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Questionnaire that measures participants emotion at the time of completion of the questionnaire

Questionnaire that measures participants emotion at the time of completion of the questionnaire



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Could anyone help with finding a questionnaire that measures participants emotion at the time of completion of the questionnaire about the effect on memory recall in eyewitness testimony?


Your question is very specific in that it pertains to memory recall in eyewitness testimony. But presumably the context is not the main point. The main point is that you want to get a measures of state emotion.

The most common measure that I'm familiar with is the PANAS (Watson, Clark, Tellegen, 1988). It asks about the frequency of experiencing a set of common positive and negative emotions. It yields overall scores positive affect and negative affect. It can be asked with different time-frame instructions (e.g., last hour, today, last week, etc.). Choose the time frame that most aligns with your construct of interest.

See also

https://en.wikipedia.org/wiki/Affect_measures

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. doi:10.1037/0022-3514.54.6.1063.


General Discussion

The goal of the current research was to describe the development and validation of a comprehensive parent report measure of feeding practices. Whereas previous scales have measured only a subset of feeding practices, the 12 subscales of the CFPQ allow researchers and clinicians to measure many aspects of this complex behavior. Another benefit of this measure is that the factor structure of the items appears to be consistent for mothers and fathers and across multiple modalities of survey administration (i.e., paper and pencil and computer based). Thus, the scale provides flexibility for use in multiple settings and can be adapted to suit the needs of a particular project.

This initial examination of the validity of the CFPQ yielded positive results. Factor analysis suggested that the items form coherent scales. Furthermore, relationships between feeding practices and parents’ attitudes about their child's weight and their responsibility for feeding their child, provided further support for the instrument. For example, parents concerned that their child is overweight reported more restriction of both types, whereas parents concerned that their child is too thin reported less restriction for weight control and more pressure to eat. Furthermore, parents who avowed greater responsibility for the feeding of their child reported more monitoring of what their child ate and were less likely to grant their child control over feeding interactions.

Although the goal was to create a measure that would be as comprehensive as possible, there remain some feeding behaviors that may not be adequately represented in the CFPQ. During the open-ended item generation by parents, many parents’ responses included the word “snack.” In most cases, these responses were coded as either restriction (e.g., “don't allow child to eat snacks between meals”) or as child control (e.g., “allow children to get snacks without asking”). However, the word snack is ambiguous. Indeed, in completing the questionnaires, some parents indicated confusion about the word snack in one of the items, noting that it could be interpreted either as meaning food eaten between meals or as typically unhealthy snack foods. Although typically snack foods are energy dense and nutrient poor (e.g., chips, cookies), snacks of fruits and vegetables may be beneficial to a child's health. Thus, snacking is a very complicated issue and one that deserves further attention in both measurement and substantive work. Future research should work to develop questions that adequately measure the quality, quantity, and frequency of children's snacks, as well as the relationships between snacking and outcomes of interest.

It also appears that there is a distinction between two types of food as reward items. The first set of items referred to using food as a reward for behavior. These are the items that ultimately were included in this subscale. However, there were other items that were not included in the final CFPQ that referred to using food as a reward for food (e.g., promising dessert if a child eats his/her vegetables). Unfortunately, an insufficient number of items were available for this construct in this validation study to create distinct subscales. This distinction, however, is theoretically quite important. Research suggests that using sweet foods as a reward for eating healthy foods may alter taste preferences, encouraging increased liking of the sweet food and decreased liking of the healthy food (Capaldi, 1996). Thus, it is our hope that future work will explore this distinction fully.

Another set of feeding constructs that may be useful is parental behaviors that alleviate children's food neophobia. For example, one such construct that was not included in the scale is exposing children to foods repeatedly to encourage them to develop a preference for these foods. Items tapping this construct were not found in the review of the literature, nor was the construct suggested in the open-ended portion of this study. Nonetheless, research suggests that repeated exposure to a food (8–15 tries) is an effective way to encourage acceptance of a new food (Sullivan & Birch, 1990), and that most parents give up too soon when introducing a new food to a child (Wardle et al., 2003). Future research should examine whether repeated exposure to foods may be part of the healthy environment or encouraging balance and variety subscales described in this study or, alternatively, represent an independent construct. Given the evidence that this practice can support the development of healthy eating habits in children, future research should explore this more fully.

Of the three new subscales suggested by parents’ responses, only involvement in food preparation was included in the final scale. Future research may determine that the two other constructs (presentation and routine) are important predictors of eating outcomes for children and thus find that this is a weakness of the CFPQ. On the other hand, future research on these constructs may find that parents are not well-informed about what is really important in encouraging healthy eating outcomes in their children. It is possible that an overemphasis on food presentation or routine obscures other more important feeding issues.

This study also demonstrated that the constructs that parents spontaneously report as being central to feeding practices did not always overlap with those that have been emphasized in the literature. For example, a large number of parents mentioned providing a healthy food environment and modeling healthy eating habits however, these constructs have been the focus of only a small amount of research. The results also indicated that parents did not spontaneously differentiate between restriction motivated by weight and by health. Nonetheless, research has supported the necessity for this distinction (Musher-Eizenman & Holub, 2006). Thus, it seems critical to have a well-constructed and sensitive scale to distinguish these constructs that on the surface may appear to be similar.

It is important to note that although the participants in this study were geographically diverse, all three samples were predominantly Caucasian, and of a high educational background, and socio-economic status. In addition, the recruitment method used in Study 3 may have yielded biases inherent to chain-sampling such that the final sample is likely to resemble the initial sample. Steps were taken to minimize this bias (Penrod, Preston, Cain, & Starks, 2003) such as clearly defining the population in question and initiating chains appropriately. Nonetheless, the samples included in these studies should not be taken as representative of all parents.

Notably, several studies suggest that scales that appropriately measure feeding practices in this population do not capture the feeding processes of more diverse ethnic/racial or socioeconomic groups (Anderson et al., 2005), that these items may be misunderstood by more diverse samples (Jain, Sherman, Chamberlain, & Whitaker, 2004), and that ethnic/racial group may impact the frequency of use of some feeding practices (Faith et al., 2003 Hoerr, Utech, & Ruth, 2005). Previous research also suggests that there are differences in feeding practices depending on maternal education (Vereecken, Keukelier, & Maes, 2004) and income level (Baughcum et al., 2001). The creation of the CFPQ took this into account by including feeding constructs that have been found to be relevant in nonCaucasian samples. However, its applicability to these groups has not yet been confirmed. Given that cultural background undoubtedly impacts feeding practices and that rates of childhood obesity are elevated in groups with lower socio-economic status and some ethnic groups in the US, it is of high importance to validate this measure with additional samples.

In addition, although the current study provided considerable support for the validity of the CFPQ, less is currently known about the reliability of the measure. The internal consistency (coefficient alpha) of most of the scales was moderate to high, but this index of reliability was lower than desired for some of the scales in some of the samples ( Table I). Furthermore, a study of test–retest reliability would increase researchers’ confidence in the measure.

The development of a comprehensive, valid and reliable tool to measure parental feeding practices opens up many possible research directions. Research priorities in this area include a better understanding of the impact that various feeding practices have on child health, eating habits, and weight outcomes in both the short and long term, as well as an exploration of the parent and child characteristics that are related to the use of these feeding practices. Carefully controlled longitudinal research that sheds light on causal relationships is of particular importance. Thus, measures such as the CFPQ that can be used to assess the feeding practices of parents with children of a wide range of ages are especially useful. It is important to remember that further work needs to be done to assure the psychometric properties of this measure are appropriate for work in more diverse samples.

Furthermore, the support provided by this research for the computer-implementation of the scale is very promising. Although web-based data collection makes it difficult to know what biases may be inherent in the sample (i.e., how parents who did not complete the survey might differ from those who did), computer implementation (either on-line or in an office or laboratory setting) may make it easier for researchers and clinicians to gather information from large numbers of parents. This may also improve responding from fathers, who tend to have a lower response rate than mothers for paper and pencil questionnaires.

Finally, although this scale was primarily developed for use as a research tool, additional uses are possible. This scale has potential as a clinical instrument. As norms on the various subscales are determined, it is possible that clinicians working with overweight children or children with eating problems could use the CFPQ as part of a familial intake. Furthermore, the CFPQ could be used as an evaluation tool to assess the effectiveness of teacher or parent training programs that intend to improve the parent–child feeding relationship. It is our hope that a valid, reliable, and comprehensive tool for the measurement of parents’ feeding practices will help advance research in this arena and will allow for much needed clarity in the ways in which parents feed their children.

Confilict of Interest: None declared.

Items from these deleted scales are available upon request from the authors.


Likert Scale

Participants in the PANAS Scale respond to all 20 terms according to the Likert scale. This implies the following options:

  1. this concept applies very little or not at all to the participant
  2. this concept applies a little to the participant
  3. this concept applies moderately to the participant
  4. this concept applies a lot to the participant
  5. this concept applies very much to the participant

The final score of the PANAS Scale / Positive and Negative Affect Schedule (PANAS) test is the sum of the 10 terms on the positive scale and the sum of the 10 terms on the negative scale. The value assigned is positive for answers on the positive scale and negative for answers on the negative scale.


Top 15 Psychology Survey Questions for Questionnaires

Psychology survey questions are survey questions asked to collect information about an individual to evaluate the mental state of the respondent. Such questions enable the researcher to categorize different behaviors, traits, and conditions. Such survey questions are used by a number of industries such as healthcare, corporates, recruitment firms, defense services and many more. Psychology survey questions are created and analyzed by psychologists, mental health professionals, psychiatrists, members of the judicial system and other psychology experts. Some examples of psychology surveys are anxiety surveys, Depression questionnaires , happiness surveys , personality surveys , quality of life survey , life attitudes survey and many more.

For example, a psychologist wants to understand the factors that may be the cause of a student’s depression problem. For such a situation the mental health professional can ask the patient to answer a depression survey for students which includes psychology questions that will enable them to understand the psyche of the patient and evaluate their behavior, traits, lifestyle, and other parameters that may have impacted the student’s mental state. Such questions can be very helpful as it may sometimes shed light on patients who might even have suicidal tendencies. Using the responses from such questions can help the psychologist devise corrective actions for their patients.

Another instance where psychology surveys can be used is while conducting psychological studies. These surveys can help the researcher to collect data on the behavior, traits, attitudes, and lifestyle of a person. Using this data will enable the researcher to prove a psychological study that is based on the psyche of a person. For example, the color red and yellow is known to increase hunger in a person. In such a case, a psychology survey will help the researcher to collect the right information needed for their research and prove the hypothesis.

Psychology survey questions for questionnaires

Following are a few psychology survey questions that can help you evaluate the behaviors, traits, and attitudes of your respondent. An important point to remember when conducting psychology surveys is that wording the questions appropriately to create an effective research design will help you get good response rates . Also, demographic questions are important in such surveys as they play a vital role in shaping the psyche of a person.


Emotion Regulation Questionnaire for use with athletes

Three studies examine the factorial validity, internal consistency, test–retest stability, and criterion validity of the Emotion Regulation Questionnaire (ERQ: Gross & John, 2003) for use with athletes.

Design

Factorial validity, internal consistency, test–retest stability and criterion validity of the ERQ were examined over three stages, using three separate samples.

Method

In stage 1 the factorial validity and internal consistency of the ERQ subscales were examined based on responses from 433 sport participants. In stage 2, 176 sport participants completed the ERQ on two occasions separated by an interval of two weeks. In stage 3, the criterion validity of the ERQ was examined. Sport participants (n = 88) completed the ERQ and reported the intensity, frequency and direction of a range of emotions experienced when competing in sport.

Results

Confirmatory factor analysis results lend some support to a two-factor model when reappraisal and suppression are allowed to correlate. Alpha coefficients were acceptable. Test–retest stability analyses indicated poor agreement and a greater influence of situational, as opposed to trait factors, in the variance of item scores on the second test administration. In addition, results were partially consistent with findings of Gross and John (2003): reappraisal scores were associated with pleasant emotions, but suppression scores were not associated with unpleasant emotions.

Conclusion

Results provide mixed support for the validity of the ERQ in sport. Because the ERQ is intended to assess stable patterns of emotion regulation, the instability of items is a concern and reasons for this require further investigation.

Highlights

► The validity of the Emotion Regulation Questionnaire (ERQ) is explored among athletes. ► The ERQ assesses individuals' use of reappraisal and suppression to manage emotion. ► Consistency, stability, factorial and predictive validity of the ERQ are examined. ► The validity of the ERQ is generally supported, but scores are unstable over time. ► The ERQ is a valid measure of athletes' use of reappraisal and suppression.


Background

The rapid increase in the number of infected cases and mortalities due to the 2019 novel coronavirus diseases (COVID-19) has led to the closures of all academic institutions including elementary and high schools with the hope of slowing the transmission of the virus among the population [1, 2]. Furthermore, all students have been advised to be home quarantined regarding their safety.

A report by the United Nation Educational, Scientific and Cultural Organization (UNESCO) showed that until April 2020, school functions were affected by the COVID-19 pandemic and almost 196 countries experienced national wide closure of schools, subsequently affecting almost 1.6 billion young learners [3]. Furthermore, the Ministry of Health in Iran ordered a country wide closure of schools on March 2020 as a preventive measure in order to reduce the risk of viral transmission among the students and staff [4].

School closure in COVID-19 pandemic era has directly impacted today’s young learners. Although more than two thirds of the countries have introduced a platform for distance learning, this program was not successful in underdeveloped countries compared to developed ones with almost 30% of them being able to run a similar program. Even before the pandemic, almost 30% of world’s young population did not have access to digital educational programs, which has only gotten worse during the COVID-19 pandemic [3].

However, this is not the first incidence of school closure. During the pandemic of the H1N1 Influenza, US health officials suggested temporary school closures. A study during this period and regarding the impact of school closure on students reported that students' activities, with respect to their grades, didn’t decline during this time period however, their interactions with other classmates remarkably decreased [5]. The absence from the academic and educational environment can affect the students’ behavior and emotions towards education and school attendance. Therefore, it can be stated that students' emotion is influenced by public health emergencies which necessitates adequate devotion and support from authorities. It is proposed that schools should collaborate in managing these situations by providing crisis-oriented psychological support and facilities for their students [6, 7].

Pekrun et al. described “emotions of progress” as emotions that are directly linked to either emotion during the activities or its consequences, which consists of various situations. Their study findings revealed that academic emotions were remarkably associated with the students’ enthusiasm, academic achievement, self-regulation, cognitive resources, and learning strategies, as well as class experiences and character [8]. Positive emotions include pride, hope, and enjoyment, while negative ones include anger, anxiety, hopelessness, shame, and boredom. The public opinion considers positive emotions to have positive consequences and negative emotions to have negative consequences however, each of these two categories of emotions has its own benefits. Positive emotions broaden the circle of human thinking spreading creativity, curiosity, and bonding with others discovering social perspectives and connections and acquiring physical and social skills. On the other hand, negative emotions are the motivational sources for self-defense, spirit of cooperation (feeling guilty), seeking justice (anger), informative aspects (for example, sadness about deficiency), and assist in learning. Negative feeling indicates a problem and, therefore, motivates us to solve that problem [8,9,10]. In another study, it was demonstrated that positive emotions positively predicted subsequent achievement (math test scores and end-of-the-year grades), and that achievement positively predicted these emotions, controlling for the students’ family socioeconomic status, intelligence and sex however, negative emotions negatively predicted achievement, and achievement negatively predicted these emotions [9]. Also, Sakiz et al. state that the total effect of perceived teacher affective support on behavioral engagement was as effective as that of the students’ perceived academic self-efficacy beliefs in science [10].

Given the fact that school closure may affect the students in a variety of aspects, this study was conducted to investigate the students’ positive and negative attitudes and emotions toward the closure of schools due to the COVID-19 pandemic and to evaluate its correlation with related academic factors. Findings of this study would help to establish a well-scheduled strategy to optimize learning in the prone population.


Methods: Does measuring people change them?

People who are aware that they are in a psychological study may not behave in their normal way. For this reason, ‘unobtrusive’ measurement has been advocated, whereby people are not aware they are being studied (Webb et al., 1966). A good recent example of this was a study that varied messages about hand-washing outside a motorway service station, and assessed the number of people who entered the toilets and number of uses of soap dispensers using electronic sensors (Judah et al., 2009). This procedure allowed different messages to be presented at different times of day, in a randomly determined order, and the effects on handwashing to be objectively assessed, with the people whose behaviour was manipulated unaware that an experiment was taking place.

Although such approaches have much to recommend them and are probably under-used, in many instances psychologists wish to measure constructs such as emotions or beliefs, which are difficult to assess unobtrusively with good levels of validity. Consequently, self-report measures are widespread.

Unfortunately, the mere act of measurement may be sufficient to affect the people who complete the measures. This possibility was noted in relation to mental testing over 40 years ago: ‘we can perhaps repeat a measurement once or twice, but if we attempt further repetitions, the examinee’s response changes substantially because of fatigue or practice effects’ (Lord & Novick, 1968, p.13). Despite this observation, psychologists (and others) usually assume that the process of participants being interviewed or completing questionnaires does not result in them having different thoughts, feelings or behaviour as a consequence. However, there is increasingly strong evidence that this assumption is not always valid: the process of psychological measurement can affect people’s thoughts, feelings and behaviour, and is therefore ‘reactive’ (French & Sutton, 2010).

Emotion
One area where the evidence of measurement reactivity is compelling relates to emotional reactions in people completing measures concerning personally salient illness. For example, one study involving women with breast cancer placed a measure of general anxiety at either the beginning or the end of a questionnaire, according to experimental condition (Johnston, 1999). The questionnaire assessed ‘demographic and clinical factors, social support, and attitudes for attending a social support group for women with breast cancer’ (p.78). Women who completed the anxiety measure at the end of the questionnaire had significantly higher anxiety scores than those who completed it at the beginning, with the most plausible explanation being that the other questionnaire items raised the anxiety of these women.

A reduction in negative emotion between the first and subsequent occasions of measurement has also been observed. For example, one study asked undergraduate students to complete a battery of emotion measures on two occasions, one week apart (Sharpe & Gilbert, 1998). A significant reduction in depression scores was observed on the second occasion of measurement. Similar results were obtained for other measures of affect, and this drop in scores upon repeated completion of measurements was replicated in a second sample.

These two measurement artefacts have the potential to bias the conclusions drawn from research studies, if ignored (French & Sutton, 2010). One such area of research concerns emotional reactions to the results of health screening tests (Johnston et al., 2004). It has been observed on many occasions that receipt of a positive screening test result is associated with elevated anxiety in the short term, with this anxiety returning to normal levels in the longer term (Shaw et al., 1999). Given that few studies examining this issue obtain true baseline measures of anxiety (i.e. before screening), it is not possible to be sure if the higher anxiety in the short-term is generated by receipt of a positive screening test result or if the higher scores are an artefact of measurement, due to participants completing questions that require them to consider the potentially distressing consequences of the illness. Equally, the reduction in anxiety over the longer term may reflect a coming to terms with the initially distressing result or it may be an instance of the observation that people’s negative affect scores tend to drop on the second occasion of measurement.

Recent research suggests that it is unlikely that measurement reactivity is responsible for the typical pattern of distress scores observed after receiving a positive screening test (i.e. increased anxiety in the short term returning to normal levels in the long term) (Shaw et al., 1999). Specifically, postal questionnaires concerning diabetes have not elicited any discernible effects on anxiety on subsequent occasions of measurement, relative to people who have not previously completed such measures (French et al., 2009). Possible reasons for the postal questionnaire study not finding the effects on emotion scores obtained in earlier studies include the absence of an interviewer, respondents having more time to complete the measures (and for any distress to dissipate), and those who are most distressed being more easily able to drop out of the study.

Behaviour
Say you provide people with pedometers, and conclude that people given pedometers are subsequently more physically active (Bravata et al., 2007). Is it the process of measurement that is having these effects or the associated use of effective behaviour change techniques such as goal setting (Michie et al, 2009)? Similar findings include the observation that completing an alcohol disorders screening questionnaire affects reporting of alcohol consumption two to three months later (McCambridge & Day, 2007), and being asked to complete a questionnaire relating to blood donation, along with postal reminders and thank-you letters, appears to lead to higher rates of objectively assessed blood donation behaviour (Godin et al., 2008).

If measurement leads to such changes in health-related behaviours, then it may be more difficult for deliberate interventions to have additional effects. For example, a recent study (ProActive) that attempted to bring about increases in physical activity found that all three experimental groups increased their physical activity by the equivalent of 20 minutes brisk walking per day (Kinmonth et al., 2008). The authors attributed this, at least in part, to a measurement reactivity effect: by assessing motivation using questionnaires, objective behaviour using heart-rate monitors and a treadmill exercise test, and a variety of physiological measures, participants may not only have become more convinced of the importance of physical activity but may also have become aware of their own low levels of activity and gained some insight into the psychological processes by which an increase might be brought about. The combined effects of these measures may have been sufficient to pre-empt any effects of the behaviour change intervention that differed between experimental conditions. If this reasoning is correct, future interventions to increase healthy behaviour could profit from a greater understanding of the processes underlying measurement reactivity.

Dealing with measurement reactivity effects
Exactly how measurement has the reactivity effects described is currently poorly understood (French & Sutton, 2010) we await more thorough theorising of the multiple mechanisms that may be involved and empirical tests of such theories. However, most researchers will be most interested in how to avoid such reactivity effects biasing their own research findings. There are a number of steps researchers can take with regard to measurement reactivity effects, some of which are helpful in identifying these effects, and some are helpful with avoiding their impact.

Given sufficient resources, the ideal approach involves the use of ‘Solomon designs’, where people are randomised not only to receive an intervention or not, but also to receive pre-test measures or not (e.g. Spence et al., 2009). The use of this design thereby not only examines the effects of an intervention, which is usually what researchers are interested in, but also any reactivity to measurement, and crucially any interaction between intervention and measurement, such as was proposed above for the ProActive study.

More generally, the order of any outcome measures that are thought particularly likely to be affected by order effects could be counter-balanced for order. This is common practice within many laboratory studies, but less common in more ‘applied’ field studies. However, although this will control for order effects (see Schuman & Presser, 1981), this does not control for any changes in measurement across multiple periods of follow-up. Thus, although this simple approach may be useful for some purposes, it does not control for all forms of measurement reactivity. For this to be possible, a greater understanding of the multiple mechanisms involved in producing reactivity effects is required, to be able to control for each possible source of reactivity.

Another simple method that has been proposed is to place the most reactive measure at the beginning of the questionnaire (Johnston et al., 2004). Thus, given that asking people questions about the (distressing) consequences of their illness appears to result in higher anxiety scores (Johnston, 1999), it may be sensible to place the anxiety measure at the beginning of a set of questionnaire measures. It is important to note that counterbalancing of the order of measures, an approach which is useful for detection of measurement reactivity effects, would only reduce the impact of such effects in this example, not eliminate them completely.

The impact of measurement reactivity can also be reduced by requiring participants to only complete a measure on one occasion: if there is good reason to believe that measures are reactive, this approach removes any possibility of reactivity effects on subsequent occasions of measurement. This approach does not, however, avoid reactivity effects within a single occasion of measurement, such as completing questions about an illness resulting in higher anxiety. Further, assessing different samples at single (different) timepoints is less statistically efficient, and therefore requires larger sample sizes. Equally, experiments that use post-test-only designs do not allow us to assess the possibility that there is a difference between samples at baseline.

It is sensible to be particularly cautious when the effects of measurement are likely to be similar to those of the phenomenon being studied. For example, questions testing knowledge should be carefully designed not to provide information about the topic being assessed. The use of the same items on multiple occasions would be particularly unwise. Another example would be when an intervention encourages self-monitoring of own performance against a criterion such as a personal goal (see Michie et al., 2009). Given that psychological measurement appears to encourage such monitoring, and potentially similar effects as the deliberate self-monitoring intervention, it may be prudent to consider post-test-only designs in such situations.

A final area that warrants caution is where the research involves assessing beliefs about an issue that the participant has not previously thought about. ‘Non-attitudes’ can be said to be present when a sample of respondents choose the ‘don’t know’ option in response to a question designed to assess attitude, whereas a similar sample choose an apparently meaningful option to the same question, when the ‘don’t know’ option is not included (Schuman & Presser, 1981). More recently, studies have looked at this in more depth by asking people to ‘think aloud’ whilst they complete questionnaires about issues such as physical activity and drinking alcohol (e.g. Darker & French, 2009 French et al., 2007). These studies have shown that when people are asked to complete questions about issues they have not previously considered, they provide questionnaire answers that are generated on the spot, on the basis of inferences from what they do know.

It is clear from examination of the literature on measurement reactivity effects that they are poorly understood. The best future defence against research conclusions being biased by these effects is to increase our understanding of why they are likely to arise, and under what circumstances.

David P. French is at the Applied Research Centre in Health and Lifestyle Interventions, Faculty of Health and Life Sciences, Coventry University [email protected]

Stephen Sutton is at the Institute of Public Health, University of Cambridge [email protected]


Part 2: Does the Blirt Scale Measure What It Claims to Measure?

No one wants to use a scale that hasn’t been shown to be valid. And validity is really hard to show.

Analyzing Validity

Here is our challenge. Remember that blirtatiousness is the extent to which people respond to friends and partners quickly and effusively. Our questions may look good, but we need evidence that the numbers we get actually measure the trait.

There is no one way to determine the validity of a scale. Test developers like Dr. Swann usually take several different approaches. They may compare the test results with other personality tests of similar traits (convergent validity), or compare scores from the BLIRT test with other dissimilar tests (discriminant validity). Researchers may also compare the results of the BLIRT test to real-world outcomes (criterion validity), or see if the results work to predict people’s behavior in certain situations (predictive validity).

In the sections below, we will peek at some studies that try to assess these different aspects of validity.

Convergent and Discriminant Validity

One way to test the validity of a test is to compare it to results from tests of other traits for which validated tests already exist. There are two types of comparisons that researchers look for when they validate a test. One is called convergent validity and the other is called discriminant validity.

When testing for convergent validity, the researcher looks for other traits that are similar to (but not identical to) the trait they are measuring. For example, we are studying blirtatiousness. It would be reasonable to think that a person who is blirtatious is also assertive. The two traits—blirtatiousness and assertiveness—are not the same, but they are certainly related. If our blirtatiousness scale is not at all related to assertiveness, then we should be worried that we are not really measuring blirtatiousness successfully.

We can use the correlation between the BLIRT score and a score on a test of assertiveness to measure convergent validity. The researchers gave a set of tests, including the BLIRT scale and a measure of assertiveness [5] to 1,397 college students. Assertiveness was just one of several traits that were expected to be similar to blirtatiousness. [6]

Try It

We want our BLIRT score to have a moderate-to-strong relationship with traits that are similar, but we also want it to be unrelated to traits or abilities that are not similar to blirtatiousness. Tests of discriminant validity compare our BLIRT score to traits that should have weak or no relationship to blirtatiousness. For example, people who are blirtatious may be good students or poor students or somewhere in-between. Knowing how blirtatious you are should not tell us much about how good a student you are.

The researchers compared the BLIRT score of the 1,397 students mentioned earlier to their self-reported GPA. [7]

Try It

Dr. Swann’s team compared 21 different traits and abilities to the blirtatiousness scale. Some assessed convergent validity and others tested discriminant validity. The results were generally convincing: BLIRT scores were similar to traits that should be related to blirtatiousness (good convergent validity) and unrelated to traits that should have no connection to blirtatiousness (good discriminant validity).

Criterion Validity

Another way to test the validity of a measure is to see if it fits the way people behave in the real world. The BLIRT researchers conducted two studies to see if BLIRT scores fit what we know about people’s personalities. Criterion validity is the relationship between some measure and some real-world outcome.

Librarians or Salespeople?

Who do you think is more likely to be blirtatious, a salesperson or a librarian? The researchers found thirty employees of car dealerships and libraries in central Texas and gave them the BLIRT scale. Their ages ranged from 20 to 66 (average age = 34.3 years).

Try It

Using the bar graph below, adjust the bars based on your prediction about who will be more blirtatious. Then click the link below to see if your prediction is correct.

Most people expect salespeople to be more blirtatious than librarians. The researchers explained that we assume that high blirters will look for a work environment that rewards “effusive, rapid responding,” while low blirters would prefer a workplace that encourages “reflection and social inhibition.” As you can see, the results of the study were consistent with this idea: salespeople had significantly higher blirt scores (on the average) than librarians.

Asian Americans or European Americans?

How blirtatious a person is can be influenced by a lot of factors, including “cultural norms”—ways of acting that we learn from our families and the people around us as we grow up. Although we shouldn’t overstate the difference, Asian cultures tend to emphasize restraint of emotional expression, while European cultures are more likely to encourage direct and rapid expression.

The researchers were able to get BLIRT scores from 2,800 students from European-American cultures and 698 students from Asian-American cultures. What would you predict about the BLIRT scores for these two groups?

Try It

Using the bar graph below, adjust the bars based on your prediction about who will be more blirtatious. Then click the link below to see if your prediction is correct.

As you can see, the results were consistent with the researchers’ expectations. The difference between the groups was small, but statistically significant. The small difference indicates that we shouldn’t turn these modest differences into cultural stereotypes, but the statistically significant difference suggests that cultural experiences may have a real—if modest—effect on people’s blirtatiousness.

Predictive Validity

Another way to assess validity of the BLIRT scale is to see if it predicts people’s behavior in specific situations. Based on research about first impressions, the experimenters believed that people who are open and expressive should, in general, make better first impressions than people who are reserved and relatively quiet.

To test this hypothesis, the researchers recruited college students and put them into pairs. The members of each pair had a 7-minute “getting acquainted” telephone conversation. The members of the pairs did not know each other and, in fact, they never saw each other. The participants also completed several personality measures, including the BLIRT scale. Note that they were NOT paired based on their BLIRT scores, so there were different combinations of blirtatiousness across the 32 pairs tested.

Try It

After the conversations, the students rated their conversation partners on several different qualities. For example, who do you think would be perceived as more responsive—a high blirter or a low blirter?

Keeping in mind that this was a first-impression 7-minute conversation, who do you think would be seen as more interesting: a high blirter or a low blirter?

Here are some other qualities that were rated. Make your prediction for each one, and then check out the results.

Who was rated as more likeable?

Who was rated as someone who “I’d like to be friends with?”

Who was rated as more intelligent?

Measuring Personality

You now know more about creating a personality test than most people do. Scales like the BLIRT or the Big Five test you took at the beginning of this exercise are used for serious purposes. Psychological researchers use them in their studies, of course. But psychological tests are also used by companies in their hiring process, by therapists trying to understand their patients, school systems assessing strengths and weaknesses of their students, and even sports teams trying to identify the best athletes to fit their system.

Blirtatiousness is simply an example of a personality trait, and it is not among the most widely used scales. There are hundreds of personality tests in use today. For example, the Big Five personality factors (conscientiousness, agreeability, neuroticism, openness to experience, and extraversion) are among the most widely used scales, and they have been extensively studied and validated. Other qualities, like intelligence, self-esteem, and general anxiety level, have also been widely studied, and they have well validated measures.

We hope that this exercise has given you some insight into the characteristics of a good personality test, and the work that goes into developing a useful scale. Next time you take one, consider the process that went into its development.


Discussion

This study used multiple brain phenotypes and retrospectively reported AEs in childhood and adulthood, reflecting early adversity and partner abuse, respectively, in a large population sample of adults, aiming to contribute to current efforts of understanding the neural consequences of adversity. We found that participants who experienced emotional abuse in childhood had smaller global cerebellar volumes and ventral striatum. Cerebellar lobules I-IV were particularly affected, and to a lesser extent, Crus I. While this result remained significant in a random subsample, we note the small effect sizes ( < .01). We also report uncorrected individual associations in Table S10 to illustrate the presence of other putative findings (e.g. amygdala–physical neglect, β = −0.1). Such associations did not meet the current statistical rigours, lending further support to the conclusion that associations between AEs and brain structures in our sample showed small effects. Finally, there were no significant differences in total grey and white matter volumes on either of the adversity items, suggestive of localized effects.

The finding that the volumes of the cerebellum and ventral striatum were smaller in individuals exposed to childhood adversity is in agreement with previous reports (De Bellis & Kuchibhatla, 2006 Edmiston et al., 2011 Walsh et al., 2014 ). Indeed, the cerebellum supports emotional processing (Schmahmann & Sherman, 1998 ), has abundant glucocorticoid receptors (Sanchez et al., 2000 ), and it has a protracted developmental time course, thus remaining vulnerable to environmental factors for longer (Giedd et al., 2007 ). The ventral striatum is implicated in reward processing, a function which is affected by childhood AEs, predicting depressive symptoms (Hanson et al., 2015 ) and neuroendocrine hyperactivation (Pruessner et al., 2004 ). Interestingly, the cerebellum and basal ganglia are strongly connected via disynaptic projections, suggesting that changes in one node may influence the other. While it is yet to be determined whether the ventral striatum part of the basal ganglia receives these projections (Bostan & Strick, 2018 ), empirical evidence lends support in this direction (Li et al., 2014 ). Computationally, the striatum associates reward/punishment signals to cerebellar processes (Doya, 2000 ). Conversely, cerebellar granule cells compute expectations of reward (Wagner et al., 2017 ). Having identified associations between both structures and reports of childhood emotional abuse (albeit small in magnitude), this finding may further support the argument of their interconnected activity, specifically in the realm of emotional processing.

We acknowledge a series of important weaknesses, which caution the interpretation of these results beyond the limitations imposed by the use of retrospective and nonstandard adversity measures in a cross-sectional design. First, the adversity information available in UK Biobank is limited. We refer here to individual items, using convention, such as ‘emotional abuse’, given ‘yes’ responses on the item: ‘felt hated by family member as a child’ (anywhere on the spectrum: rarely–very often). Indeed, to qualify as adversity the quantifiable experiences need to suggest chronicity (e.g. institutional rearing) or represent single events of increased severity (e.g. sexual abuse) (Mclaughlin et al., 2019 ). Furthermore, evidence on the effects of childhood AEs on the brain is consistent with a dimensional model of adversity related to the concepts of threat and deprivation (McLaughlin et al., 2014 ), which, arguably, may not be reflected in all items used here. This might explain why some consistent findings, such as reductions in the prefrontal cortex, were not replicated. Second, by assessing adversity retrospectively, the analysis does not account for other probable sources of pre-existing vulnerability in brain phenotypes (Lupien et al., 2018 ). A recent meta-analysis demonstrated that childhood AEs collected prospectively identify a different group of individuals based on mental illness risks, compared to studies including adults who recall AEs retrospectively from their childhood (Baldwin et al., 2019 ). It is plausible that the associated neural signatures are also different. Third, a longitudinal design, as opposed to the current cross-sectional approach, would be more suitable to capture neural changes and their timing, without being confounded by other factors influencing brain structure. Fourth, we acknowledge the small effect sizes observed throughout, which suggest that the biological effects in this sample, while significant, may not translate in altered behaviour. Finally, it is important to note that given the volume of neuroimaging data, identification of the IDPs included here cannot be ensured by expert human operators. Therefore, while there is comparable overlap between the phenotypes obtained using hand tracing vs automated processes, we acknowledge the potential biases introduced by the latter (Morey et al., 2009 ).

In conclusion, this cross-sectional study shows that retrospective reports related to emotional abuse are associated with small reductions in the cerebellum and ventral striatum, in a large population of adults. Future studies should continue to use large samples to build on a literature consensus. However, longitudinal cohort designs acquiring prospective and precise assessments of adversity are needed.


Study 1

Our first study was a confirmatory factor analysis of the factors recovered in the previous scale-construction effort. Our goal was to ensure that the factor decomposition we observed in previous exploratory efforts was a good description of the full scale. Participants were not screened for parenthood, thus we expect that the sample would contain both parents and non-parents the goal of this initial study was simply to make a confirmatory test of the factor structure we recovered in previous samples.

Methods

Participants

Our final sample consisted of a new group of 250 participants recruited on Amazon Mechanical Turk. Of these participants, 47.6% reported having one or more children.

Participants reported their gender as 45.6% male, 52.0% female 2.4% other or decline to state. The race breakdown among participants was 79.2% White, 2.4% Black, 10.8% Asian or Asian-American, and 7.6% multiple, other, or decline to state. 9.2% of the sample reported Hispanic ethnicity. The large majority of our participants were between 20 and 49 years old: 41.2% of participants were 20–29, 35.2% were 30–39, 10.8% were 40–49. The modal level of education was a four-year college degree (38.0%) with a large additional proportion reporting some college education (33.6%) and the remainder evenly split between a high school degree (11.2%) and a graduate degree (13.6%).

Procedure and Materials

Participants filled out the EPAQ items using a 7-point Likert scale. The final set of items comprising the three subscales is presented in Table1. Participants were asked to fill out a short demographic form, providing age, gender, number of children, level of education, language, race, ethnicity, and age of youngest and oldest child. Participants additionally completed the MacArthur Ladder, a measure of subjective social status that asks participants to rate their status from 1–10 using a picture of a ladder. 3

Early Parenting Attitudes Questionnaire items.

Category . Item .
AAChildren should be comforted when they are scared or unhappy.
Its important for parents to help children learn to deal with their emotions.
Parents should pay attention to what their child likes and dislikes.
A child who has close bonds with his or her parents will have better relationships later on in life.
Children who receive too much attention from their parents become spoiled.*
Too much affection, such as hugging and kissing, can make a child weak.*
Children and parents do not need to feel emotionally close as long as children are kept safe.*
Parents should not try to calm a child who is upset, it is better to let children calm themselves.*
ELIt is good to let children explore and experiment.
Parents can help babies learn language by talking to them.
Parents can prepare young children to succeed in school by teaching them things, such as shapes and numbers.
Babies can learn a lot just by playing.
It is not helpful to explain the reasons for rules to young children because they wont understand.*
Children dont need to learn about numbers and math until they go to school.*
Reading books to children is not helpful if they have not yet learned to speak.*
Babies cant learn about the world until they learn to speak.*
RRIt is very important that children learn to respect adults, such as parents and teachers.
It is very important for young children to do as they are told, for example, waiting when they are told to wait.
Children should be grateful to their parents.
It is very important that there are consequences when a child breaks a rule, big or small.
It is okay if young children boss around their caregivers.*
It is okay if children see adults as equals rather than viewing them with respect.*
Young children should be allowed to make their own decisions, like what to play with and when to eat.*
Parents do not need to worry if their child misbehaves a lot.*
Category . Item .
AAChildren should be comforted when they are scared or unhappy.
Its important for parents to help children learn to deal with their emotions.
Parents should pay attention to what their child likes and dislikes.
A child who has close bonds with his or her parents will have better relationships later on in life.
Children who receive too much attention from their parents become spoiled.*
Too much affection, such as hugging and kissing, can make a child weak.*
Children and parents do not need to feel emotionally close as long as children are kept safe.*
Parents should not try to calm a child who is upset, it is better to let children calm themselves.*
ELIt is good to let children explore and experiment.
Parents can help babies learn language by talking to them.
Parents can prepare young children to succeed in school by teaching them things, such as shapes and numbers.
Babies can learn a lot just by playing.
It is not helpful to explain the reasons for rules to young children because they wont understand.*
Children dont need to learn about numbers and math until they go to school.*
Reading books to children is not helpful if they have not yet learned to speak.*
Babies cant learn about the world until they learn to speak.*
RRIt is very important that children learn to respect adults, such as parents and teachers.
It is very important for young children to do as they are told, for example, waiting when they are told to wait.
Children should be grateful to their parents.
It is very important that there are consequences when a child breaks a rule, big or small.
It is okay if young children boss around their caregivers.*
It is okay if children see adults as equals rather than viewing them with respect.*
Young children should be allowed to make their own decisions, like what to play with and when to eat.*
Parents do not need to worry if their child misbehaves a lot.*

Note: A * indicatesndicates reverse coded items.

Results and Discussion

Cronbach’s alpha for the whole scale was 0.90. For the AA subscale alpha was 0.82, for the EL subscale 0.83, and for the RR subscale 0.81 (see Figure1 for item means). Items within subscales were highly correlated, but items across subscales are highly correlated as well, which may reflect response biases to rate all items particularly high or low.

Average ratings for individual EPAQ items.

Average ratings for individual EPAQ items.

We next examined the loadings of individual items onto the three factors (Figure2). Items loaded onto the three factors in a way that was roughly consistent with our established subscales, but the factors did not pull apart completely. Specifically, several EL items loaded strongly (above .40) on the AA factor (“It is good to let children explore and experiment,” loading = 0.56 “Babies can learn a lot just by playing,” loading = 0.69 “Parents can help babies learn language by talking to them,” loading = 0.61 “Parents can prepare young children to succeed in school by teaching them things, such as shapes and numbers,” loading = 0.48) and several AA items loaded strongly on the EL factor (“Parents should not try to calm a child who is upset, it is better to let children calm themselves,” loading = 0.47 “Children and parents do not need to feel emotionally close as long as children are kept safe,” loading = 0.63 “Too much affection, such as hugging and kissing, can make a child weak,” loading = 0.41). Additionally, several RR items loaded strongly onto the EL factor (“It is okay if young children boss around their caregivers,” loading = 0.57 “It is okay if children see adults as equals rather than viewing them with respect,” loading = 0.42, “Young children should be allowed to make their own decisions, like what to play with and when to eat,” loading = 0.42 and “Parents do not need to worry if their child misbehaves a lot,” loading = 0.46). In contrast, no AA or EL items loaded strongly onto the RR factor, suggesting that in the population we sampled this was the most separable of the three proposed intuitive theories.

Factor loadings on individual EPAQ questions from Study 1.

Factor loadings on individual EPAQ questions from Study 1.

Given the partial overlap of the EL and AA factors in particular, it is possible that participants’ responses on these items were driven to some extent by a more general attitude towards more involved parenting. For example, in attachment theory, the primary caregiver is posited to be a primary source for learning interactions, whether those interactions are about behavior/emotion regulation or more academic topics (Sroufe, 2005). Additionally, although it was unexpected, the relatively strong loading of these items onto the EL factor could reflect a general attitude that relates children’s autonomy with their early learning. For example, some parents may believe that children who are strong-willed will be at an advantage for learning from their mistakes.

Despite the partial overlap of the EL and AA factors in the population we sampled – which was mostly White, educated, and from the United States – it is possible that these factors would be more separable in other cultural contexts. For example, perhaps there is a particular focus among White, educated U.S. parents on promoting early learning and affectionate parenting. Because the EL and AA subscales measure theoretically separable constructs, we nevertheless decided to retain this distinction. With these points in mind, we decided to move forward with the present version of the scale.


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