Ray W Cooksey - Profile on Academia.edu (original) (raw)
Papers by Ray W Cooksey
How Should I Approach Data Analysis and Display of Results?
Springer eBooks, 2019
The basic goal of all forms of data analysis is to build meaning from the raw data and convey tha... more The basic goal of all forms of data analysis is to build meaning from the raw data and convey that meaning to one or more specific audiences. This chapter reviews approaches to data analysis which provide key pathways for telling the stories about what you have learned through your research journey by helping readers/users connect evidence, including strategic data displays, with those stories. Quantitative analysis deals with data in the form of numbers, measurements and indices whereas qualitative analysis deals with data that are in non-numerical form, which can include recordings, documents and transcripts, images, websites and films/videos. For certain purposes, via the Transformative data-shaping strategy, qualitative data may be transformed (i.e., ‘quantitised’) into a quantitative form prior to analysis, e.g., participants, words and codes can be categorised, counted, ranked or rated, yielding quantitative data. Equally, quantitative measurements can be ‘qualitised’ such that richer interpretive meaning is attached to the numbers. No matter what type of data you have gathered, analysis will almost always transform, condense, aggregate, re-represent, thematise or categorise the raw data to build meaning.
Behavior Research Methods, Sep 1, 1978
How Do I Frame and Conceptualise My Research Problem and Questions?
Springer eBooks, 2019
This chapter presents the concept of research frames (i.e., Action Research, Case Study, Evaluati... more This chapter presents the concept of research frames (i.e., Action Research, Case Study, Evaluation, Survey, Cross-Cultural, Indigenous, Transdisciplinary, Developmental Evaluation, Explanatory, Exploratory, Descriptive, Feminist) as ways of providing more holistic pictures of how your research purposes can/will be translated into research strategies and tactics that will generate or apply knowledge and learning that will speak to and influence specific audiences. We show that research frames emerge from the dynamic and synergistic intersection of researcher positioning, research contexts, participants’ contexts and positioning and research sponsor/reader/user contexts, all embedded within the larger social, political and physical worlds. We then discuss strategies for identifying and clarifying researchable problems and for appropriately setting out your research questions and/or hypotheses in the context of your chosen research frame(s).
How Can I Gain Access to Data Sources?
Springer eBooks, 2019
In this chapter, we discuss issues and considerations associated with negotiating and gaining acc... more In this chapter, we discuss issues and considerations associated with negotiating and gaining access to data sources, something that many research texts are relatively circumspect about. Difficulty in obtaining access, restrictions placed on access, or withdrawal of support can be devastating to a postgraduate researcher and can cause not only considerable emotional upheaval, but such a setback can also have ramifications on project quality as well as completion. Data access could involve digital and/or personal interaction with participants as well as access to documents and other artefacts and access to these may be controlled by one or more gatekeepers. Negotiating access is, essentially, an ongoing process of determining what is required, getting into an organisation or community and gaining their permission, obtaining access to physical materials/artefacts and/or participant involvement and then possibly doing this all over again with another organisation or community. We suggest a ten-step process for successfully gaining access to data sources.
How Should I Contextualise and Position My Study?
Springer eBooks, 2019
The focus of this chapter is on contextualising and positioning your research, which involves cla... more The focus of this chapter is on contextualising and positioning your research, which involves clarifying your assumptions, stating your intentions and goals and drawing boundaries around your research and its context(s). When you appropriately contextualise your study, you are making clear (1) where you, as researcher, well as your data sources, as participants, are coming from (‘positioning’ arguments) as well as larger contextual considerations (e.g., ethics, stakeholders); (2) what assumptions you are making in order to make your research ‘do-able’; (3) what you will/will not be addressing in your research (e.g., encompassing research goals, frames, literature foundations, questions and/or hypotheses) and why; (4) what contributions and impacts you see your research making, and, importantly, (5) what your research context(s) are and their implications for what you can do and what you may learn. Influences on your research may come from any of these domains, so a crucial part of your research journey will be recognising, balancing and managing the most relevant and impactful of these contextual influences, dealing effectively with constraints and being opportunistic where the need arises – all in pursuit of carrying out a convincing research project.
What Data Gathering Strategies Should I Use?
Springer eBooks, 2019
In this chapter, we review many of the data gathering strategies that can be used by postgraduate... more In this chapter, we review many of the data gathering strategies that can be used by postgraduates in social and behavioural research. We explore three major domains of data gathering strategies: strategies for connecting with people (encompassing interaction-based and observation-based strategies), exploring people’s handiworks (encompassing participant-centred and artefact-based strategies) and structuring people’s experiences (encompassing data-shaping and experience-focused strategies). In light of our pluralist perspective, we consider each data gathering strategy, not only as a distinct and self-contained strategy (which may encompass a range of more specific data gathering approaches), but also as part of a larger more interconnected and dynamic toolkit. Our goal is to highlight some key considerations and issues associated with each strategy that might be relevant to your decision making about which might be appropriate for you to use as part of your research journey, given your research frame, pattern(s) of guiding assumptions, contextualisations, positionings, research questions/hypotheses, scoping and shaping considerations and MU configuration.
An anthropometrically adjustable seat for low seam mining applications
PsycEXTRA Dataset, 1982
Low seam underground coal mines require use of heavy machinery having interior cab heights that m... more Low seam underground coal mines require use of heavy machinery having interior cab heights that may be lower than 33 inches. Current mining machines typically provide operators with non-adjustable seats consisting of heavy metal slabs or, in many cases, provide no seat at all. However, the limited workspace height forces the operator to control his machine from a reclined or supine position that requires special support. The problem may be exacerbated by the presence of a canopy that may further reduce the workspace height by several inches or more. While some Air Force research has examined low-profile seating anthropometry, the special ruggedness and low-level technology of the mining environment imposes unique design requirements not addressed by the Air Force research. This paper describes the development of a special anthropometrically adjustable seat that can provide comfortable body and head support in mining machines having very low workspace heights. Anthropometric analyses using published data and 1/4 inch scale drawing board manikins were used to establish design parameters for a medium fidelity adjustable seat mockup. Formal evaluation of the mockup confirmed, and in some cases altered, seat design parameters. The final seat design integrates refinements from the evaluation results and solutions to the adjustment problems.
Behavior Research Methods, 1981
Complexity, Context, and Constraints in Human Decision Making
PsycEXTRA Dataset, 1996
Ability of high school pupils to estimate vocational interests: Some influences of demographic factors and context
Australian Educational and Developmental Psychologist, Nov 1, 1994
ABSTRACTThis study examines the influence of demographic factors such as age, sex, and school set... more ABSTRACTThis study examines the influence of demographic factors such as age, sex, and school setting on self-estimate ability. The subjects (N = 1814) in this study were administered an interest inventory (Vocational lnterest Survey) and a self-rating scale (Work Interest Survey). Similarity between self-estimate and measured interest profiles was assessed using the correlation between individual's profiles and the squared Euclidean distance (D2), and its components (elevation, scatter, and shape by scatter). There were significant differences between boys and girls on profile parameters of elevation, the overall distance between profiles, and self-estimate ability. Girls, on the whole, were better able to estimate the pattern of measured interests (0.62), compared to boys (0.55), but the magnitude of this difference between these coefficients (i.e., 0.07) was very small. Age differences between four age groups (14, 15, 16, and those over 16 years) were small. The mean correlation at 14 years was 0.64 compared with 0.57 at 16 years and 0.4 for those over 16 years. Differences between single-sex schools and co-educational schools were the third factor considered. Girls' schools had the highest correlation between the VIS and WIS profiles (0.63), followed by co-educational schools (0.58) and boys' schools had the lowest profile correlation (0.55).
Springer eBooks, 2020
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Example Research Context & Quantitative Data Set
Springer eBooks, 2020
Nearly all the statistical procedures are illustrated with reference to data emerging from a sing... more Nearly all the statistical procedures are illustrated with reference to data emerging from a single coherent research context, described in this chapter. As you read, the variables used in the illustrations will remain familiar to you. The research context has been constructed to resonate not only with readers from a business background, but also with readers from social science, psychological, educational and health-related backgrounds. The research context and database are entirely fictitious and were created so that specific relationships and comparisons could be illustrated. Most of the procedures to be illustrated are performed on variables from this database using SPSS, with periodic use of SYSTAT, NCSS, STATGRAPHICS or R, as appropriate, for specific analytical and illustrative purposes. For certain more sophisticated procedures (such as time series analysis and Rasch models) and procedures involving different research designs (such as repeated measures designs), we extend the QCI research context to encompass new design aspects and additional data for the illustrations and to demonstrate the use of more specialised computer software programs. Finally, in this chapter, we discuss a range of quantitative stories that could be pursued in this database using the procedures illustrated in this book.
Coefficient Beta and Hierarchical Item Clustering
Organizational Research Methods, 2006
Summated scales are widely used in management research to measure constructs such as job satisfac... more Summated scales are widely used in management research to measure constructs such as job satisfaction and organizational commitment. This article suggests that Revelle’s (1979) coefficient beta, implemented in Revelle’s (1978) ICLUST item-clustering procedure, should be used in conjunction with Cronbach’s coefficient alpha measure of internal consistency as criteria for judging the dimensionality and internal homogeneity of summated scales. The approach is demonstrated using ICLUST reanalyses of sample responses to Warr’s (1990) affective well-being scale and O’Brien, Dowling, and Kabanoff’s (1978) job satisfaction scale. Coefficient beta and item clustering are shown to more clearly identify the homogeneity and internal dimensional structure of summated scale constructs than do traditional principal components analyses. Given these benefits, Revelle’s approach is a viable alternative methodology for scale construction in management, organizational, and cross-cultural contexts, especially when researchers need to make defensible choices between using whole scales or subscales.
The Methodology of Social Judgement Theory
Thinking & Reasoning, Jul 1, 1996
ABSTRACT
Chapter 9 Social Judgment Theory in Education: Current and Potential Applications
Elsevier eBooks, 1988
Publisher Summary This chapter reviews the recent progress made in the application of social judg... more Publisher Summary This chapter reviews the recent progress made in the application of social judgment theory (SJT) to a variety of problems in education. The study of educational decision ecologies entails working with cue structures and task characteristics as they exist naturally, not as they are experimentally manipulated. The utility of SJT in education is that it provides both a theoretical model of the judgment process and a methodological system within which one can investigate the model's specific implications. The work in SJT has given rise to a particularly useful theory of human cognition: cognitive continuum theory (CCT). Many decisions are accomplished using a mixture of analysis and intuition, which gives rise to a general mode of cognition termed “quasirationality.” This is regarded the fundamental characteristic of CCT: human cognition ranges on a continuum from highly intuitive to highly analytic. The middle and largest region of the continuum is quasi-rational where facets of both intuitive and analytic thinking blend.
HRM: A Management Science in Need of Discipline
Journal of Management & Organization, 1995
Human Resource Management (HRM), as a sub-discipline of management science, is in its infancy. HR... more Human Resource Management (HRM), as a sub-discipline of management science, is in its infancy. HRM practices are often Utopian in expectation and fail to incorporate a realistic view of existing knowledge bases in the psychological, social, and biological sciences. The HRM discipline relies upon theoretical approaches (eg theories of motivation, satisfaction, and performance) which are: (1) almost invariably linear in conceptualisation and depend largely upon correlational evidence, (2) frequently validated within nonrepresentative contexts that are overly constrained by researchers and (3) overly simplistic in that the constraints and patterns imposed by our biological, psychological and social systems are frequently ignored or assumed to constitute random error within the models. This frequently translates into HRM practices which map reasonably well onto theory yet fall short of yielding expected outcomes. The theories do not match the realities observed. We point to nonlinear dynamics and chaos theory as a way of conceptualising how common HRM practices may translate into observable outcomes. Such an approach will force managers to pull back from simple reliance on linear predictions and realise that truly effective HRM practices should be sensitive to the unique, complex and less systematically predictable patterns of human behaviour.
Common Pitfalls
Springer eBooks, 2019
Making Judgements and Decisions
BRILL eBooks, 2008
Other Commonly Used Statistical Procedures
Springer eBooks, 2020
In this chapter, we explore some other commonly used but less ‘traditional’ statistical procedure... more In this chapter, we explore some other commonly used but less ‘traditional’ statistical procedures. While these procedures are commonly reported in behavioural and social research, they tend not to be well-covered in standard statistical texts. Procedures discussed and illustrated include: reliability analysis & classical item analysis (useful for assessing measurement quality); data screening & missing value analysis (useful for preliminary explorations looking for anomalous data patterns); confidence intervals (useful for assessing the precision of statistical estimates); bootstrapping and jackknifing (useful for estimating errors associated with statistic estimates where traditional methods are not available or do not work); time series analysis (useful for understanding data patterns over time, with or without an intervention); confirmatory factor analysis (useful for evaluating theorised factor structures); structural equation models (useful for evaluating theorised causal models); and meta-analysis (useful for exploring data patterns evident in samples of published, and occasionally unpublished, research).
How Should I Approach Data Analysis and Display of Results?
Springer eBooks, 2019
The basic goal of all forms of data analysis is to build meaning from the raw data and convey tha... more The basic goal of all forms of data analysis is to build meaning from the raw data and convey that meaning to one or more specific audiences. This chapter reviews approaches to data analysis which provide key pathways for telling the stories about what you have learned through your research journey by helping readers/users connect evidence, including strategic data displays, with those stories. Quantitative analysis deals with data in the form of numbers, measurements and indices whereas qualitative analysis deals with data that are in non-numerical form, which can include recordings, documents and transcripts, images, websites and films/videos. For certain purposes, via the Transformative data-shaping strategy, qualitative data may be transformed (i.e., ‘quantitised’) into a quantitative form prior to analysis, e.g., participants, words and codes can be categorised, counted, ranked or rated, yielding quantitative data. Equally, quantitative measurements can be ‘qualitised’ such that richer interpretive meaning is attached to the numbers. No matter what type of data you have gathered, analysis will almost always transform, condense, aggregate, re-represent, thematise or categorise the raw data to build meaning.
Behavior Research Methods, Sep 1, 1978
How Do I Frame and Conceptualise My Research Problem and Questions?
Springer eBooks, 2019
This chapter presents the concept of research frames (i.e., Action Research, Case Study, Evaluati... more This chapter presents the concept of research frames (i.e., Action Research, Case Study, Evaluation, Survey, Cross-Cultural, Indigenous, Transdisciplinary, Developmental Evaluation, Explanatory, Exploratory, Descriptive, Feminist) as ways of providing more holistic pictures of how your research purposes can/will be translated into research strategies and tactics that will generate or apply knowledge and learning that will speak to and influence specific audiences. We show that research frames emerge from the dynamic and synergistic intersection of researcher positioning, research contexts, participants’ contexts and positioning and research sponsor/reader/user contexts, all embedded within the larger social, political and physical worlds. We then discuss strategies for identifying and clarifying researchable problems and for appropriately setting out your research questions and/or hypotheses in the context of your chosen research frame(s).
How Can I Gain Access to Data Sources?
Springer eBooks, 2019
In this chapter, we discuss issues and considerations associated with negotiating and gaining acc... more In this chapter, we discuss issues and considerations associated with negotiating and gaining access to data sources, something that many research texts are relatively circumspect about. Difficulty in obtaining access, restrictions placed on access, or withdrawal of support can be devastating to a postgraduate researcher and can cause not only considerable emotional upheaval, but such a setback can also have ramifications on project quality as well as completion. Data access could involve digital and/or personal interaction with participants as well as access to documents and other artefacts and access to these may be controlled by one or more gatekeepers. Negotiating access is, essentially, an ongoing process of determining what is required, getting into an organisation or community and gaining their permission, obtaining access to physical materials/artefacts and/or participant involvement and then possibly doing this all over again with another organisation or community. We suggest a ten-step process for successfully gaining access to data sources.
How Should I Contextualise and Position My Study?
Springer eBooks, 2019
The focus of this chapter is on contextualising and positioning your research, which involves cla... more The focus of this chapter is on contextualising and positioning your research, which involves clarifying your assumptions, stating your intentions and goals and drawing boundaries around your research and its context(s). When you appropriately contextualise your study, you are making clear (1) where you, as researcher, well as your data sources, as participants, are coming from (‘positioning’ arguments) as well as larger contextual considerations (e.g., ethics, stakeholders); (2) what assumptions you are making in order to make your research ‘do-able’; (3) what you will/will not be addressing in your research (e.g., encompassing research goals, frames, literature foundations, questions and/or hypotheses) and why; (4) what contributions and impacts you see your research making, and, importantly, (5) what your research context(s) are and their implications for what you can do and what you may learn. Influences on your research may come from any of these domains, so a crucial part of your research journey will be recognising, balancing and managing the most relevant and impactful of these contextual influences, dealing effectively with constraints and being opportunistic where the need arises – all in pursuit of carrying out a convincing research project.
What Data Gathering Strategies Should I Use?
Springer eBooks, 2019
In this chapter, we review many of the data gathering strategies that can be used by postgraduate... more In this chapter, we review many of the data gathering strategies that can be used by postgraduates in social and behavioural research. We explore three major domains of data gathering strategies: strategies for connecting with people (encompassing interaction-based and observation-based strategies), exploring people’s handiworks (encompassing participant-centred and artefact-based strategies) and structuring people’s experiences (encompassing data-shaping and experience-focused strategies). In light of our pluralist perspective, we consider each data gathering strategy, not only as a distinct and self-contained strategy (which may encompass a range of more specific data gathering approaches), but also as part of a larger more interconnected and dynamic toolkit. Our goal is to highlight some key considerations and issues associated with each strategy that might be relevant to your decision making about which might be appropriate for you to use as part of your research journey, given your research frame, pattern(s) of guiding assumptions, contextualisations, positionings, research questions/hypotheses, scoping and shaping considerations and MU configuration.
An anthropometrically adjustable seat for low seam mining applications
PsycEXTRA Dataset, 1982
Low seam underground coal mines require use of heavy machinery having interior cab heights that m... more Low seam underground coal mines require use of heavy machinery having interior cab heights that may be lower than 33 inches. Current mining machines typically provide operators with non-adjustable seats consisting of heavy metal slabs or, in many cases, provide no seat at all. However, the limited workspace height forces the operator to control his machine from a reclined or supine position that requires special support. The problem may be exacerbated by the presence of a canopy that may further reduce the workspace height by several inches or more. While some Air Force research has examined low-profile seating anthropometry, the special ruggedness and low-level technology of the mining environment imposes unique design requirements not addressed by the Air Force research. This paper describes the development of a special anthropometrically adjustable seat that can provide comfortable body and head support in mining machines having very low workspace heights. Anthropometric analyses using published data and 1/4 inch scale drawing board manikins were used to establish design parameters for a medium fidelity adjustable seat mockup. Formal evaluation of the mockup confirmed, and in some cases altered, seat design parameters. The final seat design integrates refinements from the evaluation results and solutions to the adjustment problems.
Behavior Research Methods, 1981
Complexity, Context, and Constraints in Human Decision Making
PsycEXTRA Dataset, 1996
Ability of high school pupils to estimate vocational interests: Some influences of demographic factors and context
Australian Educational and Developmental Psychologist, Nov 1, 1994
ABSTRACTThis study examines the influence of demographic factors such as age, sex, and school set... more ABSTRACTThis study examines the influence of demographic factors such as age, sex, and school setting on self-estimate ability. The subjects (N = 1814) in this study were administered an interest inventory (Vocational lnterest Survey) and a self-rating scale (Work Interest Survey). Similarity between self-estimate and measured interest profiles was assessed using the correlation between individual's profiles and the squared Euclidean distance (D2), and its components (elevation, scatter, and shape by scatter). There were significant differences between boys and girls on profile parameters of elevation, the overall distance between profiles, and self-estimate ability. Girls, on the whole, were better able to estimate the pattern of measured interests (0.62), compared to boys (0.55), but the magnitude of this difference between these coefficients (i.e., 0.07) was very small. Age differences between four age groups (14, 15, 16, and those over 16 years) were small. The mean correlation at 14 years was 0.64 compared with 0.57 at 16 years and 0.4 for those over 16 years. Differences between single-sex schools and co-educational schools were the third factor considered. Girls' schools had the highest correlation between the VIS and WIS profiles (0.63), followed by co-educational schools (0.58) and boys' schools had the lowest profile correlation (0.55).
Springer eBooks, 2020
The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Example Research Context & Quantitative Data Set
Springer eBooks, 2020
Nearly all the statistical procedures are illustrated with reference to data emerging from a sing... more Nearly all the statistical procedures are illustrated with reference to data emerging from a single coherent research context, described in this chapter. As you read, the variables used in the illustrations will remain familiar to you. The research context has been constructed to resonate not only with readers from a business background, but also with readers from social science, psychological, educational and health-related backgrounds. The research context and database are entirely fictitious and were created so that specific relationships and comparisons could be illustrated. Most of the procedures to be illustrated are performed on variables from this database using SPSS, with periodic use of SYSTAT, NCSS, STATGRAPHICS or R, as appropriate, for specific analytical and illustrative purposes. For certain more sophisticated procedures (such as time series analysis and Rasch models) and procedures involving different research designs (such as repeated measures designs), we extend the QCI research context to encompass new design aspects and additional data for the illustrations and to demonstrate the use of more specialised computer software programs. Finally, in this chapter, we discuss a range of quantitative stories that could be pursued in this database using the procedures illustrated in this book.
Coefficient Beta and Hierarchical Item Clustering
Organizational Research Methods, 2006
Summated scales are widely used in management research to measure constructs such as job satisfac... more Summated scales are widely used in management research to measure constructs such as job satisfaction and organizational commitment. This article suggests that Revelle’s (1979) coefficient beta, implemented in Revelle’s (1978) ICLUST item-clustering procedure, should be used in conjunction with Cronbach’s coefficient alpha measure of internal consistency as criteria for judging the dimensionality and internal homogeneity of summated scales. The approach is demonstrated using ICLUST reanalyses of sample responses to Warr’s (1990) affective well-being scale and O’Brien, Dowling, and Kabanoff’s (1978) job satisfaction scale. Coefficient beta and item clustering are shown to more clearly identify the homogeneity and internal dimensional structure of summated scale constructs than do traditional principal components analyses. Given these benefits, Revelle’s approach is a viable alternative methodology for scale construction in management, organizational, and cross-cultural contexts, especially when researchers need to make defensible choices between using whole scales or subscales.
The Methodology of Social Judgement Theory
Thinking & Reasoning, Jul 1, 1996
ABSTRACT
Chapter 9 Social Judgment Theory in Education: Current and Potential Applications
Elsevier eBooks, 1988
Publisher Summary This chapter reviews the recent progress made in the application of social judg... more Publisher Summary This chapter reviews the recent progress made in the application of social judgment theory (SJT) to a variety of problems in education. The study of educational decision ecologies entails working with cue structures and task characteristics as they exist naturally, not as they are experimentally manipulated. The utility of SJT in education is that it provides both a theoretical model of the judgment process and a methodological system within which one can investigate the model's specific implications. The work in SJT has given rise to a particularly useful theory of human cognition: cognitive continuum theory (CCT). Many decisions are accomplished using a mixture of analysis and intuition, which gives rise to a general mode of cognition termed “quasirationality.” This is regarded the fundamental characteristic of CCT: human cognition ranges on a continuum from highly intuitive to highly analytic. The middle and largest region of the continuum is quasi-rational where facets of both intuitive and analytic thinking blend.
HRM: A Management Science in Need of Discipline
Journal of Management & Organization, 1995
Human Resource Management (HRM), as a sub-discipline of management science, is in its infancy. HR... more Human Resource Management (HRM), as a sub-discipline of management science, is in its infancy. HRM practices are often Utopian in expectation and fail to incorporate a realistic view of existing knowledge bases in the psychological, social, and biological sciences. The HRM discipline relies upon theoretical approaches (eg theories of motivation, satisfaction, and performance) which are: (1) almost invariably linear in conceptualisation and depend largely upon correlational evidence, (2) frequently validated within nonrepresentative contexts that are overly constrained by researchers and (3) overly simplistic in that the constraints and patterns imposed by our biological, psychological and social systems are frequently ignored or assumed to constitute random error within the models. This frequently translates into HRM practices which map reasonably well onto theory yet fall short of yielding expected outcomes. The theories do not match the realities observed. We point to nonlinear dynamics and chaos theory as a way of conceptualising how common HRM practices may translate into observable outcomes. Such an approach will force managers to pull back from simple reliance on linear predictions and realise that truly effective HRM practices should be sensitive to the unique, complex and less systematically predictable patterns of human behaviour.
Common Pitfalls
Springer eBooks, 2019
Making Judgements and Decisions
BRILL eBooks, 2008
Other Commonly Used Statistical Procedures
Springer eBooks, 2020
In this chapter, we explore some other commonly used but less ‘traditional’ statistical procedure... more In this chapter, we explore some other commonly used but less ‘traditional’ statistical procedures. While these procedures are commonly reported in behavioural and social research, they tend not to be well-covered in standard statistical texts. Procedures discussed and illustrated include: reliability analysis & classical item analysis (useful for assessing measurement quality); data screening & missing value analysis (useful for preliminary explorations looking for anomalous data patterns); confidence intervals (useful for assessing the precision of statistical estimates); bootstrapping and jackknifing (useful for estimating errors associated with statistic estimates where traditional methods are not available or do not work); time series analysis (useful for understanding data patterns over time, with or without an intervention); confirmatory factor analysis (useful for evaluating theorised factor structures); structural equation models (useful for evaluating theorised causal models); and meta-analysis (useful for exploring data patterns evident in samples of published, and occasionally unpublished, research).