Psychosocial Measurement Issues in Sport and Exercise Settings
Summary and Keywords
Trustworthy measurement is essential to make inferences about people and events, as well as to make scientific inquiries and comprehend human behaviors. Measurement is used for validating and building theories, substantiating research endeavors, contributing to science, and supporting a variety of applications. Sport and exercise psychology is a theoretical and practical domain derived from two domains: psychology and kinesiology. As such, the measurement methods used by scientists and practitioners relate to the acquisition of motor skills (i.e., genetics and environment-deliberate practice), physiological measures (e.g., heart rate pulse, heart rate variability, breathing amplitude and frequency, galvanic skin response, and electrocardiogram), and psychological measures including introspective instruments in the form of questionnaires, interviews, and observations.
Sport and exercise psychology entails the measurement of motor performance (e.g., time-trials, one repetition maximum tests), cognitive development (e.g., knowledge base and structure, deliberate practice, perception-cognition, attention, memory), social aspects (e.g., team dynamics, cohesion, leadership, shared mental models, coach-performer interaction), the self (e.g., self-esteem, self-concept, physical self), affective and emotional states (e.g., mood, burnout), and psychological skills (e.g. imagery, goal-setting, relaxation, emotion regulation, stress management, self-talk, relaxation, and pre-performance routine). Sport and exercise psychologists are also interested in measuring the affective domain (e.g., quality of life, affect/emotions, perceived effort), psychopathological states (e.g., anxiety, depression), cognitive domain (e.g., executive functioning, information processing, decision making, attention, academic achievements, cognition and aging), social-cognitive concepts (e.g., self-efficacy, self-control, motivation), and biochemical markers of human functioning (e.g., genetic factors, hormonal changes). The emergence of neuroscientific methods have ushered in new methodological tools (e.g., electroencephalogram; fMRI) to assess central markers (brain systems) linked to performance, learning, and well-being in sport and exercise settings. Altogether, the measures in the sport and exercise domain are used to establish linkages among the emotional, cognitive, and motor systems.
Measurement in Sport and Exercise: Definition and Requirements
Research in sport and exercise settings has grown exponentially over the past century (Filho & Tenenbaum, 2015). Sport and exercise scientists (e.g., psychologists, sociologists, kinesiologists) are concerned with developing and implementing measurement tools to examine bio-psycho-social processes linked to performance, learning, and well-being in sport and exercise settings. Measurement consists of scaling, by attributing either a quality or quantity value, the properties of latent (abstract) or manifest (concrete, physical) variables. To move science in sport and exercise forward, measurements must be (a) trustworthy and accurate; and (b) “conceptually useful for testing theoretical contentions about how people behave in sport and exercise setting” (Tenenbaum, Eklund, & Kamata, 2012, p. 3). Like other behavioral and social domains, sport and exercise psychology measures can be classified as either quantitative or qualitative. A variable can be described by a quantitative mathematical formulation if it establishes a perceptual image of “less” or “more” on an abstract linear continuum. The description of the measure in quantitative terms requires the presence of origin, equal units of measurement, and linearity (Rasch, 1961; Wright & Douglas, 1977; Wright & Masters, 1982). Specifically, an origin or “ground zero” must be defined to indicate the lack of the attribute being measured (e.g., 0 °C—the measure’s origin). Furthermore, a given unit of measurement (e.g., 1 °C) must denote the same (equal) value to allow for reliable comparisons across quantities within a continuum. Finally, linearity allows for inferences of direction (e.g., increase or decrease in temperature) as well as the establishment of a relationship pattern (i.e., normalization) among the several quantities represented in each population.
Once a measure satisfies these three properties (e.g., origin, equal units of measurement, and linearity), and is reliable and valid, it allows objectivity and generalizability to be inferred with confidence. Of note, quantification is performed on manifest variables; that is, those variables that can be described via mathematical or physical properties However, when measures apply to subjective behaviors, such as some artistic performances, the measure of its quality changes into verbal and semantic expressions (e.g., interview and observation). Thus, qualitative measurement consists of an evaluative form, where judgments, feelings, and opinions are used to describe the event or experience (for a review, see Tenenbaum & Filho, 2015). It is important to note that quantitative measurement in the sport and exercise psychology domain paralleled the developments in the psychological and educational domains at large. Common measurement procedures in psychology and education include the use of validity evolution, reliability and local precision, equating and item banking, computerized adaptive testing (CAT), test fairness and differential item functioning (DIF), cognitive diagnostic assessment, and technological procedures (for an extensive review, see Zhu, 2012). Of note, in addition to assessing the reliability and validity of measurement tools per se (e.g., questionnaires), researchers must assess the reliability and validity of the scores and responses gathered by these very tools. For instance, some measurement tools might be appropriate to quantify the psycho-social states of children but might not be reliable to gauge the psycho-social of young-adults. Put plainly, reliability and validity scores are sample dependent insofar that they are influenced by individuals’ characteristics and contextual constraints.
Two essential measurement requirements in the behavioral-social domain relate to the reliability and validity of the measures. We do not elaborate on these in detail because of their extensive coverage in the psychometric literature (for a review, see Tenenbaum et al., 2012). Instead, we briefly relate to them and emphasize their centrality in the sport and exercise psychology domain. Reliability pertains to the accuracy (error-free), repeatability, consistency, and reproducibility of a given measure. Once a measure is free of error (E = 0), it is trustworthy, and thus a score (X) on the measure is a true (T) score. In other words: X = T + E.
Random errors of measurement stem from internal (e.g., anxiety, lack of preparation, fatigue) and external (e.g., climate, humidity, settings, conditions) sources of the person being measured. The main methods used to estimate reliability are temporal stability (i.e., test-retest estimate) and internal consistency coefficient (e.g., split half, Cronbach alpha, Kuder-Richardson KR, and inter-rater agreement rate). Although reliability and validity are not mutually exclusive, validity depends greatly on reliability. Validity pertains to an estimate of a desired/target variable. Thus, an unreliable test cannot be valid, but a valid test must be reliable (for a review, see Vaughn, Lee, & Kamata, 2012).
Validity signifies the truthfulness and confidence of the measure (or in what it is intended to measure). Various types of validity are used to generate the measure’s trustfulness, each of which covers different aspects of the entire evidence concept. Common validity assessments in sport and exercise include face, content, predictive, concurrent, construct, convergent, and discriminant (see Nideffer & Sagal, 2001). Within this concept, the multi-trait multi-methods (MTMM; Campbell & Fiske, 1959) procedure was developed to validate psychological constructs that share one or more dimensions that are not significantly correlated. Exploratory factor analyses (EFA) and confirmatory factor analyses (CFA) are used to verify the dimensionality of a given psychological measure based on the assumptions that different dimensions are comprised of items which correlate among themselves stronger than in comparison with items which define other dimensions.
An additional measurement issue related to validity, and usually discussed in the sport and exercise psychology literature, relates to the factorial invariance of the tools used in both research and applied settings. The degree to which the factorial structure (i.e., dimensions, latent variables) remains stable across gender, age category, and sport type is vital for the generalizability and validity of the scores derived from the measure itself. New procedures have been developed to test factorial, configural, metric, strict, and partial invariances of introspective measures, as well as other psychometric procedures such as idiographic fitting, fitting invariance models, and likelihood ratio testing (see Estabrook, 2012). Recent methods have also been introduced to study model changes over time, thus advancing knowledge on how developmental processes unfold and are affected by interventions (for a review, see Grimm & Ram, 2012). Finally, the probabilistic Rasch modeling in sport, which is implemented in an effort to standardize the measures on a linear continuum, is typical to physical variables with a definite origin (i.e., zero point) and units (for a review, see Strauss, Busch, & Tenenbaum, 2012).
The number of methods used in a given research study can influence the reliability, validity, and generalizability of its findings (Tenenbaum & Filho, 2015). First, research based on only one method (mono-method) is more susceptible to researchers’ subjective bias (Parker, 2004). A researcher’s subjective bias influences one’s theoretical formulations, methodological decisions, and analytical interpretations. Second, a mono-method assessment is less robust to response biases (e.g., leniency, halo, and social desirability effects) and common method biases (e.g., length and item priming effects; for a review, see Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Hence, the recommendation is to engage in triangulation, which pertains to the use of different methods and/or measures within a given study (Tenenbaum & Filho, 2015). In this regard, Rothbauer (2008) noted that “the concept of triangulation is borrowed from navigational and land surveying techniques that determine a single point in space with the convergence of measurements taken from two other distinct points” (p. 892). Thus, a multi-method approach is recommended to increase reliability and validity in research and practice, not only in the sport and exercise domain, but also across fields of human activity. Within sport and exercise psychology, there is a general agreement that researchers and practitioners should rely on myriad bio-psycho-social variables to study performance, learning, and well-being (Berger, Weinberg, & Eklund, 2015; Eklund & Tenenbaum, 2014; Tenenbaum & Filho, 2015). Although the emphasis on biological, psychological, or social factors depends on the research question and rationale for a given study, the recommendation is that researchers and practitioners use a combination of methods from biology, psychology, and sociology when trying to establish functional relationships among variables.
Researchers and practitioners should strive to mix subjective qualitative measurements (e.g., introspective self-report measures in the form of questionnaires, open-ended interviews or verbal protocols) with objective quantitative measurements (e.g., electromyography, cardiac output, and electroencephalography readings). The use of new technologies, including biofeedback learning environments, multidimensional simulation training, virtual reality, and brain-computer interfaces have also been incorporated in sport and exercise psychology (Filho, 2015). In effect, there are numerous, both traditional and emerging, qualitative and quantitative bio-psycho-social methods and measurements being used in the field, as well as several ways to triangulate these various approaches. An extensive review of these methods and measurements is available in the literature for those interested in the specifics of this subject matter (see Tenenbaum et al., 2012).
In addition to methodological triangulation, it is also important to consider investigator triangulation and theoretical triangulation (Rothbauer, 2008). The former refers to the use of critical friends, external judges, inter-rater reliability ratings and double-blind experimental designs to ensure impartiality in psychosocial research. The latter, also known as interpretative pluralism (see Coyle, 2010), pertains to the juxtaposition of different theories, epistemologies, and ontological routes to increase trustworthiness of the findings (Kincheloe, 2005). Overall, researchers should consider methodological, investigator, and theoretical triangulation to develop new theories, best practices, and evidence-based intervention guidelines.
Areas of Measurement Interests
Measurements are essential to the development of descriptive and explanatory theoretical frameworks, as well as in the advancement of holistic and systemic theory in sport and exercise settings. The development of reliable and valid measures is also a condition sine qua non in the development of applied psychosocial interventions in sport and exercise settings.
One of the main areas of measurement pertains to theory development. To develop valid theoretical frameworks, scholars propose and test different measurement models (see Kline, 2011), while contrasting competing hypotheses and alternative models related to a given phenomenon (Tenenbaum & Filho, 2015). It is important to note that theories vary in their scope. To this extent, Chomsky (2014) has long noted that there are theoretical models can meet descriptive and explanatory adequacy. Descriptive theories enumerate detail and list the properties of a given phenomenon. Explanatory theories focus on revealing how these properties work in the real world. Moreover, descriptive theories relate to declarative knowledge, whereas explanatory theories are linked to functional knowledge (Biggs, 2002). Accordingly, scholars interested in developing measurement tools should consider whether the instrument reflects a descriptive or explanatory theoretical instance.
The validity and generalizability of a theory also depends on its scope. To this extent, conceptual frameworks can be holistic or systemic in nature (Kim, Hannafin, & Bryan, 2007). Holistic frameworks aim to summarize an entire set of rules into a single overarching rule. For instance, physicists are currently trying to develop a “theory of everything” by merging principles of mechanics and quantum physics. In sport sciences and psychology, researchers have tried to establish thresholds, as in the case of the attention association-dissociation shift.
On the other hand, systemic theories try to explain the relationship among a myriad of phenomena. Meta-theorists in applied psychology have all spoken of systemic theories, whereby variables of a given nomological network mutually influence one another. For instance, Bandura (1997) speaks of reciprocal determinism (i.e., variables mutually influence one another and can be both cause and effect) when discussing the relationship between collective efficacy and other group processes. Cacioppo, Tassinary, and Berntson (2007) maintain that psychophysiological responses have a many-to-many rather than one-to-one relationship, and that is why different individuals may show different physiological responses to the same given stimuli. Overall, systemic theories conform to the principle of parsimony (see Gigerenzer, 2010), which advocates for simple models based on the minimum number of parameters needed to explain a given phenomenon. Whether holistic or systemic, theoretical models inform measurement development, which in turn is used to inform applied interventions.
A countless number of measures have been developed within the field of sport and exercise psychology to diagnose and monitor individuals, and help in the establishment of mental training protocols. Simply put, practitioners select their working measures depending on the needs and goals of their clients. Filho and Tenenbaum (2015) have noted that applied work in sport and exercise psychology usually consists of either performance restoration or performance optimization. Performance restoration refers to applied work directed at restoring performance to functional levels, as in the case of injury, over-training, burnout, or other dysfunctional bio-psycho-social conditions. Performance optimization consists of training protocols aimed at increasing the likelihood of individuals experiencing optimal performance, as when athletes report being in “flow” or “in the zone.” Whether working toward restoring or optimizing performance, practitioners should recognize that cognitions, affective states, and behaviors are intertwined.
Measurement Domains in Sport and Exercise Psychology
A cognitive measure and approach might work best in a certain situation, whereas in other instances an affective or behavioral measure might yield better answers to a given performance barrier. It is for this reason that sport and exercise psychologists have developed different measures aimed at capturing cognitive, affective, and behavioral states and patterns.
The Cognitive-Behavioral Domain
Sport and exercise have been found to change cognitive and behavioral tendencies in humans. The measures used to quantify the cognitive changes include but are not limited to: (a) behavioral, such as measures of reaction time, movement time, decision time, and performance accuracy; and (b) cognitive, such as measurement paradigms on attention allocation, anticipation, memory recall, pattern recognition, and situational awareness. The theoretical and experimental accounts for the linkage between exercising features (e.g., duration, type, mode) and cognitive functioning have been discussed elsewhere (Etnier, 2012), and are not within the scope of this chapter.
To measure cognitive attention-related variables, reaction time (RT) and choice reaction time (CRT) are indicators of information-processing speed and efficiency. Specifically, the measures refer to the duration it takes the cognitive system to respond to any internal or external stimulus that was detected by any of the senses. The measure becomes more complex when more stimuli or tasks are presented simultaneously, as the cognitive load on the information-processing system increases with complexity.
Short-term memory (STM) and long-term memory (LTM) tests within the exercise domain consist of various forms of word lists to be recalled immediately and after various delay periods (15 min, 30 min). Another form of testing STM is through a 2–8 digit span test, in which participants are asked to repeat the digits forwards or backwards. Working, episodic, visual, and spatial memories can also be tested using visual scenarios expressed verbally before and after exposure to an exercise protocol varying in intensity, duration, and type (e.g., aerobic, anaerobic, strength).
As noted by Etnier (2012), measures of executive functioning have become very popular in the exercise literature. Executive functioning refers to a higher-order cognitive ability that determines low cognitive functions such as planning, scheduling, inhibition, and working memory (Etnier & Chang, 2009). The dominant measures within this domain are the Stroop Test and the Flanker Test. The Stroop Test is known as an “interference/inhibition test” where the color of the font must be expressed when the color word may be in conflict with the font color. The test has different forms that consist of processing color and words where time and error are considered for its assessment. The Trail Making Test (TMT), another measure of executive functioning, has two versions that require drawing lines between consecutive numbers and numbers and letters. The time duration for completing the task indicates the extent that people can attend and orient themselves in the environment. The Flanker Test is matched usually with cerebral activity. The test requires subjects to pay attention to five symbols, with one central arrow directing the response to congruent and incongruent formations. The measures used for eliciting perceptual-cognitive capability related to exercise are shown in Figure 1.
Measuring Perception-Cognition-Action in Sport
Sport activities are usually viewed via the perspectives of skill-type (open vs. closed; see Wang & Chen, 2014), movement/action (dynamic vs. static), and complexity (e.g., number of elements to be processed at the same time). Each sport is characterized by unique motor and environmental demands, which rely on several perceptual-cognitive capabilities. These capabilities include: visual attention (e.g., width, direction, selection, focus, concentration-sustained attentional mode), anticipation, information-processing, decision making, decision alteration, memory types (short term memory, working memory, long term memory, and long term working memory), and mental representation (see Tenenbaum, 2003). For example, in shooting, which represents a closed, static and stable environment, the shooter directs attention to the target, and attends to his/her heart beat, breathing pattern, and rifle tremors before releasing the trigger. In basketball, an open, dynamic, fast, and complex environment, the players pay attention to the relevant cues such as the formation of the opposing team and the ball location, anticipates the upcoming moves, and selects a response that best matches the situation (i.e., results in an advantageous outcome). In both cases, the response selection must be executed at the right time, and in both cases be altered and replaced by an alternative response. Each of these processes (see Figures 2 and 3) can be measured in the laboratory and field conditions.
Other methods for capturing the perceptual-cognitive capabilities in the sport domain were reviewed by Ericsson and Williams (2007), and Williams and Abernethy (2012). Most of these measurement paradigms use tasks representing the particular sport, and tailor it to the component of interest. Overall, most measures have targeted the visual system to derive perceptual cognitive capabilities in sport.
Visual attention is usually measured through the use of eye-tracker devices, which provide data on the location of visual attention, number of eye-fixations, and their duration. In closed tasks, it provides data that indicates the “quiet-eye” duration (i.e., last eye fixation before initiating an action). Eye movement recording, which measures visual attention, is believed to determine anticipatory and decision-making behaviors (for a review, see Williams, Janelle, & Davids, 2004). This technology detects the position of the pupil and the reflection of the light source of the cornea in a video image of the eye. This allows for the detection of the point of eye gaze. Verbal reports can complement the data gathered and shed more information on attention to peripheral cues in the environment. Visual attention, mainly the dimensions of width and direction, are measured introspectively via the test of attention and interpersonal style (TAIS; Nideffer, 1976), but their ecological validity is questionable.
Anticipatory capabilities in sport are measured through the use of Advance Cue Utilization (ACU) paradigms, mainly the temporal and special occlusion paradigms. The temporal occlusion methods limit the time available for the viewer to watch a scenario and require anticipating the next moves based on what has been captured during the short exposure. The shorter the time, the harder is the anticipatory decision. The scenarios are filmed from the viewer’s visual field to mimic as much as possible the real world. The films are usually edited into separate segments varying in exposure time and tasks, and presented randomly to the viewer in order to eliminate familiarization and learning effects. Using films, wherein images are either occluded or frozen, and assigning “next event probabilities” is an additional method for measuring anticipated event probabilities in sport settings. Groups of experts rank the possible follow-up events to which the viewer’s choices are compared.
The temporal occlusion method can be used to measure situational awareness and STM by asking the viewer to reconstruct the scene that has been viewed and occluded. Moreover, it can be used for measuring decision making by asking the viewer to report his/her moves had they been in a given position. Some measurement issues relate to the time-windows duration between the scenarios, its representativeness of the real-world information processing, and its isolation from other sources of information that are usually present in real-life situations (Williams & Abernethy, 2012).
Visual attention can also be measured by wearing liquid crystal occlusion glasses that can be used in the laboratory and field. The glasses are triggered and controlled by a range of electronic devices, and better mimic environmental constraints, though it poses some safety concern when athletes are asked to move. The emergent technology of virtual reality (VR) coupled with the use of eye-trackers will likely enable more ecologically valid measurement of the perceptual-cognitive components of the athletic performance.
Additional methods used to capture the cue utilization capabilities of athletes include the event occlusion paradigm. A filmed scenario is viewed and then occluded along with occlusion of different areas of the display. The assumption is that, when important areas are occluded, anticipatory capability declines only when the occluded cue provides crucial information for making anticipatory or action decisions. Some variations to this method include the presentation of the key sources of information only or exclusion of them sequentially. The occlusion paradigms can also use point-light images of movements performed by an opponent, instead of a real opponent, and they require the viewer to anticipate the upcoming move based on kinematic information only. This method relies on the assumption that, as skill level develops, less information is needed to make either anticipatory or response-related decisions.
An additional method for measuring anticipation and decision making relies on the response time paradigm, where video clip presentations are viewed and the athlete initiates a response, which is then measured by “time to decision” and accuracy. Similar to the temporal occlusion paradigm, this method does not provide the sources of information for the decision made. Another method is to film real-life plays and measure the initial response time made when viewing the opponent player’s move. Timing and anticipatory decision are the dependent measures derived from a real-world environment that includes all the typical pressures.
Recognition paradigms use real films, consisting of 3- to 10-second sequences from the sport of interest. The filmed segments vary in their structure. The viewer is asked to view the films, and then, after some time, view a new sequence of film that consists of the same segments viewed before and new unwatched segments. The viewer is then asked to identify which are the new segments and which segments s/he recognizes from the previous exposure. Accuracy of recall is the dependent measure, which indicates the capacity to store sport-specific information in the form of templates and retrieve it accurately. The recognition method also incorporates the presentation of actions in the form of point-light displays, which make the task more cognitively demanding. The recall paradigm consists of presenting structured and unstructured scenarios for 3 to 10 seconds and asking the viewer to recall the entire scene or part of it. The number of elements recalled, as well as radial, vertical, and horizontal errors, are indicators of recall accuracy. Verbal reports, eye tracking, and special occlusion data complement the array of information about the perceptual-cognitive capabilities of the performer.
Decision making relates to the action choice selected from several choice alternatives. When shown scenarios, players are asked which action (e.g., shoot, pass, dribble) they select, or to select one of the options given to them. Think aloud is another variation of measuring decision making, especially when individuals are asked to make decisions “on the fly.” These measurements are usually accompanied by eye-tracking measures in an attempt to link gaze behaviors with decision-making quality. Retrospective reports can complement how and why decisions have been made and shed more light on the perceptual-cognitive process.
Finally, a method termed structural dimensional analysis of mental representation (SDA-M), which places mental representations of basic action concepts (BACs) into a mental map, is used to quantify the knowledge base of performers as a function of their skill level (for an extensive review, see Schack, 2012). Because mental representations play a central role in the control and organization of actions, the BACs mental map describes its elements and structure symbolically in a cluster-type figure. The method assumes that effect representations mediate between perception of events and the execution of actions (Ericsson, 2003). By linking pairs of elements related to each other, a dendrogram is established that represents the structure of the action elements in long-term memory. This method allows for the observation of how knowledge base and structure are developed and deepened via the developmental stages of skill acquisition.
The Affective Domain
Affect, mood, and emotions are used interchangeably within the general sport and exercise psychology literature. Core affect is considered a primitive non-reflective feeling within the mind (e.g., pleasure, tension, relaxation, energy, tiredness). Mood refers to a more general and diffuse state of mind and lasts longer than emotion (for a review, see Ekkekakis, 2012). Emotions are reactions to events that are appraised (e.g., anger, fear, jealousy, pride, love). To this extent, there is an ongoing debate on the dimensionality of affect, mood, and emotional states in applied psychology (Blascovich, Vanman, Mendes, & Dickerson, 2011). Specifically, most methodological intakes in sport and exercise settings are based on a multidimensional approach to the study of affect, mood, and emotions, as these concepts are thought to have many antecedents, covariates, and outcomes (i.e., a many-to-many basis relationship; see Cacioppo et al., 2007) in the natural world. For instance, an increase in heart rate may signal both positive (e.g., happiness) and negative (e.g., anger) emotional states. Notwithstanding, there is a debate on whether certain concepts, including the pervasive notion of social-physic anxiety, might better be represented by a one-dimensional model (see Eklund, 1998; Martin, Rejeski, Leary, McAuley, & Bane, 1997). The continuous contrasting of alternative hypothesis and models needs to continue if we are to develop stronger theories and evolve evidence-based practice recommendations (see Popper, 2005).
Studies on the affective domain have examined the effect of exercise on individuals’ bio-psycho-social states, particularly through introspective reports. Previous research has focused on the notion of global negative/positive affect (PANAS; Watson, Clark, & Tellegen, 1988), and on the monitoring of discrete mood states, such as tension, depression, anger, vigor, fatigue, and confusion (POMS; McNair, Lorr, & Droppleman, 1971). A parsimonious conceptualization of affect (see Russel, 1980; Russell, 2003), which is comprised of arousal (activation) and pleasantness (valence) levels, has been widely used in the sport and exercise psychology domain (see Tenenbaum, Basevitch, Gershgoren, & Filho). Specifically, high pleasantness and high arousal result in excitation; low scores on both result in boredom. High on pleasantness and low on activation results in a relaxing mood, while the opposite results in anxiety and negative feelings. According to this model, distinct feelings and emotions are embedded within the interactions of the general states of arousal and pleasantness, making the affect-emotion distinctiveness difficult. Ekkekakis (2012), aware of the problematic nature of core affect and emotions, introduced an alternative concept (hierarchical structure of affective domain), which better specifies how related emotions are clustered within the affect dimensions. As a consequence, Ekkekakis suggested using measures of affect, mood, and emotions that typify the purpose of the study.
Ekkekakis (2012) divided the measures used in exercise into categories. These are: (a) single-items dimensional measures of affect (e.g., affect grid, feeling scale, and felt-arousal scale); (b) multi-items measures of distinct mood states, such as the multiple affect adjective checklist, and the POMS; (c) multi-item dimensional measures of affect, such as the positive and negative affect schedule activation-deactivation adjective check list; (d) multi-item measures of specific emotions; (e) multi-item measures of specific moods; and (f) exercise specific measures of affect. In addition to the measurements pertaining to affect-mood-emotions, the most commonly used exercise-related measures pertain to the self, exercise self-efficacy, quality of life, moral behaviors, motivation, and perception of effort and attention allocation.
Self-esteem is a subjective account of how people feel about themselves, whereas self-concept is a more objective account of “who we are” academically, socially, emotionally, and physically (see Buckworth, Dishman, O’Connor, & Tomporowski, 2013). As such, self-concept has been more studied in the sport and exercise psychological domain (see Marsh & Cheng, 2012). Marsh and Cheng (2012) and Sabiston, Whitehead, and Eklund (2012) reviewed a plethora of introspective measures of the physical self (exercise identity, physical activity self-definition, exercise self-schema, and possible self), which are mostly suited to the physical activity and movement sciences. Overall, they recommend the use of questionnaires, which are derived from a sound theoretical background and have undergone rigorous psychometric testing.
McAuley, White, Mailey, and Wojcicki (2012) identified three broad categories of exercise self-efficacy: barrier (overcome obstacles), adherence (maintain exercising), and task-specific. Introspective scales are usually unidimensional and consist of one item. However, there are also multi-dimensional scales with various items. Also, general physical self-efficacy scales contain several items related to various physical activity aspects. Last, hierarchical scales consist of physical tasks that gradually increase and/or decrease in difficulty and/or load level, and that ask the respondent to rate his/her self-efficacy to accomplish each level on a Likert-type scale.
Quality of Life
Measures of quality of life are also related to the concept of “self.” That is, quality of life measures pertain to estimating one’s perceived self-satisfaction with life in general. Quality of life is a broad construct involving physical health, psychological well-being, level of independence, number and quality of social relationships, relationship to the environment, and personal belief (Berger et al., 2015). Given the numerous factors that influence this construct, multi-measures are used to gauge one’s perceived quality of life. In particular, interviewing and survey methods have been widely used to assess quality of life. Quality of life is usually not investigated in isolation. Rather, scholars and practitioners generally investigate how individuals’ habits and background (e.g., age, gender, social economic status, exercise adherence) might be linked to other bio-psycho-social factors, such as cardiovascular endurance and psychological well-being.
Within the field of sport and exercise psychology, moral behaviors have generally been assessed through the use of multidimensional self-report measures. Moral disengagement, for instance, is a particularly well-studied phenomenon which involves displacement of responsibility, distortion of consequences, and diffusion of responsibility, among other moral justification practices (for a review, see Hodge, Hargreaves, Gerrard, & Lonsdale, 2013). Positive moral behaviors in sports are usually operationalized through the concept of sportsmanship. The study of sportsmanship has relied on multi-measures and methods, particularly self-report surveys and observational analysis and interviewing. Importantly, as is usually the case in sport and exercise psychology, moral behaviors are not analyzed in isolation. In effect, moral and amoral behaviors are most often linked to individuals’ motivations to participate in sport and physical activity.
Self-determination theory (SDT) and achievement goal theory are influential theories of motivation within the field of sport and exercise psychology. SDT purports that people are motivated by gaining competence, developing relatedness to others (i.e., developing social relationships), and feeling and behaving autonomously (for a review, see Ryan & Deci, 2000). Achievement goal theory reasons that task and ego orientations drive direction and intensity of effort towards one’s goals (Nicholls, 1984, 1992; Roberts & Treasure, 2012). Success for task-oriented individuals is a self-reference process, whereas success for ego-oriented individuals depends on outperforming others. Individuals’ task and ego orientation influences the development of two motivational climates: (a) mastery climate, which is task involving and focuses on personal improvement and effort; and (b) performance climate, which is ego involving and focuses on competition and rivalry among individuals. Scholars and practitioners use both SDT and achievement goal theory to inform the development and use of motivational tools.
Noteworthy, social cognitive theory and theory of planned behavior have also been used to inform the development of measurement tools in sport and exercise settings. Social cognitive theory purports that iteration among personal factors, behaviors and environmental influences drives motivation. Theory of planned behavior discusses how behavioral outputs depend on the linkage among attitudes, subjective norms, perceived control, and intention. Self-report measures are used to establish the motivational profile of different individuals, and link these profiles with other psychological constructs such as grit, mental toughness, and perception of effort.
Perception of Effort and Attention Allocation
How one perceives physical effort was once limited to the terms and scales that measured perception of exertion (Borg’s scales, which have dominated the field since 1960; for a review, see Razon, Hutchinson, & Tenenbaum, 2012). Borg distinguished between rating scales and ratio scales, and used a range of 6–20 with semantics varying in exertion level for rating scales, and 0–10 for ratio scales. Borg (1998) assumed that the term exertion encompasses a gestalt perspective where feelings such as pain and fatigue are embedded with the term exertion. Thus, all the scales used to be one-item scales that were shown to the exerciser during a graded exercise regimen. The rating of exertion corresponded to physiological changes such as heart rate, breathing, lactic acid accumulation, ventilation rate, and oxygen consumption. Prior to administering the scales, subjects would undergo familiarization and anchoring procedures to assure reliable exertive ratings. In recent years, perceived exertion has been viewed as one dimension within the construct of effort sensation (Hutchinson & Tenenbaum, 2006, 2007). Specifically, the dimension of perceived pain, which stems from the gate-control theory (GCT; Melzack & Wall, 1965), was found to represent an array of symptoms felt while being engaged in physical activity, including sensory-discriminative, cognitive-evaluative, and motivational-affective. Specific scales were developed for different populations and for children—all sharing a single item with ratings located on a linear continuum.
Idiosyncratic Methodology for Linking Emotions-Performance
Measures in the applied sport psychology domain do not provide sufficient information to practitioners about how measures of arousal, mood, and emotions are linked to performance level. Norm-related interpretation of emotions, coping, and self-regulation measures provide limited information for working practices. Criterion-related interpretation, wherein the level of any measure of affect/mood/emotion is idiosyncratically linked to performance, is more informative and practical (Tenenbaum et al., 2013). To this end, several descriptive methods have been developed to estimate the level of emotions that corresponds to optimal performance. This approach relies on many performance observations, collecting measures of emotions and performance quality simultaneously, followed by calculating the mean and SD coefficients for each performer and establishing the individual zone of optimal functioning (IZOF; see Hanin, 2000 for specific IZOF determination). Hanin’s original IZOF methodology was descriptive and lacked probabilistic estimation of performance. Thus, Kamata, Tenenbaum, and Hanin (2002) developed a method termed individual affect-related performance zone (IAPZ), which consists of simultaneously observing any measure of affect, emotion, mood, coping, or self-regulation, and performance quality. Upon completion of the measurement procedure, performance is classified into 3 to 5 categories (e.g., poor below the zone, moderate below the zone, the zone, moderate above the zone, poor above the zone) in correspondence to the inverted U function (Yerkes & Dodson, 1908). At this stage, a logistic regression (see equation below) is performed where any of the predicting measures is regressed individually on the categorized performance variable,
where Pi is the probability of any performance category, Xi is the measure of affect/mood/emotion, coping, or self-regulation separately, and β0 and βi are the regression coefficients. The coefficient estimates of the regression are entered into a statistical software along the scale’s score ranges resulting in a probabilistic curve for each performance category. At each point on the measure continuum, the sum of the probabilistic estimates of the curves is 1 (or 100%). For more details, see Edmonds, Johnson, Tenenbaum, and Kamata, 2012. An example of IAPZs is presented in Figure 4.
Psychological Skills, Coping, and Performance Routine
Psychological skills refer to mental self-regulatory strategies used before, during, and after competitive events. They are learned and used to secure competent behaviors, enhance or optimize performance, and improve well-being (Filho & Tenenbaum, 2015). Basic mental skills refer to self-awareness, self-confidence, productive thinking, and achievement drive. Performance skills include energy management, attention focus, and perceptual-cognitive skills. Performance developing skills consist of identity achievement and interpersonal competence. Team skills include leadership, communication, and team confidence (for an extensive review, see Vealey, 2007). Other categorizations of mental skills consist of five dimensions: foundation, psychosomatic, and cognitive skills (for a review, see Durand-Bush, Salmela, & Green-Demers, 2001).
Weinberg and Forlenza (2012) reviewed the main state and trait psychological skills inventories that were developed to measure psychological skills, and provide their specific dimensions along with psychometric properties of reliability and validity. The main dimensions of psychological skills measured by these introspective measures were: mental preparation, concentration, confidence, team emphasis, anxiety control, coping with adversity, freedom from worry, goal setting, achievement motivation, negative thinking, emotional control, self-talk, imagery, attention control, activation, relaxation, automaticity, fear control, refocusing, mental practice, activation, focusing, commitment, and competitive planning. The psychological skills most discussed in the literature include mental toughness and resilience, which consist of concentration, confidence, motivation, coping with pressure, control, commitment, challenge, and self-belief. Specific multi-dimensional scales have been developed to measure mental toughness and resilience (see Weinberg & Forlenza, 2012). Other inventories, developed to measure psychological skills, pertain to one skill, such as imagery, self-talk, concentration, and goal setting.
Coping and Self-Regulation
An additional line of measures related to psychological skills and self-regulation is coping. Measuring coping aims at quantifying the person’s ability to utilize cognitive and behavioral effort to manage stress and emotions stemming from internal and external resources (Aldwin, 2007). Micro-analytical approaches identified three broad functional categories: problem-focused, emotion-focused, and avoidance. Other categories were distraction-oriented coping, and approach-avoidance coping (for an extensive review, see Lidor, Crocker, & Mosewich, 2012). Lidor et al. (2012) also outlined some micro-analytical components of coping such as increasing effort, problem-solving, planning, seeking social support, imagery, relaxation, logical analysis, acceptance, positive reappraisal, mental disengagement, behavioral disengagement, wishful thinking, help-seeking, humor, confrontation, venting, arousal control, turning to religion, suppression of competing activities, dietary restrictions, appearance management, and self-blame. Lidor et al. (2012) list a number of inventories that were developed by using both inductive and deductive perspectives for dispositional and situational purposes. Some of the inventories were adapted to the sport and exercise domain from the general psychology domain, and others were developed specifically for sport and exercise. Some questionnaires also pertain to the approach-avoidance concept where coping and self-regulatory behaviors are active or attempt to eliminate threat and the sources of it. Other inventories were designed specifically for competitive conditions in which task-oriented, distraction, and disengagement coping are quantified. Specific strategies are similar to the ones outlined by the micro-analytical approach.
Since coping is a dynamic, contextual, and personal construct, the use of introspective measures to capture these features is limited, and thus requires the use of qualitative methods. Phenomenological methods, narrative analysis, thinking aloud, diaries, focus groups, and interview methods are recommended to capture the essence of coping with stressors for achieving adaptation (Crocker, Mosewich, Kowalski, & Besenski, 2010).
Closed-Skills Performance Coping Routines
Self-paced tasks are performed in a stable, non-dynamic environment and are mostly repetitious in nature. In some cases, temporal constraints pose some cognitive demands, and attention is directed both inward and outward on several familiar cues (e.g., shooting archery, bowling, basketball free-throw, golf, weightlifting). According to Lidor et al. (2012), the coping strategies in such an environment pertain mainly to biofeedback, performance routines, learning strategies, and attention instructions. Process-related components measured during the sessions are psycho-physiological (e.g., electroencephalogram, heart rate) and introspective (e.g., feelings, perceptions, and thoughts). The psycho-physiological state of the performer is obtained via a biofeedback system, in which means and variation of each measure can be calculated at a designated time interval. The data is stored and can be used later for practical and research purposes.
A pre-performance routine consists of a sequence of motor, emotional, and cognitive behaviors performed immediately before performing the task (Lidor, 2009). Motions like dribbling-throwing balls, body position location and relocation, breathing, concentration, and imagery, are typical actions that can be quantified by observation and later by introspection using specific inventories. The consistency of physical behaviors (e.g., time laps among various elements) and introspective reports are used to measure process variables, which are then contrasted to outcome variables (usually accuracy and speed).
Learning strategies refer to behaviors and thoughts activated deliberately or not when learning a skill; the most known is the 5-step strategy, which consists of readying, imaging, focusing attention, executing, and evaluating (Lidor & Singer, 2005). Measures of strategy use are mainly introspective in nature. Attentional strategies (e.g., where is attention being allocated—internal cues or external—task or non-task related?) are measured via interviews, gaze location using eye-trackers, and introspective reports. It is also important to measure performance and learning in team sports, particularly how team members’ cognitive-behavioral states change as a function of team characteristics and processes, such as team size, cohesiveness and collective efficacy.
Traditionally, team processes have been measured using paper-pencil questionnaires that target key team processes, such as leadership, cohesion, collective efficacy, and communication (see Connors, Rende, & Colton, 2016). More recently, advances in sport technology have made it possible to link team processes to position monitoring hardware (e.g., GPS and accelerometers) and software (i.e., sport performance video analysis). Position monitoring analysis allows researchers and practitioners to identify tactical patterns adopted by players, including the relative spread and direction in the field space of actions in interactive team sports. Recently, multi-subject monitoring of peripheral and central physiological responses has been conducted to monitor team processes and performance (see Filho, Bertollo, Robazza, & Comani, 2015). In monitoring multi-subjects, it is essential to reliably synchronize the data collection devices, as well as normalize the team-level responses to each subject’s baseline. Of note, both subjective and objective measures are considered in analyzing team processes. The nature of the sport (coactive vs. interactive), the size of the team, and the research questions or intended application of the study inform the methodology and measures chosen to assess team processes. It is also important to take into account that team processes change dynamically, and thus it may require multiple, longitudinal assessments to gather valid information (Myers & Feltz, 2007). Further, given the nested nature of the data (individuals within teams), it is important to apply multi-level statistical analysis before drawing conclusions from the data.
A number of signal processing principles are considered in the measurement of psychophysiological data. Attention must be given to the different types of psychological activity and the concept of directional fractionation. Furthermore, different peripheral and central physiological processes must also be considered when measuring psychophysiological data.
Types of Psychophysiological Activity
Three types of somatic activity are implicated in psychophysiological research: spontaneous activity, tonic activity, and phasic activity. Spontaneous activity pertains to psychophysiological responses that occur in a situation without an identified stimulus (Stern, Ray, & Quigley, 2001). For instance, a person’s heart rate may increase due to myriad reasons, including a physical threat, an acute auditory stimulus, or a number of distinct affective-emotional responses (e.g., surprise or anger). Accordingly, the assessment of objective psychophysiological data should be done in conjunction with the collection of subjective reports (see Tenenbaum & Filho, 2015). If the subject being assessed is not asked about his/her perception of a given stimuli, researchers may misinterpret the acquired data.
Tonic activity is the activity level of a given biological organ (e.g., heart rate, skin conductance, alpha brain waves) prior to stimulation. Tonic activity complies with the law of initial values, which states “the direction of response of a body function to any agent depends to a large degree on the initial level of that function.” According to Wilder (1967), any increase in psychophysiological function will be smaller if the pre-stimulus levels are high, and larger if the pre-stimulus levels are low. For instance, there is greater room for variability in a maximal aerobic capacity test (VO2 max) if the subject’s baseline heart rate is at a lower level (e.g., 60 bpm) as compared to a higher level (e.g., 90 bpm). It follows that a baseline assessment is paramount in psychophysiological measurement. The idea is that the baseline measurement captures the state of homeostasis (equilibrium) of a given organism prior to testing. Noteworthy, there is no “recipe” for how long the baseline assessment should last. Overall, the assessment must last long enough to provide a stable pre-stimulus signal (within normal ranges to attest for validity) that allows for subsequent data collection and analysis. However, a baseline assessment should not be too long in duration to avoid negative affective responses (e.g., boredom, stress) from the subjects that may interfere with the data collection.
Phasic activity refers to an evoked, event-related response. In other words, phasic activity is a discrete reaction to a specific stimulus. To conduct reliable psychophysiological research, it is important to present a clear stimulus (or set of stimuli), while considering the baseline tonic activity. Different somatic responses may occur for the same given stimulus. Hence, familiarization with the specific psychophysiology of various systems, including the muscular, cardiovascular, respiratory and gastrointestinal systems, as well as electro-dermal activity and eye-movement analysis, is required.
Directional fractionation is a concept in psychophysiological research that relies on the notion that a single given stimulus (e.g., video of aggressive behavior in a football stadium) can lead to changes in various psychophysiological systems (e.g., cardiovascular, central nervous system; see Stern et al., 2001). As such, directional fractionation reflects a multidimensional understanding of human functioning. The idea of directional fractionation advances on the initial notion of “stimulus-specific response,” which stems from the idea that each unique stimulus carries an idiosyncratic psychophysiological reaction. This concept is particularly important for measurement of elite athletes and exercisers. With respect to sports, extant empirical evidence suggests that high-skilled athletes exhibit different ranges of bio-psycho-social states linked to optimal and less-than-optimal performance states (Tenenbaum et al., 2013). There is also a general agreement that exercise testing and guidelines should be individualized due to the variability in individual reaction and adaptation to stimuli within the population. These recommendations should be taken into account to ensure reliable measurement of both peripheral and central processes.
The most commonly used peripheral physiological measures in sport and exercise psychology include electrocardiogram (EKG), electrodermal activity, electromyography (EMG), and respiration response. Using EKG involves placing two electrodes on the skin surface. The electrodes can be placed in different locations on the skin but are generally situated on the torso to minimize movement artifacts, the most common problem with EKG recording (Stern et al., 2001). Heart rate monitors have also been used increasingly to determine heart rate and heart rate variability. The interest in measuring cardiovascular responses rests on the notion that changes in bio-psycho-social states leads to changes in the sympathetic-parasympathetic co-regulation, which leads, in turn, to changes in cardiovascular response.
Electrodermal activity is measured by placing electrodes on the skin surface. As the largest organ in the human body, changes in bio-psycho-social states will inevitably be reflected on the skin, either by changes in temperature or skin conductance. Electrodermal activity is usually measured by placing electrodes on the palms of hands or feet, locations where there is a high density of eccrine sweat glands, which are linked to the autonomous nervous system and reflect changes in psychological states (see Plowman & Smith, 2013). Researchers and practitioners measuring electrodermal activity are interested in latency (time from stimulus to the beginning of a response) or recovery time (time from stimulus onset to the re-establishment of pre-stimulus/baseline levels).
EMG is the recording of electrical activity created by skeletal muscles through the use of surface electrodes placed on the skin (Basmajian & Blumenstein, 1980). EMG is primarily used to determine where tension develops within a muscle, in relation to the recruitment of other skeletal muscles (Konrad, 2005). Scholars and practitioners interested in the measure of EMG activity rely on knowledge of anatomy and kinesiology, especially muscle origin and insertions, to determine the proper location for attachment of electrodes. A pair of electrodes is needed to record EMG activity and specific guidelines on proper placement are available in the literature (see Basmajian & Blumenstein, 1980; Konrad, 2005). The size of the muscle, the distribution of motor units within the muscle, and the type of contraction under study (e.g., concentric, isometric, eccentric) all influence EMG responses.
Respiration response, especially breathing rate and amplitude, is another variable studied by applied psychologists, who are particularly interested in relating respiration responses to performance and other physiological variables. For instance, respiration is related to cardiovascular activity, as changes in breathing patterns correlate with changes in heart rate (Plowman & Smith, 2013). Changes in respiration are also reflected in electrodermal activity. Spirometry testing is used to identify the volume and speed of air that can be inhaled and exhaled, and is widely used in sport sciences to measure, max, and in medical sciences to diagnose clinical conditions (e.g., asthma). Furthermore, computer programs connected to bodily sensors (hardware) are used to mark events in respiration, and derive breathing rate and amplitude, as well as other events, including inspiration time, inspiration pause time, expiration time and expiration pause time (see Stern et al., 2001). Biofeedback training relies heavily on measuring and teaching individuals how to modify respiration responses (Filho, 2015).
In addition to measuring peripheral markers, which pertain mostly to capturing changes in the autonomous nervous system, applied psychologists are also interested in measuring central processes in the brain. In particular, they are interested in the spatiotemporal dynamics of electrical or hemodynamic responses that occur in the brain in response to a given stimulus (Tenenbaum & Filho, 2015). Applied psychologists are also interested in examining both structural and functional changes in the brain. Structural changes refer to the physical composition of the brain, which may change as a result of physical ailments or deliberate processes, such as learning. Functional changes pertain to transformations in the inter-neuronal connections in the brain, which also change due to illness, learning, or environmental stimuli (Collin, Sporns, Mandl, & van den Heuvel, 2014). A number of different instruments have been used to measure spatiotemporal dynamics in the brain, with respect to both structural and functional properties (see Table 1).
Table 1. Methods used to measure or stimulate spatiotemporal dynamics in the brain
Measures functional changes in the brain
Measures functional changes in the brain
Metabolic, functional, molecular
Measures areas of brain change in response to given stimuli or condition
Measures areas of brain change in response to given stimuli or condition
Uses near-infrared light to evaluate changes in hemodynamic activity in the brain
Brain activation or inhibition
Application of low current pulses to scalp; alter the activity of the brain
Low spatial and temporal
Brain activation or inhibition
Application of low current pulses to scalp; alter the activity of the brain
Electroencephalogram (EEG) and magnetoencephalogram (MEG) are noninvasive techniques with high-temporal resolution. Both have been used in the movement sciences to describe functional changes in the brain. Position Emission Tomography (PET) and Functional Magnetic Resonance Imaging (fMRI) are high in spatial resolution and have been used to measure which areas of the brain change in response to a given stimuli or condition. Other instruments commonly used to measure brain activity include near-infrared spectroscopy (NIRS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS). NIRS is a noninvasive method with excellent temporal resolution and uses near-infrared light to evaluate changes in hemodynamic activity in the brain. TMS and tDCS are used to alter the activity of the human brain through the application of low current pulses to the scalp. Neuroscientific methods are a large area of investigation, and it is beyond the scope of this chapter to describe in detail the aforementioned methods and measures used to capture brain activity. The most important issue is to understand that these different methods vary in their ability to measure structural and functional changes in the spatiotemporal dynamics of the human brain.
In summary, applied psychologists have relied on numerous methods and measures to describe, explain, and promote performance across domains of human activity. It is essential that scientists and practitioners understand the importance of developing and using reliable and valid measures in sport and exercise psychology. The importance of theoretical, investigator, and data triangulation is also paramount for the advancement of research and applied approaches within the field of sport and exercise psychology. Furthermore, the future of measurement in the field is tied to the advancement of research paradigms able to connect covert and overt measures. Brain imaging studies are a growing force, not only EEG studies, which historically dominated the scene in sport and exercise, but also research relying on modalities such as fMRI, TMS, tDCS, and NIRS. Furthermore, multi-brain studies have been gaining momentum and can greatly advance our understanding of the neural correlates of team processes during highly interactive cooperative actions (Filho, 2015). Sports-related genetic testing is an ever-growing area that will bring new data, theories, interventions, and ethical challenges to scientists and practitioners (Wagner & Royal, 2012). Overall, scholars and practitioners should strive to meaningfully link covert and overt measures in an effort to bring together the various methods and measures covered here, from self-report questionnaires to state-of-the-art brain imaging methods.
Aldwin, C. M. (2007). Stress, coping, and development: An integrative approach (2d ed.). New York: Guilford.Find this resource:
Anderson, R. D., & Helms, J. V. (2001). The ideal of standards and the reality of schools: Needed research. Journal of Research in Science Teaching, 38(1), 3–16.Find this resource:
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman/Times Books/Henry Holt.Find this resource:
Basmajian, J. V., & Blumenstein, R. (1980). Electrode placement in EMG biofeedback. Philadelphia, PA: Williams & Wilkins.Find this resource:
Berger, B. G., Weinberg, R. S., & Eklund, R. C. (2015). Foundations of exercise psychology (3d ed.). Morgantown, WV: Fitness Information Technology.Find this resource:
Biggs, J. (2002). Aligning the curriculum to promote good learning. Presented at the Imaginative curriculum symposium, LTSN Generic Centre, York, U.K.Find this resource:
Blascovich, J., Vanman, E., Mendes, W. B., & Dickerson, S. (2011). Social psychophysiology for social and personality psychology. Thousand Oaks, CA: SAGE.Find this resource:
Borg, G. (1998). Borg’s perceived exertion and pain scales. Champaign, IL: Human Kinetics.Find this resource:
Buckworth J., Dishman, R. K., O’Connor, P. J. and Tomporowski, P. D. (2013) Exercise psychology (2d ed.). Champaign, IL: Human Kinetics.Find this resource:
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (2007). Psychophysiological science: Interdisciplinary approaches to classic questions about the mind. In J. T. Cacioppo, L. G. Tassinary, & G. Berntson (Eds.), Handbook of psychophysiology (3d ed., pp. 1–24). New York: Cambridge University Press.Find this resource:
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.Find this resource:
Chomsky, N. (2014). Aspects of the theory of syntax (Vol. 11). Cambridge, MA: MIT Press.Find this resource:
Collin, G., Sporns, O., Mandl, R. C., & van den Heuvel, M. P. (2014). Structural and functional aspects relating to cost and benefit of rich club organization in the human cerebral cortex. Cerebral Cortex, 24(9), 2258–2267.Find this resource:
Connors, B. L., Rende, R., & Colton, T. J. (2016). Beyond self-report: Emerging methods for capturing individual differences in decision-making process. Frontiers in Psychology, 7, 312.Find this resource:
Coyle, A. (2010). Qualitative research and anomalous experience: A call for interpretative pluralism. Qualitative Research in Psychology, 7(1), 79–83.Find this resource:
Crocker, P. R. E., Mosewich, A. D., Kowalski, K. C., & Besenski, L. J. (2010). Coping: Research design and analysis issues. In A. R. Nicholls (Ed.), Coping in sport: Theory, methods, and related constructs (pp. 53–76). Hauppauge, NY: Nova Science.Find this resource:
Durand-Bush, N., Salmela, J. H., & Green-Demers, I. (2001). The Ottawa Mental Skills Assessment Tool (OMSAT-3*). The Sport Psychologist, 15(1), 1–19.Find this resource:
Edmonds, W. A., Johnson, M. B., Tenenbaum, G., & Kamata, A. (2012). Idiographic approaches in sport. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology. Champaign, IL: Human Kinetics.Find this resource:
Ekkekakis, P. (2012). Affect, mood, and emotion. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 321–332). Champaign, IL: Human Kinetics.Find this resource:
Eklund, R. C. (1998). With regard to the Social Physique Anxiety Scale (conceptually speaking). Journal of Sport and Exercise Psychology, 20(2), 225–227.Find this resource:
Eklund, R. C., & Tenenbaum, G. (Eds.). (2014). Encyclopedia of sport and exercise psychology. Thousand Oaks, CA: SAGE.Find this resource:
Ericsson, K. A. (2003). The development of elite performance and deliberate practice: An update from the perspective of the expert-performance approach. In J. Starkes and K. A. Ericsson (Eds.), Expert performance in sport: Recent advances in research on sport expertise (pp. 49–81). Champaign, IL: Human Kinetics.Find this resource:
Ericsson, K. A., & Williams, A. M. (2007). Capturing naturally occurring superior performance in the laboratory: Translational research on expert performance. Journal of Experimental Psychology: Applied, 13(3), 115–123.Find this resource:
Estabrook, R. (2012). Factorial invariance: Tools and concepts for strengthening research. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 53–63). Champaign, IL: Human Kinetics.Find this resource:
Etnier, J. L. (2012). Cognitive measures related to exercise and physical activity. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 179–190). Champaign, IL: Human Kinetics.Find this resource:
Etnier, J. L., & Chang, Y. K. (2009). The effect of physical activity on executive function: A brief commentary on definitions, measurement issues, and the current state of the literature. Journal of Sport and Exercise Psychology, 31(4), 469–483.Find this resource:
Filho, E. (2015). Biofeedback learning environments. In J. M. Spector (Ed.), The SAFE encyclopedia of education technology. Thousand Oaks, CA: SAGE.Find this resource:
Filho, E., & Tenenbaum, G. (2015). Sports psychology. Oxford bibliographies. Oxford: Oxford University Press.Find this resource:
Filho, E., Bertollo, M., Robazza, C., & Comani, S. (2015). The juggling paradigm: A novel social neuroscience approach to identify neuropsychophysiological markers of team mental models. Frontiers in Psychology, 6.Find this resource:
Gigerenzer, G. (2010). Personal reflections on theory and psychology. Theory & Psychology, 20(6), 733–743.Find this resource:
Grimm, K. J., & Ram, N. (2012). Modeling change over time. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 65–73). Champaign, IL: Human Kinetics.Find this resource:
Gucciardi, D., & Gordon, S. (2011). Mental toughness in sport: Developments in theory and research. Abingdon-on-Thames, U.K.: Routledge.Find this resource:
Hanin, Y. L. (Ed.). (2000). Emotions in sport. Champaign, IL: Human Kinetics.Find this resource:
Hodge, K., Hargreaves, E., Gerrard, D., & Lonsdale, C. (2013). Psychological mechanisms underlying doping attitudes in sport: Motivation and moral disengagement. Journal of Sport & Exercise Psychology, 35(4), 419–432.Find this resource:
Hutchinson, J. C., & Tenenbaum, G. (2006). Perceived effort: Can it be considered gestalt? Psychology of Sport and Exercise, 7(5), 463–476.Find this resource:
Hutchinson, J. C., & Tenenbaum, G. (2007). Attention focus during physical effort: The mediating role of task intensity. Psychology of Sport and Exercise, 8(2), 233–245.Find this resource:
Kamata, A., Tenenbaum, G., & Hanin, Y. L. (2002). Individual zone of optimal functioning (IZOF): A probabilistic estimation. Journal of Sport and Exercise Psychology, 24(2), 189–208.Find this resource:
Kim, M., Hannafin, M., & Bryan, L. (2007). Technology-enhanced inquiry tools in science education: An emerging pedagogical framework for classroom practice. Science Education, 96, 1010–1030.Find this resource:
Kincheloe, J. (2005). On to the next level: Continuing the conceptualization of the bricolage. Qualitative Inquiry, 11(3), 323–350.Find this resource:
Kline, R. B. (2011). Principles and practice of structural equation modeling (3d ed.). New York: Guilford.Find this resource:
Konrad, P. (2005). The ABC of EMG. A practical introduction to kinesiological electromyography. Scottsdale, AZ: Noraxon.Find this resource:
Lidor, R. (2009). Free throw shots in basketball: Physical and psychological routines. In E. Tsung-Min Hung, R. Lidor, & D. Hackfort (Eds.), Psychology of sport excellence (pp. 53–61). Morgantown, WV: Fitness Information Technology.Find this resource:
Lidor, R., Crocker, P. R. E., & Mosewich, A. D. (2012). Coping in sport and exercise. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 393–408). Champaign, IL: Human Kinetics.Find this resource:
Lidor, R., & Singer, R. N. (2005). Learning strategies in motor skill acquisition: From the laboratory to the gym. In D. Hackfort, J. L. Duda, & R. Lidor (Eds.), Handbook of research in applied sport and exercise psychology: International perspectives (pp. 109–126). Morgantown, WV: Fitness Information Technology.Find this resource:
Marsh, H. W., & Cheng, J. H. S. (2012). Physical self-concept. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 215–226). Champaign, IL: Human Kinetics.Find this resource:
Martin, K. A., Rejeski, W. J., Leary, M. R., McAuley, E., & Bane, S. (1997). Is the Social Physique Anxiety Scale really multidimensional? Conceptual and statistical arguments for a unidimensional model. Journal of Sport and Exercise Psychology, 19(4), 359–367.Find this resource:
McAuley, E., White, S. M., Mailey, E. L., & Wojcicki, T. R. (2012). Exercise-related self-efficacy. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 239–250). Champaign, IL: Human Kinetics.Find this resource:
McNair, D. M., Lorr, M., & Droppleman, L. F. (1971). Manual for the profile of mood states. San Diego, CA: Educational and Industrial Testing Services.Find this resource:
Melzack, R., & Wall, P. D. (1965). Pain mechanisms: A new theory. Science, 150, 971–979.Find this resource:
Myers, N. D., & Feltz, D. L. (2007). From self-efficacy to collective efficacy in sport: Transitional methodological issues. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (3d ed., pp. 799–819). Hoboken, NJ: Wiley.Find this resource:
Nicholls, J. G. (1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91(3), 328–346.Find this resource:
Nicholls, J. G. (1992). The general and the specific in the development and expression of achievement motivation. In G. C. Roberts (Ed.), Motivation in sport and exercise (pp. 31–56). Champaign, IL: Human Kinetics.Find this resource:
Nideffer, R. M. (1976). Test of attentional and interpersonal style. Journal of Personality and Social Psychology, 34(3), 394–404.Find this resource:
Nideffer, R. M., & Sagal, M. S. (2001). Assessment in sport psychology. Morgantown, WV: Fitness Information Technology.Find this resource:
Parker, I. (2004). Criteria for qualitative research in psychology. Qualitative Research in Psychology, 1(2), 95–106.Find this resource:
Plowman, S. A., & Smith, D. L. (2013). Exercise physiology for health fitness and performance. Philadelphia, PA: Lippincott Williams & Wilkins.Find this resource:
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.Find this resource:
Popper, K. (2005). The logic of scientific discovery. New York: Routledge.Find this resource:
Rasch, G. (1961). On general laws and meaning of measurement in psychology. Proceedings of the Fourth Berkeley Symposium on mathematical statistics and probability, 4, 321–333.Find this resource:
Razon, S., Hutchinson, J., & Tenenbaum, G. (2012). Effort perception. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 265–277). Champaign, IL: Human Kinetics.Find this resource:
Roberts, G. C., & Treasure, D. C. (Eds.). (2012). Advances in motivation in sport and exercise (3d ed.). Champaign, IL: Human Kinetics.Find this resource:
Rothbauer, P. (2008). Triangulation. In L. Given (Ed.), The SAGE encyclopedia of qualitative research methods (pp. 892–894). Thousand Oaks, CA: SAGE.Find this resource:
Russel, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178.Find this resource:
Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172.Find this resource:
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68.Find this resource:
Sabiston, C., Whitehead, J. R., & Eklund, R. C. (2012). Exercise and self-perception constructs. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 227–237). Champaign, IL: Human Kinetics.Find this resource:
Schack, T. (2012). Measuring mental representations. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 203–214). Champaign, IL: Human Kinetics.Find this resource:
Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological recording. Oxford: Oxford University Press.Find this resource:
Strauss, B., Busch, D., & Tenenbaum, G. (2012). Rasch modeling in sports. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.). Measurement in sport and exercise psychology (pp. 75–80). Champaign, IL: Human Kinetics.Find this resource:
Tenenbaum, G. (2003). Expert athletes: An integrated approach to decision making. In J. Starkes, & A. Ericsson (Eds.), Expert performance in sports: Advances in research on sport expertise (pp. 192–218). Champaign, IL: Human Kinetics.Find this resource:
Tenenbaum, G., Basevitch, I., Gershgoren, L., & Filho, E. (2013). Emotions: Decision-making in sport: Theoretical conceptualization and experimental evidence. International Journal of Sport and Exercise Psychology, 11(2), 151–168.Find this resource:
Tenenbaum, G., Eklund, R., & Kamata, A. (2012). Measurement in sport and exercise psychology. Champaign, IL: Human Kinetics.Find this resource:
Tenenbaum, G., & Filho, E. (2015). Measurement considerations in performance psychology. In M. Raab, B. Lobinger, S. Hoffmann, A. Pizzera, & S. Laborde (Eds.), Performance psychology: Perception, action, cognition, and emotion (pp. 31–44). Philadelphia, PA: Elsevier.Find this resource:
Vaughn, B. K., Lee, H., & Kamata, A. (2012). Reliability. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.). Measurement in sport and exercise psychology (pp. 25–32). Champaign, IL: Human Kinetics.Find this resource:
Vealey, R. S. (2007). Mental skills training in sport. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (3d ed.). New Jersey: WileyFind this resource:
Wagner, J. K., & Royal, C. D. (2012). Field of genes: An investigation of sports-related genetic testing. Journal of Personalized Medicine, 2(3), 119–137.Find this resource:
Wang, J., & Chen, S. (2014). Transfer of learning: The key principle of motor skill training implementation. Applied motor learning in physical education and sports. Morgantown, WV: Fitness Information Technology.Find this resource:
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070.Find this resource:
Weinberg, R., & Forlenza, S. (2012). Psychological skills. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 381–392). Champaign, IL: Human Kinetics.Find this resource:
Wilder, J. (1967). Stimulus and response: The law of initial value. Bristol, U.K.: Wright.Find this resource:
Williams, A. M., & Abernethy, B. (2012). Anticipation and decision making: Skills, methods and measures. In G. Tenenbaum, R. C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 191–202). Champaign, IL: Human Kinetics.Find this resource:
Williams, A. M., Janelle, C. M., & Davids, K. (2004). Constraints on the search for visual information in sport. International Journal of Sport and Exercise Psychology, 2(3), 301–318.Find this resource:
Wright, B. D., & Douglas, G. A. (1977). Conditional versus unconditional procedures for sample-free item analysis. Educational and Psychological Measurement, 37(3), 573–586.Find this resource:
Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. Chicago, IL: MESA.Find this resource:
Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18(5), 459–482.Find this resource:
Zhu, W. (2012). Measurement practice in sport and exercise psychology: A historical, comparative, and psychometric view. In G. Tenenbaum, R. Eklund, & A. Kamata (Eds.). Measurement in sport and exercise psychology (pp. 9–21). Champaign, IL: Human Kinetics.Find this resource: