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).
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.
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.
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.
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.
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.
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).
This chapter is concerned with sampling, which is all about making choices related to data source... more This chapter is concerned with sampling, which is all about making choices related to data sources that will be the focus of your data gathering strategies. In social and behavioural research, sampling refers to much more than selecting human participants, especially when patterns of guiding assumptions, specific research configurations and specific data gathering strategies are considered in conjunction with the research context, research frames and associated research questions and/or hypotheses. This includes choices of the data sources themselves as well as choices of the circumstances, contexts and occasions in which data sources are encountered. We review a range of probability (e.g., various types of random sampling scheme, useful in research guided by positivist pattern of guiding assumptions) and non-probability sampling schemes (schemes, such as purposive, convenience, quota and theoretical, that can be useful under any pattern of guiding assumptions, especially interpretivist/constructivist assumptions). We also discuss two critical constraints associated with implementing any sampling scheme, ethical constraints and feasibility constraints, and reinforce the importance of having a Plan B for sampling.
This paper pursues a line of critique that was put forward by Cooksey and Gates (1995a,b). Our ap... more This paper pursues a line of critique that was put forward by Cooksey and Gates (1995a,b). Our approach reviews, critically, the recent reports of Karpin and Hilmer. In particular we examine some of the basic assumptions underlying the approaches taken in these reports with specific emphasis on contemporary human resource management theory. We first explore the conditions under which these reports were generated and how these conditions may have impacted on report recommendations. In addition this exploration points to significant difficulties with the quality of the reports themselves. We then examine the lack of critical attention given by the reports to the vagaries of the 'human condition' which are complex and unpredictable and which tend to lead the reports to produce overly simplistic and unrealistic recommendations and expectations for business. Finally, the implications for SMEs are considered.
The first broad category of statistics we discuss concerns descriptive statistics. The purpose of... more The first broad category of statistics we discuss concerns descriptive statistics. The purpose of the procedures and fundamental concepts in this category is quite straightforward: to facilitate the description and summarisation of data. By 'describe' we generally mean either the use of some pictorial or graphical representation of the data or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. These statistical procedures are designed to identify or display specific patterns or trends in the data. What remains after their application is simply for us to interpret and tell the story. Reflect on the QCI research scenario and the associated data set discussed in Chap. 4. Consider the following questions that Maree might wish to address with respect to decision accuracy and speed scores: • What was the typical level of accuracy and decision speed for inspectors in the sample? [see Procedure 5.4-Assessing central tendency.] • What was the most common accuracy and speed score amongst the inspectors? [see Procedure 5.4-Assessing central tendency.] • What was the range of accuracy and speed scores; the lowest and the highest scores? [see Procedure 5.5-Assessing variability.] • How frequently were different levels of inspection accuracy and speed observed? What was the shape of the distribution of inspection accuracy and speed scores? [see Procedure 5.1-Frequency tabulation, distributions & crosstabulation.]
... are context independent and have rather poor performance records as predictive devices for ..... more ... are context independent and have rather poor performance records as predictive devices for ... TR & Lusk, CM (1994)Seven components of judgmental forecasting skill: implications ... and the improvement of forecasts, Organizational Behavior and Human Decision Processes, 13 ...
W hat is complexity science? This question invokes systemic and dynamic thinking at many levels, ... more W hat is complexity science? This question invokes systemic and dynamic thinking at many levels, which means that arriving at a coherent answer will be a complex process in and of itself. I will argue in this article that a coherent answer to the question must, of necessity, draw on a diverse range of disciplines, while at the same time demanding a reconceptualization of those disciplines in the context of the new science we are describing. A coherent answer must also address the theoretical, methodological, and practical implications of the emergent science. Finally, a coherent answer must, itself, be contextually shaped and sensitized so that complexity science in the context of organizations and management will look, operate, and feel somewhat different to a complexity science for medicine or engineering. The essential foundation of complexity science in organizational contexts is multidimensional diversity, consistent with the thesis developed by Flood and Romm (1996): diversity in thinking, theorizing and conceptualizing organizational and management problems and issues, diversity in methodologies and pathways for achieving understanding of those problems and issues, and diversity in predictive capabilities and solution
Proceedings of the Human Factors Society annual meeting, Oct 1, 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.
This chapter discusses and illustrates inferential statistics for hypothesis testing. The procedu... more This chapter discusses and illustrates inferential statistics for hypothesis testing. The procedures and fundamental concepts reviewed in this chapter can help to accomplish the following goals: (1) evaluate the statistical and practical significance of the difference between a specific statistic (e.g. a proportion, a mean, a regression weight, or a correlation coefficient) and its hypothesised value in the population; and/or (2) evaluate the statistical and practical significance of the difference between some combination of statistics (e.g. group means) and some combination of their corresponding population parameters. Such comparisons/tests may be relatively simple or multivariate in nature. In this chapter, you will explore various procedures (e.g. t-tests, analysis of variance, multiple regression, multivariate analysis of variance and covariance, discriminant analysis, logistic regression) that can be employed in different hypothesis testing situations and research designs to inform the judgments of significance. You will also learn that statistical significance is not the only way to address hypotheses—practical significance (e.g., effect size) is almost always relevant as well; in some cases, even more relevant. Finally, you will explore several fundamental concepts dealing with the logic of statistical inference, the general linear model, research design, sampling and, for complex designs, the concept of interaction.
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).
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.
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.
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.
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.
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.
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).
This chapter is concerned with sampling, which is all about making choices related to data source... more This chapter is concerned with sampling, which is all about making choices related to data sources that will be the focus of your data gathering strategies. In social and behavioural research, sampling refers to much more than selecting human participants, especially when patterns of guiding assumptions, specific research configurations and specific data gathering strategies are considered in conjunction with the research context, research frames and associated research questions and/or hypotheses. This includes choices of the data sources themselves as well as choices of the circumstances, contexts and occasions in which data sources are encountered. We review a range of probability (e.g., various types of random sampling scheme, useful in research guided by positivist pattern of guiding assumptions) and non-probability sampling schemes (schemes, such as purposive, convenience, quota and theoretical, that can be useful under any pattern of guiding assumptions, especially interpretivist/constructivist assumptions). We also discuss two critical constraints associated with implementing any sampling scheme, ethical constraints and feasibility constraints, and reinforce the importance of having a Plan B for sampling.
This paper pursues a line of critique that was put forward by Cooksey and Gates (1995a,b). Our ap... more This paper pursues a line of critique that was put forward by Cooksey and Gates (1995a,b). Our approach reviews, critically, the recent reports of Karpin and Hilmer. In particular we examine some of the basic assumptions underlying the approaches taken in these reports with specific emphasis on contemporary human resource management theory. We first explore the conditions under which these reports were generated and how these conditions may have impacted on report recommendations. In addition this exploration points to significant difficulties with the quality of the reports themselves. We then examine the lack of critical attention given by the reports to the vagaries of the 'human condition' which are complex and unpredictable and which tend to lead the reports to produce overly simplistic and unrealistic recommendations and expectations for business. Finally, the implications for SMEs are considered.
The first broad category of statistics we discuss concerns descriptive statistics. The purpose of... more The first broad category of statistics we discuss concerns descriptive statistics. The purpose of the procedures and fundamental concepts in this category is quite straightforward: to facilitate the description and summarisation of data. By 'describe' we generally mean either the use of some pictorial or graphical representation of the data or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. These statistical procedures are designed to identify or display specific patterns or trends in the data. What remains after their application is simply for us to interpret and tell the story. Reflect on the QCI research scenario and the associated data set discussed in Chap. 4. Consider the following questions that Maree might wish to address with respect to decision accuracy and speed scores: • What was the typical level of accuracy and decision speed for inspectors in the sample? [see Procedure 5.4-Assessing central tendency.] • What was the most common accuracy and speed score amongst the inspectors? [see Procedure 5.4-Assessing central tendency.] • What was the range of accuracy and speed scores; the lowest and the highest scores? [see Procedure 5.5-Assessing variability.] • How frequently were different levels of inspection accuracy and speed observed? What was the shape of the distribution of inspection accuracy and speed scores? [see Procedure 5.1-Frequency tabulation, distributions & crosstabulation.]
... are context independent and have rather poor performance records as predictive devices for ..... more ... are context independent and have rather poor performance records as predictive devices for ... TR & Lusk, CM (1994)Seven components of judgmental forecasting skill: implications ... and the improvement of forecasts, Organizational Behavior and Human Decision Processes, 13 ...
W hat is complexity science? This question invokes systemic and dynamic thinking at many levels, ... more W hat is complexity science? This question invokes systemic and dynamic thinking at many levels, which means that arriving at a coherent answer will be a complex process in and of itself. I will argue in this article that a coherent answer to the question must, of necessity, draw on a diverse range of disciplines, while at the same time demanding a reconceptualization of those disciplines in the context of the new science we are describing. A coherent answer must also address the theoretical, methodological, and practical implications of the emergent science. Finally, a coherent answer must, itself, be contextually shaped and sensitized so that complexity science in the context of organizations and management will look, operate, and feel somewhat different to a complexity science for medicine or engineering. The essential foundation of complexity science in organizational contexts is multidimensional diversity, consistent with the thesis developed by Flood and Romm (1996): diversity in thinking, theorizing and conceptualizing organizational and management problems and issues, diversity in methodologies and pathways for achieving understanding of those problems and issues, and diversity in predictive capabilities and solution
Proceedings of the Human Factors Society annual meeting, Oct 1, 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.
This chapter discusses and illustrates inferential statistics for hypothesis testing. The procedu... more This chapter discusses and illustrates inferential statistics for hypothesis testing. The procedures and fundamental concepts reviewed in this chapter can help to accomplish the following goals: (1) evaluate the statistical and practical significance of the difference between a specific statistic (e.g. a proportion, a mean, a regression weight, or a correlation coefficient) and its hypothesised value in the population; and/or (2) evaluate the statistical and practical significance of the difference between some combination of statistics (e.g. group means) and some combination of their corresponding population parameters. Such comparisons/tests may be relatively simple or multivariate in nature. In this chapter, you will explore various procedures (e.g. t-tests, analysis of variance, multiple regression, multivariate analysis of variance and covariance, discriminant analysis, logistic regression) that can be employed in different hypothesis testing situations and research designs to inform the judgments of significance. You will also learn that statistical significance is not the only way to address hypotheses—practical significance (e.g., effect size) is almost always relevant as well; in some cases, even more relevant. Finally, you will explore several fundamental concepts dealing with the logic of statistical inference, the general linear model, research design, sampling and, for complex designs, the concept of interaction.
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Papers by Ray W Cooksey