Moreover, studies on the effectiveness of behavioral interventions, specifically SMT, have shown positive effects on stress and stress-related outcomes [ 16 ].

Yet, occupational stress prevention measures are not or only inadequately implemented in many companies [ 17 , 18 ]. Insufficient dissemination and depth of implementation is particularly prevalent among MSE [ 19 ]. In Germany, for example, mental health prevention in the context of occupational health management is often neglected in MSE even though the inclusion of PRA in general risk assessments is legally mandatory since 2013: Most micro (85%) and small (67%) enterprises do not conduct a work place risk assessment including psychosocial factors and only 4% of micro and 7% of small enterprises complete a whole PRA cycle [ 17 ].

Barriers to occupational stress prevention in MSE

Implementation research investigates the real-world application of evidence-based interventions and the factors that might hinder it [ 20 ]. In addition to the effectiveness in terms of the desired changes, outcomes related to the process of implementation in a particular setting are equally important for the evaluation of (adapted) interventions [ 21 ]; also see Table  1 ). Implementation research also tries to identify all determinants that help to explain the success or failure of an intervention. According to the Consolidated Framework For Implementation Research (CFIR) [ 22 , 23 ] barriers can be found in any of the five main domains: (1) innovation (intervention) characteristics, (2) outer setting, (3) inner setting, (4) individuals and (5) the implementation process. Although research on the topic is rather scarce in the setting of MSE (or refers to samples of small- to medium-sized enterprises (SME)), some international studies (e.g. Netherlands, Ireland, Germany) have identified possible barriers in regard to these domains:

Regarding the intervention characteristics, Benning et al. [ 24 ] reported that the (perceived) complexity of prevention measures is a main barrier to the implementation in MSE. Pavlista et al. [ 25 ] found a negative image of PRA among representatives of MSE who also considered it the wrong approach for MSE.

With regard to the outer setting, a general lack of resources, specifically a shortage of staff and limited financial resources, has been reported to be one of the most important barriers to implement occupational health measures (including mandatory risk assessments for physical risks at the workplace) in SME and MSE [ 24 ]. Resource constraint is one of the typical characteristics of SME and likely to be even more prominent in micro-sized enterprises [ 26 ]. It has been argued that due to limited financial or human resources, the room for maneuver of MSE is often restricted so that they focus more on securing their existence and managing their day-to-day business instead of practicing occupational health management [ 27 ]. In addition, barriers to the implementation of PRA in MSE include ignorance of the legal obligation to conduct a PRA, not understanding the necessity of the assessment and an assumption of lack of acceptance by employees [ 25 ]. Managers of MSE may prefer to deal with work-related stress in an informal manner, outside the framework of occupational health management [ 28 ]. This is exacerbated by the ongoing stigmatization of mental health [ 29 , 30 ].

Facilitation factors to the implementation of (general) occupational prevention measures in the inner setting include awareness of (long term) health risks, high commitment among employers and openness among staff, good communication strategies, integration in the organizational policy and high trust and autonomy of the employees [ 24 ]. A handful of previous studies in the setting of MSE have shown that the awareness of and knowledge about different forms of occupational stress prevention is low on average, especially when it comes to structural interventions [ 29 , 30 ]. MSE have also been reported to consider the estimated benefit of stress prevention (specifically PRA) as too low [ 31 ].

Furthermore, in the individual domain, conducting a PRA involves some diagnostic skills from the employers or managers for the obtainment and interpretation of the questionnaire results, which might be a barrier for small enterprises (outside the health sector) without the necessary expertise [ 24 ]. Earlier qualitative studies have also stressed that owners of MSE regard prevention as a personal responsibility of the employee [ 32 ].

Finally, a few studies on barriers in the implementation process of general prevention programs (e.g. promoting exercise) show rather low participation rates (e.g. 47% in [ 18 ]).

Previous research provides a number of possible barriers to occupational stress prevention as well as factors that may cause it to fail. Nevertheless, to the best of our knowledge, there are no previous implementation studies with a comprehensive process evaluation of a complete implementation of occupational stress prevention in MSE.

Web-based solutions for occupational stress prevention in MSE

Given the reoccurring issue of constrained resources and expertise in small enterprises, web-based interventions could offer a possible alternative for occupational stress prevention in MSE, as they are associated with flexible use and low costs. The effectiveness of web-based interventions has been shown in several studies:

Research in a large Dutch healthcare setting has shown that a web-based PRA could be effective for stress prevention at the organizational level [ 33 ]. Recently, more web-based PRA tools for structural interventions in smaller organizations have been developed [e.g. 34 , 35 ], but not specifically for MSE. When developing an online training to facilitate the implementation of PRA, researchers from Germany and the Netherlands observed great interest from (representatives of) SME (42% of all participants) and participants reported a significant simplification of the process of PRA after the training [ 36 ].

For occupational stress prevention at the individual level, web-based SMT provides a way in which employees can train stress coping at anytime and anywhere, without disclosure to their employers or anyone else [ 37 ]. Meta-analytic evidence is still heterogeneous but shows that targeted web-based SMTs are effective in reducing stress and promoting well-being [ 38 , 39 ]. Web-based SMTs including additional guidance from e-coaches show slightly higher effects than unguided (self-help) interventions [ 40 ]. Moreover, there have been first studies that indicate positive results in terms of cost-effectiveness [ 39 , 41 , 42 ].

However, there is a lack of implementation research for web-based SMT in the setting of MSE and it is still unknown if such systems can promote the implementation of preventive measures in small companies.

Aim and research questions

The aim of the present study is to evaluate the implementation process of an integrated online platform for occupational stress prevention (combining established structural and behavioral preventive measures; named “System P”) in MSE. By addressing some of the barriers from previous studies, we hope to facilitate the uptake and use (implementation) of common stress prevention measures in MSE.

In the context of the present study, we explore different implementation outcomes according to Peters et al. [ 21 ] and use the CFRI framework [ 22 ] in order to evaluate specific determinants of the implementation in MSE from a quantitative and qualitative perspective. Besides important indicators of the intervention use (e.g., penetration, fidelity), we were especially interested in the implementation outcomes at the early stage of implementation (e.g., adoption, reach, acceptability and feasibility) to identify additional barriers and facilitators for the uptake and use of web-based stress prevention in this specific (undersupplied) setting.

The study is guided by the following research questions:

How can MSE be reached to implement web-based interventions for occupational stress prevention?

What kind of MSE decided to adopt and implement System P?

How did participating managers and employees of MSE use the web-based system (over a course of 6 months)?

How did MSE representatives and external stakeholders perceive the usefulness and fit of System P for the MSE setting?

The research questions will be answered in two parts, with a quantitative study focusing on questions 1), 2) and 3) and a qualitative study addressing question 4). The results will be reflected in a joint discussion at the end of this article.

Part 1 – quantitative study

Study design.

This implementation study was conducted according to a previously published study protocol [ 43 ] and was approved by the ethics commission of the Medical Faculty of the Heinrich-Heine University Düsseldorf (reference No: 2021−1588). The implementation study originally followed a hybrid approach to implementation research [ 44 ] to evaluate both the implementation process and effectiveness of the web-based interventions in MSE at the same time. No adjustments were made to System P during the implementation study. However, due to a low number of registrations, the effectiveness of the interventions could not be analyzed appropriately. Therefore, this article focuses on the implementation outcomes from a quantitative and qualitative perspective. Part I of the study has a pre-post design with two measurements (T1 – baseline, T2 – after 6 months). Self-assessment questionnaires were administered directly via the intervention platform System P at baseline (after registration) and T2. During registration in System P, the potential participants received written study information. Before each questionnaire, the participants had to agree to a written declaration of consent. Figure  1 summarizes all relevant steps of this study part.

Intervention: integrated platform system P

For the purpose of this study, we combined existing web-based interventions for occupational stress prevention on structural and behavioral levels (PRA and SMT), implemented them into an integrated online platform, called “System P” and adapted the platform to the specific needs of MSE. For example, we developed features that allow MSE to independently carry out a PRA from anywhere without external help by use of a simplified approach with online questionnaires and automatized reports of the results.

More specifically, the web-based PRA intervention provides a simplified tool to follow a complete PRA cycle as recommended in the German guidelines for structural stress prevention [GDA; 45 ]. The original seven steps of the PRA cycle were summarized into three steps for managers to actively implement: (1) Preparation, (2) Analysis and Actions and (3) Evaluation, with some other measures (e.g., documentation) automated within the process. In step 1 of the PRA component, employers can generate customized questionnaires for their MSE to assess possible psychosocial risks (per division). PRA within System P provides a pool of 55 questions from validated questionnaires about the main psychosocial stressors at work (e.g., organization, workload, social support, physical environment and boundaries) [ 46 , 47 ], which are expected to take between 30 and 60 min for individual employees to complete, depending on the number of chosen questions. The standard pre-selected questionnaire is a short version with nine questions on the most important stressors according to the GDA guidelines, which could be filled out in less than 10 min.

The web-based SMT is based on the established online training “GET.ON Stress” [ 48 , 49 ]. In total, the SMT consists of seven sessions, which can be completed at your own pace. Yet, it is recommended to complete them on a weekly schedule [ 48 , 49 ]. Each session is designed to last about 45 min, but can be interrupted at any time if necessary and continued at another time. The SMT includes two main strategies of stress coping: problem-solving [ 50 ] and emotion regulation [ 51 ]. GET.ON Stress has been shown to be effective in reducing stress and depressive symptoms in employees with elevated levels of perceived stress with intensive [ 48 ] and minimal guidance [ 52 ] as well as without any guidance [ 53 ]. It has also been shown to be effective as universal prevention [ 54 ] and for employees experiencing adverse working conditions [ 55 ]. Moreover, this intervention has been proved to be cost-effective compared to waiting controls [ 41 ]. Before integration into System P, the training was adapted to the MSE setting by introducing employees and managers of MSE as examples in the exercises [ 43 ].

System P further contains other non-intervention components that were designed to address common barriers to implementation. To overcome a potential lack of knowledge [ 25 ], the platform provides educational information about occupational stress prevention and its legal frameworks as well as mechanisms of work stress and health through a web-based “stress lexicon” (for a detailed description see appendix: 01 System P description). Although System P is available to registered users only, an overview of its most important benefits and three self-tests (checklist for organizational interventions, personal stress level and a quiz to test knowledge about stress) are provided on the public landing page in order to emphasize the usefulness of occupational stress prevention. The platform includes short video and audio instructions to increase accessibility as well as application examples from different industrial sectors. Typical obstacles in the implementation process with recommended solutions (e.g., communication towards employees) are listed in a section with frequently asked questions (FAQ) (see appendix: 02 FAQ). The manager version of System P also provides access to a moderated forum where employers can share advice and experiences [ 27 ] (see appendix: 03 Forum). The project team organized regular one-hour introductory webinars to explain all components of System P, which could be attended live or viewed afterwards.

The full development of System P has been described in the study protocol [ 43 ].

Recruitment

A two-stage recruitment strategy was developed through an extensive media and literature analysis to address and activate MSE managers to register their enterprise and subsequently invite their employees to participate. Recruitment strategy one (R1) included a structured approach and addressed MSE managers via e-mails sent out by external recruitment partners. Recruitment partners were institutions and networks who support occupational health and safety activities and are already known by MSE (e.g., accident insurance institutions, health insurance companies, company physicians and local networks). The standardized e-mail contained an invitation to register on System P, information about System P and an individualized link to the project website (see appendix: 04 Translation of standardized e-mail). On the project website, MSE managers received information about System P, including a short introduction video, a visual summary of the advantages of occupational stress prevention, self-administered tests and checklists, best practice scenarios and the opportunity to participate in an introduction event (or watch a recording of it). Interested MSE managers could register for System P if they fulfilled the inclusion criteria. Participants had to be either a manager of an MSE (enterprise with less than 50 employees – full-time equivalent) or an employee of an MSE invited by their manager. Participants were excluded from the study if they were under 16 years old. There were no further exclusion criteria.

Recruitment strategy two (R2) involved an unstructured approach in order to recruit additional participants for the study. The communication channels in R2 involved, among others, distributing information about System P via printed flyers, face-to-face seminars for MSE conducted by the recruitment partners, online distribution via newsletters and online posts on websites of recruitment partners, extensive articles in organizational health and safety journals or magazines, as well as presentations during events of recruitment partners and personal presentations for interested MSE. In addition, R2 involved an extensive social media campaign and search engine advertising (see appendix: 05 Example image social-media post).

Dealing with non-responders during recruitment: Participants who registered but did not fill out the baseline questionnaire after 3 months (non-responders = NR) were asked about their reasons for not using System P via a short e-mail survey with open questions. The NR feedback could be sent openly via a direct response to the e-mail or via a link to a short structured anonymous non-responder questionnaire.

Measurements and procedure

Socio-demographic variables of all respondents were collected at baseline measurement (age, gender, family status, first language, education, occupational position, working hours, income, ). Managers were additionally asked to provide information about the enterprise (number of employees, history of health program use in the organization, location (region) and industrial sector).

To systematically evaluate the success of the implementation of System P in the context of MSE, we collected data on the following outcomes as defined by Proctor et al. [ 21 ]: adoption/reach, appropriateness, feasibility, fidelity/dose, penetration, acceptability and costs. These outcomes specifically relate to the quality of the implementation (process) and help to map the (potential) real-world impact (or failure) of an intervention in a specific setting [ 21 ]. Each outcome has a theoretical basis and the implementation stages at which the outcomes are most salient vary. Implementation outcomes can be linked to different determinants in the CFIR framework [ 22 ], which helps to explain the necessary preconditions as well as potential barriers for successful implementation. Sustainability, another outcome defined by Proctor, was not included because of a relatively short follow-up period. Table  1 provides an overview of the operationalization of the main quantitative outcomes (see study protocol for further details).

Operationalization of implementation outcomes

Adoption: Adoption refers to the initial action taken by an organization interested in implementation (in our case: registering for the use of System P) [ 21 ]. Adoption rate was calculated by dividing the number of registered MSE at T0 by the number of MSE invited via e-mail during recruitment strategy one (R1). We additionally calculated the number of website visits per individualized link of our recruitment partners to see whether general interest varied by network/industry.

Reach: Reach was analyzed by comparing socio-demographic characteristics of MSE at T1 with data from the German population, partly gathered as aggregated data from our recruitment partners and partly from earlier representative studies. MSE were compared with regard to company size, industry and prior experience with occupational (stress) prevention.

Penetration: Penetration of an intervention refers to the reach within the participating organization to see if all employees can potentially benefit from it. It was analyzed by dividing the number of employees who filled in a baseline questionnaire by the number of actual employees in the MSE (as reported by the employer at T1).

Fidelity & Dose: To evaluate the adherence to the intended implementation process, we collected usage data directly via System P. Important indicators of dose were the number of logins, the number of steps completed in the web-based tool for PRA (minimum two of three) and the number of completed sessions in the web-based training (minimum five of seven).

Acceptability: In the quantitative measurements at T1 and T2, general acceptability of stress prevention interventions was assessed with three items on “Readiness for Change” adapted from earlier process evaluations for stress prevention [ 56 ] (scores 1 “do not agree at all” to 5 “completely agree”). Both managers and employees were asked whether they agreed that occupational stress prevention is valuable, positive for the organization and necessary. For a better classification, mean scores were grouped into three categories: high (4.0–5.0), moderate (2.6–3.9) and low (1.0-2.5) readiness for change. At T2, users were also asked to give a rating of one to five star to evaluate the usefulness of each intervention component of the system. Another indicator of acceptability was the user experience at T2. User experience was assessed with the safety subscale of the System Usability Scale [ 57 ] and the Questionnaire for Modular Evaluation based on the Components model of User Experience (meCUE) [ 58 ], which includes the aspects of visual aesthetics, usability and usefulness.

Costs: Although the use of the System P was free of charge, companies still required (human) resources to implement the web-based interventions. Therefore, usage times served as an estimate for the indirect costs for MSE and the providers. Hours spent on the web-based platform were assessed at T2 and login times were monitored for managers, employees and e-coaches over the course of the 6 months.

Additional measures

The following measures were additionally included in the questionnaires at T1 and T2 to account for pre-existing differences between MSE:

General conditions regarding stress prevention (inner setting) were measured with a German adaptation of the short version of the Psychosocial Safety Climate Questionnaire (PSC-4; [ 59 , 60 , 61 ].

We also assessed individual levels of mental health before and after the implementation (T1 and T2). The primary indicator were depressive symptoms measured by the short version of the Patient Health Questionnaire (PHQ-8) [ 62 ]. The eight items of the PHQ-8 describe different depressive symptoms and ask how often they have occurred in the last two weeks (scores 0 “not at all” to 3 “almost every day”). Usually, the cut-off score for clinical relevance is ten [ 62 ].

Finally, the general attitude towards new technological systems was measured with four items of the Affinity for Technology Interaction Short Scale (ATI-S; [ 63 ]) for managers and two items for employees. The ATI-S is a four-item scale with scores ranging from 1 “not at all true” to 6 “completely true”. The questionnaire asks whether the participants are generally open to new technologies and whether they want to try out new functions (in contrast to being satisfied with just basic functions). The employees were only asked about their openness towards the use of new technologies or new functions of a technology.

Further details on all measurements and target values for the implementation outcomes can be found in the original study protocol [ 43 ].

Due to the high drop-out between T1 and T2, analysis of quantitative data is restricted to descriptive results. Multilevel analysis with sufficient power was not possible. The quantitative analyses were calculated using IBM SPSS 27. A response rate was analyzed for structured recruitment. Furthermore, frequencies for the use of System P were calculated as well as mean values and sum values. A detailed description of the evaluation process allows for a specific overview of the reached target group and the adoption and use of System P. To explore possible determinants associated with characteristics of the individual and the inner setting, we compared the descriptive values of the individuals and MSE who participated in the study with the general German working population, as well as benchmark and reference values from other studies (e.g. psychosocial safety climate scale).

In R1, a standardized e-mail was sent to a pre-determined number of MSE ( n  = 5413) by recruitment partners. Knowing the exact number of contacted MSE allowed us to track their adoption behavior and calculate an adoption rate. In response to the standardized e-mails, the project website was visited 157 times within the time of analysis (i.e., between December 2021 and January 2022) Footnote 1 . On the project website, the short introduction video of system P was watched 57 times and seven visitors enrolled for the introduction event. In total, seven of these MSE registered for System P. Thus, addressing MSE managers via standardized e-mails and recruitment partners in R1 yielded an adoption rate of 0.13%.

Figure  2 provides an overview of the timeline of R1 and R2 as well as the resulting number of visits to the project website and subsequent registrations Footnote 2 .

Timeline of used recruitment channels during structured and unstructured approach

In R2, additional communication channels were used to increase the number of MSE registering in System P. R2 started in February 2022 and was unstructured, as the number of MSE managers that could potentially be reached via the different communications channels was unknown. Hence, it was not possible to calculate an adoption rate for R2.

In total, 40 MSE registered to use System P as a result of the two recruitment strategies. Figure  3 indicates how these 40 MSE learned about System P and further differentiates between MSE who only registered in System P and MSE who also filled in the baseline questionnaire.

Numbers of MSE by communication channels via which they were contacted (own info)

During the registration period from December 2021 to September 2022, 102 participants from 40 MSE (40 managers and 62 employees) registered in System P. Out of the 40 MSE registrations, 15 managers and eight employees did not complete the enrollment process or never filled out the baseline questionnaire. These managers and employees were contacted again after three to six months and asked to provide short feedback on why they did not use System P. From a total of 19 NR contacted, five NR replied. Reasons given were: not enough time to use System P, other important operational issues with higher priority in the enterprise and (mis-)believing that the size of the enterprises was too small to implement the measures in System P.

Penetration

Twenty-four of the registered 40 managers (representing their MSE) completed the baseline questionnaire (60%). These 24 MSE reported having a total of 359 employees. However, only eight of the 24 MSE invited some of their employees to use System P. Out of the 91 invited employees, 62 registered and 54 completed the baseline questionnaire (response rate of 59.3%). The overall resulting penetration rate (participating employees / all employees of the participating MSE) was 15%, with only 3 MSE having more than 50% of their staff included in the web-based stress prevention program.

Of the participating companies 58.3% were small-sized enterprises with 10–49 employees, while the remaining 41.7% were micro-sized enterprises with one to nine employees. Regarding the industrial sectors, healthcare and social services were overrepresented with 7 MSE. Other indicated sectors were professional, scientific and technical services (6 MSE), other services (3 MSE) as well as construction and trade (2 MSE) and manufacturing/production of goods (2 MSE).

Compared with the general German working population, the individual participants in the study had higher educational and vocational qualifications and were slightly younger. Among managers, 58.3% and among employees, 77.8% had a high school diploma, compared to 34.5% in the general working population [ 64 ]. The proportion of women in the present study (managers 54.2% and employees 70.4%) fits to the general figures in healthcare and social services in Germany [ 65 ].

For detailed sample information see Table  2 .

It is important that, the majority of participating MSE reported having prior experience with occupational health measures and 62.5% had already implemented occupational stress prevention interventions (PRA or SMT) in the past (see Fig.  4 ).

Previous experience with occupational prevention measures ( n  = 15)

The analyses of the Psychosocial Safety Climate (PSC) revealed a generally good climate in the participating MSE according to the benchmark standards and recommendations of Berthelsen et al. [ 59 ] (see Fig.  5 ). At the MSE level, 80% had scale values indicating a good PSC (mean 15.20 and 3.35 SD, n  = 20). An additional 15% of MSE had a moderate PSC for implementing psychosocial occupational health and safety measures and only one enterprise ranked below the cut-off value of eight [ 61 ].

Benchmark standards and recommendations [ 59 ]

The analysis of other characteristics revealed that the managers were mostly affiliated with technology (ATI-S: Managers: M = 3.85; 0.60 SD). Employees were also willing to try out new functions (74.1%) and were interested in new technical systems (70.4%).

Acceptability

On average, the readiness for change was high among managers (mean 4.36 (0.59 SD)) and employees (mean 4.13 (0.61 SD)) [ 56 , 66 ]. When looking at the readiness for change categorically (high, moderate and low readiness for change), 86.4% ( n  = 19) of managers and 69.8% ( n  = 37) of employees showed a high readiness for change, believing in the value of stress prevention interventions. A moderate readiness for change was found among the remaining users.

General acceptability of System P at T2 was measured by a simple star-rating (1 = lowest and 5 = highest acceptability) or higher. More than two-thirds of the participants rated their experience with the system with three or more stars (see Fig.  6 ). Overall, the user experience in T2 ( n  = 10) regarding the interaction with System P was satisfactory (mean = 2.83 (SD 0.88)). The usability aspect was rated highest by the participants ( n  = 14) (M = 3.62; SD 0.91), which showed that System P was commonly perceived as easy to use and that participants were able to quickly learn how to operate. Likewise, the design of System P (stylish, creative, attractive) was perceived as satisfactory by the users ( n  = 12) (M = 2.86; SD 1.15). Regarding the aspect of usefulness ( n  = 10), i.e., the extent to which System P is useful in achieving the user’s goals in the area of stress prevention, System P was rated as partially satisfying (M = 2.43; SD 1.04).

General experience with System P at T2 ( n  = 27)

Fidelity and dose (usage data)

The minimum number of completed steps for the web-based tool for PRA was set at two out of three. No MSE finished step two of the web-based PRA, but one MSE started to implement a measure to improve working conditions. In total, eleven MSE initiated a questionnaire-based assessment of psychosocial risks in the first step of the web-based PRA and invited 86 employees. The surveys of the web-based PRA were answered by 31 employees. Only two MSE looked at the results. On average, 31 questions were included in the survey, mainly questions related to the nine key factors of the PRA. Out of the eleven MSE that had sent out a survey, eight MSE used the standardized short version of the questionnaire (44 items), two MSE only used the questionnaire concerning key factors (9 items) related to the German guidelines for PRA and one MSE used the full questionnaire (64 items). Two MSE additionally developed their own questions. Beyond the key factors, managers were particularly interested in topics such as professional development, team, responsibility and accountability. The results of the key factors in the PRA step one ( n  = 31) showed that the employees were not exposed to any serious work-related stress, except for the area of work organization, where 33.3% of the employees ( n  = 10) stated that they often or always have to work under time pressure. However, most employees only worked overtime occasionally (46.7%; n  = 14). At the end of the observation period, no MSE in System P had implemented a targeted action to counteract risks (Step 2) or carried out an assessment of the targeted measures (Step 3).

The web-based SMT was initiated by 25 users. Nine participants completed the minimum number of training sessions (five out of seven) and thus achieved the implementation goal. Of these, eight participants, including two managers and six employees, completed the entire training. However, a more detailed analysis of the usage data of the web-based SMT shows that only three people engaged in the exercises of the individual sessions in the SMT as intended.

To evaluate indirect costs, the managers in T2 were asked how much time the processing of the web-based PRA had taken. In addition, the corresponding usage times for the first step (the employee survey) of the PRA were recorded for the managers. In T2, the managers ( n  = 4) stated that they had spent an average of two and a half hours with the PRA. According to the System P usage data ( n  = 13), the processing of the first step of the PRA took an average of 17.1 min (with a maximum of two hours). The same was applied for SMT use. In T2, participants ( n  = 10) stated that they had spent an average of one hour with the SMT. According to System P usage data ( n  = 23), the average time spent in the SMT was 13.4 min. Each training session is scheduled to last about 45 min (seven training sessions in total).

In total, participants logged in 302 times after the baseline assessment. Until the end of the observation period, the most active managers logged into the system 47 times. The average number of logins was 6.3 per manager. Employees logged into System P notably less often than managers. The most active employee logged in nine times within the observation period. The average number of logins was 2.8 per employee.

To enable participants to use System P as efficiently as possible, all users had the possibility to ask questions about the content or request help with technical difficulties via a support service when using the system. The support could be contacted by e-mail, or a return call could be requested. The support received a total of 52 requests from December 2021 to March 2023. There were only a few requests regarding content, e.g., inviting employees to System P. Most support requests were technical issues, e.g., regarding registration or access to the PRA results. None of the participants made use of the optional individual e-coaching.

Part 2 – qualitative study

The qualitative part of the study consists of semi-structured interviews conducted with managers of MSE and intercorporate stakeholders, e.g., occupational physicians or safety supervisors. Three researchers were responsible for the conception of the study and the analysis, and three researchers conducted the interviews. The research team is experienced in occupational health research and qualitative research methods. Due to the low adoption rate for part I, the interviews took place in parallel with an independent sample, deviating from the study protocol [ 43 ]. The sample included (a) managers of MSE who were not drawn from the registered users of System P and (b) intercorporate stakeholders who were experts on occupational prevention in the context of MSE. The research team aimed to identify barriers to adoption for potential users and experts. A corresponding interview guide (see appendix: 06 Interview questions managers and stakeholders) was pilot-tested.Recruitment procedure.

Participants for the interviews were recruited at the same time as participants for the quantitative study (R2). To facilitate the recruitment process of the qualitative study, a convenience and snowball sampling approach was selected, and participants were mainly recruited through personal contacts within the research environment and via the recruitment partners.

Participants

A total of six managers of MSE and six intercorporate stakeholders took part in the interviews between February 2022 and August 2022.

Four of the participating managers were female and had an average age of 49 years (11.21 SD). In terms of business size and sector, four managers led a micro-sized and two a small-sized enterprise in the following sectors: craft ( n  = 3), healthcare ( n  = 2) and tourism ( n  = 1). Of the participating intercorporate stakeholders two were female and on average 44 years (9.88 SD) old. They were occupational physicians ( n  = 2), (digital) coordinators for occupational health management ( n  = 2), a safety supervisor ( n  = 1) and a funding supporter for start ups ( n  = 1).

To gain further information about potential barriers to the implementation of System P, we collected additional data on the implementation outcomes acceptability, appropriateness and feasibility [ 21 ] (see Table  3 ).

Acceptability: Based on three short video sequences introducing the web-based platform, managers and intercorporate stakeholders were asked about the expected advantages and disadvantages of implementing System P and whether managers intended to use System P.

Appropriateness & Feasibility: During the interviews, managers of MSE and stakeholders were asked about the perceived fit of System P for the setting of MSE in general and for their own MSE. Additionally, participants were asked which components they liked and disliked and whether the instructions for the use of the system were clear. The semi-structured interviews also included questions about potential barriers to the use of the platform.

The interviews were conducted via a web conferencing software with audio recording. Each participant received written study information before the interview and gave written consent to participate. The duration of the interviews was 27 to 50 min for the managers, and 24 to 68 min for the intercorporate stakeholders. While conducting the interviews, the research team regularly discussed data saturation and concluded in the end that 12 participants was a sufficiently large number to reach it.

Data analysis

Qualitative data were analyzed by transcribing the recordings and applying qualitative content analysis as described by Kuckartz [ 67 ]. The software MAXQDA 2020 was used. In this approach, a dynamic categorization system that is replicable and valid was developed by three researchers. With regard to the coding of the data, a mixed deductive-inductive approach was used. The deductive approach comprises the development of main categories based on the literature and the research questions, whereas the inductive approach includes the development of subcategories from the interview data. Three researchers conducted these two coding approaches in combination with regular intensive discussion aiming for consistent coding. Additionally, three researchers reviewed the category system and coding for plausibility, consistency and interpretability. The results of the content analysis were summarized according to indicators of a successful implementation process, specifically addressing acceptability, appropriateness and feasibility [ 21 ] and the five domains of determinants according to the CFIR: innovation, outer setting, inner setting, individuals and the implementation process (see Table  3 ).

In an overall assessment, System P was predominantly evaluated to be acceptable and appropriate from the perspective of MSE managers and intercorporate stakeholders, especially in relation to the operating conditions of MSE. With regard to the feasibility of the intervention, results are mixed in terms of intervention format, adaptability, complexity and indirect costs (e.g., time investment) (see Table  4 for a summary).

Results predominantly showed that System P was acceptable and satisfactory from the perspective of managers as well as intercorporate stakeholders. Positive beliefs about the intervention prevail, the perception of advantages was much stronger than the perception of disadvantages and the entrepreneurs mainly favoured the use of the intervention.

As an indicator for acceptability, we first analyzed the knowledge and (perceived) beliefs about the intervention [ 22 ]. Regarding the knowledge about the intervention, the functions of the system and its components were understood very well by the participants. The participants expressed mainly positive and a few negative viewpoints about the intervention: The managers described both PRA and SMT as an opportunity to learn and reflect about problems at the workplace. While most of them perceived the additional components (e.g., stress lexicon and forum) of System P as useful, others stated that too many features could discourage the use of the system. Intercorporate stakeholders emphasized that System P meets the needs for tailored occupational stress prevention in MSE and described the platform as a “bundling of good offers” (IS4) and “help for self-help” (IS1). Some stakeholders believed that convincing MSE managers about the usefulness of the system could be difficult and that the implementation of the structural intervention modules requires expert knowledge so that it cannot necessarily be performed by MSE without third-party support. Apart from that, the managers considered a support of intercorporate stakeholders in the introduction of the system as beneficial for employee acceptability.

Furthermore, the participants approved the intervention source. They perceived the externally and independently developed platform as positive for employees´ acceptance. Additionally, the intercorporate stakeholders evaluated the evidence strength and quality of System P as positive due to the scientific findings behind the intervention modules.

We furthermore investigated the perception of advantages and disadvantages of implementing the intervention [ 22 ] as a key indicator of acceptability. Expected benefits were, among others, the removal of taboos concerning work stress, improvement of working conditions, fulfilment of legal requirements, increased team bonding and employee satisfaction, motivation, increased productivity, improved employee health and well-being and prevention of mental illnesses, absenteeism and employee fluctuation. Managers described a variety of possible gains for themselves and their companies (e.g., showing interest in employees’ health and improving their own stress management). Expected disadvantages included increased workload and time investment at both levels as well as psychological pressure for employees to take part.

The interviews revealed a general tension caused by societal changes and the managers’ aspiration to lead a sustainable, successful company. Reasons for the use of the system were, among others, a perceived need for and individual interest in occupational stress prevention as well as signalled interest in employee well-being and in increasing job satisfaction. Arguments against usage were data protection concerns and time intensity of the intervention. In sum, the results predominantly show a tendency of the managers towards accepting and implementing System P.

Appropriateness

To investigate the appropriateness (the perceived fit of an intervention for a specific setting) of occupational stress prevention with System P in MSE, we analyzed relevant CFIR domains such as: the inner setting of the company, characteristics of the managers, culture/implementation climate and perceived costs.

Generally, some participants indicated that the web-based intervention is fitting for small businesses, while others highlighted that the interventions only suit larger enterprises and that no “bureaucratic monster” (MSE5) is needed. The intercorporate stakeholders tended to consider System P as suitable for small (not micro-sized) enterprises in general. Facilitating conditions for a good fit were the prevailing open climate of change and the conviction of the managers, which came along with the willingness to concede time for the use of the system, as well as a generally available willingness of the employees for the implementation of the intervention. Furthermore, the participants perceived a fit for companies with a high degree of digitalization. In this context, some stakeholders affirmed that MSE in general have the necessary digital competencies to use the system, while others suggested that not every enterprise has them. System P was seen as more appropriate for MSE with a preference for structured work and compliance with legal requirements and for MSE with an open team culture and prior experience in the use of formal and/or digital interventions. Some intercorporate stakeholders indicated that occupational stress prevention is not seen as a priority in many MSE, so that raising awareness for occupational stress prevention is difficult. In contrast, others emphasized the increasing awareness for work stress, the willingness to use prevention offers like System P and good communication due to the size of MSE. The assessment of perceived indirect costs in terms of time investment was mixed, both appropriate and inappropriate.

Feasibility

Participants named a few additional barriers to a successful implementation in the setting(s). While some participants appreciated the digital format, which enables flexibility in use (i.e., from home or after working hours), some preferred a non-digital approach. The intercorporate stakeholders emphasized that accompanying face-to-face support in addition to the digital approach would improve feasibility.

Findings with regard to the adaptability of the system were mixed. Some of the participants valued that the PRA can be easily adapted to the needs of the enterprise (including question pool, different assessment modes, differentiation of departments) and that the modules of System P were optional to use (e.g., FAQ, forum). In contrast, some other participants assessed the high number of features as potentially overwhelming. The complexity of the intervention is a further important factor for feasibility. With respect to the design complexity, participants valued the structure of the system, which provides a good overview of the modules and is easy and intuitive to use. On the other hand, the text load within the system was mentioned as a barrier. Regarding the pool of interventions provided by the system, some participants perceived the level of complexity as adequate, while others raised concerns that it was too complex and described the interventions as challenging to put into practice (i.e., difficult to implement measures after Step 1 of PRA). Some participants of both groups mentioned that managers need to be able to communicate the benefits of stress prevention to their employees and stated that they might lack the specific knowledge about occupational stress prevention to be able to use System P appropriately without a supporting third party.

Barriers mentioned by the managers were the fact that users need to be able and willing to invest time in using System P. This means that managers need to provide their employees with the respective time. There was a general disagreement with regard to the feasibility of the time investment/indirect costs (partly related to an uncertainty about the actual time necessary for a successful implementation). The stakeholders perceived the communication regarding the implementation process as challenging and even more time-consuming than the actual use of the system.

The aim of the present study was to comprehensively evaluate the implementation of web-based interventions for stress prevention in MSE. For this purpose, a combined web-based platform (including PRA and SMT) was developed considering the specific needs of MSE and designed to enable location- and time-independent stress prevention without external help. We observed the implementation process in 40 MSE over the course of 6 months and evaluated the success of System P according to the implementation outcomes by Proctor et al. [ 21 ]. We also analyzed possible determinants according to the CFIR [ 22 ].

In the past, stress prevention has hardly been implemented in MSE [ 17 ]. The reasons for these low implementation rates are manifold, with the main barriers being the perceived complexity of prevention measures for implementation in MSE [ 24 ] and a lack of resources [ 24 , 26 ]. System P addressed these barriers (e.g., through a simplified PRA and location- and time-independent access) in order to increase the likelihood of an adoption of the interventions. The results of the study show that System P partly fell short of the expectations regarding the outcomes of the implementation (process) within the observation period. System P reached only a small part of the target group, which was already sensitized to stress prevention. However, even in this informed and engaged group, usage was low in terms of fidelity and penetration, although System P was accepted by users and received good ratings for usability and appropriateness.

With regard to the first two research questions (How to reach MSE and which MSE decide to implement the system? ), it becomes clear that despite extensive recruitment strategies, only a small part of the target group could be reached. These were in particular MSE who were already sensitized to stress prevention. However, even in this informed and engaged group, the use in terms of fidelity and penetration was low, although System P was accepted by users and received good ratings for usability and appropriateness. Users were predominantly female, had a high educational status and good mental health. The high number of female participants is particularly striking in the context of MSE, where the proportion of female managers is usually low [ 68 , 69 ]. However, the gender proportion is in line with the strongly represented health sector, which is generally more familiar with health and prevention measures [ 24 ].

Compared to other implementation studies, the adoption rate of System P among the MSE was quite low [ 18 ]. Yet, in relation to other communication strategies used during the unstructured approach (e.g., social media campaign), the conversion rate (from website visits to registrations) for e-mail contact via the recruitment partners was higher and compares well to other studies in small enterprises [ 70 ]. Accordingly, communication via intercorporate stakeholders seems to be generally suitable for the target group, but the appropriate communication strategy alone did not lead to the desired adoption of the intervention.

The MSE that decided to adopt System P were mainly from the health sector and professional, scientific and technical services and already sensitized to the issue of stress prevention (i.e., they had already dealt with measures to reduce stress, some had even carried out a PRA). They also described a good working atmosphere for their organization. These higher levels of expertise are in line with Benning et al. [ 22 ], who describe that conducting a PRA does require some diagnostic skills and knowledge of work demands and health risks in order to interpret the results. This could be an obstacle for small companies from other sectors that do not have the necessary expertise, thus discouraging them from adopting and using System P without external support. Although further verification is needed, these results may indicate that the potential health benefit (and cost effectiveness) of web-based interventions in the setting of MSE is low, since it is precisely companies that already have good working conditions and a commitment to stress prevention that implement them.

The managers and employees of MSE who adopted System P also showed a high affinity for technology and a high readiness for change. This confirms that the characteristics of the individuals involved, such as abilities and motivation [ 71 ] and attitude towards the intervention [ 72 ], contribute to the decision to implement an intervention. Other characteristics, such as general self-efficacy, might also play a role and should be further investigated [ 22 , 23 ].

From the non-responder survey, we know that some MSE did not adopt System P because other business aspects were prioritized over the introduction of stress prevention or because there was no time (inner setting). This suggests that there is a lack of knowledge and conviction about the benefits of stress prevention measures and that existing legal regulations and scientific recommendations (outer setting) do not sufficiently motivate MSE to deal with the topic of stress prevention [ 22 , 23 ]. The qualitative results underline the statements of the non-responders, as they indicate that stress prevention is not a priority in MSE. It is not clear whether the low priority of stress prevention results from a lack of intrinsic motivation of MSE (who did not adopt System P) or inhibiting external circumstances [ 26 ] such as coping with day-to-day business due to the shortage of skilled workers [ 27 ] or a combination of both.

When we explore the actual use of the individual components of System P by the registered MSE (research question three), the results reveal that even the sensitized and committed sample did not implement System P as intended. The targeted penetration rate was achieved in just three MSE. Moreover, not all employees of the participating MSE were invited to System P by their managers. A potential explanation for this may be that MSE find it complex to introduce an extensive and structural intervention for several people at the same time [ 24 ]. This is supported by the finding that the MSE in System P tend to initially introduce the intervention to only some of the employees (e.g., in individual departments), as indicated by the usage data of the PRA. Another reason why MSE managers did not invite all employees to System P could be difficulties in understanding the technical procedure of inviting employees. However, this is contradicted by the good evaluations of the usability of System P in the follow-up survey. It is also conceivable that the MSE consider the estimated benefit of System P or the relative advantage of the intervention to be too low [ 22 , 31 ], or that the managers of the MSE consider prevention to be the personal responsibility of the employees [ 32 ].

During the study period, only two MSE carried out an analysis of possible work-related risk factors (step 1 of the PRA) and looked at the results. There was no introduction of appropriate countermeasures (only the effort to implement a measure in one MSE), which is in line with previous studies among MSE [ 73 ]. It is possible that managers of MSE prefer to deal with work-related stress in an informal way, outside the formalized framework of workplace health management [ 14 , 74 ]. They may not see a real connection between their own practical work and PRA or perceive PRA as something imposed on them by others. This could be exacerbated by the stigma still attached to mental health in MSE and the consequent negative image of PRA [ 25 ]. Another explanation for the low usage of the PRA could be the perceived indirect costs. Stakeholders indicated that they expect a time-consuming communication effort accompanying the implementation of these stress prevention interventions. At the same time, the actual usage data from System P show that MSE spent only a very limited amount of time with the PRA. Consequently, there seems to be a gap between perceived and actual time investment, and between the direct and indirect costs of implementation. The reasons for this can be manifold, possibly the perception of time is distorted in stressful everyday life or the calculations include internal thoughts and discussions with the team.

The qualitative results indicate that managers need to be able to communicate the benefits of the PRA to their staff and that they may lack the specific knowledge of workplace stress prevention to be able to use System P appropriately without an external expert. This may be linked to concerns about the possible consequences of PRA, which may lead to unrest in the organization, unrealistic expectations or resistance that has to be dealt with [ 75 , 76 ]. More offline time investments for communication, participation and change management within the MSE might be needed, which may itself become an additional barrier according to the qualitative results. Beck & Lehnhardt [ 17 ] also argued that increased contact with professional Occupational Safety and Health (OSH) experts by companies may help improve the use of stress prevention interventions. The use of the SMT also fell short of expectations based on prior studies which showed a higher compliance of participants with the program [ 37 , 41 ]. Even though the training is available online and can be paused at any time, the usage data show that the participants did not spend much time with the training. It emerged from the qualitative results that managers have to provide their employees with the appropriate time to use System P. This time requirement in turn represents an additional investment that is less easy to compensate for in MSE than in larger companies [ 26 ]. In contrast to this argument, the participating MSE showed a high readiness for change and a good working atmosphere, which generally describes a good inner setting for the introduction of an intervention [ 22 , 23 ]. However, personal characteristics such as learning style, seniority or values of the users may lead to the SMT not being used to its full extent [ 71 ].

System P was also designed to increase the adaptability of measures to the specific needs of MSE [ 25 ]. In response to our final research question (How do stakeholders perceive System P? ), the web-based intervention was described as appropriate for MSE, especially for those who already work with digital technologies. Overall, this fits with the stated affinity for technology of managers and employees in System P, who are generally open to engaging with new digital technologies. It can only be speculated that the openness to new digital technologies is less pronounced in other sectors that are hardly or not at all represented in the present sample, such as craft enterprises, and thus hindered the adoption of System P [ 77 ]. There is some evidence in the literature, which shows that the level of digitalization in MSE is less advanced than in larger companies [ 78 , 79 ]. In addition, digital technologies are primarily used in the context of everyday working tasks (e.g., e-mail communication) and hardly or not at all when it comes to improve work processes [ 78 ].

Implications

Overall, the results of the implementation of System P show that in order to adopt and fully integrate stress prevention in the daily routine of MSE, awareness and knowledge of stress prevention must continue to increase so that the issue becomes more of a priority in organizations than it has been so far. In any case, it was clear from our study that a state-of-the-art web-based system alone had little effect on the ability of MSE to initiate and carry out stress prevention interventions.

Certainly, some good and perhaps unconscious efforts to reduce stress prevention are already taking place in MSE on an informal level [ 74 ], but in our opinion this does not replace systematic stress prevention. However, for a concrete and comprehensive establishment of stress prevention in MSE, it will be necessary to demonstrate the actual benefit or return on investment. This is because the indirect costs (e.g., time investment) to adopt and use an intervention such as System P (despite the high flexibility offered by a web-based solution) appear to be too high for MSE, as they seem to have already reached the limit of their capacity with their day-to-day business due to external circumstances such as a shortage of skilled workers or inflation [ 24 , 26 , 27 ]. The MSE which have been active in System P are likely to perceive a lower cost and lower risk associated with the introduction of a new intervention due to their good working climate, the employees’ willingness for change and the already increased knowledge of stress prevention and are therefore higher willingness to engage in System P than other MSE. For other MSE, one could consider stronger regulations or more control mechanisms as facilitators to implement stress prevention. Yet, these measures are likely to increase the pressure on small companies and might potentially lead to additional resistance [ 80 ]. Instead, increased support for MSE at various levels (e.g., instrumental, financial, bureaucratic relief) could be a more effective strategy. The use of web-based interventions could be accompanied and guided by professional OSH experts, given that the lack of knowledge about stress prevention and the mechanisms for implementing an intervention such as System P (including sensitive communication and careful interpretation of the results) also appears to be a criterion for non-adoption, especially outside the health and technical services sectors. Future research on stress prevention in MSE should therefore focus more on the return on investment for MSE and allow for a much greater degree of freedom in implementation and documentation as well as better (external) support for the integration with general entrepreneurial and management tasks.

Strengths and limitations

This study is one of the first systematic evaluations of the implementation of stress prevention in the setting of MSE and it has several strengths. First of all, we used many different communication strategies and media channels to reach the target group and were therefore able to compare their effectiveness. This provided insight for practitioners and public health stakeholders who want to target MSE (e.g., that a more targeted and personal communication is overall more effective than mass or social media campaigns).

Secondly, we worked together with intercorporate stakeholders who are in direct exchange with MSE from various sectors and who were mostly mentioned as the main source of information on prevention issues by the users of System P. Researchers should continue to work together with these stakeholders when developing/adapting interventions and choosing communication approaches. However, personal contact, which was most effective, is also the most time-consuming type of recruitment and increases the indirect costs on both provider and user side.

Finally, we combined the quantitative results with qualitative data to gain specific and further insights into the perceptions and assessments of System P among the target group and stakeholders. This approach fills knowledge gaps about the implementation process in MSE and answers some questions about barriers to the implementation process raised by the low dose and fidelity in the first part of the study.

The study also has several limitations. It was originally set up with three measurement points but due to the long recruitment phase and slow uptake, T3 (another 6 months later) could not be completed. Therefore, this study lacks information on the sustainability of the implementation process. Overall, the recruitment took longer than expected and the drop-out between measurements was high. Therefore, several of the originally planned comparisons (pre-post measurements) were not possible within the given timeframe and no conclusions on the real-world effectiveness of the interventions could be made. One possible explanation for the drop-out among managers and employees is that the recruitment took place during the ongoing COVID-19 pandemic, which challenged companies in a fundamental way [ 81 ].

Due to the small sample size, the results may not be representative for all MSE. However, because the intervention was rolled out across the country and through many media channels, we can assume that theoretically a large proportion of the target group was aware of the intervention.

Moreover, due to the extensive amount of measured outcomes and control variables the questionnaires within the implementation study were quite long. This could have stopped some interested users from participating further after the registration. To take this into account, we often used short versions or single item measures instead of full questionnaires. Therefore, the validity and reliability is restricted. Future studies should use fully validated instruments to measure a selection of process outcomes when possible (e.g. ORIC for Organizational Readiness for Implementing Change [ 82 ] and adapt them for the MSE setting.

Finally, the interview data only captured perceived suitability and usefulness as well as potential barriers from the perspective of MSE managers and intercorporate stakeholders. In light of the convenience sampling, the qualitative results might not be fully representative of all MSE in Germany. Ideally, we would have interviewed actual users of System P at the start and again during or after the implementation period, but this was not possible without a study extension, given the slow and difficult recruitment. Due to the high drop-out, conclusions on the actual suitability and usefulness are based on a few quantitative measures from T2 and cannot be generalized. Future studies should include the view of employees on usefulness and fit and also assess how the perception might change over time during use.

In conclusion, the results of the present study point towards several barriers to the implementation process of stress prevention interventions among MSE. Despite of a high general acceptance of web-based interventions, the overall complexity and perceived (indirect) costs, e.g. time investment, make it less feasible for small enterprises without external help. This results in minimal use of the intervention. Considering the potentially high long-term costs of stress-related illnesses for MSE, communication efforts should be increased and additional support from intercorporate stakeholders who are already sensitized and well-informed is necessary to facilitate the implementation process.

Data availability

No datasets were generated or analysed during the current study.

Additional visits to the project websites from other sources explain the discrepancies between visits due to the standardized e-mails from R1 reported in Fig.  1 ( n  = 157) and total visits in December 2021 and January 2022 reported in Fig.  2 ( n  = 467).

The reported visits to the project website only refer to the number of tracked visits per month for those who enabled the necessary cookies in their browser. The real number of visits is assumed to be higher.

Abbreviations

Gemeinsame Deutsche Arbeitsschutzstrategie (Joint German Occupational Safety and Health Strategy)

Statistisches Bundesamt (German Federal Statistical Office)

Micro and Small-Sized Enterprises

Psychosocial Risk Assessment

Stress Management Training

Occupational Safety and Health

Niedhammer I, Bertrais S, Witt K. Psychosocial work exposures and health outcomes: a meta-review of 72 literature reviews with meta-analysis. Scand J Work Environ Health. 2021;47:489–508. https://doi.org/10.5271/sjweh.3968 .

Article   PubMed   PubMed Central   Google Scholar  

Hassard J, Teoh KRH, Visockaite G, Dewe P, Cox T. The cost of work-related stress to society: a systematic review. J Occup Health Psychol. 2018;23:1–17. https://doi.org/10.1037/ocp0000069 .

Article   PubMed   Google Scholar  

Eurostat. Small and medium-sized enterprises (SMEs). 2023. https://ec.europa.eu/eurostat/web/structural-business-statistics/information-on-data/small-and-medium-sized-enterprises . Accessed 18 Sep 2023.

Eurostat Data Browser. Number of enterprises in the non-financial business economy by size class of employment. 2023. https://ec.europa.eu/eurostat/databrowser/view/TIN00145/default/table?lang=en . Accessed 18 Sep 2023.

Unternehmen DESTATIS. Tätige Personen, Umsatz und weitere betriebs- und volkswirtschaftliche Kennzahlen: Deutschland, Jahre, Unternehmensgröße. 2023. https://www-genesis.destatis.de/datenbank/beta/statistic/48121/table/48121-0001 . Accessed 18 Sep 2023.

Health and Safety Executive (HSE). Risk assessment: a brief guide to controlling risks in the workplace. Sudbury, Suffolk: Health and Safety Executive; 2014.

Google Scholar  

Janetzke H, Ertel M. Psychosocial risk management in a European comparison 2017: bundesanstalt für Arbeitsschutz Und Arbeitsmedizin (BAuA). https://doi.org/10.21934/BAUA:BERICHT20170106 .

GDA. Empfehlungen zur Umsetzung der Gefährdungsbeurteilung psychischer Belastung: Arbeitschutz in der Praxis. 3rd ed.; 2017.

Klenke B. Psychische Gefährdungsbeurteilungen in Deutschen Unternehmen – Anforderungen, Aktueller stand und Vorgehensweisen. In: Ghadiri A, Ternès A, Peters T, editors. Trends Im Betrieblichen Gesundheitsmanagement: Ansätze Aus Forschung Und Praxis. Wiesbaden: Springer Gabler; 2016. pp. 17–26.

EU-OSHA. ESENER 2019: What does it tell us about safety and health in Europe’s workplaces? 2020.

Joyce S, Modini M, Christensen H, Mykletun A, Bryant R, Mitchell PB, Harvey SB. Workplace interventions for common mental disorders: a systematic meta-review. Psychol Med. 2016;46:683–97. https://doi.org/10.1017/S0033291715002408 .

Article   CAS   PubMed   Google Scholar  

Fox KE, Johnsona ST, Berkmana LF, Sianojad M, Soh Y, Kubzanskyc LD, Kellyd EL. Organisational- and group-level workplace interventions and their effect on multiple omains of worker well-being: a systematic review. Work Stress. 2022;30–59. https://doi.org/10.1080/02678373.2021.1969476 .

Montano D, Hoven H, Siegrist J. Effects of organisational-level interventions at work on employees’ health: a systematic review. BMC Public Health. 2014;14:135. https://doi.org/10.1186/1471-2458-14-135 .

Schuller K, Beck D. Arbeitsgestaltung Im Rahmen Der Gefährdungsbeurteilung Psychischer Belastung. ASU. 2023;2023:145–8. https://doi.org/10.17147/asu-1-257880 .

Article   Google Scholar  

Beck D, Taşkan E, Elskamp E, Gold M, Gregersen S, Klamroth H et al. Berücksichtigung psychischer Belastung in der Gefährdungsbeurteilung: Empfehlungen zur Umsetzung in der betrieblichen Praxis. 4th ed.; 2022.

Miguel C, Amarnath A, Akhtar A, Malik A, Baranyi G, Barbui C, et al. Universal, selective and indicated interventions for supporting mental health at the workplace: an umbrella review of meta-analyses. Occup Environ Med. 2023;225–36. https://doi.org/10.1136/oemed-2022-108698 .

Beck D, Lenhardt U. Consideration of psychosocial factors in workplace risk assessments: findings from a company survey in Germany. Int Arch Occup Environ Health. 2019;92:435–51. https://doi.org/10.1007/s00420-019-01416-5 .

McCoy K, Stinson K, Scott K, Tenney L, Newman LS. Health promotion in small business: a systematic review of factors influencing adoption and effectiveness of worksite wellness programs. J Occup Environ Med. 2014;56:579–87. https://doi.org/10.1097/JOM.0000000000000171 .

Beck D, Lenhardt U, Schmitt B, Sommer S. Patterns and predictors of workplace health promotion: cross-sectional findings from a company survey in Germany. BMC Public Health. 2015;15:343. https://doi.org/10.1186/s12889-015-1647-z .

Peters DH, Adam T, Alonge O, Agyepong IA, Tran N. Implementation research: what it is and how to do it. BMJ. 2013;347:f6753. https://doi.org/10.1136/bmj.f6753 .

Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38:65–76. https://doi.org/10.1007/s10488-010-0319-7 .

Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. https://doi.org/10.1186/1748-5908-4-50 .

Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated Consolidated Framework for Implementation Research based on user feedback. Implement Sci. 2022;17:75. https://doi.org/10.1186/s13012-022-01245-0 .

Benning FE, van Oostrom SH, van Nassau F, Schaap R, Anema JR, Proper KI. The Implementation of Preventive Health Measures in small- and medium-Sized Enterprises-A combined Quantitative/Qualitative study of its determinants from the perspective of Enterprise representatives. Int J Environ Res Public Health. 2022. https://doi.org/10.3390/ijerph19073904 .

Pavlista V, Angerer P, Diebig M. Barriers and drivers of psychosocial risk assessments in German micro and small-sized enterprises: a qualitative study with owners and managers. BMC Public Health. 2021;21:1–12. https://doi.org/10.1186/s12889-021-11416-1 .

Harney B, Gilman M, Mayson S, Raby S. Advancing understanding of HRM in small and medium-sized enterprises (SMEs): critical questions and future prospects. Int J Hum Resource Manage. 2022;33:3175–96. https://doi.org/10.1080/09585192.2022.2109375 .

Brandt M, Holtermann I, Kunze D. Betriebliches Gesundheitsmanagement für Klein- Und Kleinstunternehmer. In: Badura B, Ducki A, Schröder H, Klose J, Meyer M, editors. Fehlzeiten-Report 2015: Neue Wege für mehr Gesundheit - Qualitätsstandards für ein zielgruppenspezifisches Gesundheitsmanagement. 1st ed. Springer-Verlag Berlin Heidelberg; 2015. pp. 61–9.

Beck D, Schuller K, Schulz-Dadaczynski A. Aktive Gefährdungsvermeidung bei psychischer Belastung: Möglichkeiten Und Grenzen Betrieblichen Handelns. Praev Gesundheitsf. 2017;12:302–10.

Wulf IC, Süß S, Diebig M. Akteure Der Gefährdungsbeurteilung psychischer Belastung – Perspektiven Und Konflikte Im Betrieblichen Arbeits- Und Gesundheitsschutz. Z Arb Wiss. 2017;71:296–304. https://doi.org/10.1007/s41449-017-0085-4 .

Pavlista V, Angerer P, Diebig M. Barriers and drivers of psychosocial risk assessments in German micro and small-sized enterprises: a qualitative study with owners and managers. Under review.

Sommer S, Kerschek R, Lenhardt U. Gefährdungsbeurteilung in Der Betrieblichen Praxis: Ergebnisse Der GDA-Betriebsbefragungen 2011 und 2015. Dortmund: Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA); 2018.

Saito J, Odawara M, Takahashi H, Fujimori M, Yaguchi-Saito A, Inoue M, et al. Barriers and facilitative factors in the implementation of workplace health promotion activities in small and medium-sized enterprises: a qualitative study. Implement Sci Commun. 2022;3:23. https://doi.org/10.1186/s43058-022-00268-4 .

Havermans BM, Boot CR, Brouwers EP, Houtman IL, Heerkens YF, Zijlstra-Vlasveld MC, et al. Effectiveness of a digital platform-based implementation strategy to prevent work stress in a healthcare organization: a 12-month follow-up controlled trial. Scand J Work Environ Health. 2018;44:613–21. https://doi.org/10.5271/sjweh.3758 .

Diebig M, Dragano N, Körner U, Lunau T, Wulf IC, Angerer P. Development and validation of a questionnaire to measure Psychosocial Work stressors in Modern Working environments. J Occup Environ Med. 2020;62:185–93. https://doi.org/10.1097/JOM.0000000000001779 .

Dragano N, Wulf IC, Diebig M. Digitale Gefährdungsbeurteilung Psychischer Belastung. Fehlzeiten-Report 2019. Berlin, Heidelberg: Springer; 2019. pp. 111–25. https://doi.org/10.1007/978-3-662-59044-7_8 .

Chapter   Google Scholar  

Auweiler L, Lemmens V, Hülsheger U, Lang J. Digital training for psychosocial risk assessment as an approach to foster primary prevention for SMEs: an evaluation study. Work. 2022;72:1549–61. https://doi.org/10.3233/WOR-211264 .

Lehr D, Geraedts A, Asplund RP, Khadjesari Z, Heber E, de Bloom J, et al. In: Kryspin-Exner Ilse PN, editor. Occupational e-mental health: current approaches and promising perspectives for promoting mental health in workers. Healthy at Work: Springer; 2016. pp. 257–81. https://doi.org/10.1007/978-3-319-32331-2_19 .

Stratton E, Lampit A, Choi I, Malmberg Gavelin H, Aji M, Taylor J, et al. Trends in Effectiveness of Organizational eHealth interventions in addressing employee Mental Health: systematic review and Meta-analysis. J Med Internet Res. 2022;24:e37776. https://doi.org/10.2196/37776 .

Carolan S, Harris PR, Cavanagh K. Improving Employee Well-Being and Effectiveness: systematic review and Meta-analysis of web-based psychological interventions delivered in the Workplace. J Med Internet Res. 2017;19:e271. https://doi.org/10.2196/jmir.7583 .

Heber E, Ebert DD, Lehr D, Cuijpers P, Berking M, Nobis S, Riper H. The benefit of web- and computer-based interventions for stress: a systematic review and Meta-analysis. J Med Internet Res. 2017;19:e32. https://doi.org/10.2196/jmir.5774 .

Ebert DD, Kählke F, Buntrock C, Berking M, Smit F, Heber E, et al. A health economic outcome evaluation of an internet-based mobile-supported stress management intervention for employees. Scand J Work Environ Health. 2018;44:171–82.

PubMed   Google Scholar  

Carolan S, Harris PR, Cavanagh K. Multimedia Appendix: improving Employee Well-Being and Effectiveness: systematic review and Meta-analysis of web-based psychological interventions delivered in the Workplace. J Med Internet Res. 2017;19:e271. https://doi.org/10.2196/jmir.7583 .

Engels M, Boß L, Engels J, Kuhlmann R, Kuske J, Lepper S, et al. Facilitating stress prevention in micro and small-sized enterprises: protocol for a mixed method study to evaluate the effectiveness and implementation process of targeted web-based interventions. BMC Public Health. 2022;22:591. https://doi.org/10.1186/s12889-022-12921-7 .

Landes SJ, Mcbain SA, Curran M. Reprint of: an introduction to e ff ectiveness-implementation hybrid designs. Psychiatry Res. 2019;112630. https://doi.org/10.1016/j.psychres.2019.112630 .

Gemeinsame Deutsche Arbeitsschutzstrategie. Empfehlungen zur Umsetzung der Gefährdungsbeurteilung psychischer Belastung: Arbeitschutz in der Praxis; 2017.

Diebig M, Angerer P. Description and application of a method to quantify criterion-related cut-off values for questionnaire-based psychosocial risk assessment. Int Arch Occup Environ Health. 2020. https://doi.org/10.1007/s00420-020-01597-4 .

Diebig M, Angerer P. Description and application of a method to quantify criterion-related cut-off values for questionnaire-based psychosocial risk assessment. Int Arch Occup Environ Health. 2021;94:475–85. https://doi.org/10.1007/s00420-020-01597-4 .

Heber E, Lehr D, Ebert DD, Berking M, Riper H. Web-based and mobile stress management intervention for employees: a Randomized Controlled Trial. J Med Internet Res. 2016;18:e21. https://doi.org/10.2196/jmir.5112 .

Heber E, Ebert DD, Lehr D, Nobis S, Berking M, Riper H. Efficacy and cost-effectiveness of a web-based and mobile stress-management intervention for employees: design of a randomized controlled trial. BMC Public Health. 2013;13:655. https://doi.org/10.1186/1471-2458-13-655 .

D’Zurilla TJ, Nezu AM. Problem-solving therapies. In: Dobson KS, editor. Handbook of cognitive-behavioral therapies. 3rd ed. New York: Guilford Press; 2010. pp. 211–45.

Berking M, Whitley B. Affect regulation training: a practitioners’ Manual. New York, NY, s.l.: Springer New York; 2014.

Book   Google Scholar  

Nixon P, Boß L, Heber E, Ebert DD, Lehr D. A three-armed randomised controlled trial investigating the comparative impact of guidance on the efficacy of a web-based stress management intervention and health impairing and promoting mechanisms of prevention. BMC Public Health. 2021;21:1511. https://doi.org/10.1186/s12889-021-11504-2 .

Ebert DD, Lehr D, Heber E, Riper H, Cuijpers P, Berking M. Internet- and mobile-based stress management for employees with adherence-focused guidance: efficacy and mechanism of change. Scand J Work Environ Health. 2016;42:382–94.

Ebert DD, Franke M, Zarski A-C, Berking M, Riper H, Cuijpers P, et al. Effectiveness and moderators of an internet-based Mobile-supported Stress Management Intervention as a Universal Prevention Approach: Randomized Controlled Trial. J Med Internet Res. 2021;23:e22107. https://doi.org/10.2196/22107 .

Nixon P, Ebert DD, Boß L, Angerer P, Dragano N, Lehr D. The efficacy of a web-based stress management intervention for employees experiencing adverse working conditions and occupational self-efficacy as a mediator: Randomized Controlled Trial. J Med Internet Res. 2022;24:e40488. https://doi.org/10.2196/40488 .

Havermans BM, Boot CRL, Brouwers EPM, Houtman ILD, Anema JR, van der Beek AJ. Process evaluation of a Digital platform-based implementation strategy aimed at Work Stress Prevention in a Health Care Organization. J Occup Environ Med. 2018;60:e484–91. https://doi.org/10.1097/JOM.0000000000001402 .

Brooke J. Sus: a quick and dirty’usability. Usability Evaluation Ind. 1996;189.

Minge M, Thüring M, Wagner I, Kuhr CV. The meCUE Questionnaire: A Modular Tool for Measuring User Experience. In: Soares M, Falcão C, Ahram TZ, Cham. Cham: Springer International Publishing; 2016. pp. 115–128.

Dollard MF. The PSC-4; a short PSC Tool. In: Dollard MF, Dormann C, Awang Idris M, editors. Psychosocial Safety Climate: a new work stress theory. Cham: Springer International Publishing; 2019. pp. 385–409. https://doi.org/10.1007/978-3-030-20319-1_16 .

Otto W, Neuert C, Meitinger K, Beitz C, Schmidt R, Stiegler A. Psychosocial Safety Climate - Weiterentwicklung und Validierung eines Instrumentes für die Erfassung Der Handlungsbereitschaft Zum Schutz Der Psychischen Gesundheit Der Beschäftigten auf organisationaler Ebene. GESIS – Pretest Lab; 2016.

Berthelsen H, Muhonen T, Bergström G, Westerlund H, Dollard MF. Benchmarks for evidence-based Risk Assessment with the Swedish Version of the 4-Item Psychosocial Safety Climate Scale. Int J Environ Res Public Health. 2020;17:8675. https://doi.org/10.3390/ijerph17228675 .

Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114:163–73. https://doi.org/10.1016/j.jad.2008.06.026 .

Wessel D, Attig C, Franke T, ATI-S -. An Ultra-short Scale for assessing Affinity for Technology Interaction in user studies. In: Alt F, Bulling A, Döring T, editors. MuC’19: mensch-und-Computer; 08 09 2019 11 09 2019; Hamburg Germany. New York, New York: The Association for Computing Machinery, Inc; 2019. pp. 147–54. https://doi.org/10.1145/3340764.3340766 .

DESTATIS. Kleine und mittlere Unternehmen. 2021. https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen /Kleine-Unternehmen-Mittlere-Unternehmen/_inhalt.html;jsessionid=2DF8779B9BB68114C815B8892DECA138.live741#sprg475846. Accessed 16 Jun 2021.

DESTATIS, Gesundheitspersonal. Deutschland, Jahre, Einrichtungen, Geschlecht. 2021. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitspersonal/_inhalt.html . Accessed 13 Feb 2023.

Hoek RJA, Havermans BM, Houtman ILD, Brouwers EPM, Heerkens YF, Zijlstra-Vlasveld MC, et al. Stress Prevention@Work: a study protocol for the evaluation of a multifaceted integral stress prevention strategy to prevent employee stress in a healthcare organization: a cluster controlled trial. BMC Public Health. 2018;18:26. https://doi.org/10.1186/s12889-017-4585-0 .

Kuckartz U. Qualitative inhaltsanalyse. Methoden, Praxis, Computerunterstützung. 69 469. Weinheim: Beltz Verlagsgruppe; 2018.

Elam AB, Brush CG, Greene PG, Baumer B, Dean M, Heavlow R, et al. Women’s Entrepreneurship Report 2018/2019. Babson College: Smith College and the Global Entrepreneurship Research Association; 2019.

Cardella GM, Hernández-Sánchez BR, Sánchez-García JC. Women entrepreneurship: a systematic review to Outline the boundaries of Scientific Literature. Front Psychol. 2020;11:1557. https://doi.org/10.3389/fpsyg.2020.01557 .

Martin A, Kilpatrick M, Cocker F, Sanderson K, Scott J, Brough P. Recruitment and Retention Challenges of a Mental Health Promotion Intervention Targeting Small and Medium Enterprises. In: Karanika-Murray M, Biron C, editors. Derailed organizational interventions for stress and Well-Being: confessions of failure and solutions for success. Dordrecht: Springer Netherlands; 2015. pp. 191–200. https://doi.org/10.1007/978-94-017-9867-9_22 .

Frambach RT, Schillewaert N. Organizational innovation adoption a multi-level framework of determinants and opportunities for future research. J Bus Res. 2002:163–76.

VanDeusen LC, Meterko MM, Mohr D, Seibert MN, Parlier R, Levesque O, Petzel RA. Implementation of a clinical innovation: the case of advanced clinic access in the Department of Veterans affairs. J Ambul Care Manage. 2008;31:94–108. https://doi.org/10.1097/01.JAC.0000314699.04301.3e .

Pavlista V, Angerer P, Kuske J, Schwens C, Diebig aM. Exploring the barriers to the implementation of Psychosocial Risk Assessment in Micro- and small-sized firms. Z für Arbeits- und Organisationspsychologie A&O. 2022;66:170–83. https://doi.org/10.1026/0932-4089/a000398 .

Beck D, Berger S, Breutmann N, Fergen A, Gregersen S, Morschhäuser M et al. Arbeitsschutz Der Praxis Empfehlungen Zur Umsetzung Der Gefährdungsbeurteilung Psychischer Belastung 2017:1–25. 3rd ed.

Aust B, Rugulies R, Finken A, Jensen C. When workplace interventions lead to negative effects: learning from failures. Scand J Public Health. 2010;38:106–19. https://doi.org/10.1177/1403494809354362 .

Srivastava S, Agrawal S. Resistance to change and turnover intention: a moderated mediation model of burnout and perceived organizational support. JOCM. 2020;33:1431–47. https://doi.org/10.1108/JOCM-02-2020-0063 .

Brockhaus CP, Bischoff TS, Haverkamp K, Proeger T, Thonipara A. Digitalisierung von kleinen und mittleren Unternehmen in Deutschland - ein Forschungsüberblick. Göttinger Beiträge zur Handwerksforschung 2020. https://doi.org/10.3249/2364-3897-GBH-46 .

Öz F. Digitalisierung in Kleinbetrieben: Ergebnisse aus Baugewerbe, Logistik und ambulanter Pflege. Forschung Aktuell. 2019.

Runst P, Proeger T. Digitalisierungsmuster Im Handwerk - Eine regionale und sektorale Analyse Des digitalisierungs-checks des Kompetenzzentrums Digitales Handwerk. Göttinger Beiträge Zur Handwerksforschung. 2020. https://doi.org/10.3249/2364-3897-GBH-39 .

Hagqvist E, Vinberg S, Toivanen S, Landstad BJ. A balancing act: Swedish occupational safety and health inspectors’ reflections on their bureaucratic role when supervising micro-enterprises. Small Bus Econ. 2021;57:821–34. https://doi.org/10.1007/s11187-020-00384-2 .

Vinberg S, Danielsson P. Managers of micro-sized enterprises and Covid-19: impact on business operations, work-life balance and well-being. Int J Circumpolar Health. 2021;80:1959700. https://doi.org/10.1080/22423982.2021.1959700 .

Buhrmann L. Organizational readiness for implementation change (ORIC). ORIC_German; 2018.

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Acknowledgements

The study is embedded in the collaborative project “PragmatiKK – Pragmatische Lösungen für die Implementation von Maßnahmen zur Stressprävention in Kleinst- und Kleinbetrieben“ (= Pragmatic solutions for the implementation of stress prevention interventions in micro and small-sized enterprises)” (for more information on roles and responsibilities within the project see www.pragmatikk.de ). We thank our colleagues Thorsten Lunau, Ingo Klingenberg, Sarah Lepper, Christoph Landers and Babette Schneckener for their help in conceptualizing the web-based platform “System P”. We would also like to thank our colleague Anna Feisthauer for her support in collecting the qualitative data.

This study is funded by the German Federal Ministry of Education and Research (BMBF) within the Framework Concept “Future of work” (fund number 02L16D020 to 02L16D023) and managed by the Project Management Agency Forschungszentrum Karlsruhe, Production and Manufacturing Technologies Division (PTKA).

Open Access funding enabled and organized by Projekt DEAL.

Author information

Miriam Engels and Louisa Scheepers are shared-first authors.

Authors and Affiliations

Department of Work and Organisational Psychology, Faculty of Psychology, Open University of the Netherlands, Valkenburgerweg 177, Heerlen, 6419 AT, The Netherlands

Miriam Engels & Judith Engels

Institute of Medical Sociology, Centre for Health and Society, Medical Faculty and University Hospital, Heinrich-Heine-University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany

Louisa Scheepers, Ines C. Wulf & Nico Dragano

Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital, Heinrich-Heine-University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany

Louisa Scheepers, Valeria Pavlista, Kira Schmidt-Stiedenroth & Peter Angerer

Chair of Business Administration, in particular Work, Human Resource Management and Organization Studies, Faculty of Business Administration and Economics, Heinrich-Heine-University Dusseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany

Judith Engels, Rebekka Kuhlmann & Stefan Süß

Department of Health Psychology and Applied Biological Psychology, Institute of Psychology, Leuphana University Luneburg, Universitätsallee 1, 21335, Lüneburg, Germany

Leif Boß, Sascha A. Ruhle & Dirk Lehr

Chair for Entrepreneurship and Management, Faculty of Management, Economics and Social Sciences, University of Cologne, Albertus‑Magnus‑Platz, 50923, Köln, Germany

Johanna Kuske & Christian Schwens

K12 Agentur für Kommunikation und Innovation GmbH, Schirmerstr. 76, 40211, Düsseldorf, Germany

Lutz Lesener & Jörg Hoewner

Department of Human Resource Studies, Tilburg University, Prof. Cobbenhagenlaan 225, Tilburg, 5037 DB, The Netherlands

Sascha A. Ruhle

Institute for International Business, Department of Global Business and Trade, Vienna University of Economics and Business, Welthandelsplatz 1, Wien, 1020, Austria

Florian B. Zapkau

German Social Accident Insurance Institution for the Administrative Sector, Markgrafenstraße 18, 10969, Berlin, Germany

Ines C. Wulf

Department of Work and Organisational Psychology, Faculty I - Psychology, Trier University, Universitätsring 15, 54296, Trier, Germany

Mathias Diebig

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Contributions

All authors have made substantial contributions to the conception of the study design and the development of the integrated web-based platform (System P). ME, LS, ND and IW were primarily responsible for the coordination and execution of the proposed quantitative study. JE, JK and KS were responsible for the coordination and execution of the qualitative study. ME, LS, LB, JE, JK and KS have written the first draft of the manuscript. The authors RK, LL, JH, VP, MD, SR, FZ, JB, DL, CS and SS have critically revised the first draft of the manuscript. All authors read and approved the final manuscript. LS is the corresponding author ([email protected]).

Corresponding author

Correspondence to Louisa Scheepers .

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Ethics approval and consent to participate.

The study was performed in accordance with the Declaration of Helsinki. The ethics commission of the Medical Faculty of the Heinrich-Heine University Düsseldorf (Germany) has approved subject recruitment and all other procedures involved in the proposed study prior to any subject recruitment (reference No: 2021 − 1588). The study participation was voluntary; participants received adequate information about the study and data protection measures before giving written consent. At their request, participants could also obtain a summary of the trial results after completion.

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Engels, M., Scheepers, L., Engels, J. et al. Web-based occupational stress prevention in German micro- and small-sized enterprises – process evaluation results of an implementation study. BMC Public Health 24 , 1618 (2024). https://doi.org/10.1186/s12889-024-19102-8

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1. Introduction

2. formulation of the proposed framework, 3. formulation of a multicomponent monodisperse spheres model, 4. numerical experiments, 5. discussion, 6. conclusions.

research papers \(\def\hfill{\hskip 5em}\def\hfil{\hskip 3em}\def\eqno#1{\hfil {#1}}\)

Qu­antitative selection of sample structures in small-angle scattering using Bayesian methods

a Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8561, Japan, b Japan Synchrotron Radiation Research Institute, Sayo, Hyogo 679-5198, Japan, c National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan, and d Facalty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan * Correspondence e-mail: [email protected]

Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.

Keywords: small-angle X-ray scattering ; small-angle neutron scattering ; nanostructure analysis ; model selection ; Bayesian inference .

SAS measurement data are expressed in terms of scattering intensity that corresponds to a scattering vector, a physical quantity representing the scattering angle. Data analysis requires selection and parameter estimation of a mathematical model of the scattering intensity that contains information about the structure of the specimen. This selection process is critical as it involves assumptions about the structure of the specimen.

We conducted numerical experiments to assess the effectiveness of our proposed method. These experiments are based on synthetic data used to estimate the number of distinct components in a specimen, which was modeled as a mixture of monodisperse spheres of varying radii, scattering length densities and volume fractions. The results demonstrate the high accuracy, interpretability and stability of our method, even in the presence of measurement noise. To discuss the utility of the proposed method, we compare our approach with traditional model selection methods based on the reduced χ -squared error.

In this section, we present a detailed formulation of our algorithm for selecting mathematical models for SAS specimens using Bayesian model selection. The pseudocode for this algorithm is provided in Algorithm 1.

2.1. Bayesian model selection

The likelihood is thus expressed as

Let φ ( K ) be the prior distribution of the parameter K that characterizes the model, and φ ( Ξ | K ) be the prior distribution of the model parameters Ξ . Then, from Bayes' theorem, the posterior distribution of the parameters given the measurement data can be written as

2.2. Calculation of marginal likelihood

Sampling from the joint probability distribution at each inverse temperature gives

2.3. Estimation of model parameters

In this paper, we consider isotropic scattering and focus on the scattering vector's magnitude q , defined as

Monodisperse spheres are spherical particles of uniform radius. The scattering intensity I ( q ,  ξ ) of a specimen composed of sufficiently dilute monodisperse spheres of a single type for the scattering vector magnitude q is given by

To formulate the scattering intensity of a specimen composed of K types of monodisperse sphere, we assume a dilute system and denote the particle size of the k th component in the sample as R k and the scale as S k . The scattering intensity of a sample composed of K types of monodisperse sphere is then given by

The numerical experiments reported in this section were conducted with a burn-in period of 10 5 and a sample size of 10 5 for the REMC. We set the number of replicas for REMC, the values of inverse temperature and the step size of the Metropolis method taking into consideration the state exchange rate and the acceptance rate.

4.1. Generation of synthetic data

(i) Set the number of data points to N = 400 and define the scattering vector magnitudes at N equally spaced points within the interval [0.1, 3] to obtain { q i } i =1 N =400 (nm −1 ).

In this section, we consider cases with pseudo-measurement times of T = 1 and T = 0.1. Generally, smaller values of T indicate greater effects from measurement noise.

4.2. Setting the prior distributions

In the Bayesian model selection framework, prior knowledge concerning the parameters Ξ and the model-characterizing parameter K is set as their prior distributions.

In this numerical experiment, the prior distributions for the parameters Ξ were set as Gamma distributions based on the pseudo-measurement time T used during data generation, while the prior for K was a discrete uniform distribution over the interval [1, 4].

4.3. Results for two-component monodisperse spheres based on scale ratio

The ratio of the scale parameters S 1 and S 2 for spheres 1 and 2 during data generation, denoted r S , is defined as

Parameter values used for data generation with varying   Sphere 1 Sphere 2 Radius (nm) 2 10 Scale 250 {250, 100, 20, 0.5, 0.1, 0.05} Background (cm ) 0.01 Pseudo-measurement time {1, 0.1} Fitting to synthetic data generated at various values and residual plots. Panels and show cases for pseudo-measurement times of = 1 and = 0.1, respectively. In plots ( )–( ) and ( )–( ), the scale ratio is displayed in descending order for = 1 and = 0.1, respectively. Black circles represent the generated data and the black dotted lines indicate the true scattering intensity curves. For models = 1, = 2, = 3 and = 4, the fitting curves and residual plots are represented by blue dashed–dotted lines, red dashed lines, orange solid lines and green dotted lines, respectively. Fitting curves were plotted using 1000 parameter samples that were randomly selected from the posterior probability distributions for each model. The width of the distribution of these fitting curves reflects the confidence level at each point. Results of Bayesian model selection among models = 1–4 for varying values. Panel shows the posterior probability for each model using data generated with a pseudo-measurement time of = 1, and panel shows results for = 0.1. In cases ( )–( ) and ( )–( ), the scale ratio is displayed in descending order for = 1 and = 0.1, respectively. The height of each bar corresponds to the average values calculated for ten data sets generated with different random seeds, with maximum and minimum values shown as error bars. Areas highlighted in red indicate cases where, on average, the highest probability was found for the true model with = 2, while blue backgrounds indicate that models other than = 2 were associated with the highest probability on average. The number of times each model was associated with the highest probability in numerical experiments for ten data sets generated with different random seeds at each value ) = 1   1 2 3 4 ( ) 1.0 0 0 0 ( ) 0.4 0 0 0 ( ) 0.08 0 0 0 ( ) 0.002 0 0 0 ( ) 0.0004 0 0 0 ( ) 0.0002 2 0 0 ) = 0.1   1 2 3 4 ( ) 1.0 0 0 0 ( ) 0.4 0 0 0 ( ) 0.08 0 0 0 ( ) 0.002 0 0 0 ( ) 0.0004 1 0 0 ( ) 0.0002 0 0 0 4.4. Results for two-component monodisperse spheres based on radius ratio During synthetic data generation, the ratio of the radii R 1 and R 2 of spheres 1 and 2, denoted r R , was defined as In this setup, we generated seven types of data by varying the value of r R for pseudo-measurement times of T = 1 and T  = 0.1. Parameter values used for data generation when varying   Sphere 1 Sphere 2 Radius (nm) {9.9, 9.7, 9.5, 0.5, 0.5, 0.4, 0.3} 10 Scale 250 100 Background (cm ) 0.01   Pseudo-measurement time {1, 0.1}   Fitting to synthetic data generated at various values and residual plots. Panels and show cases for pseudo-measurement times of = 1 and = 0.1, respectively. In plots ( )–( ) and ( )–( ), the radius ratio is displayed in descending order for = 1 and = 0.1, respectively. Black circles represent the generated data and the black dotted lines indicate the true scattering intensity curves. For models = 1, = 2, = 3 and = 4, the fitting curves and residual plots are represented by blue dashed–dotted lines, red dashed lines, orange solid lines and green dotted lines, respectively. Fitting curves were plotted using 1000 parameter samples that were randomly selected from the posterior probability distributions for each model. The width of the distribution of these fitting curves reflects the confidence level at each point. Results of Bayesian model selection among models = 1–4 for varying values. Panel shows the posterior probability of each model using data generated with a pseudo-measurement time of = 1, and panel shows results for = 0.1. In cases ( )–( ) and ( )–( ), the radius ratio is displayed in descending order for = 1 and = 0.1, respectively. The height of each bar corresponds to the average values calculated for ten data sets generated with different random seeds, with the maximum and minimum values shown as error bars. Areas highlighted in red indicate cases where the true model = 2 was most highly supported, while the blue backgrounds indicate that the likelihood of a model other than = 2 was the highest. The number of times each model was most highly supported in numerical experiments for ten data sets generated by varying values ) = 1   1 2 3 4 ( ) 0.99 1 0 0 ( ) 0.97 0 0 0 ( ) 0.95 0 0 0 ( ) 0.5 0 0 0 ( ) 0.05 0 0 0 ( ) 0.04 1 0 0 ( ) 0.03 0 0 0 ) = 0.1   1 2 3 4 ( ) 0.99 0 0 0 ( ) 0.97 2 0 0 ( ) 0.95 0 0 0 ( ) 0.5 0 0 0 ( ) 0.05 1 0 0 ( ) 0.04 3 0 0 ( ) 0.03 0 0 0 5.1. Limitations of the proposed method 5.2. model selection based on χ -squared error. In SAS data analysis, selecting an appropriate mathematical model for the analysis is a crucial but challenging process. In this subsection, we compare the conventional model selection method based on the χ -squared error with the results of model selection using our proposed method. The fitting results and residual plots for the data shown in Fig. 3 ( ) were derived using parameters that minimize the χ-squared error from the posterior probability distributions for models ranging from = 1 to = 4. For each of these models, the fitting curves and their corresponding residual plots are represented by blue dashed–dotted lines, red dashed lines, orange solid lines and green dotted lines, respectively. The legend indicates the reduced χ-squared values for each model ( = 1 to = 4). Model selection results based on reduced χ-squared values -squared value to 1 for ten data sets generated with different random seeds for each setting = 1. Labels ( ) to ( ) refer to the settings in Figs. 3–4 and Table 2. The cases with the highest level of support for each data set are shown in bold.   1 2 3 4 ( ) 1.0 0 2 0\sim ( ) 0.4 0 0 1 ( ) 0.08 0 0 1 ( ) 0.002 0 0 0 ( ) 0.0004 0 4 1 ( ) 0.0002 0 2 0 In this paper, we have introduced a Bayesian model selection framework for SAS data analysis that quantitatively evaluates model validity through posterior probabilities. We have conducted numerical experiments using synthetic data for a two-component system of monodisperse spheres to assess the performance of the proposed method. We have identified the analytical limits of the proposed method, under the settings of this study, with respect to the scale and radius ratios of two-component spherical particles, and compared the performance of traditional model selection methods based on the reduced χ -squared. The numerical experiments and subsequent discussion reveal the range of parameters that can be analyzed using the proposed method. Within that range, our method provides stable and highly accurate model selection, even for data with significant noise or in situations in which qualitative model determination is challenging. In comparison with the traditional method of selecting models based on fitting curves and data residuals, it was found that the proposed method offers greater accuracy and stability. SAS is used to study specimens with a variety of structures other than spheres, including cylinders, core–shell structures, lamellae and more. The proposed method should be applied to other sample models to determine the feasibility of expanding the analysis beyond the case examined here to broader experimental settings. Future work could benefit from using the proposed method to conduct real data analysis, which is expected to yield new insights through our more efficient analysis approach. Funding information This work was supported by JST CREST (grant Nos. PMJCR1761 and JPMJCR1861) from the Japan Science and Technology Agency (JST) and by a JSPS KAKENHI Grant-in-Aid for Scientific Research (A) (grant No. 23H00486). This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence , which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. Follow J. Appl. Cryst. Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Methodology Qualitative vs. Quantitative Research | Differences, Examples & Methods Qualitative vs. Quantitative Research | Differences, Examples & Methods Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023. When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood. Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories. Table of contents The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research. Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions. Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s). Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe). However, some methods are more commonly used in one type or the other. Quantitative data collection methods Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone). Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships. Observations : Observing subjects in a natural environment where variables can’t be controlled. Qualitative data collection methods Interviews : Asking open-ended questions verbally to respondents. Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research. Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior. Literature review : Survey of published works by other authors. A rule of thumb for deciding whether to use qualitative or quantitative data is: Use quantitative research if you want to confirm or test something (a theory or hypothesis ) Use qualitative research if you want to understand something (concepts, thoughts, experiences) For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents. Quantitative research approach You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?” You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”. Qualitative research approach You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?” Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns. Mixed methods approach You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale. It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends. Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data. Analyzing quantitative data Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables. Applications such as Excel, SPSS, or R can be used to calculate things like: Average scores ( means ) The number of times a particular answer was given The correlation or causation between two or more variables The reliability and validity of the results Analyzing qualitative data Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers. Some common approaches to analyzing qualitative data include: Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases Thematic analysis : Closely examining the data to identify the main themes and patterns Discourse analysis : Studying how communication works in social contexts If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples. Chi square goodness of fit test Degrees of freedom Null hypothesis Discourse analysis Control groups Mixed methods research Non-probability sampling Quantitative research Inclusion and exclusion criteria Research bias Rosenthal effect Implicit bias Cognitive bias Selection bias Negativity bias Status quo bias Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail. In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question . The research methods you use depend on the type of data you need to answer your research question . If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods . If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data. If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods. Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations. There are various approaches to qualitative data analysis , but they all share five steps in common: Prepare and organize your data. Review and explore your data. Develop a data coding system. Assign codes to the data. Identify recurring themes. The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis . A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic. Cite this Scribbr article If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator. Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved June 19, 2024, from https://www.scribbr.com/methodology/qualitative-quantitative-research/ Is this article helpful? Raimo Streefkerk Other students also liked, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, mixed methods research | definition, guide & examples, "i thought ai proofreading was useless but..". I've been using Scribbr for years now and I know it's a service that won't disappoint. 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In the results section, writers systematically report their findings, and in discussion, they interpret these findings. 9 Presenting the Results of Quantitative Analysis After that comes a literature review, which ends with a summary of the research question(s) and/or hypotheses. A methods section, which explains the source of data, sample, and variables and quantitative techniques used, follows. Many analysts will include a short discussion of their descriptive statistics in the methods section. PDF Discussion Section for Research Papers The discussion section is one of the final parts of a research paper, in which an author describes, analyzes, and interprets their findings. They explain the significance of those results and tie everything back to the research question(s). In this handout, you will find a description of what a discussion section does, explanations of how to ... 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Step 5: Share your recommendations. Discussion section example. Writing results and discussion chapters for quantitative research Martin McMorrow. This document provides guidance on writing the results and discussion chapters for quantitative research theses. It discusses the structure and style of these chapters, including how to present tables and figures, summarize results, and compare findings to previous research. Examples are given from published theses. A Practical Guide to Writing Quantitative and Qualitative Research INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ... PDF Chapter 4-Quantitative Results and Discussion Chapter 4-Quantitative Results and Discussion 4.1. Introduction In the previous chapter, the research design used in this study was described in detail. This included both the quantitative data collection involving the two questionnaires: BALLI and PELLEM, and the qualitative data collection which entailed a semistructured interview. (PDF) Results and Discussion This chapter 5 presents the results of the study. First, an outline of the informants included in the study and an overview of the statistical techniques employed in the data analyses are given ... Chapter 3 Results and Discussion Chapter III RESULTS AND DISCUSSION. The presentation, analysis, and interpretation of the data acquired for this study are all included in this chapter. According to the methodology, statistical tools are utilized to determine the student's perceptions towards the preservation and improvement of Central Luzon State University's landmark. What Is Quantitative Research? Revised on June 22, 2023. Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. Quantitative research is the opposite of qualitative research, which involves collecting and analyzing ... Full article: Characterization of public space forms in traditional In the realm of spatial research on traditional villages, a prevailing trend involves supporting qualitative research through quantitative analysis using measurement tools. This trend primarily concentrates on two key aspects: the characterization of spatial patterns and their causal analysis, along with the impacts resulting from these spatial ... Web-based occupational stress prevention in German micro- and small The research questions will be answered in two parts, with a quantitative study focusing on questions 1), 2) and 3) and a qualitative study addressing question 4). The results will be reflected in a joint discussion at the end of this article. Quantitative selection of sample structures in small-angle scattering F (K) is referred to as the Bayesian free energy, also known as the stochastic complexity.The posterior probability of the model, , can be rephrased as the validity of model K for the measurement data .In other words, calculating and comparing the value of for all candidate models {K} thus enables quantitative model selection.Note that in Bayesian model selection the parameter K does not need ... Qualitative vs. Quantitative Research When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. assignment essay homework paper phd report resume review thesis