Advanced Personality Psychology
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Continue with personality dynamics. We are going to be focusing on today and next class, within person dynamics, mechanisms and proceses. Why do we not always act in accord with our personality? Why do people not strictly adhere to the traits they have. People seem surprinsgly and substantially different from the traits they report. Semi random sampling of trait terms. There terms, like many, focus on what someone is like in general. How they typically behave or think or feel. Characteristic of personality psychology in general. We are preoccupied with understanding what people are like in general. How people behave on average. What are you like on average. What mean levels to people show in their thinking, in their feelings, in their behaviours. Mean levels, average tendencies they show. We use these terms as they make reference to average levels Say someone is trusting, you say they are on average more trusting than others. Fragile? Labile? Erratic? Percetly good descriptors for thinking, feeling and behavior. But they do not make reference to what people are like on average. To say someone is fragile odesn’t mean they break frequently, instead in average circumstances they are more likely to break. Erractic is a description not of behaviour, but of how erratic their behavior is. How variable people are? Worthwhile to understand that people are variable? Not just the typical levels that people show. After all people are variable.
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William fleeson: risen to the rank of president in personality research. Outlined a set of principles for how we engage in research to explore intraindivdual varability. They have historically been defined by mean levels. Trait = mean of someone’s behaviour. But fleeson has argued that we need to overturn this focus, need to focus on not just typical behaviours, but full spread. Other element’s of people’s behaviour as well. Not just levels. Broad claims, not hypothesis 1) Even though it is likely the case that someone’s typical level of behaviour differs from other, it is the case that this idnividual may show more of a characteristic, and less of a characteristic across time. So each indvidual will express every level of a trait over time. Scale 1 -7. sometimes you will be a 4, 4 may not describe you equally well in all circumstances. It may happen most of the time, but there might be ocassions where the person reports a 5,6 or 7. the same individual who orignially described tehsemelves as 4, may on other ocassions be higher or lower. Situations require us to change our behaviour. Over time, our behaviour varies around a central tendency. Given a sufficent number of observations, people make use of the entire range of a scale. People’s reports can be described with a density distribution. Sometimes more talkative than usual, soemtimes less talkative than usual. Not one trait, not one person. But all traits, and all people 2) The mean is still the most important, most stable, most predictable characteristics
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of all of psychology. The mean is one parameter, but not the only parameter. 3) The mean is not the only way, there are other paramaters. Should not equate traits with the means of traits. Reconceptualize traits as not just the mean, but all the paramaters from which you can describe a distribution. Understand and explore how stable and how meaningful are these other density distrubtions of traits and states.
Need to know the concept of a trait. A state. (today we use state and behaviour interchangeably) both are distinct from traits. Traits express themselves in behaviours. Being in a particular state or showing a behaviour is the expression of at rait. Trait is what someone is like in general. State or behaviour is over a narrow period of time. Differ from one situation to the next because of many variables.
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Paper focuses on 3 studies, lecture today focuses on the first. Studies 2 and 3 are qualifications on the first study. Important to know why study 2 and 3 were conductged, but the substitive results are reported in the first study. Explore how well we can describe people’s behaviours using density distributions. We can describe the state you are in right now using the same words to describe your traits. We can use the same scale for assessing a trait of an indvidual to assess their state. (in general vs past hour) Fleeson collected multiple state records. Records states as they go about the day. Complete them mutliple times daily to assess how their states vary from one moment to the next along the big 5 dimensions. Mutiple records of behaviours and how the big 5 have been expressing htemselves over circumscribed periods of time. Vertical axis = how frequently a state has been reported. The curve describes distribution of frequencies Frequency dsitribution = density distribution. The universe is normal for some reason.
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Density distribution can be summarized with 3 or 4 paramaters. Location, size, shape. The mean tells you typically where the scores are generally clustered. Ususally most important thing to know. Know the average. Mean is imperfect distribution however. There is typically some or a lto above, ro some beloew Look at the size or standard deviation. How much spread or how much diversion there is around the mean. The standard deviation of how badly a job the mean does in describing the distribution. A smaller standard deviation means that the central tendency is a good indicator of the distribution. Shape Skewness. Where the distribution is shifted to one side of the scale or another side of the scale. Is the distribution lop sided? If yes, then it is skewed. Violation of symmetry. If the distribution is not symmetrical, it is skewed. How peaked or shallow is it? Is the distribution more peaked than the other? Kurtosis. Kurtosis and skew describe the shape Skew describes lopsided to the left or right Kurtosis describes the peak or shallowness. Location size shape, skew, kurtosis. This is how we describe traits. Historically just concentrated on traits. But maybe we can learn about people from the spread of their behaviour and the shape that it takes.
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May or may not be the case where we need these paramaters to describe behaviours. Left: distribution roated 90 degree. Their staes of extraversion over time, for 3 indviduals. What’s important to see is how non-overlapping these distributions are. Very few occasions where person 2 behaves similarly to person 1 or person 3. 3 doesn’t behave much like 1 or 2 either. Etc. all we really need for this one is the mean. Person 1 is more extraverted on average than person 2, person 3. person 3 more extraverted than 3. etc. we do not actually need more than the mean. Very little within person variation. Most of their behaviour indicates their average. Vey little variablitiy within the person. Right: Some of these distributions are more skwed, they differ in how kerutotic they are as well. To describe these 3, we need more than the location of their distributiosn. Need their size and shape as well. There is so much in person variabiltiy, so they now overlap. Much less infomration to just know the location of these distributions. Need to know how they differ in terms of size and shaep of distributions as well. Which one of these hypothesized sitautions are more close to real life? If left is the closest real life, then we can just focus on the locaiton or average. If right is closer to real life, then we need size and shape of distribution as well.
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5 outcomes would support that psychologists need to redfine personality traits. Not just average. 1) Within person variability. If we need more than the mean, there has to be substatinal variabiltiy. Have to have people that differ a lot. 2) Momentary states are not that predictable. Needs to be little resemblance from one day to the day. Single state indices are notoriously unreliable. Shouldn’t correlate well. Measure someone on one ocassion then measure on another ocassion, there should be variability. Single states of behaviour should not show high levels of consistency. 3) The mean of the distribution would be very stable. Average level of behaviour is likely to be very stable. Its frequent use of perosnality characteristics. 4) But one of the other parmaters should also be stable. His goal needs to be to show that not just the mean is stable, at least one other feature of the density distribution should be stable as well. Liberal test and conservative test: liberally he only needs to show that one other paramter is stable. More conservatively, he would need to show that all other paramaters are stable as well. There is some leeway here. At the very least he has to show that one other paramter is stablke. At the very most he has to show that all other paratmters are stable. 5) The variability that people show has to be meangiful. Cannot be simply error or nusiance factor. Cannot be osmoen randomly picking numbers. Has to be something meanfiul as to why you are picking different numbers on different
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ocassions to show how you behave. How do you show this> show that it is predictable. Some lawfulness to how it occurs. Lawfulness indicates predictability. Fleeson shows for 1 of 5 traits, he does not have time for all the other, that the variability we show on extraversion is not just random, but there are meaningful contextual cues that explain why you are more extraverted now than in other situatoins Memorize! 5 hurdles.
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3 studies. Studies 2 and 3 qualifiy on the first 46 participants complete experience sampling procedres for 2 weeks approximately. Participants collected record forms and carried them around, 5 times a day used them to describe behaviour in the last hour. 65 opportunities to measure people’s behaviour. On average, people compelted 3 quarters of the records available. There is missing data, sometimes participants failed to supply records. On average people were able to submit just under 50 records. Reports would gauge big 5 behaviour. In the last hour, not on average.
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How descriptiive are these adjectives with regards to your behaviour in the last hour.
Measured their different affective states as well. Moods are known to vary. Postive affect and negative affect substantially wihtin people change. Looking at within person variability of personal affect and negative affect gives us a benchmark to guage how much varability a perosn will have for the big 5. look to affect where we know there is variabiltiy to say whether there is a lot of variability or very little variability in trait expression. Large number of records, 22,00 records. Each record shows how much of each big 5 trait was epressed in the past hour.
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How do we summarize?
Within person variation -how much variation does each person show on average? Takes a single subject and all the data she supplies. Fro that single person she calculations the standard deviation. Standard deviation is the spread across scores from the average. Each indvidual has a SD. 46 SDs. Differ across people. Some have larger deviations, some have less devaiotns, less spread. Typical amont of within person varability. Calculate sd for each person sperately, average the sd. On average how much variabliity does a person show? -calculate the mean for each person. How extraverted was somsone across the 13 days? Each person gets their own mean. How extraverted are they on average over the 13 days? Then take the SD across everyone’s means to determine how much vrability exists between people in how they typically behave.
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1st colum is total varation Striped column is within person variation = how much do they vary in their behaviour Blank column: how much do people differ from each other on how they behave? For affect: more within person variability than between people. Your moods change, more than they change compared to others. More within variability in the big 5 than stability. Our traits, the standing on the big 5 change from one hour to the next as much as our moods do. As much within person vrability than between person. In conscientiousness and extraversion, more within person variability than between people. If we randomly sample two moments in a person’s life, that variability is as big as the differences between that person and other people. Your variability from one moment to the next is as great as variabiltiy we se between people.
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This is a hypothetical indvidual. Interproalted from the avreage across the sample. How much the typical subject varies in their big 5 across time. The amount of each trait is not relevant, but the dispersion is. How the groups are disperesed. Emotional stability and intellect seem to sue mos t of the scale rather than all of the scale. How much does a person vary across a 13 day period? Every level of possible xpression utilzied. There is within person variabiotin. Gone over hurdle 1.
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Single indices are likely to be very unstable. Not highly correlated. 2nd hurdle. Degree of correlation is modest at best. Single states are likely not stable features. We should not be very stable across two single situations. Shouldn’t have high expectations. Randomly pick two moments, and see how simliar they are between those situations. According to hurdle 2, they should not correlate well. And they do not. Single state stability column Hurdle 3: mean of distribution should be stable. This is what we typically think of. Mean level should be a stable thing. Stability of the mean is on the stablitiy column. The location of the didstribution is stable. The average on all of your reports. Substantial evidence. How we typically behave is a very stable feature of us as indviduals. Split your data in random into two halves and see if the mean of one half correlates with the mean of the other half. Hurdle 3. Hurdles 2 and 3 just prove the obvious. 4th hurdle is harder. At least one other paramater must be stable. More than just the mean. Split the sd randomly and compare them. They are quite impressive. Ranging from .58 to .67. liberal test. At least one other paramater is a stable feature. At the very leats we can think of the lcoation but the size of the distribution as stable as well.
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Shape: Skew: levels of stability are lower than for size, still reasonbily good. Ranging from .41 to .48. the size is stable, as is the skew. Not only is the location is stable, the spread is stable, and the lopsidedness is stable as well. Kurtosis shows lower stability, too close tos ingel state stability. 3 of the paramaters show stabliity. We need to know how you are in general, how you differ, how you shhift in extremes as well.
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Way of depicitng the difference between how stable two states are and how stable two means are. One state/moment is only moderately predictive of how you are in at another moment. Some degree of relationship, but modest. Not copmletely flat line. When we look at the mean however, the regression line is better. Average in one half of data better predicts the other half much better. Limited use of state, but average is stable. Basically hurdles 2 and 3.
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Is the within person varability not simple errors? Is how you describe yourself in the last hour actually relevant to the context you were in the last hour. We should be able to understand why you were in a state of more than one trait than you usually are, or less. Contextual cues were collected with the records as well. Time of day and number of others present. Relying on folk theory of extraversion, thought that people differed in levels of extraversion in relation to time of day and number of people around you. More extraversion when there are greater number of other around you. How many other people were you with in the last hour. Also releveant is the time of day. Young adults and old adults: morning/afternoon is more strucuted: need to be at work/school. More free at night. People may become more extraverted in the evening when they have opporutunies to engage in other socially, not bound by tasks in work and school. Expects that there can be variation in extraversion related to time of day. Regression coefficents. Yes, there is meanigful varability. There would be no significant results if it was random. But there is lawfulness. Variation is meangiful, contextual cues.
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What are the sources?
Error? Necessairly the case that when we take a measurmenet, we make an error. Never measure anything perfectly. Simply impossible to have a perfect measurement. Some of the variability that people show is due to error. Participant failures to use the scales well. Some people take questionnares in an error prone method. Measurement is imperfect. We error when trying to measure. Variations in situations: sitautiosn change, and these sitautions have normative influences on us. In normal circumstances a situation will affect us all in the same way. More people around us, we become more extravereted. Normative infkuence, we all respond in this way. When stiautiuons change, we all change with them Person x sitaution interactions: not the normative responses that we all hvae,, but the unique particular idoscrncratic responses to certain situations. Number of people may be nromative, but ther are unique sitautions where you differ from everyone else, how you interpret stituations differently. A given stiaution may move us all in the same way, and may move some in a unique way.
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Substantial varability in how we are across situations. Mean is not the only mneasure. Need tro consider the other paramaters as well.
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Continue with personality dynamics. We are going to be focusing on today and next class, within person dynamics, mechanisms and proceses. Why do we not always act in accord with our personality? Why do people not strictly adhere to the traits they have. People seem surprinsgly and substantially different from the traits they report. Semi random sampling of trait terms. There terms, like many, focus on what someone is like in general. How they typically behave or think or feel. Characteristic of personality psychology in general. We are preoccupied with understanding what people are like in general. How people behave on average. What are you like on average. What mean levels to people show in their thinking, in their feelings, in their behaviours. Mean levels, average tendencies they show. We use these terms as they make reference to average levels Say someone is trusting, you say they are on average more trusting than others. Fragile? Labile? Erratic? Percetly good descriptors for thinking, feeling and behavior. But they do not make reference to what people are like on average. To say someone is fragile odesn’t mean they break frequently, instead in average circumstances they are more likely to break. Erractic is a description not of behaviour, but of how erratic their behavior is. How variable people are? Worthwhile to understand that people are variable? Not just the typical levels that people show. After all people are variable.
3
William fleeson: risen to the rank of president in personality research. Outlined a set of principles for how we engage in research to explore intraindivdual varability. They have historically been defined by mean levels. Trait = mean of someone’s behaviour. But fleeson has argued that we need to overturn this focus, need to focus on not just typical behaviours, but full spread. Other element’s of people’s behaviour as well. Not just levels. Broad claims, not hypothesis 1) Even though it is likely the case that someone’s typical level of behaviour differs from other, it is the case that this idnividual may show more of a characteristic, and less of a characteristic across time. So each indvidual will express every level of a trait over time. Scale 1 -7. sometimes you will be a 4, 4 may not describe you equally well in all circumstances. It may happen most of the time, but there might be ocassions where the person reports a 5,6 or 7. the same individual who orignially described tehsemelves as 4, may on other ocassions be higher or lower. Situations require us to change our behaviour. Over time, our behaviour varies around a central tendency. Given a sufficent number of observations, people make use of the entire range of a scale. People’s reports can be described with a density distribution. Sometimes more talkative than usual, soemtimes less talkative than usual. Not one trait, not one person. But all traits, and all people 2) The mean is still the most important, most stable, most predictable characteristics
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of all of psychology. The mean is one parameter, but not the only parameter. 3) The mean is not the only way, there are other paramaters. Should not equate traits with the means of traits. Reconceptualize traits as not just the mean, but all the paramaters from which you can describe a distribution. Understand and explore how stable and how meaningful are these other density distrubtions of traits and states.
Need to know the concept of a trait. A state. (today we use state and behaviour interchangeably) both are distinct from traits. Traits express themselves in behaviours. Being in a particular state or showing a behaviour is the expression of at rait. Trait is what someone is like in general. State or behaviour is over a narrow period of time. Differ from one situation to the next because of many variables.
4
Paper focuses on 3 studies, lecture today focuses on the first. Studies 2 and 3 are qualifications on the first study. Important to know why study 2 and 3 were conductged, but the substitive results are reported in the first study. Explore how well we can describe people’s behaviours using density distributions. We can describe the state you are in right now using the same words to describe your traits. We can use the same scale for assessing a trait of an indvidual to assess their state. (in general vs past hour) Fleeson collected multiple state records. Records states as they go about the day. Complete them mutliple times daily to assess how their states vary from one moment to the next along the big 5 dimensions. Mutiple records of behaviours and how the big 5 have been expressing htemselves over circumscribed periods of time. Vertical axis = how frequently a state has been reported. The curve describes distribution of frequencies Frequency dsitribution = density distribution. The universe is normal for some reason.
5
Density distribution can be summarized with 3 or 4 paramaters. Location, size, shape. The mean tells you typically where the scores are generally clustered. Ususally most important thing to know. Know the average. Mean is imperfect distribution however. There is typically some or a lto above, ro some beloew Look at the size or standard deviation. How much spread or how much diversion there is around the mean. The standard deviation of how badly a job the mean does in describing the distribution. A smaller standard deviation means that the central tendency is a good indicator of the distribution. Shape Skewness. Where the distribution is shifted to one side of the scale or another side of the scale. Is the distribution lop sided? If yes, then it is skewed. Violation of symmetry. If the distribution is not symmetrical, it is skewed. How peaked or shallow is it? Is the distribution more peaked than the other? Kurtosis. Kurtosis and skew describe the shape Skew describes lopsided to the left or right Kurtosis describes the peak or shallowness. Location size shape, skew, kurtosis. This is how we describe traits. Historically just concentrated on traits. But maybe we can learn about people from the spread of their behaviour and the shape that it takes.
6
May or may not be the case where we need these paramaters to describe behaviours. Left: distribution roated 90 degree. Their staes of extraversion over time, for 3 indviduals. What’s important to see is how non-overlapping these distributions are. Very few occasions where person 2 behaves similarly to person 1 or person 3. 3 doesn’t behave much like 1 or 2 either. Etc. all we really need for this one is the mean. Person 1 is more extraverted on average than person 2, person 3. person 3 more extraverted than 3. etc. we do not actually need more than the mean. Very little within person variation. Most of their behaviour indicates their average. Vey little variablitiy within the person. Right: Some of these distributions are more skwed, they differ in how kerutotic they are as well. To describe these 3, we need more than the location of their distributiosn. Need their size and shape as well. There is so much in person variabiltiy, so they now overlap. Much less infomration to just know the location of these distributions. Need to know how they differ in terms of size and shaep of distributions as well. Which one of these hypothesized sitautions are more close to real life? If left is the closest real life, then we can just focus on the locaiton or average. If right is closer to real life, then we need size and shape of distribution as well.
7
5 outcomes would support that psychologists need to redfine personality traits. Not just average. 1) Within person variability. If we need more than the mean, there has to be substatinal variabiltiy. Have to have people that differ a lot. 2) Momentary states are not that predictable. Needs to be little resemblance from one day to the day. Single state indices are notoriously unreliable. Shouldn’t correlate well. Measure someone on one ocassion then measure on another ocassion, there should be variability. Single states of behaviour should not show high levels of consistency. 3) The mean of the distribution would be very stable. Average level of behaviour is likely to be very stable. Its frequent use of perosnality characteristics. 4) But one of the other parmaters should also be stable. His goal needs to be to show that not just the mean is stable, at least one other feature of the density distribution should be stable as well. Liberal test and conservative test: liberally he only needs to show that one other paramter is stable. More conservatively, he would need to show that all other paramaters are stable as well. There is some leeway here. At the very least he has to show that one other paramter is stablke. At the very most he has to show that all other paratmters are stable. 5) The variability that people show has to be meangiful. Cannot be simply error or nusiance factor. Cannot be osmoen randomly picking numbers. Has to be something meanfiul as to why you are picking different numbers on different
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ocassions to show how you behave. How do you show this> show that it is predictable. Some lawfulness to how it occurs. Lawfulness indicates predictability. Fleeson shows for 1 of 5 traits, he does not have time for all the other, that the variability we show on extraversion is not just random, but there are meaningful contextual cues that explain why you are more extraverted now than in other situatoins Memorize! 5 hurdles.
8
3 studies. Studies 2 and 3 qualifiy on the first 46 participants complete experience sampling procedres for 2 weeks approximately. Participants collected record forms and carried them around, 5 times a day used them to describe behaviour in the last hour. 65 opportunities to measure people’s behaviour. On average, people compelted 3 quarters of the records available. There is missing data, sometimes participants failed to supply records. On average people were able to submit just under 50 records. Reports would gauge big 5 behaviour. In the last hour, not on average.
9
How descriptiive are these adjectives with regards to your behaviour in the last hour.
Measured their different affective states as well. Moods are known to vary. Postive affect and negative affect substantially wihtin people change. Looking at within person variability of personal affect and negative affect gives us a benchmark to guage how much varability a perosn will have for the big 5. look to affect where we know there is variabiltiy to say whether there is a lot of variability or very little variability in trait expression. Large number of records, 22,00 records. Each record shows how much of each big 5 trait was epressed in the past hour.
10
How do we summarize?
Within person variation -how much variation does each person show on average? Takes a single subject and all the data she supplies. Fro that single person she calculations the standard deviation. Standard deviation is the spread across scores from the average. Each indvidual has a SD. 46 SDs. Differ across people. Some have larger deviations, some have less devaiotns, less spread. Typical amont of within person varability. Calculate sd for each person sperately, average the sd. On average how much variabliity does a person show? -calculate the mean for each person. How extraverted was somsone across the 13 days? Each person gets their own mean. How extraverted are they on average over the 13 days? Then take the SD across everyone’s means to determine how much vrability exists between people in how they typically behave.
11
1st colum is total varation Striped column is within person variation = how much do they vary in their behaviour Blank column: how much do people differ from each other on how they behave? For affect: more within person variability than between people. Your moods change, more than they change compared to others. More within variability in the big 5 than stability. Our traits, the standing on the big 5 change from one hour to the next as much as our moods do. As much within person vrability than between person. In conscientiousness and extraversion, more within person variability than between people. If we randomly sample two moments in a person’s life, that variability is as big as the differences between that person and other people. Your variability from one moment to the next is as great as variabiltiy we se between people.
12
This is a hypothetical indvidual. Interproalted from the avreage across the sample. How much the typical subject varies in their big 5 across time. The amount of each trait is not relevant, but the dispersion is. How the groups are disperesed. Emotional stability and intellect seem to sue mos t of the scale rather than all of the scale. How much does a person vary across a 13 day period? Every level of possible xpression utilzied. There is within person variabiotin. Gone over hurdle 1.
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Single indices are likely to be very unstable. Not highly correlated. 2nd hurdle. Degree of correlation is modest at best. Single states are likely not stable features. We should not be very stable across two single situations. Shouldn’t have high expectations. Randomly pick two moments, and see how simliar they are between those situations. According to hurdle 2, they should not correlate well. And they do not. Single state stability column Hurdle 3: mean of distribution should be stable. This is what we typically think of. Mean level should be a stable thing. Stability of the mean is on the stablitiy column. The location of the didstribution is stable. The average on all of your reports. Substantial evidence. How we typically behave is a very stable feature of us as indviduals. Split your data in random into two halves and see if the mean of one half correlates with the mean of the other half. Hurdle 3. Hurdles 2 and 3 just prove the obvious. 4th hurdle is harder. At least one other paramater must be stable. More than just the mean. Split the sd randomly and compare them. They are quite impressive. Ranging from .58 to .67. liberal test. At least one other paramater is a stable feature. At the very leats we can think of the lcoation but the size of the distribution as stable as well.
15
Shape: Skew: levels of stability are lower than for size, still reasonbily good. Ranging from .41 to .48. the size is stable, as is the skew. Not only is the location is stable, the spread is stable, and the lopsidedness is stable as well. Kurtosis shows lower stability, too close tos ingel state stability. 3 of the paramaters show stabliity. We need to know how you are in general, how you differ, how you shhift in extremes as well.
15
Way of depicitng the difference between how stable two states are and how stable two means are. One state/moment is only moderately predictive of how you are in at another moment. Some degree of relationship, but modest. Not copmletely flat line. When we look at the mean however, the regression line is better. Average in one half of data better predicts the other half much better. Limited use of state, but average is stable. Basically hurdles 2 and 3.
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Is the within person varability not simple errors? Is how you describe yourself in the last hour actually relevant to the context you were in the last hour. We should be able to understand why you were in a state of more than one trait than you usually are, or less. Contextual cues were collected with the records as well. Time of day and number of others present. Relying on folk theory of extraversion, thought that people differed in levels of extraversion in relation to time of day and number of people around you. More extraversion when there are greater number of other around you. How many other people were you with in the last hour. Also releveant is the time of day. Young adults and old adults: morning/afternoon is more strucuted: need to be at work/school. More free at night. People may become more extraverted in the evening when they have opporutunies to engage in other socially, not bound by tasks in work and school. Expects that there can be variation in extraversion related to time of day. Regression coefficents. Yes, there is meanigful varability. There would be no significant results if it was random. But there is lawfulness. Variation is meangiful, contextual cues.
18
What are the sources?
Error? Necessairly the case that when we take a measurmenet, we make an error. Never measure anything perfectly. Simply impossible to have a perfect measurement. Some of the variability that people show is due to error. Participant failures to use the scales well. Some people take questionnares in an error prone method. Measurement is imperfect. We error when trying to measure. Variations in situations: sitautiosn change, and these sitautions have normative influences on us. In normal circumstances a situation will affect us all in the same way. More people around us, we become more extravereted. Normative infkuence, we all respond in this way. When stiautiuons change, we all change with them Person x sitaution interactions: not the normative responses that we all hvae,, but the unique particular idoscrncratic responses to certain situations. Number of people may be nromative, but ther are unique sitautions where you differ from everyone else, how you interpret stituations differently. A given stiaution may move us all in the same way, and may move some in a unique way.
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Substantial varability in how we are across situations. Mean is not the only mneasure. Need tro consider the other paramaters as well.
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