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Er=Wpa + Ipi : The summation of panoramic wisdom and piercing insight.

Nightmares and Worries in the United States.

During October 15th-19th, 2019, Raven's Eye conducted an online survey about common nightmares and worries of people located in the United States. We utilized Amazon's Mechanical Turk to conduct our poll1.

Our report.

This report presents the worldview derived from our findings, including: the foremost model statements, panorama, mood, viewpoints, theme structure, brief elaborations on the three most popular concepts produced in response to our open-ended questions, as well as this study's horizon. These findings are the result of a quantitative phenomenology performed in October, 2019, by the Raven's Eye team using our online natural language analysis software2. Examples of the worldviews on other topics may be found here.

Our attitude.

This survey analysis results from a quantitative phenomenology utilizing Raven's Eye. Our attitude is one that seeks to:

• reliably and validly understand people's thoughts according to both phenomenological and scientific processes, and
• create synthetic model statements that reproduce both the most popular themes in those thoughts, and the ways that participants express them.

What results are plain English statements that express the thoughts of the participants, in the way that they are ordered and structured by the participants themselves. We seek when possible to produce themes that may be generalized with confidence to various groups and populations. This is the case for this survey with respect to Mechanical Turk workers in the United States.

Our survey.

In an online survey, we first asked participants acquired through Amazon's Mechanical Turk their age, gender and state of residence. Age and gender were formatted to allow free-responses, while state of residence was selected from a dropdown menu. Next, we asked participants to respond to the following item over the course of 2-3 sentences:

In your own words, please describe your most recent nightmare.

We followed this question by asking participants to rate on a scale of 1-7 (with 1 being "never" and 7 being "all the time") how often in your daily life are you concerned about the main fear expressed in your nightmare?

Our sixth item asked participants to respond to the following directions over the course of 2-3 sentences:

In your own words, please tell us what most worries you in your daily life.

We then asked participants to rate on a scale of 1-7 (with 1 being "not at all" and 7 being "overwhelming") how much of a daily worry is this to you?

The main concepts utilized in our model statements were found at rates in excess of 5 times their typical use, and as such provide a measure of confidence in their association with the question posed3, 4. All such words in the model statements are emphasized in bold font, while the concepts around which the statements are created are also in blue text.

 

The worldview.

In your own words, please…

Question 1

describe your most recent nightmare.

Foremost model statements.

In my most recent nightmare, someone was in my house and I was trying to get out.
In a recent nightmare, I was being chased by people and I couldn't get away from them.
In a recent dream, I was driving in my car and I lost control and got into an accident.

…and
then I woke up.

Question 1

tell us what most worries you in your daily life.

Foremost model statements.

In my daily life, I am most worried about being able to have enough money to pay the bills.
I worry
a lot about the health of my family members, and that I will be able to take care of them in the future.
Losing my
job is what worries me most.


The panorama.

Revealing the worldview's panorama involves understanding the general content and form of the natural language data acquired in response to our free response item. To do this, we first identify the gists, or individual word forms that are relatively overrepresented in them3. We then consider the overall style of the response set through measures of its vocabulary and verbosity. These gists and this style information then give us a sense of the whole—or as we call it, the panorama—presented by the worldview.

  • In your own words, please describe your most recent nightmare.

    Top_50_nightmare_words

    Figure 1. The fifty most frequent words in response to the item: In your own words, please describe your most recent nightmare. The vertical axis represents the number of times that a word is found to be relatively overrepresented in the response set.

    As visualized in Figure 1, in addition to words related to the question (such as nightmare, most, and recent), concepts such as I, was, me, had, up, out, dream, woke, we, house, then, get, being, trying, could, and like are central to the messages of our respondents. The readability and grade-level scores indicate a simple sentence structure and low-average vocabulary, while the average response consists of 24 words over 2-3 sentences. Table 1 displays these measures of verbosity and vocabulary.

    Measure

    Score

    Flesch Readability

    79.2

    Flesch-Kincaid Grade Level

    6.11

    Average Cell Word Count

    24.13

    Table 1. Measures of vocabulary and verbosity for responses to the item: In your own words, please describe your most recent nightmare.

  • In your own words, please tell us what most worries you in your daily life.

    Top_50_worries_words

    Figure 2. The fifty most frequent words in response to the item: In your own words, please tell us what most worries you in your daily life. The vertical axis represents the number of times that a word is found to be relatively overrepresented in the response set.

    As visualized in Figure 2, in addition to words related to the question (such as most, worries, daily, and life), concepts such as I, my, about, worry, me, am, have, money, being, will, enough, what, able, family, worried, I'm, having, health, job, something, get, work, and bills are central to the messages of our respondents. Similar to the responses to the nightmares question, the readability and grade-level scores for people's responses about their worries indicate a simple sentence structure and low-average vocabulary. However, the responses to this question were substantially shorter than the responses to the nightmares question, with the average response to this item consisting of of 11 words over 2-3 sentences. Table 2 displays these measures of verbosity and vocabulary.

    Measure

    Score

    Flesch Readability

    72.56

    Flesch-Kincaid Grade Level

    6.72

    Average Cell Word Count

    11.37

    Table 2. Measures of vocabulary and verbosity for responses to the item: In your own words, please tell us what most worries you in your daily life.


The mood.

The foremost ranked few words of any dataset utilizing quantitative phenomenology reveal the general mood of that dataset. A mood comprises the general way in which a topic or event is approached or considered, i.e., whether it is self-reflective, object-oriented, or other-oriented. The primary aspect of mood in both the nightmares and worries is self-reflective, as revealed by the words, "I," "my," and, "me" This indicates that the content is primarily about the self and its possessions. The secondary aspect of mood for nightmares is object-oriented and past-tense, as revealed by the words "and," "was," "the," "a," "it," "that," and, "had," among others. That is, the language produced in response to the nightmares question is secondarily about objects, generally in the past tense. The secondary aspect of mood for worries, however, is relationally object-oriented in the present-tense, as revealed by the words, "to," "and," "the," "about," "a," "of," "in," and "is." Prepositions such as "to," "about," "of," and, "in," are generally used to describe an object in relation to another object or person, and are therefore relational. The word, "is," reveals the present-tense, as may become more readily apparent when comparing this word to the words, "was" and, "had," which were frequently used in response to the nightmares question, but not as frequently used in response to the worries question.

Thus, while both questions primarily invoke self-reflective moods, responses to the nightmares question tend to be about the past (as in a recollection), while the responses to the worries question tend to be relational and an in the present tense. When combined with the low-average 6th-grade Flesch-Kincaid score, we conclude that response content to the nightmares question is likely informally written recollection about a past event. The responses to the question about worries is likewise informally written, however it more frequently pertains to a current situation, and due to the lower average word-count per response is more direct and concise in phrasing.


The viewpoints.

Worldviews are determined by the people, places, and times that co-create them. Therefore, in order to more fully understand the worldview on the nightmares and worries presented in this survey, we highlight those aspects that contextualize and influence our findings. We call these contextualizing and influencing aspects viewpoints. We consider these viewpoints according to their origin in human factors (kuturgeist), spatial or environmental factors (ortgeist), or time-based factors (zeitgeist).

Kulturgeist

Our participants were recruited from Amazon Mechanical Turk, and were paid $0.15 for completing our 7-question survey. A total of 1,003 responses were received. After eliminating non-compliant responses5, 994 were deemed eligible for inclusion.

Age_distribution_of_participants

Figure 3. A frequency distribution of our participants' reported ages.

We asked participants, "how old are you?" As is depicted in Figure 3, the respondents included in our analysis were all adults, with an average age of 35.9 years old and a median age of 33 years old. This median age is 5.1 years younger than the 2018 US Census median age of 38.1.

The age of participants did not have an influence on the content or structure of the foremost model statements in this worldview.

Gender_distribution_of_our_participants

Figure 4. The proportion of responses received by reported gender/sex.

We asked participants, "what is your gender?" The majority of participants responded by stating their biological sex. We therefore utilized these terms as categorical labels, and grouped variants of "Female" and "Male" together. We grouped variant spellings of "Nonbinary," but did not include in this category the single participant who wrote, "Gender fluid." As depicted in Figure 4, Females were more greatly represented than Males. Further comparison with US Census data indicates that these rates vary significantly from expected proportions in the US population (p<.01). Nonbinary individuals expressed a rate that is on the lower bounds of most recent US population estimates. Two participants did not respond to this question.

Participant gender did not influence the content or structure of the foremost model statements reported in this worldview.

How_often_daily_concern_about_nightmare

Figure 5. Proportional distribution of ratings about how often the main fear of their nightmare is of concern to participants in their daily lives.

We asked participants, "on a scale of 1-7 (with 1 being "never" and 7 being "all the time") how often in your daily life are you concerned about the main fear expressed in your nightmare? The mean response on this item was 3.36, while the median was 3 and the modal response was 2, indicating a non-normal distribution of responses that tended toward not being very often concerned about the main fear expressed in their nightmare during their daily lives. Figure 5 depicts the percentage of respondents selecting each point along the 7-point scale.

The level of concern about the main fear expressed in the nightmare did not appear to influence the content or structure of the foremost model statements produced for this worldview.

How_much_of_a_daily_worry_is_this

Figure 6. Proportional distribution of participant ratings on how much of a daily worry their described worries are to them.

We asked participants, "on a scale of 1-7 (with 1 being "not at all" and 7 being "overwhelming"), how much of a daily worry is [their described worries] to you? The mean response on this item was 4.83, while the median was 5 and the modal response was 7, indicating a non-normal distribution of responses that tended toward being quite affected by their described worries. Figure 6 depicts the percentage of respondents selecting each point along the 7-point scale.

As with the previous rating scale, the degree of intensity of the daily worry did not influence the content or structure of the model statements produced for this worldview.

Ortgeist

We asked participants, "In what state do you live?" The participants who took part in our study did so online through Amazon's Mechanical Turk. We limited our solicitation to those who were located in the United States of America, and as noted in bold above, requested that participants select the State in which they reside. We grouped States according to US Census Regions, and compared this to 2018 US Census population estimates. Doing so indicates that our sample can be considered as being regionally representative (within +1.6 % to -3.4% of US Census population estimates per region). Figure 7 displays the number of participants reporting residence in each US State.

The regional residence of participants did not appear to substantially influence the content of the model statements in this report, nor did it meaningfully affect the vocabulary or verbosity of the responses.

Geographic_distribution_of_participants

Figure 7. The geographic distribution of responses, according to the number of participants reporting residence in each US State.

Zeitgeist

This survey began on October 15th, 2019 at 7:08 p.m., PST and was continuously available until its conclusion on October 19th, 2019 at 7:37 p.m PST. Mechanical Turk automatically records and reports the time at which each response is submitted. Figure 8 reveals the distribution of completed surveys during the 96.5-hour period that the survey was available.

As with geography, the date of submission did not appear to influence the content or order of the model statements presented in this report, nor did it affect the vocabulary or verbosity of the responses.

Date_that_response_was_received

Figure 8. The cumulative percentage of responses received according to the date.


The parts.

The parts of a worldview consist of the major units of meaning in the responses. We asked our participants to respond in their own words to two distinct questions. We therefore partition our meaning units accordingly, resulting in two distinct parts of the worldview:

  • A part containing the themes and model statements derived from their most recent nightmares, and
  • A part containing the themes and model statements derived from their descriptions of that which worries them most in their daily lives.


The theme structure.

Themes are structured from word networks, which center on the most frequent and overrepresented non-mood related (e.g., I, my, me, was, about, am) and stimulus associated (e.g., nightmare, most, recent, worries, daily, life) concepts. These networks are based on the frequency of actual association between words in the original dataset. They graphically display the most frequent ordered relations between concepts as they are expressed in the language of our participants. Figures 9-11 present three such word networks for the most frequent overrepresented concepts (house, get, and trying) used by respondents when asked to describe their most recent nightmare. Figures 12-14 present three such word networks for the most frequent overrepresented concepts (have, being, and money) used by respondents when asked to tell us what most worries them in their daily lives.

Nightmares.

  • word_network_for_house

    Figure 9. The word network for house.

  • word_network_for_get

    Figure 10. The word network for get.

  • word_network_for_trying

    Figure 11. The word network for trying.

Figure 9 reveals the most popular environment in which the nightmare occurred: house; The word house is most frequently directly preceded by the words: my, the, and a. The most popular word preceding all of these three words is, in turn: in. House is most frequently followed by the words: and, I, and with. Among those words following in order after the words and and with are the self-referent I, my, and me, while the word was is also a popular choice to follow the words and and I.

Figure 10 reveals the action most often occurring in the house. By putting the words surrounding the word get in this network together according to the most popular order of placement in our participants responses, we arrive at the phrases: trying to get out of, or trying to get away from; I couldn't get out of, or I couldn't get away from; and could not get out of, or could not get away from.

The third most frequent and overrepresented word not associated with the mood or question was trying. As may be apparent from inspection of the networks presented in Figure 11, the concept trying is most frequently immediately preceded by the three words: was, kept, and were. These are, in turn, frequently preceded by I, they, it, he, we, and there. The three most frequent words immediately following they are: to, my, and with. The word to is frequently followed by get, kill, and find; the word my is frequently followed by most, husband, and dream; and the word with is frequently followed by a, my, and me. The most frequent 5-word phrase in this network is: I was trying to get. Various permutations include: it was trying to kill, they were trying to find, I kept trying to get, we were trying to find.

Daily worries.

  • word_network_for_have

    Figure 12. The word network for have.

  • word_network_for_money

    Figure 13. The word network for money.

  • word_network_for_being

    Figure 14. The word network for being.

The most frequent and overrepresented word not associated with the mood or question was have. Figure 12 depicts the word network centering on this word. By putting the words preceding the word have in this network together according to the most popular order of placement in our participants responses, we arrive at the phrases: that I have, able to have, and I will have. Then, by putting the words following the word have in this network together according to the most popular order of placement in our participants responses, we arrive at the phrases: have a lot, have to have, and have enough money. Connecting the phrases preceding and following the word have reveal the combined phrases: that I have a lot, able to have enough money, and I will have to have.

The word network depicted in Figure 13 focuses on the word money. Examining the most popular words preceding this word reveal its connection to the word have: having enough money, have enough money, and making enough money. These are followed by: worry about money, worried about money, and lot about money. The third most popular word directly preceding the word money is and, and the word and is preceded by the words bills and job, as well as the word money itself. The words directly following the word money are: to, and, and for. These reveal that money is worried about because of its relation to and for something else, as well as being one among other worries. Creating three-word phrases from the most popular words following the word money result in: money to pay, money and my, and money for my. Alternatives include money to make, money to be, money and I, money and not, money for the, and money for things.

As may be apparent in Figure 14, the word being is surrounded by a somewhat more diverse set of words than the words of focus in the two preceding word networks. Three-word phrases constructed from the most popular words preceding the word being reveal its connection to worry, as in: and not being, about not being, is not being, worry about being, worried about being, and most about being. Through its relationship with the preceding word and, being also serves as connector between one worry and another, as in: health and being, money and being, and bills and being. Examining the words following the word being reveals three popular phrases: being able to, being in life, and being alone without.

Elaboration on the themes.

We can further elaborate on the themes so far presented to construct more detailed model statements. In this demonstration, we show how we can do so by expanding the word network surrounding each concept. Doing so identifies ways that existing concepts connect with other concepts in the dataset. Here, we present such elaborations for the the top three most popular concepts produced in response to both parts (or questions): our participants' most recent nightmare, and our participants' daily worries.

Though it was not needed to construct the model statements in this demonstration, reducing the meaning unit to include only those sentences or responses containing the concept is another way of elaborating on themes. See this demonstration for an example of doing so.

Expanding the word networks.

Nightmares.

The expanded network surrounding the target words.
  • Expanded_house_word_network

    Figure 15. The expanded word network surrounding house.

  • Expanded_get_word_network

    Figure 16. The expanded word network surrounding get.

  • expanded_word_network_trying

    Figure 17. The expanded word network surrounding trying.

In the expanded word networks presented in Figures 15-17, more elaboration becomes visible. In this figure, the target word (house, get, or trying) is depicted in orange, while the words following it are depicted in lighter shades of red for each word-level away from help they are located. Similarly, the words preceding the target word are depicted in lighter shades of yellow as they are found at an increasing number of words away from that word.

Comparing Figures 9 and 15, those words additionally associated with house become apparent, such as: chasing, couldn't, found, get, getting, standing, terrifying, they, trying, and we, among others. Finally, comparing Figures 10 and 16 reveals the following words additionally associated with the word get, among others: away, chasing, couldn't, daughter, family, friends, found, house, move, mutilated, running, someone, started, stuck, through, trying. Comparing Figures 11 and 17, those words additionally associated with trying include: about, away, but, chasing, cops, could, couldn't, dream, eventually, friends, had, him, is, nightmare, not, off, one, out, recent, ran, running, she, showed, that, tried, up, wake, where, and would.

Daily worries.

The expanded networks surrounding the target words.
  • Have_word_network

    Figure 18. The expanded word network surrounding the word have.

  • money_expanded_word_network

    Figure 19. The expanded word network surrounding the word money.

  • Expanded_being_word_network

    Figure 20. The expanded word network surrounding the word being.

In the expanded word networks presented in Figures 18-20, more elaboration becomes visible. In this figure, the target word (have, money, or being) is depicted in orange, while the words following it are depicted in lighter shades of red for each word-level away from help they are located. Similarly, the words preceding the target word are depicted in lighter shades of yellow as they are found at an increasing number of words away from that word.

Comparing Figures 12 and 18, those words additionally associated with have become apparent, such as: bills, children, day, family, job, make, pay, time, and work, among others. Comparing Figures 13 and 19, such words additionally associated with money include: able, alone, anything, being, family, knowing, leaving, little, mother, things, trying, and will, among others. Finally, comparing Figures 14 and 20 reveals the following words additionally associated with being, among others: children, daily, during, enough, finances, friends, future, happens, having, how, members, pay, and well-being.

These expanded word networks elaborate on the actions, people, places, qualities, and things associated with each of the target words, and facilitate the construction of the foremost model statements listed at the beginning of this worldview for each open-ended question posed to our participants.


The interpretation.

Given the foremost model statements, panorama, and mood of the natural language analyzed in this survey, we find its contents to be informal self-reflection. The responses about nightmares is secondarily object oriented in the past tense, while the responses to daily worries are relationally object oriented. Nightmares predominately occur in—or related to—an otherwise safe place (such as a house, home, or car), and generally involve a pursuit or threat to individuals, in terms of their sense of safety or security. Daily worries generally pertain to access to resources or those things that provide resources, such as money and jobs. They are secondarily about the health of family members, but again generally in terms of having the resources to take care of them.

The foremost model statements presented herein do not vary in their content across the people, places, and time involved in our survey, though some less predominant themes (not presented in this worldview) do vary according to gender. Age, location, and time of submission also do not impact the content of the foremost model statements presented in this worldview, though again less popular themes not presented in this worldview do vary according to age (such as increasing likely hood of mentioning high school in younger ages). Our foremost model statements can then be applied generally across age, gender, location, and time of submission.

When asked how often in your daily life are you concerned about the main fear expressed in your nightmare, the tendency was to respond with a rating below the mid-point on the 7-point scale, indicating that such fears are not consciously considered in the daily life of our respondents. However, when asked how much of a daily worry their described worries are to our respondents, the tendency was to select a response above the mid-point, with the response of 7 (equivalent to, "overwhelming") on the scale of 1-7 being the most frequent selection. This would indicate that the worries reported by our participants are often present to them in an intense manner during their day-to-day lives. As with age, gender, location, and time of submission, the rating selected on these scales did not appear to influence the foremost model statements presented in this worldview. Such model statements can then be generally applied across levels of concern about the nightmares being expressed, as well as level of impact of the worries being described.


The horizon.

Our results are bounded by the people, places, and time in which they were produced. For example, our participants were drawn from Amazon Mechanical Turk workers located in the United States. The cut-off threshold of 5 that we used for our analysis makes it such that our confidence estimate for our results exceeds the total likely population of available Mechanical Turk workers in the United States at any given time4. We are, therefore, quite confident that our results can be generally applied to this population, at least at this point in history.

There is an increasing tendency among American researchers in the social sciences to utilize Mechanical Turk workers to complete surveys intended for generalization to the U.S. population. Doing so requires consideration as to whether they maintain certain characteristics or experiences that vary from that expressed by the typical person in this nation. These characteristics or experiences may, in turn, systematically relate to variation in thoughts about a given topic. Current studies comparing the demographics of Mechanical Turk workers to the background US population indicate that they may be somewhat younger, more educated, less racially diverse, and less affluent than the general US population.

Generalization beyond Mechanical Turk workers to Americans at-large would then depend on whether or not the aforementioned differences between in affluence, age, education, and race influence the foremost model statements produced by this survey. Given that we found no need to vary the content of the foremost model statements based on age, gender, location, or time of submission, we believe age differences between the average Mechanical Turk worker and the average person in the United States would have no bearing on the foremost model statements being produced by these two groups. We are aware of no existing research indicating that affluence, education, or race has an effect on nightmare content. As a result, we therefore believe that the foremost model statements related to nightmares can be generalized to people in the United States broadly.

Disparities in affluence are systematically related to educational attainment and race in the United States. Since Mechanical Turk workers tend to have higher levels of education and are less diverse than the US population, such differences would logically lead to the expectation that such individuals should be relatively less concerned about health, job security, money, and resources than the typical person in the United States. At the same time, however, Mechanical Turk workers are as a group relatively less affluent than the average person in the United States. Given the same logic then, this would likely predispose them to relatively more concern about health, job security, money, and resources. We believe, therefore, that these mutually conflicting probabilities allow us to apply the foremost model statements related to daily worries to the middle and lower economic segments in the United States broadly.

The worldview.

In your own words, please…

Question 1

describe your most recent nightmare.

Foremost model statements.

In my most recent nightmare, someone was in my house and I was trying to get out.
In a recent nightmare, I was being chased by people and I couldn't get away from them.
In a recent dream, I was driving in my car and I lost control and got into an accident.

…and
then I woke up.

Question 1

tell us what most worries you in your daily life.

Foremost model statements.

In my daily life, I am most worried about being able to have enough money to pay the bills.
I worry
a lot about the health of my family members, and that I will be able to take care of them in the future.
Losing my
job is what worries me most.


The panorama.

Revealing the worldview's panorama involves understanding the general content and form of the natural language data acquired in response to our free response item. To do this, we first identify the gists, or individual word forms that are relatively overrepresented in them3. We then consider the overall style of the response set through measures of its vocabulary and verbosity. These gists and this style information then give us a sense of the whole—or as we call it, the panorama—presented by the worldview.

  • In your own words, please describe your most recent nightmare.

    Top_50_nightmare_words

    Figure 1. The fifty most frequent words in response to the item: In your own words, please describe your most recent nightmare. The vertical axis represents the number of times that a word is found to be relatively overrepresented in the response set.

    As visualized in Figure 1, in addition to words related to the question (such as nightmare, most, and recent), concepts such as I, was, me, had, up, out, dream, woke, we, house, then, get, being, trying, could, and like are central to the messages of our respondents. The readability and grade-level scores indicate a simple sentence structure and low-average vocabulary, while the average response consists of 24 words over 2-3 sentences. Table 1 displays these measures of verbosity and vocabulary.

    Measure

    Score

    Flesch Readability

    79.2

    Flesch-Kincaid Grade Level

    6.11

    Average Cell Word Count

    24.13

    Table 1. Measures of vocabulary and verbosity for responses to the item: In your own words, please describe your most recent nightmare.

  • In your own words, please tell us what most worries you in your daily life.

    Top_50_worries_words

    Figure 2. The fifty most frequent words in response to the item: In your own words, please tell us what most worries you in your daily life. The vertical axis represents the number of times that a word is found to be relatively overrepresented in the response set.

    As visualized in Figure 2, in addition to words related to the question (such as most, worries, daily, and life), concepts such as I, my, about, worry, me, am, have, money, being, will, enough, what, able, family, worried, I'm, having, health, job, something, get, work, and bills are central to the messages of our respondents. Similar to the responses to the nightmares question, the readability and grade-level scores for people's responses about their worries indicate a simple sentence structure and low-average vocabulary. However, the responses to this question were substantially shorter than the responses to the nightmares question, with the average response to this item consisting of of 11 words over 2-3 sentences. Table 2 displays these measures of verbosity and vocabulary.

    Measure

    Score

    Flesch Readability

    72.56

    Flesch-Kincaid Grade Level

    6.72

    Average Cell Word Count

    11.37

    Table 2. Measures of vocabulary and verbosity for responses to the item: In your own words, please tell us what most worries you in your daily life.


The mood.

The foremost ranked few words of any dataset utilizing quantitative phenomenology reveal the general mood of that dataset. A mood comprises the general way in which a topic or event is approached or considered, i.e., whether it is self-reflective, object-oriented, or other-oriented. The primary aspect of mood in both the nightmares and worries is self-reflective, as revealed by the words, "I," "my," and, "me" This indicates that the content is primarily about the self and its possessions. The secondary aspect of mood for nightmares is object-oriented and past-tense, as revealed by the words "and," "was," "the," "a," "it," "that," and, "had," among others. That is, the language produced in response to the nightmares question is secondarily about objects, generally in the past tense. The secondary aspect of mood for worries, however, is relationally object-oriented in the present-tense, as revealed by the words, "to," "and," "the," "about," "a," "of," "in," and "is." Prepositions such as "to," "about," "of," and, "in," are generally used to describe an object in relation to another object or person, and are therefore relational. The word, "is," reveals the present-tense, as may become more readily apparent when comparing this word to the words, "was" and, "had," which were frequently used in response to the nightmares question, but not as frequently used in response to the worries question.

Thus, while both questions primarily invoke self-reflective moods, responses to the nightmares question tend to be about the past (as in a recollection), while the responses to the worries question tend to be relational and an in the present tense. When combined with the low-average 6th-grade Flesch-Kincaid score, we conclude that response content to the nightmares question is likely informally written recollection about a past event. The responses to the question about worries is likewise informally written, however it more frequently pertains to a current situation, and due to the lower average word-count per response is more direct and concise in phrasing.


The viewpoints.

Worldviews are determined by the people, places, and times that co-create them. Therefore, in order to more fully understand the worldview on the nightmares and worries presented in this survey, we highlight those aspects that contextualize and influence our findings. We call these contextualizing and influencing aspects viewpoints. We consider these viewpoints according to their origin in human factors (kuturgeist), spatial or environmental factors (ortgeist), or time-based factors (zeitgeist).

Kulturgeist

Our participants were recruited from Amazon Mechanical Turk, and were paid $0.15 for completing our 7-question survey. A total of 1,003 responses were received. After eliminating non-compliant responses5, 994 were deemed eligible for inclusion.

Age_distribution_of_participants

Figure 3. A frequency distribution of our participants' reported ages.

We asked participants, "how old are you?" As is depicted in Figure 3, the respondents included in our analysis were all adults, with an average age of 35.9 years old and a median age of 33 years old. This median age is 5.1 years younger than the 2018 US Census median age of 38.1.

The age of participants did not have an influence on the content or structure of the foremost model statements in this worldview.

Gender_distribution_of_our_participants

Figure 4. The proportion of responses received by reported gender/sex.

We asked participants, "what is your gender?" The majority of participants responded by stating their biological sex. We therefore utilized these terms as categorical labels, and grouped variants of "Female" and "Male" together. We grouped variant spellings of "Nonbinary," but did not include in this category the single participant who wrote, "Gender fluid." As depicted in Figure 4, Females were more greatly represented than Males. Further comparison with US Census data indicates that these rates vary significantly from expected proportions in the US population (p<.01). Nonbinary individuals expressed a rate that is on the lower bounds of most recent US population estimates. Two participants did not respond to this question.

Participant gender did not influence the content or structure of the foremost model statements reported in this worldview.

How_often_daily_concern_about_nightmare

Figure 5. Proportional distribution of ratings about how often the main fear of their nightmare is of concern to participants in their daily lives.

We asked participants, "on a scale of 1-7 (with 1 being "never" and 7 being "all the time") how often in your daily life are you concerned about the main fear expressed in your nightmare? The mean response on this item was 3.36, while the median was 3 and the modal response was 2, indicating a non-normal distribution of responses that tended toward not being very often concerned about the main fear expressed in their nightmare during their daily lives. Figure 5 depicts the percentage of respondents selecting each point along the 7-point scale.

The level of concern about the main fear expressed in the nightmare did not appear to influence the content or structure of the foremost model statements produced for this worldview.

How_much_of_a_daily_worry_is_this

Figure 6. Proportional distribution of participant ratings on how much of a daily worry their described worries are to them.

We asked participants, "on a scale of 1-7 (with 1 being "not at all" and 7 being "overwhelming"), how much of a daily worry is [their described worries] to you? The mean response on this item was 4.83, while the median was 5 and the modal response was 7, indicating a non-normal distribution of responses that tended toward being quite affected by their described worries. Figure 6 depicts the percentage of respondents selecting each point along the 7-point scale.

As with the previous rating scale, the degree of intensity of the daily worry did not influence the content or structure of the model statements produced for this worldview.

Ortgeist

We asked participants, "In what state do you live?" The participants who took part in our study did so online through Amazon's Mechanical Turk. We limited our solicitation to those who were located in the United States of America, and as noted in bold above, requested that participants select the State in which they reside. We grouped States according to US Census Regions, and compared this to 2018 US Census population estimates. Doing so indicates that our sample can be considered as being regionally representative (within +1.6 % to -3.4% of US Census population estimates per region). Figure 7 displays the number of participants reporting residence in each US State.

The regional residence of participants did not appear to substantially influence the content of the model statements in this report, nor did it meaningfully affect the vocabulary or verbosity of the responses.

Geographic_distribution_of_participants

Figure 7. The geographic distribution of responses, according to the number of participants reporting residence in each US State.

Zeitgeist

This survey began on October 15th, 2019 at 7:08 p.m., PST and was continuously available until its conclusion on October 19th, 2019 at 7:37 p.m PST. Mechanical Turk automatically records and reports the time at which each response is submitted. Figure 8 reveals the distribution of completed surveys during the 96.5-hour period that the survey was available.

As with geography, the date of submission did not appear to influence the content or order of the model statements presented in this report, nor did it affect the vocabulary or verbosity of the responses.

Date_that_response_was_received

Figure 8. The cumulative percentage of responses received according to the date.


The parts.

The parts of a worldview consist of the major units of meaning in the responses. We asked our participants to respond in their own words to two distinct questions. We therefore partition our meaning units accordingly, resulting in two distinct parts of the worldview:

  • A part containing the themes and model statements derived from their most recent nightmares, and
  • A part containing the themes and model statements derived from their descriptions of that which worries them most in their daily lives.


The theme structure.

Themes are structured from word networks, which center on the most frequent and overrepresented non-mood related (e.g., I, my, me, was, about, am) and stimulus associated (e.g., nightmare, most, recent, worries, daily, life) concepts. These networks are based on the frequency of actual association between words in the original dataset. They graphically display the most frequent ordered relations between concepts as they are expressed in the language of our participants. Figures 9-11 present three such word networks for the most frequent overrepresented concepts (house, get, and trying) used by respondents when asked to describe their most recent nightmare. Figures 12-14 present three such word networks for the most frequent overrepresented concepts (have, being, and money) used by respondents when asked to tell us what most worries them in their daily lives.

Nightmares.

  • word_network_for_house

    Figure 9. The word network for house.

  • word_network_for_get

    Figure 10. The word network for get.

  • word_network_for_trying

    Figure 11. The word network for trying.

Figure 9 reveals the most popular environment in which the nightmare occurred: house; The word house is most frequently directly preceded by the words: my, the, and a. The most popular word preceding all of these three words is, in turn: in. House is most frequently followed by the words: and, I, and with. Among those words following in order after the words and and with are the self-referent I, my, and me, while the word was is also a popular choice to follow the words and and I.

Figure 10 reveals the action most often occurring in the house. By putting the words surrounding the word get in this network together according to the most popular order of placement in our participants responses, we arrive at the phrases: trying to get out of, or trying to get away from; I couldn't get out of, or I couldn't get away from; and could not get out of, or could not get away from.

The third most frequent and overrepresented word not associated with the mood or question was trying. As may be apparent from inspection of the networks presented in Figure 11, the concept trying is most frequently immediately preceded by the three words: was, kept, and were. These are, in turn, frequently preceded by I, they, it, he, we, and there. The three most frequent words immediately following they are: to, my, and with. The word to is frequently followed by get, kill, and find; the word my is frequently followed by most, husband, and dream; and the word with is frequently followed by a, my, and me. The most frequent 5-word phrase in this network is: I was trying to get. Various permutations include: it was trying to kill, they were trying to find, I kept trying to get, we were trying to find.

Daily worries.

  • word_network_for_have

    Figure 12. The word network for have.

  • word_network_for_money

    Figure 13. The word network for money.

  • word_network_for_being

    Figure 14. The word network for being.

The most frequent and overrepresented word not associated with the mood or question was have. Figure 12 depicts the word network centering on this word. By putting the words preceding the word have in this network together according to the most popular order of placement in our participants responses, we arrive at the phrases: that I have, able to have, and I will have. Then, by putting the words following the word have in this network together according to the most popular order of placement in our participants responses, we arrive at the phrases: have a lot, have to have, and have enough money. Connecting the phrases preceding and following the word have reveal the combined phrases: that I have a lot, able to have enough money, and I will have to have.

The word network depicted in Figure 13 focuses on the word money. Examining the most popular words preceding this word reveal its connection to the word have: having enough money, have enough money, and making enough money. These are followed by: worry about money, worried about money, and lot about money. The third most popular word directly preceding the word money is and, and the word and is preceded by the words bills and job, as well as the word money itself. The words directly following the word money are: to, and, and for. These reveal that money is worried about because of its relation to and for something else, as well as being one among other worries. Creating three-word phrases from the most popular words following the word money result in: money to pay, money and my, and money for my. Alternatives include money to make, money to be, money and I, money and not, money for the, and money for things.

As may be apparent in Figure 14, the word being is surrounded by a somewhat more diverse set of words than the words of focus in the two preceding word networks. Three-word phrases constructed from the most popular words preceding the word being reveal its connection to worry, as in: and not being, about not being, is not being, worry about being, worried about being, and most about being. Through its relationship with the preceding word and, being also serves as connector between one worry and another, as in: health and being, money and being, and bills and being. Examining the words following the word being reveals three popular phrases: being able to, being in life, and being alone without.

Elaboration on the themes.

We can further elaborate on the themes so far presented to construct more detailed model statements. In this demonstration, we show how we can do so by expanding the word network surrounding each concept. Doing so identifies ways that existing concepts connect with other concepts in the dataset. Here, we present such elaborations for the the top three most popular concepts produced in response to both parts (or questions): our participants' most recent nightmare, and our participants' daily worries.

Though it was not needed to construct the model statements in this demonstration, reducing the meaning unit to include only those sentences or responses containing the concept is another way of elaborating on themes. See this demonstration for an example of doing so.

Expanding the word networks.

Nightmares.

The expanded network surrounding the target words.
  • Expanded_house_word_network

    Figure 15. The expanded word network surrounding house.

  • Expanded_get_word_network

    Figure 16. The expanded word network surrounding get.

  • expanded_word_network_trying

    Figure 17. The expanded word network surrounding trying.

In the expanded word networks presented in Figures 15-17, more elaboration becomes visible. In this figure, the target word (house, get, or trying) is depicted in orange, while the words following it are depicted in lighter shades of red for each word-level away from help they are located. Similarly, the words preceding the target word are depicted in lighter shades of yellow as they are found at an increasing number of words away from that word.

Comparing Figures 9 and 15, those words additionally associated with house become apparent, such as: chasing, couldn't, found, get, getting, standing, terrifying, they, trying, and we, among others. Finally, comparing Figures 10 and 16 reveals the following words additionally associated with the word get, among others: away, chasing, couldn't, daughter, family, friends, found, house, move, mutilated, running, someone, started, stuck, through, trying. Comparing Figures 11 and 17, those words additionally associated with trying include: about, away, but, chasing, cops, could, couldn't, dream, eventually, friends, had, him, is, nightmare, not, off, one, out, recent, ran, running, she, showed, that, tried, up, wake, where, and would.

Daily worries.

The expanded networks surrounding the target words.
  • Have_word_network

    Figure 18. The expanded word network surrounding the word have.

  • money_expanded_word_network

    Figure 19. The expanded word network surrounding the word money.

  • Expanded_being_word_network

    Figure 20. The expanded word network surrounding the word being.

In the expanded word networks presented in Figures 18-20, more elaboration becomes visible. In this figure, the target word (have, money, or being) is depicted in orange, while the words following it are depicted in lighter shades of red for each word-level away from help they are located. Similarly, the words preceding the target word are depicted in lighter shades of yellow as they are found at an increasing number of words away from that word.

Comparing Figures 12 and 18, those words additionally associated with have become apparent, such as: bills, children, day, family, job, make, pay, time, and work, among others. Comparing Figures 13 and 19, such words additionally associated with money include: able, alone, anything, being, family, knowing, leaving, little, mother, things, trying, and will, among others. Finally, comparing Figures 14 and 20 reveals the following words additionally associated with being, among others: children, daily, during, enough, finances, friends, future, happens, having, how, members, pay, and well-being.

These expanded word networks elaborate on the actions, people, places, qualities, and things associated with each of the target words, and facilitate the construction of the foremost model statements listed at the beginning of this worldview for each open-ended question posed to our participants.


The interpretation.

Given the foremost model statements, panorama, and mood of the natural language analyzed in this survey, we find its contents to be informal self-reflection. The responses about nightmares is secondarily object oriented in the past tense, while the responses to daily worries are relationally object oriented. Nightmares predominately occur in—or related to—an otherwise safe place (such as a house, home, or car), and generally involve a pursuit or threat to individuals, in terms of their sense of safety or security. Daily worries generally pertain to access to resources or those things that provide resources, such as money and jobs. They are secondarily about the health of family members, but again generally in terms of having the resources to take care of them.

The foremost model statements presented herein do not vary in their content across the people, places, and time involved in our survey, though some less predominant themes (not presented in this worldview) do vary according to gender. Age, location, and time of submission also do not impact the content of the foremost model statements presented in this worldview, though again less popular themes not presented in this worldview do vary according to age (such as increasing likely hood of mentioning high school in younger ages). Our foremost model statements can then be applied generally across age, gender, location, and time of submission.

When asked how often in your daily life are you concerned about the main fear expressed in your nightmare, the tendency was to respond with a rating below the mid-point on the 7-point scale, indicating that such fears are not consciously considered in the daily life of our respondents. However, when asked how much of a daily worry their described worries are to our respondents, the tendency was to select a response above the mid-point, with the response of 7 (equivalent to, "overwhelming") on the scale of 1-7 being the most frequent selection. This would indicate that the worries reported by our participants are often present to them in an intense manner during their day-to-day lives. As with age, gender, location, and time of submission, the rating selected on these scales did not appear to influence the foremost model statements presented in this worldview. Such model statements can then be generally applied across levels of concern about the nightmares being expressed, as well as level of impact of the worries being described.


The horizon.

Our results are bounded by the people, places, and time in which they were produced. For example, our participants were drawn from Amazon Mechanical Turk workers located in the United States. The cut-off threshold of 5 that we used for our analysis makes it such that our confidence estimate for our results exceeds the total likely population of available Mechanical Turk workers in the United States at any given time4. We are, therefore, quite confident that our results can be generally applied to this population, at least at this point in history.

There is an increasing tendency among American researchers in the social sciences to utilize Mechanical Turk workers to complete surveys intended for generalization to the U.S. population. Doing so requires consideration as to whether they maintain certain characteristics or experiences that vary from that expressed by the typical person in this nation. These characteristics or experiences may, in turn, systematically relate to variation in thoughts about a given topic. Current studies comparing the demographics of Mechanical Turk workers to the background US population indicate that they may be somewhat younger, more educated, less racially diverse, and less affluent than the general US population.

Generalization beyond Mechanical Turk workers to Americans at-large would then depend on whether or not the aforementioned differences between in affluence, age, education, and race influence the foremost model statements produced by this survey. Given that we found no need to vary the content of the foremost model statements based on age, gender, location, or time of submission, we believe age differences between the average Mechanical Turk worker and the average person in the United States would have no bearing on the foremost model statements being produced by these two groups. We are aware of no existing research indicating that affluence, education, or race has an effect on nightmare content. As a result, we therefore believe that the foremost model statements related to nightmares can be generalized to people in the United States broadly.

Disparities in affluence are systematically related to educational attainment and race in the United States. Since Mechanical Turk workers tend to have higher levels of education and are less diverse than the US population, such differences would logically lead to the expectation that such individuals should be relatively less concerned about health, job security, money, and resources than the typical person in the United States. At the same time, however, Mechanical Turk workers are as a group relatively less affluent than the average person in the United States. Given the same logic then, this would likely predispose them to relatively more concern about health, job security, money, and resources. We believe, therefore, that these mutually conflicting probabilities allow us to apply the foremost model statements related to daily worries to the middle and lower economic segments in the United States broadly.

Notes.

1Amazon's Mechanical Turk (mturk.com) is an online marketplace of individuals willing to complete short Human Intelligence Tasks (HITs) for modest payment.

2https://ravens-eye.net Though, since you're already here, you can click on Home in the menu at the top of your browser window to find out more about us.

3To derive our rate of use, we compare the proportionality of a word in the sample to the proportionality of that same word in our 4.8 billion-word corpus of the English language. Based on a nomothetic approach to the lexical hypothesis, the amount to which a word is overrepresented (i.e., found at proportions greater than 1.0) also expresses the degree to which that word is associated with the stimulus (e.g., our open-ended survey questions) by the participants.

4The cut-off threshold that we used for our analysis makes it such that our confidence in the generalization of our results exceeds the average likely total population of Mechanical Turk workers available in the United States at any given time, and approaches maximum estimates. Current published Mechanical Turk population estimates indicate that such employment is transient and fluid. However, best estimates indicate an average likelihood of approximately 1,835 Mechanical Turk workers being available in the United States at any given time, and a maximum likelihood of 5,625 workers at any given time.

An overrepresentation score of 5 means that a given word is found in response to the question at 5 times its rate in the 4.8 billion-word background corpus of everyday English. Setting the overrepresentation cut-off score at 5 then, means that we do not create model statements from words with overrepresentation scores below this score. All else being equal, in order for a given word to be overrepresented by chance, we would need to acquire 4 times our current pool of respondents (994) who never once use the word again.

This would mean acquiring 3,976 more respondents (994 x 4 = 3,976 + 994 = 4970, or 5 times the original pool of 994 respondents), each of whom would have to refrain from ever mentioning the word again. Given population estimates for Mechanical Turk workers noted previously in this footnote, this additional pool would be .71 times the maximum population estimate, and 2.2 times the likely population estimate of the total number of Mechanical Turk workers in the United States at any given time. Moreover, many of the words used to create our foremost model statements exceed the inclusionary threshold of an overrepresentation score of 5. For instance, in the responses to the nightmares, house is found at approximately 8.4 times its background rate, get is found at approximately 16.3 times its background rate, and trying is found at approximately 48.8 times its background rate. Therefore we maintain a high degree of confidence that the words used to create our model statements were not overrepresented simply by chance.

5The nine eliminated responses consisted of single words or letters, such as "good," "nice," "n/a," and "f."