Systems are deeply embedded in the way an organisation manages health and safety (H&S). Over the last century there are recognizable shifts in the approaches taken toward H&S systems. Four Health and Safety System Approaches are identified and covered showing how the perspective taken by each of H&S and related accident analysis differ. These Health and Safety System Approaches are not substitutable options, rather they can be viewed as progressively adding to ways in which H&S is improved by organisations, in a sense reflecting a progression in the level of maturity of organisational H&S. The multi-level perspectives reflected in Health & Safety System Approaches can be similarly reflected in the law of tort and in Commissions of Inquiry into H&S failures.
Please click on image below to read the article:
The article is on Slideshare:
The article is also on Calameo, which offers reading options including “flip” pages:
With the myriad of problem situations organisations face and the wide range of options in techniques, methodologies, and models available, how do we select a “best fit” between a problem situation and a means to its solution?
The purpose of this paper is to explain Multilevel System Analysis (MSA) as an introduction to Systems Thinking, and a means to match problem situations with Systems Thinking methodologies and models for their resolution.
Please click on image below to read the article:
The article is on SlideShare:
The article is also on Calameo, which offers reading options including “flip” pages:
Human Activity System (HAS) Maps visually illustrate and capture the “flow” of causes and outcomes in a problem situation.
In HAS Mapping a problem situation is viewed as occurring within a “system”, a Human Activity System (HAS), where the “system” allows a problem situation’s causes and effects to be identified and shaped into a causal relationship flow map, so underlying issues and their interrelationships can be better recognised and addressed.
The flow of causes to outcomes within a problem situation can be developed, for example, based on using, for example, “but-for” analysis (i.e. “but for an act or omission of X, Y would not have occurred”), and “Why- Because” analysis.
HAS Maps are versatile and can be applied to investigating, assessing, and addressing a wide range of problem situations.
Please click on an image below to read the article:
The article is on Slideshare
The article is also on Calameo which offers reading options including “flip” pages
Open Surveys and their analysis
1. Open Surveys
The purpose of Open Surveys is to help understand and improve the effectiveness of an organisational change or some aspect of organisational performance based on respondent comments.
Open Surveys and their analysis are based on, and developed from, respondents’ thoughts and feelings expressed in their own words.
Open Surveys are very different to what are termed Statistical Surveys for, in a sense, they work in reverse. That is to say:
- In an Open Survey respondents answer a few questions in detail by expressing themselves in their own words. Responses are grouped into categories, and from these categories broader patterns are built showing how comments are linked. Such patterns are, or can be fitted together, into a model covering and representing the collective views of respondents. In comparison;
- Statistical Surveys use a “forced” response approach to questions that are structured around a pre-agreed model. These models have factors (categories) that are used as the basis to develop questions around an area of interest. Respondents have little to no opportunity to explain their answers. The answers provide a means of statistically (quantitatively) confirming responses against a survey’s underlying model’s factors (categories).
As Statistical Survey’s generally do not explore questions in detail or depth, this can affect how valid they are where issues are more complex and need respondents to explain reasons for believing something, as is available through Open Surveys.
Open Surveys are also termed Open Question Surveys and use respondent comments that are sometimes described as Narrative data; Text data; or Qualitative data.
2. Advantages and disadvantages of Open Surveys
- Allows respondents to answer in their own words.
- Allows for “richness” or “depth” of explanation in responses.
- Can identify and explore issues not currently fully recognised or understood.
- Can identify options for further action.
- Limited by the writing skills of respondents.
- Analysis involves a more complex interpretation.
- Stronger role of interpretation in analysing responses.
3. Designing Open Survey Questions
Questions used in Open Surveys usually use a word such as how, what, when, where, and why, to allow respondents to express their thoughts and feelings such as their attitudes, opinions, understanding, likes, dislikes, suggestions, and ideas.
Open (sometimes referred to as unstructured) questions are ones in which possible answers are not suggested in the design of the survey questions, and where respondents answer in the way they see the world.
Open ended questions are framed to encourage self explanation of thoughts and feelings in a sentence, paragraph, or more.
Open ended questions include some direction on which the feedback is sought, such as:
- Please tell us what changes you would like to see?
- Please tell us what in your view we do well?
- Where could we improve?
- Why do you use our services?
4. Analysing Open Survey Responses
4.1 Open Survey and Analysis Framework
The purpose of an Open Survey design and analysis of responses is to provide a framework that:
a) Accurately and as thoroughly as possible captures the intent of respondent comments; and
b) Analyses these comments so that they construct, step by step, an understanding of issues raised in a way that is both meaningful and relevant: That “fit and work” .
An explanation of how respondent comments are analysed, developed, and tested is provided in 4.2 Steps in Analysing Comments.
4.2 Steps in Analysing Comments
Step 1. Read through all comments to get a feeling for the responses, and themes that emerge from this overview.
Step 2 Develop Categories
2.1 Create categories. Identify categories (sometimes referred to as labels) from the different themes that emerge from comments. When a response is allocated to a category this is based on what is actually said and also any underlying meaning that might be recognised.
2.2 Test to improve Category Analysis. Test to see if there are alternative categories that have a better fit.
2.3 Decide if there are sub categories. Think about what the categories are about. Once comments have been categorised look again at the responses in each category to identify what is being expressed by different viewpoints. Are these consistent with the category, and could they also identify sub categories that are of value in the analysis?
2.4 Quantify Categories. Assign comments to at least one category (or sub category) and group them so the quantity of responses in each category can be more easily counted.
Refer to Diagram 1. Developing Categories from Respondent Comments.
Step 3 Link Categories to develop Patterns and Model(s)
Once comments have been studied and categorised (and sub categorised), the next step is to see how these categories link to form patterns and models:
- Do some categories link in some way, and how do unrelated others link to form patterns?
- Do these patterns, together, form to represent one or more models?
In developing patterns check if there are exceptions to the rule that require the patterns to be broadened to include other categories, or changed into other patterns, or cast doubt over a pattern as a “rule”.
Refer to Diagram 2. Developing Patterns from Categories.
In developing models these may be either:
a) Drawn from the patterns of analysis;
b) Brought to the analysis from a recognised published model that links to categories and their patterns. For example a recognised model relevant to the type of survey carried out such as a Hospitality, Health Service, Service Delivery, Management, Environmental Management, or Community Model.
Either approach to developing a model offers advantages, such as an internally developed model can assist in gaining commitment from those involved in its development whereas the use of an external model offers validity through its credibility as a recognised benchmark.
Whichever approach is used the selected model should be as inclusive as possible of all respondent comments.
Refer to Diagram 3. Organisational Alignment Model based on fit to Patterns.
Step 4. Write up the analysis: Once all comments are categorised and analysed, and the patterns they form identified along with any model that fits those patterns, then the next step is to write up in a summary.
The analysis can include:
- An explanation of a model and the patterns that pull the analysis together.
- Categories and sub categories identified (at the very least the more significant categories).
- The number of comments covered by a category. This can be in either broad quantitative terms covering more than one category, or specific to each category.
- An explanation about the categories and sub categories along with supporting (non identifiable) quotations drawn from respondent comments.
- Recommendations based on the analysis. Recommendations for improvements can come directly from comments where there are several similar responses or from a single, different, comment.
The survey design and analysis should demonstrate validity:
- Face Validity:
- How survey findings make sense in terms of credibility, relevance, and usefulness to respondents and those who decide to use survey recommendations;
- Use respondents own words in providing credibility to survey recommendations.
- Construct Validity: The extent to which the survey design and analysis minimise error and misinterpretation and is consistent in “fitting” with respondent comments. That is, where comments fit with constructs (or concepts) in terms of developing criteria; patterns; and models.
 In references below, see Concept Mapping as an alternative Approach for the Analysis of Open-ended Survey Responses, the term Concept Mapping is used. The term Construct Mapping is used here instead of the term Concept Mapping so that there can be a better and more logical alignment to Construct Validity. In this respect an Open Survey analysis could be viewed as a form of Construct Mapping in that comments are organised (i.e. constructed) first into categories; these categories are then organised (constructed) into patterns; and where these patterns may then be organised (constructed) into a model. When constructing such a map the outcomes (whether this relates to the construction of categories, patterns, or a model) should readily, not forcibly, “fit” with respondent comments and also “work” in the sense that categories, patterns, and any model selected can be seen to provide meaningful and relevant conclusions. “Fit” in this sense relates to Construct Validity, and “work” to Face Validity.
Outline explanations of Open Survey Analysis Frameworks
Analysing open-ended questions.Website: http://intelligentmeasurement.wordpress.com/2007/12/18/analyzing-open-ended-questions/ Downloaded 8/8/12
Analysing Qualitative Data. Website: http://learningstore.uwex.edu/assets/pdfs/g3658-12.pdf Downloaded 8/8/12
A Brief Guide to the Analysis of Open-Ended Survey Questions. Website: http://cms.cerritos.edu/uploads/ResearchandPlanning/Brief_Guide_to_Open-Ended_Survey_Questions.pdf Downloaded 8/8/12
Concept Mapping Explained
Concept Mapping as an alternative Approach for the Analysis of Open-ended Survey Responses Website: http://www.socialresearchmethods.net/research/Concept%20Mapping%20as%20an%20Alternative%20Approach%20for%20the%20Analysis%20of%20Open-Ended%20Survey%20Responses.pdf Downloaded 8/8/12
Comprehensive References on Open Survey Design and Analysis
Qualitative Data Analysis. Ian Dey. Website: http://www.drapuig.info/files/Qualitative_data_analysis.pdf Downloaded 8/8/12
Patton, M.Q. (1990). Qualitative evaluation and research methods (2nd ed). Newbury Park, California: SAGE Publications Inc.
Organisational Alignment Model in Diagram 3 adapted from Figure 3.9 in:
Addison, R., Haig. C., & Kearney. L. (2009). Performance architecture: The art and science of improving organizations. San Francisco, California: Pfeiffer.
Organisational Productivity is about assessing and improving efficiency and effectiveness throughout the organisation.
Over the last century the focus of improving organisational productivity has shifted to keep pace with the challenges facing organisations in both the public and private sectors. In this respect productivity models can be recognised as including an:
- Efficiency Model that focuses on the efficiency with which resources, such as equipment, materials, energy, and people are used to achieve outputs. In this respect productivity can be seen to focus on efficiencies at the process level.
- Quality Model focusing on the efficiency in the way resources are used, and the effectiveness of outputs/outcomes to meet or surpass customer/citizen expectations. In this respect productivity can be seen to focus in improving quality.
- Soft Productivity Model that focuses on both direct and indirect issues affecting organisational performance such as leadership; role performance; organisational structures; risk management; and governance issues. In this respect other efficiency and effectiveness aspects within an organisation are looked at in terms of how the organisation manages itself and its resources.
- SEE Sustainability Model which integrates productivity within the context of social, environmental, and economic issues. In this respect productivity takes a cost benefit approach to how the organisation impacts both internally and externally SEE Sustainability perspective.
From this it can be seen that the idea of productivity remains alive and relevant to contemporary challenges facing organisations, and that increasingly a systemic perspective is required to understand and improve organisational productivity both within and externally.
Managing and improving organisational productivity affects all organisational priorities whether public or private sector, and there are many productivity methodologies that have been developed to improve the efficiency and effectiveness of the systems that underpin organisational productivity.
For further information and explanation, please click on image below to read the supporting Organisational Productivity article: