Table 1

Helping Students Understand the Article

Section

 Tips, Comments, and Problem Areas

Abstract

Simple choice reaction task: being presented with a stimulus (in this article, the stimuli are words) and being asked to press, as quickly as possible, one button for one type of stimuli (in this article, participants  were asked to press the “9” key when presented with  a negative word such as “skull” and “snake”) and a different button for a different type of stimuli (in this article, participants were asked to press the “1” key for neutral words such as “straw” and “glass”).

Negative affect: unpleasant emotions; unpleasant feelings

Somatic symptoms: bodily symptoms of stress or illness such as headache and upset stomach.

Introduction (first part)

1^st paragraph

assimilation: integration of the new information with what is already known; making the information fit into one’s existing categories.

meaning assignment: interpreting; making sense of

proxy: substitute

2^nd paragraph

habitual categories: categories that a person typically uses.

objective criterion: a measure not vulnerable to interpretation and bias.

latency: response time, reaction time.

Problems related to …verbal report processes: what people say may not reflect what they are thinking (see 184-186 of Research design explained).

subjective coding schemes: determining how to score a participant’s response based on a gut feeling rather than on a clear-cut, observable standard.

3^rd paragraph

bipolar: having two different (often opposite) sides.

associative strength: the degree to which two things are connected.

taps: measures

hedonic consequences: the results (effects) in terms of how happy one is

Introduction

(Negative evaluations and their consequences)

1^st paragraph

latitude: room for differences; room for differences; leeway

detrimental: harmful

subjective well-being: how happy one is

2^nd paragraph

negative evaluation tendencies: speed at rating words as negative

measures of subjective well-being: measures of life satisfaction; happiness measures

retrospect: reflect back

affect: emotion, feelings (positive affect would be happy feelings, negative affect would be unhappy feelings)

3^rd paragraph

The authors expect that negative categorization leads to being unhappy with life. However, another possibility is that having unpleasant experiences may lead one to start categorizing events in a negative way. Study 3 tries to eliminate that possibility. Yet another possibility is being in a bad mood causes one to engage in negative categorization. Study 4 is designed to try to eliminate that possibility.

4^th paragraph

novel: new, original, different

Study 1

Method: Measures

Self-Report.

Read the footnotes: They make everything clearer and more concrete.

markers: indicators; measures (see footnote 1)

a : Cronbach’s alpha, a measure of internal consistency (see page 

104 of Research design explained). Alphas above .70 suggest that all the items (questions) on the measure are measuring the same thing; alphas above .85 are considered high.

Categorization.

3^rd paragraph

The idea was to try to control for things such as some people just be faster at reaction time tasks that others.

 hedonic experience: happiness

4^th paragraph

Participants’ scores were all between .3 seconds and 4 seconds because if participants took less than .3 seconds, they were given the score .3 seconds and if they took longer than 4 seconds, they were given the score 4 seconds.

log-transformed to normalize …: The idea is that some statistical tests assume that the data are normally distributed. If the data are not normally distributed, researchers may do some mathematical operation on the data (such as take the square root of all the scores or turning times [e.g., 1 hour to go a certain distance] into speeds [e.g., 60 miles per hour]) to make the distribution normal.  (For more about transforming data, see pages 69 and 70 of Research design explained.)

 “… r = -.26”: the more accurate  participants were, the less time it took them to respond.

5^th paragraph

With the regression, the authors are doing a fancy (and better) version of subtracting, for each participant, the participant’s average reaction time for the animal categorization task from that same participant’s time on the negative evaluation task. The result is that whereas a participant’s actual reaction times on the negative evaluation task are affected by the participant’s hand-eye coordination, eyesight, and attention span, the participant’s NEGEVAL score should not be affected by those factors. What the authors are actually doing is a two-step process. First, they use participants’ animal categorization  reaction time task to predict participants’ to predict participants’ scores on the negative evaluation reaction time task. Then, they calculate each participant’s NEGEVAL score by subtracting the participant’s predicted score from the participant’s actual score. Thus, if the participant’s actual reaction time on the negative evaluation task (e.g., 2 seconds) is slower than their predicted score (e.g., 1 seconds), the participant gets a positive NEGEVAL score (e.g., 2-1=+1). If, on the other hand, the participant’s actual reaction time on the negative evaluation task (e.g., 2 seconds) is faster than their predicted score (e.g., 3 seconds), the participant gets a negative NEGEVAL score (e.g., 2-3=-1).

To look at it another way, consider the graph below. We have plotted the hypothetical data of 6 participants’ scores on (a) the animal categorization reaction time task and on (b) the negative evaluation reaction time task. We have also drawn in a regression line that uses participants’ reaction times (RTs) on the animal categorization task to predict participants’reaction times on the negative evaluation task. If a participant’s actual reaction time on the negative evaluation task is the same as that participant’s predicted negative evaluation RT,  (a) the point representing that participant’s actual reaction time will be on the regression line, and (b) that participant’s NEGEVAL score will be zero. If a participant’s actual reaction time on the negative evaluation task is slower than would be expected from knowing their reaction time on the animal categorization task, (a) the point representing that participant’s actual reaction time will be above the regression line,  and (b) that participant’s NEGEVAL score will be positive.  If a participant’s actual reaction time on the negative evaluation task is faster than their predicted negative evaluation RT,  (a) the point representing that participant’s actual reaction time will be below the regression line, and (b) that participant’s NEGEVAL score will be negative.

Final paragraph of Method

Note that the authors use odd-even correlations to make the case for their measure’s internal consistency. Using odd-even correlations is a common practice with paper-and-pencil tests (see page 104 of Research design explained), but, as you can see from this article, the technique of using odd-even correlations can also be used for other kinds of measures. 

Results

…r = -.28…: participants who scored low on NEGEVAL (took less time to categorize  words as negative) had more negative emotion and more unpleasant physical symptoms such as feeling sick.

Study 2

conceptually replicate: repeat using different methods, but testing the same principle

temporal delay: time between

state dependent: due to a temporary, short-term emotional state (rather than to a long-term stable trait)

on-line: at the time it is happening; current

Method

Measures

global: general (see footnote 4 on bottom of page 523 to see exactly what the two global items were).

If you have any trouble understanding Study 2’s method section (e.g., you do not understand a or computing NEGEVAL, look at our notes for Study 1’s method section.

Results

r = .29: Participants who were fast to categorize words as negative tended to be dissatisfied with life. That is, participants who had negative [low] scores on NEGEVAL tended to have low scores on the life satisfaction scale. Conversely, participants who were slow to categorize words as negative tended to be satisfied with life. That is, participants who had positive [high] scores on NEGEVAL tended to have high scores on the life satisfaction scale.

Study 3

…negative evaluation tendencies…: Do people who have recently experienced mostly unpleasant events tend to be quicker to evaluate words as negative than people who have experienced mostly pleasant events?   If so, unpleasant life events may be causing both (a) low life satisfaction and (b) quick categorization of negative events (low NEGEVAL scores). In that case, quick categorization of negative events would not be causing lower life satisfaction.

Method

Procedure

pages: does not refer to pages of text; refers to a beeping signal from a pager telling the participant to complete a survey.

Measures

Fourth paragraph:  This paragraph will be much clearer if you read footnote 5.

Results

Preliminary analyses

The results suggest that, relative to people with high NEGEVAL scores, people with low NEGEVAL scores (those “quick-to-classify-events-as-unpleasant” people) are not less likely to have good things happen to them. However, introverts (people who are less likely to socialize), relative to extraverts (outgoing people), are less likely to have good things happen to them, and people scoring high on neuroticism (people who worry a lot, people who are less emotionally stable, and seem to have poor coping skills) seem to have fewer good things happen to them than people scoring low on neuroticism (calmer, more secure people). If you want to see where you stand on these personality scales, you may be able to take a test by going to the following web page:

http://mentalhealth.about.com/gi/dynamic/offsite.htm?zi=1/XJ&sdn=mentalhealth&zu=http%3A%2F%2Fwww.outofservice.com

Subjective experience

People with low NEGEVAL scores (people who were quick [took less time] to categorize words as negative) tended to report having (a) more negative emotions (were more likely to report being afraid, sad, etc.) and (b) more physical symptoms (headaches, upset stomachs, etc.).

Appraisal

“… r=-.20…”: People with low NEGEVAL scores (people who were quick [took less time] to categorize words as negative) tended to rate life events as more threatening than people with high NEGEVAL scores. Put another way, on when participants rated their current situation on a 1 (not at all threatening) to 5 (extremely threatening) scale, people with low NEGEVAL scores tended to rate the level of threat in their current situation as high, whereas people with high NEGEVAL scores tended to rate the level of threat in their current situation as low.

Multiple Regressions

The authors created  seven equations: one equation for each of the measures listed in Table 1. In each equation, they used three predictors (extraversion, neuroticism, and NEGEVAL) to predict scores on the measure. The authors suggest that the beta coefficients provide an index of the contribution of each variable that is independent of the other two. However,  it is probably safer to say that the beta coefficients provide an index of the contribution of each variable that is partly independent of the other two. Thus, it is probably overly optimistic to say that the beta coefficients are like correlation coefficients that only count the unique contribution of each variable of each variable to the equation.

In other words, like Pearson r correlation coefficients, beta coefficients give you an index of the relationship of the predictor with the measured variable. Indeed, if the regression equation has only one predictor, that beta coefficient will be the same as r. However, interpreting beta coefficients is tricky when you have more than one predictor and the predictors correlate with each other. One way of interpreting a beta coefficient is to say that for every increase of one standard deviation in the predictor variable, the outcome variable will change by that predictor’s beta coefficient’s number of standard deviations. In this set of studies, a beta coefficient of .21 for NEGEVAL (fifth row of Table 1) means that an increase of one standard deviation in NEGEVAL would be accompanied by a .21 standard deviation increase in the variable “appraised valence of momentary life.”

Pay attention to these three warnings about interpreting beta coefficients:

1.     Although it is tempting to say that beta coefficients tell you how important a predictor is, realize that the size of beta coefficients for one predictor is affected by what  other predictors are in the equation—and how those predictors correlate with each other (to see why, see pages 533-535 of Research design explained).

2.     In a simultaneous regression equation, the beta coefficients do not necessarily give you the unique contribution of each prediction. To understand why, visualize what you are trying to predict (for example, scores on appraised valence of momentary life”) as a whole pie. In addition, imagine that your predictors are children who have earned parts—slices—of the pie. If each child has earned a different part of the pie (each predictor is unique and accounts for an entirely different slice of the pie), you are fine. However, what if the slices they have earned overlap?  For example, what if both have claims to the third slice? Then, it gets messy about which predictor gets how much of the disputed slice of pie. The beta coefficients give each predictor its undisputed slice of pie (its unique contribution) plus some of the disputed (shared) slice(s). Thus, the beta coefficients are not telling you the unique contribution of a variable. To get a variable’s unique contribution, you would be better off looking at either the variable’s partial correlation or its semipartial correlation.

3.     Remember that, in a correlational study, beta coefficients cannot tell you the effect of a variable because correlational studies do not allow you to make cause-effect conclusions.

Table 1

See notes on Multiple Regressions (above)

Study 4

transitory: temporary

traitlike: permanent, enduring characteristics of a person’s personality

Method

Procedure

“… was parallel.”: Participants were asked to remember a recent event that made them very sad.

manipulation check: a measure to see whether the manipulation seemed to manipulate what it was designed to manipulate. In this case, the reason for the manipulation check and the manipulation check itself is described in the first paragraph of the Measures section. For more about manipulation checks, see pages 71 and 116 of Research design explained.

Measures

1^st paragraph

markers: scales, measures. As you can tell by reading footnote 6, participants rated how much they felt “happy” (a marker for pleasant feelings) on a 1 to 5 scale.

Results

M = 3.05: The mean was 3.05.

F (2,87)= 7.90, p = .00: The F tells us that the authors used an F test (see Chapter 10 of Research design explained).

The 2 in F (2,87) tells us that there were 3 groups.

The p = .00 tells us that, if the manipulation did not have an effect, it would be extremely unlikely that we would get such results. Therefore, a reasonable conclusion is that the mood manipulation did have an effect on mood. Note that they are not saying that the probability is zero; they are saying that if you round it to 2 decimal places, you would state it as .00. Thus, if p = .001 (or even p =.004), p —rounded off to two decimal places— would be .00.

F < 1.00: Fs below 1 do not provide any evidence that the treatment had an effect.

Discussion

First paragraph

Asymmetry: a relationship that does not go both ways, but instead goes only one way.  In this case, the authors suggest that being quick to evaluate events as unpleasant causes one to be in a negative mood, but being in a negative mood does not cause one to be quick to evaluate events as unpleasant. Note that because the first three studies were correlational, the authors have not actually showed that being quick to evaluate events as unpleasant causes one to be in a negative mood. Note alos that because we cannot prove the null hypothesis, the authors did not prove that being in a negative mood does not have any effect on being quick to evaluate events as unpleasant. 

Analogous: similar

How people explain failure predicts depression (e.g., people who see failure as due to a permanent problem in their basic personality are more likely to become depressed than people who see failure as a temporary setback that was due primarily to bad luck), but depression is not seen as affecting how people explain failure.

The authors argue that mood seems to have little effect on how we put new information into memory: Studies showing that mood affects memory are usually studies in which mood could affect memory by affecting what information participants get out of memory.

Toward a Categorization-Based View of Personality

1^st paragraph

Whereas a person’s score on conventional personality test tells us how people would see that person (e.g., Is the person kind? Anxious?), the authors’ believe that a measure based on their reaction time measure could tell us how a person sees the world.

2^nd paragraph

response latencies: how long it takes to respond; reaction time

deep level of semantic processing: thinking about the meaning of something

Conclusions

Instantiate: support; come up with an example that would support