Assignment 2 Table Guide

Assignment 2 Table Guide

The purpose of this document is to clarify what information you need to report in assignment 2 and how it can be presented. Information on how to run and interpret the analyses is covered in the lecture material and weekly activities across weeks 3 to 6.

Important points to note:

It usually makes more sense to present tables shortly after introducing them in-text, and to describe what is summarised in them. This allows the reader to quickly connect the results with your description.

DO NOT present statistics in a Table and then also present the same statistics in-text.

Fitting tables into a document and making them look nice can be difficult. The APA style guide does not prescribe a given font size or line-spacing for tables, as a one-size-fits-all approach would not work. So, if you are struggling to fit a particular Table on a single page, you could consider changing things like the font size.

1. Exploratory Factor Analysis

In addition to mentioning relevant information such as Bartletts Test, KMO, and the various parameters of your analyses (e.g., rotation and extraction being used, factor loading cutoff), you will need to summarise the steps taken in each successive EFA as you work towards obtaining simple structure.

The following Table is one way in which you could present the steps of your various factor analyses. You could choose to summarise these steps in-text instead.

If you choose to use the following Table, you will need to include sufficient detail for anything that is not clear within the Table itself. For example, if youve decided to remove an item for theoretical reasons, you will likely need to explain the rationale for this in-text.

You wont see a Table like this in published papers. Its mostly to provide an efficient way of outlining your steps so your tutor can follow what youve done.

The values in the Table below are for illustrative purposes. Update as required.

Table X (updated numbering as required for your assignment)

Steps taken to refine the [new scale name here]

Step Number of factor(s) % of variance explained Item(s) excluded Reason

1 4 55.67% 3 Cross loading on Factors 1 (loading = .42) and 2 (loading = .39)

2 4 53.31% 8 Not loading on any factor (highest loading = .28)

3 3 50.17% 11 Low communality (.17) and not loading on any factor (highest loading = .24)

Add more rows as needed Table 1 below gives an example of how the factor loadings for your final solution can be presented. The data is based on a factor analysis of the 7 Up 7 Down (7U7D; Youngstrom et al., 2013), a questionnaire measure of trait bipolar disorder vulnerability.

Important information to note:

Table 1 shows all of the items written out in full, which takes up a lot of room. If the full items are all shown elsewhere in the document (e.g., in a Table in the Method or an Appendix) you could just use a label (e.g., Item 1) rather than type out the entire wording for the item. The same applies if youre referring to an item in-text.

When you are running your various factor analyses you may use the supress function in SPSS to blank out loadings below your factor loading cut-off (some people find this helps them to interpret the output more easily). However, when reporting the results, its best to show all of the loadings on each factor. Table 1 presents the highest loadings for each item in bold to make it clear which factor an item is loading on. This is not compulsory though.

When doing your factor analysis you can select an option to sort the loadings by size. This will mean that all items loading on Factor 1 are listed first, then all of the items loading on Factor 2 and so on. This can be another way of helping to clearly see which items load on each factor. In Table 1 the items are presented in the same order that they were given to respondents.

Table 1

7U7D Pattern Matrix obtained via Maximum Likelihood Extraction with Promax Rotation

Item

Factor 1:

Depression-proneness Factor 2:

Mania-proneness

1. Have you had periods of extreme happiness and intense energy lasting several days or more when you also felt much more anxious or tense (jittery, nervous. uptight) than usual (other than related to the menstrual cycle)? .14 .65

2. Have there been times of several days or more when you were so sad that it was quite painful or you felt that you couldn't stand it? .69 .15

3. Have there been times lasting several days or more when you felt you must have lots of excitement, and you actually did a lot of new or different things? -.11 .78

4. Have you had periods of extreme happiness and intense energy (clearly more than your usual self) when, for several days or more, it took you over an hour to get to sleep at night? -.04 .76

5. Have there been long periods in your life when you felt sad, depressed, or irritable most of the time? .83 .05

6. Have you had periods of extreme happiness and high energy lasting several days or more when what you saw, heard, smelled, tasted, or touched seemed vivid or intense? -.03 .77

7. Have there been periods of several days or more when your thinking was so clear and quick that it was much better than most other people's? -.02 .62

8. Have there been times of a couple days or more when you felt that you were a very important person or that your abilities or talents were better than most other people's? .03 .57

9. Have them been times when you have hated yourself or felt that you were stupid, ugly, unlovable, or useless? .85 -.11

10. Have there been times of several days or more when you really got down on yourself and felt worthless? .89 -.04

11. Have you had periods when it seemed that the future was hopeless and things could not improve? .86 -.03

12. Have there been periods lasting several days or more when you were so down in the dumps that you thought you might never snap out of it? .87 .01

13. Have you had times when your thoughts and ideas came so fast that you couldn't get them all out, or they came so quickly that others complained that they couldn't keep up with your ideas? .19 .57

14. Have there been times when you have felt that you would be better off dead? .75 .04

Eigenvalues 6.92 2.09

Extraction SSL 6.44 1.74

Rotation SSL 5.93 4.90

Note. SSL = sum of squared loadings (initial SSL are equivalent to the eigenvalues).

2. Reliability

The note underneath Table 2 shows the Cronbachs alpha values for the subscales. This is because the Cronbachs alpha if item deleted statistics would be fairly meaningless without this reference point.

Tables 1 and 2 could possibly be merged if the page orientation was changed to landscape format. Either way is fine. Youll need to learn to apply section breaks in order to use landscape orientation (this is also a useful skill to have for managing page numbering in complex documents more generally).

Table 2

Item-Level Properties of the 7U7D obtained via Exploratory Factor Analysis

Item Communalities (extraction) Item response M(SD) Corrected item-total correlation Cronbachs if item deleted

Item 1 (M) .54 0.61 (.79) .65 .84

Item 2 (D) .62 0.79 (.90) .75 .93

Item 3 (M) .53 0.76 (.81) .65 .84

Item 4 (M) .55 0.73 (.87) .66 .84

Item 5 (D) .73 0.99 (.99) .82 .92

Item 6 (M) .57 0.49 (.77) .68 .84

Item 7 (M) .37 0.80 (.81) .59 .85

Item 8 (M) .34 0.65 (.80) .56 .85

Item 9 (D) .63 1.04 (.94) .75 .93

Item 10 (D) .76 0.93 (.92) .83 .92

Item 11 (D) .71 0.90 (.90) .81 .93

Item 12 (D) .76 0.80 (.91) .84 .92

Item 13 (M) .48 0.59 (.82) .64 .84

Item 14 (D) .60 0.68 (.89) .75 .93

Note. M = mania-proneness item, D = depression-proneness item. Mania-proneness Cronbachs = .86, depression-proneness Cronbachs = .94.

The Cronbachs alpha values and corrected item-total correlations do not come from the factor analysis output, but from running separate reliability analyses on the relevant items (see Week 3 content).

Communalities come from the factor analysis output

Mean scores and standard deviations (i.e., M(SD)) item response values come from running Explore or Descriptives3. DescriptivesOnce you have finished reporting your factor analysis, you need to move from an item-level focus to a scale-level. This means that your next step is to create whole-scale and subscale scores for your new scale. You can do this by using SPSS (Transform > Compute new variable) to add up the items that have loaded significantly on each factor. For example, to calculate participants mania-proneness scores (i.e., Factor 2) you would compute a variable that summed Items 1, 3, 4, 6, 7, 8, and 13 of the 7U7D. Another option is to sum and average the scores (i.e., add up those items and divide by 7). Once this is done you should report descriptive statistics for your new scale, just like you would in any research report. Since descriptive statistics for your validity indicators are useful you should include them here as well.

Table 3

Descriptive Statistics for the 7U7D and Validity Indicators

M (SD) Actual range Potential range Cronbachs

7U7D Mania-proneness 3.82 (3.68) 0-18 0-21 .86

Depression- proneness 6.13 (5.50) 0-21 0-21 .94

Validity indicators BAS-D 10.64 (2.49) 4-16 4-16 .81

BAS-FS 11.41 (2.40) 4-16 4-16 .74

BAS-RR 15.98 (2.61) 5-20 5-20 .78

Trait BIS 12.11 (2.45) 4-16 4-16 .75

N = 760 Note. Standard deviations are presented in parentheses following the relevant mean.

Important points to note:

You might be wondering why no data for a 7U7D total score here, as in many cases subscales would be summed to provide a total scale score. In this case, the assumption is that depression-proneness and mania-proneness are theoretically distinct. If they were added together and we then ran correlations using 7U7D total, we could be obscuring a lot of important data about how depression-proneness and mania-proneness relate differently to other constructs. When designing a new scale, it is up to you to think about whether your subscales can and should be meaningfully combined or not.

The actual range is the minimum and maximum values that you observed in your data set. The potential range is the minimum and maximum values that it is possible to obtain on the measure. These values can be found by multiplying the number of items by the lowest scale anchor (minimum possible response) and by the highest scale anchor (maximum possible response), and averaging this score if necessary.

For example, if you have 10 items on a 1 (Strongly Disagree) to 7 (Strongly Agree) scale, the potential range is 10-70 if you are summing the items, or 1-7 if youre using an average.

The benefit of using an average rather than summed range is that it can make the results more easily interpretable. For example, 17 items measured on a 7-point scale (1 = Strongly Disagree, 7 = Strongly agree) has a potential summed range of 17-119, with the average potential range being 1-7. If the overall average score was 68 it can be hard to figure out what this means within the range of 17 to 119. However, if the average score of 4 (68 / 17 = 4) is used, its easier to more quickly conceptualise what that means within the context of the Strongly Disagree/Strongly Agree response format.

You should always be aware of the potential range, as without knowing this you cannot spot out-of-range values (errors) in your data set.

Another purpose of looking at the actual and potential ranges is to get a sense of how people were responding (you could also look at skewness and kurtosis results, and also histograms in the Explore analysis). For example, if everyone was scoring quite low on a particular measure, that might tell us something about the sample we have, or perhaps the items are biased in such a way that most people feel that they need to give a certain type of response. These issues could influence how accurate our findings are, and could be something worth mentioning in the Discussion.

Cronbachs alphas for each measure need to calculated separately (see Week 3 workbook activity for a guide on this).

The importance of showing the alphas for the validating measures is that if a measure is unreliable it could undermine the accuracy of any conclusions you draw about the validity of the new scale.

4. Validity Correlations

Now that you have presented descriptives, it is time to present your validity correlations. Since it would also be useful to the reader to know how your new scale or subscales are correlated, you might as well present this information at the same time.

Important points to note:

The results in these tables are based on bivariate correlations between scale scores that have been created in SPSS AFTER completing the factor analysis. The matrix of factor correlations that are given in the factor analysis output is a bit different to that obtained by running bivariate correlations. This is because it runs correlations that are weighted by the factor loadings. The best approach is to run your own separate correlations (see Week 4 or Week 6 activities).

If we had a meaningful 7U7D total variable, then usually we would probably use that as a focus when testing our validation hypotheses, with the subscale data simply providing an extra level of richness. However, as noted previously, depression-proneness and mania-proneness are being considered as fairly different constructs that are not strongly correlated enough for a 7U7D total variable to be meaningful.

Table 4

Correlations between the 7U7D and BIS/BAS Scales

1. 2.

1. Mania-proneness 2. Depression-proneness .54***

3. BAS-D .11** -.12**

4. BAS-FS .13*** -.10*

5. BAS-RR -.11** -.19***

6. Trait BIS -.09 .21***

N = 760 Note: * p < .05, ** p < .01, *** p < .001

Some quick notes to begin with:

1) You do not need to remove outliers from the sample. Any major outliers have already been dealt with and removed. You also dont need to report on this.

2) If at any point you get this in your output:

Pattern Matrixaa. Rotation failed to converge in 25 iterations. (Convergence = .001).

Click on Analyze > Dimension Reduction > Factor, and then click on the Rotation button. Change the Maximum Iterations for Convergence from 25 to 100. Re-run the analysis and your Pattern Matrix should appear.

center9314

Demographic information about the sample (to be included in the Method)

N = 215

Age range: 20 to 79. M = 33.45, SD = 11.97

49 males, 164 females, one person who do not identify with binary gender labels, and one person who chose not to report their gender identity.

Questionnaires

The following questionnaires (from the list in the assignment 1 handout) were included to collect data on the new scale, and for the purposes of assessing validity of the new scale:

Note: * indicates a reverse-scored item. In SPSS these items are designated by R at the end of the name (see Name column in Variable View). These have already been reverse scored, so you do not need to do anything with them.

The following measures, including the new scale, are listed in the same order that they are presented in the SPSS data file.

Realistic optimism, unrealistic optimism, pessimism (developed by students in PSY30003)

For the following statements please select the response which best represents your views.

1 = Strongly disagree, 5 = Strongly agree

Unrealistic optimism

I believe that I am more likely than others to live a long and healthy life

I am more likely to succeed in life compared to my peers

Bad things sometimes happen to other people, but those things wont happen to me

Regardless of the challenge, I still expect things to turn out better for me than for others

Negative events are more likely to affect other people

I am much less likely than the average person to ever experience a severe illness

I am less likely to suffer a heart attack than the average person

Left to themselves my problems seem to work out

I believe that my life will work out well, no matter what

Realistic optimism

I can succeed with careful planning and persistence

If I work hard I can achieve my goals

I lower my expectations of success if the circumstances are challenging

With the right support I believe I can succeed in life

I believe I can make things happen, even in difficult circumstances

I accept that life is challenging, but I trust in my ability to overcome those challenges

I view feedback as useful for my success.

I make a concerted effort to achieve desired outcomes

I am good at many things but I cannot be good at everything

Pessimism

I often think that I will fail at a task before I start it

Even when things seem to be going well, I expect the worst to happen

I tend to believe that only bad things happen to me

Things usually go wrong for me

It doesnt matter what I do, things go wrong for me

I dont think anything positive will happen to me in the future

Things rarely work out in my favour

I often feel that nothing good is going to happen to me

No matter how hard I try, things never work out for me.

Life Orientation Test Revised (Scheier et al., 1994)

For each of the following items please select the response that best matches how you feel in general.

1 = Strongly disagree, 5 = Strongly agree

In uncertain times, I usually expect the best.

It is easy for me to relax

If something can go wrong for me, it will*

I am always optimistic about my future

I enjoy my friends a lot

It is important for me to keep busy

I hardly ever expect things to go my way*

I dont get upset too easily

I rarely count on good things to happen to me*

Overall I expect more good things to happen to me than bad

Note: Items 2, 5, 6, and 8 are distractor or filler items, intended to make it more difficult for respondents to figure out what theyre being asked about. These are not included in the overall LOT-R score that has been calculated, and should not be included in any reliability analyses.

Five-factor model of personality (Donnellan, Oswald, Baird, & Lucas, 2006)

Here are a number of personality traits that may or may not apply to you. Please indicate how accurately you think each statement applies to you.

1 = Very inaccurate, 5 = Very accurate

I have a vivid imagination

I am not interested in abstract ideas*

I have difficulty understanding abstract ideas*

I do not have a good imagination*

I get chores done right away

I often forget to put things back in their proper place*

I like order

I make a mess of things*

I am the life of the party

I dont talk a lot*

I talk to a lot of different people at parties

I keep in the background*

I can sympathise with others feelings

I am not interested in other peoples problems*

I feel others emotions

I am not really interested in others*

I have frequent mood swings

I am relaxed most of the time*

I get upset easily

I seldom feel blue*

Satisfaction With Life (Diener et al., 1994)

1 = Strongly disagree, 7 = Strongly agree

In most ways, my life is close to my ideal

The conditions of my life are excellent

I am satisfied with life

So far I have gotten the important things I want in life

If I could have my life over, I would change almost nothing

Positive Affect Negative Affect Scale (Thompson, 2007)

Thinking about yourself and how you normally feel, to what extend do you generally feel:

1 = Never, 5 = Always

Upset

Hostile

Ashamed

Nervous

Afraid

Inspired

Determined

Attentive

Active

Alert

Comparative risk judgments (Weinstein, 1983)

Compared to other people like me, my chances of developing or encountering each of the following are

1 = Much below average, 7 = Much above average

Diabetes

Heart attack

Drinking problem

Attempted suicide

Lung cancer

Other forms of cancer

Mugging

Injury in a car accident

High blood pressure

Tooth decay

Stomach ulcer

Note: A total score has been created which is the average (all risks added up and divided by 11). In accordance with Weinstein (1983), there is also a score for high risks (the average of the first 5 risks) and a score for low risks (average of the last 6). You can use whichever score corresponds with your hypothesis.

Worry about health risks

How worried are you about developing or encountering each of the following?

Diabetes

Heart attack

Drinking problem

Attempted suicide

Lung cancer

Other forms of cancer

Mugging

Injury in a car accident

High blood pressure

Tooth decay

Stomach ulcer

Note: As with the comparative health judgments, an overall worry score has been created, as well as the worry related to encountering the high and low health risks.

Calculated scale scores

Below each of the items mentioned above, there are scores that were created by summing and averaging the relevant items from each measure (i.e., if there were 10 items in a measure, they were added together and the resulting amount was divided by 10).

Correlations for validity should use these scores and not the individual items from these measures. The individual items are included in the dataset so you can calculate reliability for the relevant measure.

LOTR

Openness (this is technically about imagination or intellect in Donnellan et al.s scale, but its largely synonymous with openness to experience)

Conscientiousness

Extraversion

Agreeableness

Neuroticism

LifeSatisfactionNegativeAffectPositiveAffectRiskOverall (i.e., the comparative risk judgments)

HighRiskLowRIskWorry

WorryHighRiskWorryLowRisk