Tuesday July 28, 2009 Noon-2:00 p.m. ISR Building, Room 6050

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2. Agenda Welcome Coordinators Background Overall Purpose of Symposium 10 Minute Presentations (wide spectrum of topics) Symposium Format Closing Remarks General Q/A 2

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4. The Michigan Study of Life After Prison Andrea Garber University of Michigan-Flint Jeffrey Morenoff, PhD & Dave Harding, PhD Social Environment and Health 4 Hello. As George said, I�m Andrea Garber, and I worked on the Michigan Study of Life After Prison with Jeffrey Morenoff and Dave Harding in Social Environment and Health.Hello. As George said, I�m Andrea Garber, and I worked on the Michigan Study of Life After Prison with Jeffrey Morenoff and Dave Harding in Social Environment and Health.

5. Overview 1. The Michigan Study of Life After Prison Analysis of Michigan Department of Corrections Administrative Records Pilot Interview Study Comprehensive Evaluation of MPRI at Ionia Bellamy Creek 2. Drug Tests, Drug Screens, & Our Data 5 First, I will be looking at the main study of the Michigan Study of Life After Prison. There are a few main parts of the project. Part A is an analysis of MDOC records in order to find the parolees address before prison and their address after prison, along with the prisons that the person were in. It�s goal is to look at the effects of neighborhood context on recidivism and employment. In this case, recidivism refers to the rate of going back to prison Part B is an interview of 24 parolees that added a qualitative element to the study. I worked on parts A and B, and Gabe will be following up with part C. After I cover the main project, I will talk about my interests in substance use and drug testing and how it may affect our data.First, I will be looking at the main study of the Michigan Study of Life After Prison. There are a few main parts of the project. Part A is an analysis of MDOC records in order to find the parolees address before prison and their address after prison, along with the prisons that the person were in. It�s goal is to look at the effects of neighborhood context on recidivism and employment. In this case, recidivism refers to the rate of going back to prison Part B is an interview of 24 parolees that added a qualitative element to the study. I worked on parts A and B, and Gabe will be following up with part C. After I cover the main project, I will talk about my interests in substance use and drug testing and how it may affect our data.

6. 6 As you can see, our sample was representative of the population of 11,069 parolees. The population that we looked at was parolees released from Michigan prisons in 2003. It excluded those that were paroled to other states, and those �maxed out�. �Maxing out� refers to those people that served their full sentence in prison so when they are released, they are no longer under supervision, and therefore, there is no data on them. 2003 was chosen because it was the first year that that the MDOC databases were available statewide, and it gave us the most time to track the parolees. As you can see, our sample was representative of the population of 11,069 parolees. The population that we looked at was parolees released from Michigan prisons in 2003. It excluded those that were paroled to other states, and those �maxed out�. �Maxing out� refers to those people that served their full sentence in prison so when they are released, they are no longer under supervision, and therefore, there is no data on them. 2003 was chosen because it was the first year that that the MDOC databases were available statewide, and it gave us the most time to track the parolees.

7. 7 As I said, we chose to sample. This decision was made because of the difficulty and the time commitment that it took to go through the Michigan Department of Corrections databases: OMNI (Offender Management Network Information) and CMIS (SIMIS) (Corrections Management Information System). We sampled in two stages. First, we chose the census tracts with a probability proportionate to the number of parolees that returned there. Then, we chose individuals within that tract with a probability inversely proportionate to the tract selection rate. This gave us a representative sample with 1/3 of the population. As I said, we chose to sample. This decision was made because of the difficulty and the time commitment that it took to go through the Michigan Department of Corrections databases: OMNI (Offender Management Network Information) and CMIS (SIMIS) (Corrections Management Information System). We sampled in two stages. First, we chose the census tracts with a probability proportionate to the number of parolees that returned there. Then, we chose individuals within that tract with a probability inversely proportionate to the tract selection rate. This gave us a representative sample with 1/3 of the population.

8. Parolees� First Addresses (2003) Many parolees return to a small number of neighborhoods- 12% of tracts receive 50% of parolees But parolees are spread throughout the State- Of Michigan�s 2,707 census tracts, 78% received at least one parolee 8 After we drew our sample, we went through the databases to find the correct addresses, which was very time consuming, and we gained a lot of information by reading the parole agents case notes. We took the addresses and put them in ArcGIS and geocoded them. For those that don�t know what geocoding is, it refers to a process where we found the latitude and longitude of an address. It is sort of like placing a pin on a map. We used this information to compare the neighborhood characteristics that parolees returned to in order to look at recidivism. We also looked at number of days in prison, number of prisons, and distance from prisons to post-prison address. All of the following are possible factors that will affect recidivism rates. Here is one of the maps that was created regarding the parolees first addresses. As you can see, many parolees return to a small number of census tracts, and of Michigan�s 2,707 tracts, 78% had at least one parolee.After we drew our sample, we went through the databases to find the correct addresses, which was very time consuming, and we gained a lot of information by reading the parole agents case notes. We took the addresses and put them in ArcGIS and geocoded them. For those that don�t know what geocoding is, it refers to a process where we found the latitude and longitude of an address. It is sort of like placing a pin on a map. We used this information to compare the neighborhood characteristics that parolees returned to in order to look at recidivism. We also looked at number of days in prison, number of prisons, and distance from prisons to post-prison address. All of the following are possible factors that will affect recidivism rates. Here is one of the maps that was created regarding the parolees first addresses. As you can see, many parolees return to a small number of census tracts, and of Michigan�s 2,707 tracts, 78% had at least one parolee.

9. Qualitative Interviews 9 Part B is a study of 16 males and 8 females that follows the parolees at 1, 2, 6, 9, and 12 months following their release. The sample was chosen because they represented many different groups. Some take part in the Michigan Prisoner Reentry Initiative, while others did not. Some are from more urban areas like Wayne county, while others are not, and some subjects are black and some white and some male and some female. The goals of this research are to: Better understand the re-entry experience and possible mechanisms for outcomes and to develop and refine techniques for interviewing and tracking subjects. The interviews are face-to-face at a variety of different locations depending on where the subject wants to meet. We are reaching our goals by reading through the interview transcripts and comparing what our interviewers are told with what the parolee is telling their parole agent. On the chart, MPRI refers to the Michigan Prisoner Reentry Initiative. This is a cooperative effort led by MDOC and other state offices that aims to make communities safer and make the transition from prison to real life easier for parolees by giving them resources to succeed. Gabe will be following up with more information regarding MPRI. Part B is a study of 16 males and 8 females that follows the parolees at 1, 2, 6, 9, and 12 months following their release. The sample was chosen because they represented many different groups. Some take part in the Michigan Prisoner Reentry Initiative, while others did not. Some are from more urban areas like Wayne county, while others are not, and some subjects are black and some white and some male and some female. The goals of this research are to: Better understand the re-entry experience and possible mechanisms for outcomes and to develop and refine techniques for interviewing and tracking subjects. The interviews are face-to-face at a variety of different locations depending on where the subject wants to meet. We are reaching our goals by reading through the interview transcripts and comparing what our interviewers are told with what the parolee is telling their parole agent. On the chart, MPRI refers to the Michigan Prisoner Reentry Initiative. This is a cooperative effort led by MDOC and other state offices that aims to make communities safer and make the transition from prison to real life easier for parolees by giving them resources to succeed. Gabe will be following up with more information regarding MPRI.

10. Drug Screen v. Drug Test Drug Screen Immunoassay: Based on competitive binding, and it uses antibodies to detect the presence of a particular drug or metabolite in a urine sample Less accurate Less sensitive to small doses Drug Test Gas Chromatography/Mass Spectrometry (GC/MS): Gas chromatography separates the substances found in the urine then mass spectrometry identifies the substances found Extremely accurate Sensitive to small doses 10 I have a great interest in substance abuse and how it relates to crime, but in the 10 weeks that I was here, I didn�t have much time to focus on the subject. So when this idea came up, it was of great interest to me as well as for the project so let�s begin with the basics about a drug screen and test. There are a few main differences between a drug screen and a drug test. Drug screens and drug tests both use urine samples but with two different processes for their results. Because of this difference, drug tests are 100% accurate, while drug screens accuracies may vary. They have false answers at times. Drug tests could be referred to as giving the true results and because of this, they are used to confirm or negate the results of a drug screen. Drug screens are also not as sensitive to small doses of drugs in urine. They are much cheaper to use so it helps to make up for these differences. Drug screens usually test for five drug types that we will see soon, and they are referred to as the �federal five�- Substance Abuse and Mental Health Services Administration.I have a great interest in substance abuse and how it relates to crime, but in the 10 weeks that I was here, I didn�t have much time to focus on the subject. So when this idea came up, it was of great interest to me as well as for the project so let�s begin with the basics about a drug screen and test. There are a few main differences between a drug screen and a drug test. Drug screens and drug tests both use urine samples but with two different processes for their results. Because of this difference, drug tests are 100% accurate, while drug screens accuracies may vary. They have false answers at times. Drug tests could be referred to as giving the true results and because of this, they are used to confirm or negate the results of a drug screen. Drug screens are also not as sensitive to small doses of drugs in urine. They are much cheaper to use so it helps to make up for these differences. Drug screens usually test for five drug types that we will see soon, and they are referred to as the �federal five�- Substance Abuse and Mental Health Services Administration.

11. Preliminary Results on Drug Screening 94.1% of our 3,689 parolees have been drug screened As of May 2008, each parolee in our sample has been screened an average of 17.6 times, but some have had as many as 209 screens Of those screened, 59.12% have had a positive test 11 Drug screens and tests are an important topic for our research because it greatly affects a huge majority of our sample. Of our 3,689 parolees, 94% have been screened, and with that, they have been screened an average of 17.6 times so it is clearly something that affects their lives. Because of the large quantity of screens, I wanted to understand the accuracy of drug screens and how that might affect our measurement of substance useDrug screens and tests are an important topic for our research because it greatly affects a huge majority of our sample. Of our 3,689 parolees, 94% have been screened, and with that, they have been screened an average of 17.6 times so it is clearly something that affects their lives. Because of the large quantity of screens, I wanted to understand the accuracy of drug screens and how that might affect our measurement of substance use

12. 12 These are the estimated false rates that we could expect to see per screen according to one study by the University of Utah and the Traffic Safety Administration. The study looked at the Rapid Drug Screen that is used by MDOC on our sample and many thousands of other people statewide. So this cell is saying that for one screen, we could expect to see a 0.16% chance of a false positive for THC, and this one is saying that we could expect to see a 1.7% chance of a false negative for THC. Screens can test for one or many drugs at a time, and The Michigan Department of Corrections uses this screen to look at either 1 or 3 types of drugs. Although these numbers may look small, this is for one screen. Our sample is of almost 3,700 parolees, and they take screens at each visit to their parole officer so that certainly adds up. Please note that a false positive is when someone is not using but a screen shows results that they are, and a false negative is the opposite where they are using, and it doesn�t show. Common false positives include poppy seeds for opiates and ibuprofen for THC, the substance in marijuana. These are the estimated false rates that we could expect to see per screen according to one study by the University of Utah and the Traffic Safety Administration. The study looked at the Rapid Drug Screen that is used by MDOC on our sample and many thousands of other people statewide. So this cell is saying that for one screen, we could expect to see a 0.16% chance of a false positive for THC, and this one is saying that we could expect to see a 1.7% chance of a false negative for THC. Screens can test for one or many drugs at a time, and The Michigan Department of Corrections uses this screen to look at either 1 or 3 types of drugs. Although these numbers may look small, this is for one screen. Our sample is of almost 3,700 parolees, and they take screens at each visit to their parole officer so that certainly adds up. Please note that a false positive is when someone is not using but a screen shows results that they are, and a false negative is the opposite where they are using, and it doesn�t show. Common false positives include poppy seeds for opiates and ibuprofen for THC, the substance in marijuana.

13. 13 This table shows the rate that we could estimate and expect to see per 1,000 people who take a screen based on the average number of tests per person in our sample of 17. As you can see, the numbers do add up. This cell shows that of 1,000 taking a screen, 3.3 would expect a false positive for THC, and 225.35 would expect a false negative. The focus will be on the false positive rate because MDOC procedure says that when a parolee tests negative, there is no follow up test to see if it is a false negative. It is very interesting to note, however, that many, many people in our sample may be receiving negative results even though they were using at that point in time.This table shows the rate that we could estimate and expect to see per 1,000 people who take a screen based on the average number of tests per person in our sample of 17. As you can see, the numbers do add up. This cell shows that of 1,000 taking a screen, 3.3 would expect a false positive for THC, and 225.35 would expect a false negative. The focus will be on the false positive rate because MDOC procedure says that when a parolee tests negative, there is no follow up test to see if it is a false negative. It is very interesting to note, however, that many, many people in our sample may be receiving negative results even though they were using at that point in time.

14. Reasons for a Drug Screen or Test May be a condition of parole Agent may suspect substance use Family may suspect use May be screened if they abscond They may request a GC/MS (confirmatory test) In most cases, parolees usually admit to using 14 There are many reasons for a parolee to be screened. They may be in their biweekly or monthly check-ins with their parole officer. They may be if they are suspected of using or if their family says that they were using. Their parole officer may also screen them if they have absconded. Through conversations with MDOC staff, we learned that in most cases, people simply admit to using when they have a positive test, but if they honestly say that they didn�t use, a GC/MS test may be ordered for confirmation. There are many reasons for a parolee to be screened. They may be in their biweekly or monthly check-ins with their parole officer. They may be if they are suspected of using or if their family says that they were using. Their parole officer may also screen them if they have absconded. Through conversations with MDOC staff, we learned that in most cases, people simply admit to using when they have a positive test, but if they honestly say that they didn�t use, a GC/MS test may be ordered for confirmation.

15. Effects of a Positive Test In most instances, a parolee will be referred to a substance abuse treatment center. There are four levels of care: Outpatient, Intensive Outpatient, Domiciliary Outpatient, Residential. A return to prison rarely happens, but if it is being considered then a portion of the positive sample will be sent to the laboratory for confirmation testing using GC/MS. 15 In most situations, if a parolee receives a positives result, they are referred to substance abuse treatment. There are four types of care contracted by the Department of Corrections as listed. Parolees go to each depending on their drug of choice, level of use, and how many times they have tried to stop before. We were informed that a return to prison or confinement rarely happens, but when it does, the positive sample is sent to the lab to make certain that the parolee was using. Because MDOC does do a follow up exam, we would not have to worry about our data being skewed by people returning for false reasons.In most situations, if a parolee receives a positives result, they are referred to substance abuse treatment. There are four types of care contracted by the Department of Corrections as listed. Parolees go to each depending on their drug of choice, level of use, and how many times they have tried to stop before. We were informed that a return to prison or confinement rarely happens, but when it does, the positive sample is sent to the lab to make certain that the parolee was using. Because MDOC does do a follow up exam, we would not have to worry about our data being skewed by people returning for false reasons.

16. Should We Analyze Drug Screens? False positives are relatively rare False negatives are more widespread Parole agents may have discretion in how they react to screens Are there extra legal indicators that change parole agent responses? 16 While I found that the false positives in drug screens did no affect our data, I did find that the large number of false negatives could make our data conservative on the amount of drugs that are being used. Even if drug screens are not always accurate, it is still interesting to analyze how parole agents react to them to better understand when parolees are likely to be sent back to prison for testing positive and when they are likely to get an alternative sanction, such as being sent to a treatment program. I am particularly interested in whether extra legal factors, such as age, race, gender, and education of the parolee, predict the likelihood of being sent back to prison for a positive drug screen, after adjusting for all the legal factors that should predict the parole agent�s decision, such as indicators that the parolee was displaying a continual pattern of substance abuse.While I found that the false positives in drug screens did no affect our data, I did find that the large number of false negatives could make our data conservative on the amount of drugs that are being used. Even if drug screens are not always accurate, it is still interesting to analyze how parole agents react to them to better understand when parolees are likely to be sent back to prison for testing positive and when they are likely to get an alternative sanction, such as being sent to a treatment program. I am particularly interested in whether extra legal factors, such as age, race, gender, and education of the parolee, predict the likelihood of being sent back to prison for a positive drug screen, after adjusting for all the legal factors that should predict the parole agent�s decision, such as indicators that the parolee was displaying a continual pattern of substance abuse.

17. Thank you Jeff Morenoff and Dave Harding George Myers and Anita Johnson Barb Strane, Bianca Espinoza, Paulette Hatchett, Amy Cooter, Claire Herbet, Liz Johnston, Elena Kaltsas, Ash Siegel, Jay Borchert SRC Interns 17 I would like to thank Dave and Jeff and all the graduate and undergraduate students for their help. Anita and George, and Barb and the other interns. Next, we have Gabriel Moreno from Arizona who is going to talk more about prisoner reentry.I would like to thank Dave and Jeff and all the graduate and undergraduate students for their help. Anita and George, and Barb and the other interns. Next, we have Gabriel Moreno from Arizona who is going to talk more about prisoner reentry.

18. Matching and Randomization In A Prisoner Reentry Program Gabriel Moreno University of Arizona David Childers and Ben Hansen PhD SRC-Quantitative Methodology Program 18

19. MPRI The goal of the Michigan Prisoner Reentry Initiative (MPRI) is to reduce recidivism among prisoners. The pilot program provides various classes and services to promote successful reentry into the community. If the pilot program is deemed successful it will be expanded to more sites across the state. 19 �reduce recidivism�. Stages; stage 3. �Learning site� = pilot program�reduce recidivism�. Stages; stage 3. �Learning site� = pilot program

20. Replenishment The pilot site has a fixed number of spaces and a predetermined ratio of prisoners with low, medium and high recidivism risks. When a space becomes available, an eligible prisoner list is compiled and two of these prisoners are selected. One of these two prisoner is randomly assigned to the empty space while the other is sent to receive the standard prisoner reprogramming services. 20 Suggest introducing pairing here. �random selection� ? �Random assignment�Suggest introducing pairing here. �random selection� ? �Random assignment�

21. Tracking The outcomes of the prisoners entering the program (treatment group) and the eligible prisoners who end up receiving the standard programming (control group) are tracked. The recidivism of these groups will eventually be used to assess the program�s effectiveness. 21

22. Current Flow of Prisoners 22

23. Considerations In order to better measure the effectiveness of the program, the treatment and control groups should have a similar makeup. The current randomization scheme for keeping the overall treatment and control populations similar can be improved upon. 23 Suggest cutting point #1, revising #3. (Randomization does something to prevent dissimilarity, it�s just that we can do more.)Suggest cutting point #1, revising #3. (Randomization does something to prevent dissimilarity, it�s just that we can do more.)

24. Objective Our task was to create a randomization and matching scheme that could be used by the Michigan Department of Correction (MDOC) to better sustain the overall similarity of the treatment and control groups� compositions. 24

25. Data We began with the MDOC�s 2003 parolee demographics data. We then examined indicators of recidivism and prisoner demographic information. 25

26. Indicators of Recidivism Absconding Technical Parole Violation Recommitment 26

27. Demographic Data Race Drug Dependence Assault Risk Prior Offenses Education Level Offense Category Mental Health Status Age Sex Offenses Income Dependents 27

28. Analysis Ran logistic regressions for each recidivism indicator using the demographic data. Using the regression coefficients, we created our own risk scores. We created our own risk scores because the MDOC�s risk scores were recently introduced. So outcome measures for these scores are not available. 28

29. Function in R We created a function in the R statistical programming environment that takes a list of eligible prisoners and matches them based on their risk scores. It then randomizes the pairs into the treatment and control groups. Matching and randomizing in this way will achieve better balance between the treatment and control groups. 29 Suggest �R, a statistical package and programming environment, that takes ��Suggest �R, a statistical package and programming environment, that takes ��

30. A Problem and a Solution The MDOC employees who will use our R function are not familiar with the R programming environment. To solve this we used the RExcel package. This allowed us to design an Excel spreadsheet that would compute and present the results of our function in a familiar environment. 30

31. Eligibles 31

32. The Final Product 32

33. Acknowledgements Ben Hansen Dave Childers Dave Harding Jeff Morenoff George Myers Anita Johnson 33

34. Introducing the Telephone Survey: Verbal Interaction and the Decision to Participate Colleen McClain Survey Methodology Program Sponsor: Dr. Fred Conrad 34

35. Assessing the Introduction Why are response rates so low in telephone surveys? Why do phone answerers decline to participate? In telephone conversations, all information must be conveyed through the audio channel Variation in interviewers� delivery-- and answerer�s responses-- in the introduction may matter �Introduction� defined: From �hello� to the first question of the interview-- or the hang up, refusal, etc. 35 Introduction: an attempt to obtain an interviewIntroduction: an attempt to obtain an interview

36. Background Work in linguistics and the psychology of interaction goes beyond content of speech Use of disfluencies �to manage ongoing speech production� (Clark & Fox Tree, 2002) and to serve as �uncertainty cues� (Schober & Bloom, 2004) Linguistic analyses focusing on speech frequency or intonational patterns in the introduction 36 Literature drawn from survey methodology, linguistics, psychology, and interviewer training (e.g. Groves & McGonagle, 2001) Tailoring- adaptation of speech to the perceived characteristics of the respondent Disfluencies- fillers (uh, um, er, ah-- these are used in my analyses), repairs, restarts, filled pauses, etc.Literature drawn from survey methodology, linguistics, psychology, and interviewer training (e.g. Groves & McGonagle, 2001) Tailoring- adaptation of speech to the perceived characteristics of the respondent Disfluencies- fillers (uh, um, er, ah-- these are used in my analyses), repairs, restarts, filled pauses, etc.

37. The Interviewer Voices Project Designed to extract speech and voice variables from the recorded interaction between interviewer and telephone answerer that may predict the participation decision Levels of analysis: Interviewer Contact Turn Move 37 A �contact� is a conversation between an interviewer and an answerer at a specific point in time; a turn is a shift in speakers within a conversation. Acoustic variables include rate of speech, pitch, etc.A �contact� is a conversation between an interviewer and an answerer at a specific point in time; a turn is a shift in speakers within a conversation. Acoustic variables include rate of speech, pitch, etc.

38. The Interviewer Voices Project MSU team Transcribing conversations Measuring acoustic variables U-M team Coding speech, including content and paralinguistic features such as disfluencies and overspeech Global ratings of speaker attributes (such as gender and whether or not they are native speakers) Maryland team Conducting multivariate modeling 38

39. My Role Code speech in the recorded telephone invitations Propose and conduct preliminary analyses Specific hypotheses focus on the relationship between answerer�s participation decision and: The proportion of overspeech in a contact The proportion of backchannel moves in a contact The proportion of interviewer moves containing fillers in a contact Interviewers� phrasing of indirect invitations 39 Overspeech: both speakers talking at the same time, with one being interrupted and the other interrupting Backchannel: an utterance such as �uh-huh� or �okay� signaling engagement in the conversation by the listenetr Move: functional unit of speech Indirect invitation: to participate in the study: �I would like to interview a member of your household� instead of �will you participate?�Overspeech: both speakers talking at the same time, with one being interrupted and the other interrupting Backchannel: an utterance such as �uh-huh� or �okay� signaling engagement in the conversation by the listenetr Move: functional unit of speech Indirect invitation: to participate in the study: �I would like to interview a member of your household� instead of �will you participate?�

40. Methods Coders listened to recordings and read corresponding transcripts in order to assign codes to speech segments Transcripts organized by turns and moves Sequence Viewer used to code conversations, link audio files, run pattern analyses Some variables autocoded Onset and offset times of moves recorded Analysis of variance run using dataset exported to SPSS Currently, 589 contacts (conversations) in database Preliminary analyses: N = 491 SRO introductions from five different studies, with corresponding audio recordings 40 Turn: content spoken by a single speaker, without introduction Move: functional unit of speech within a turn Eventually, we hope to have approximately 1500 contacts coded.Turn: content spoken by a single speaker, without introduction Move: functional unit of speech within a turn Eventually, we hope to have approximately 1500 contacts coded.

41. Sequence Viewer 41 Transcripts are received from MSU broken into turns (one speaker per turn). Our first task is to break these turns into moves (functional units of speech), as you can see in this example [read]. This turn contains three moves: a self-identification, a description of the study, and a follow-up comment. You can also see some of our transcript notation, including indication of pauses, rising intonation (the backslash), and the interviewer�s section of overspeech (the asterisks). We enter a number of move-level and sequence-level codes, as well as time codes.Transcripts are received from MSU broken into turns (one speaker per turn). Our first task is to break these turns into moves (functional units of speech), as you can see in this example [read]. This turn contains three moves: a self-identification, a description of the study, and a follow-up comment. You can also see some of our transcript notation, including indication of pauses, rising intonation (the backslash), and the interviewer�s section of overspeech (the asterisks). We enter a number of move-level and sequence-level codes, as well as time codes.

42. Measures Participation decision, or outcome: (1) Hang up, (2) agree, (3) refusal, (4) other, or (5) scheduled call back Coder-determined Proportion of interviewer moves in a contact that are backchannels Proportion of overspeech in a contact Proportion of interviewer moves containing fillers in a contact Proportion of indirect invitations that are yes/no questions 42 Backchannel: an utterance such as �okay� or �uh-huh� that signals the listener�s engagement in a conversation. Overspeech: two speakers talking at the same time, one doing the interrupting and one being interrupted. Proportion is defined as the number of instances of overspeech divided by all possible instances of overspeech (the beginning and end of every turn, except the first and last) Indirect invitation: for example, �I would like to interview a member of your household� versus direct invitation-- �Will you participate?� Yes/no question: for example, �Is now a good time?� versus �I was hoping now would be a good time.� Must be able to end the conversation with an answerer�s �no.� Fillers: here: um, uh, er, ah. Backchannel: an utterance such as �okay� or �uh-huh� that signals the listener�s engagement in a conversation. Overspeech: two speakers talking at the same time, one doing the interrupting and one being interrupted. Proportion is defined as the number of instances of overspeech divided by all possible instances of overspeech (the beginning and end of every turn, except the first and last) Indirect invitation: for example, �I would like to interview a member of your household� versus direct invitation-- �Will you participate?� Yes/no question: for example, �Is now a good time?� versus �I was hoping now would be a good time.� Must be able to end the conversation with an answerer�s �no.� Fillers: here: um, uh, er, ah.

43. Results Hypothesis 1: The proportion of all moves that are backchannels will be highest in contacts resulting in agreements. 43 Analysis of variance: Contacts resulting in agreements contain a higher proportion of backchannels than any those with any other outcome Resonates with previous literature that suggests backchannel utterances signal engagement in the conversation. Can potentially serve as cues to interviewers.Analysis of variance: Contacts resulting in agreements contain a higher proportion of backchannels than any those with any other outcome Resonates with previous literature that suggests backchannel utterances signal engagement in the conversation. Can potentially serve as cues to interviewers.

44. Results 44 This pattern could indicate that within contacts that are refusals, interviewers want to appear especially engaged as answerers move toward refusal. Also, could indicate the interviewer has lost control of the conversation-- the answerer is dominating with reasons for not participating, misunderstanding of purpose, etc-- and is now more inclined to short, backchannel utterances in response to answerers� speech.This pattern could indicate that within contacts that are refusals, interviewers want to appear especially engaged as answerers move toward refusal. Also, could indicate the interviewer has lost control of the conversation-- the answerer is dominating with reasons for not participating, misunderstanding of purpose, etc-- and is now more inclined to short, backchannel utterances in response to answerers� speech.

45. Results Hypothesis 2: Proportion of overspeech in a contact will be higher for refusals than for more desirable outcomes. 45 Suggests that overspeech may be a barrier to communication; could also suggest interviewers attempting a fast-paced �last effort� to get the answerer to participate. Further pattern analyses and modeling may illuminate the relationship.Suggests that overspeech may be a barrier to communication; could also suggest interviewers attempting a fast-paced �last effort� to get the answerer to participate. Further pattern analyses and modeling may illuminate the relationship.

46. Results Hypothesis 3: The proportion of interviewer moves containing fillers will be related to the answerer�s participation decision. 46 This result resonates with literature suggesting that fillers can be used to facilitate communication. The relationship between amount of fillers (defined here as um, uh, er, ah) as well as other disfluencies (potentially including repairs, restarts, and filled pauses) with participation decision may be curvilinear; answerers may not like an interviewer who sounds too scripted, but be put off by an interviewer who is overly disfluent.This result resonates with literature suggesting that fillers can be used to facilitate communication. The relationship between amount of fillers (defined here as um, uh, er, ah) as well as other disfluencies (potentially including repairs, restarts, and filled pauses) with participation decision may be curvilinear; answerers may not like an interviewer who sounds too scripted, but be put off by an interviewer who is overly disfluent.

47. Results Hypothesis 4: Yes/no questions provide an opportunity for answerers to opt out; contacts with an indirect invitation phrased as a question will lead to less desirable outcomes than those phrased as statements. The mean proportion of indirect invitations phrased as Y/N questions is higher for desirable than undesirable outcomes, F(1, 479)=19.377, p<.001. 47 Interviewer training materials show that interviewers are trained to NOT ask yes/no questions-- questions that can be answered with a yes or no that can also end the conversation. These results run counter to conventional wisdom. It is possible that yes/no questions do not have as negative an impact on willingness to participate as assumed, although that conclusion cannot be drawn from these preliminary results. It is also possible that interviewers tailor their phrasing of indirect invitations to the respondent-- for example, asking a yes/no question when the respondent is already perceived to be interested in participating.Interviewer training materials show that interviewers are trained to NOT ask yes/no questions-- questions that can be answered with a yes or no that can also end the conversation. These results run counter to conventional wisdom. It is possible that yes/no questions do not have as negative an impact on willingness to participate as assumed, although that conclusion cannot be drawn from these preliminary results. It is also possible that interviewers tailor their phrasing of indirect invitations to the respondent-- for example, asking a yes/no question when the respondent is already perceived to be interested in participating.

48. Still to Come More transcripts to code Additional development of the coding system and consistency checks Acoustic data from the MSU team Ratings from the U-M team: masculinity/femininity, animation, nativeness, accent, and coherence Multivariate modeling by the Maryland team 48

49. Conclusions Analyses of backchannels, overspeech, fillers, and the phrasing of indirect invitations all reveal intriguing potential relationships with participation decision. While analyses were conducted on a preliminary dataset and using a coding system that is still evolving, the questions that I examined address several key variables and will allow the team to explore paths for more in-depth analyses. 49 The project is still in its early stages, and while my analyses were relatively basic, they will likely be valuable to the team as they formulate formal hypotheses and investigate further variables for analysis.The project is still in its early stages, and while my analyses were relatively basic, they will likely be valuable to the team as they formulate formal hypotheses and investigate further variables for analysis.

50. Thank you to: Dr. Fred Conrad Jessica Broome Dr. George Myers III Anita Johnson The Survey Methodology staff The coding team- Gabe Moss, Daniel Nielsen, Dave Vannette, and Dylan Vollans The SRC interns 50

51. Evaluating Analytic Methods for a Pilot Study on Dynamic Goal Setting Andrew Leslie University of Michigan Dr. Susan Murphy University of Michigan, Department of Statistics & Institute for Social Research 51

52. Goal-setting Theory and Difficulty Basic Premise: Setting goals (or having them set by an administrator) improves the level of achievement of the individual with the goal. In general, the difficulty of goals has a positive linear relationship with performance.1 This trend in mirrored in goal specificity to the point that 99 of 110 studies (from 1969-1980) found that �specific, hard goals produced better performance than medium, easy, do-your-best, or no goals.�2 52

53. Goal-setting in Medicine In classic goal-setting theory, failure to achieve the goal forces the individual to try harder. In individuals with low ability or working on complex tasks, however, such failure can impair their later performance rather than improving it.3 Additionally, goal acceptance is much harder to enforce in a lifestyle intervention possibly making hard goals less practical. 53

54. Dynamic Goal Setting Goals are set at several points based upon the history of the individual. Questions: Will individuals become demoralized if they are repeatedly assigned high goals? Should individuals be given easy goals at the beginning? Etc. . . 54

55. The Project Design Eligibility: Age >50, sedentary4 and have Body Mass Indicator (BMI) >255 and no history of cardiovascular disease or diabetes For the first week (of sixteen in the full study), the participants wear a covered pedometer to obtain a baseline step count. Each following week, they receive feedback on the step count of the previous week and are randomized to a target step increase of 400, 800 or 1200 steps for the current week. 55

56. Current Project Aim: Determine the feasibility of analytic methods like regression and bootstrapping for data like those expected from the trial. Several generative models were considered, but one with a multivariate normal error term was settled upon:Yi = �1Y1 + �2G + �3M + �4E + aHi + eLi + f + XWhere Y1 is the initial step count, a, e and f are defined by splines, G, M and E are baseline variables and X is the error term. 56

57. A Generative Model 57

58. Another Generative Model 58

59. Bootstrapping Bootstrapping is a resampling technique in which n data points are selected, with replacement, from the original sample of size n. This is done many times (here 1000 times) and the variance of the result estimates the sample variance of the coefficient. The question: is this a reliable estimator of variance given data like those expected from the study? 59

60. Bootstrapping Results 60

61. Conclusion Bootstrapping: The Bootstrapping estimator of the variance performs well and is centered on the true value of the variance. Regression: The regression functions in their current form do not reflect the history of an individual and must be changed to reflect this to reach the final goal of the study. 61

62. Acknowledgements Dr. Susan Murphy Dan Lizotte Eric Laber The Treatment Strategies Program Dr. George Myers Anita Johnson 62

63. Exploratory Analysis of Group Differences on Reenlistment Intentions Among US Army and US Air Force Personnel Ryan Tully Social Environment & Health 63

64. Presentation Overview Study Background Studies Analyzed Study Objectives Key Terms Methodologies Employed Research Analysis Theoretical Considerations Research Approach Key Findings Discussion Future Research 64

65. Studies Analyzed Data analyzed from two studies: Work, Family & Stress: Deployment & Retention (Study of US Air Force Personnel) US Army: Deployment Resilience & Retention 65

66. Study Objectives Examine the effects of wartime deployment on: Mental health and well-being Future deployment readiness Retention Construct and validate models explaining predicted effects using existing theories of: Stress and coping (Lazarus & Folkman, 1984) Conservation of Resources (Hobfoll, 2002) Planned Behavior (Ajzen, 1991) 66

67. Key Terms Deployment: On orders and performing duties at a location or under circumstances that make it infeasible for military personnel to return to their duty station or home port (Rumsfeld, 2003) Retention: Voluntary continuation of service in the military after completing an initial contractual obligation (US GAO, 1990) 67

68. Methodologies Employed Studies used similar methodologies: 2 wave panel study �Mixed Mode� data collection approach Stratified Random Sampling Technique Defense Manpower Data Center (DMDC) Strata: Military Status Gender Deployment Theater Parental Status 68

69. Theoretical Considerations Conservation of Resources Theory (COR) 69

70. Theoretical Considerations Ajzen�s (1991) Model of Planned Behavior 70

71. Research Approach Examination of Reenlistment Outcomes Outcome measure focused on respondents� reenlistment intentions for their respective military branch (7-point Likert Scale) Tested for differences in the following general groups: Demographic Occupational Deployment Primary statistical analyses utilized: Student�s T-Test (Independent Sample) Analysis of Variance (ANOVA) 71

72. Key Findings Hypothesis #1: Reserve/National Guard personnel will report significantly lower levels of intention to reenlist than Active duty (Regular) personnel. Reasoning: Reserve/National Guard personnel may have less resources to cope with the stressors caused by deployment: Economic Emotional Occupational Family-Related (Milliken, et al., 2007) 72

73. Key Findings Finding #1: Active (Regular) duty personnel reported significantly lower levels of intention to reenlist than Reserve or National Guard personnel. 73

74. Key Findings Hypothesis #2: Logistical characteristics of deployment will significantly effect levels of reenlistment intention. Reasoning: Increased combat �exposure� correlates with increased incidence of mental health disorders among military personnel (Hoge, et al., 2004) COR Theory indicates that continuous �threats� to personal resources can adversely affect reenlistment intentions Possible Logistical Characteristics of Influence: Deployment Area Length of Deployment Number of Deployments 74

75. Key Findings Finding #2: Logistical characteristics of deployment did not produce significant differences in reported reenlistment intention levels. 75

76. Key Findings Hypothesis #3: US Air Force personnel will be substantively more likely to report intentions to reenlist than US Army personnel. Reasoning: Different nature of wartime roles: Combat �exposure� Continuous �threats� to resources Uncertainty regarding deployment 76

77. Key Findings Finding #3: General observations of data indicate that US Air Force personnel are substantially more likely to report intentions to reenlist than US Army personnel. Trend was found in every sub-group analyzed Aggregate level analysis provides tentative evidence of systemic differences between branches regarding reenlistment intent 77

78. Discussion Without statistical analyses determining causality, further explanation of the results are speculative. Differences in Active duty and Reserve/Guard personnel levels of reenlistment intent may result from: Organizational experiences Personal �Resources� Lack of differences in reenlistment intent among differing logistical characteristics of deployment may result from: Influence of combat �exposure� Conflicting �Loss versus Reward� Substantive differences between US Army and US Air Force personnel may provide meaningful insight of the magnitude of war-related stressors on outcome measures. 78

79. Future Research Use of more sophisticated statistical analyses (Multivariate Regression and Structural Equational Modeling) will: Establish causal relationships among study variables Provide understanding of complex interaction effects between personal resources and deployment experiences on outcome measures Future analysis of combined data sets allows for: Assessing the validity of suspected substantive differences between US Army and US Air Force personnel Statistical comparisons between branches will help further identify and clarify the impacts of war-stressors and outcome measures 79

80. Acknowledgements Dr. Penny Pierce Dr. Amiram Vinokur Dr. Lisa Lewandowski-Romps Mrs. Susan Clemmer Mrs. Lillian Berlin Mrs. Elli Georgal Dr. George Myers Mrs. Anita Johnson Fellow SRC Interns 80

81. Acknowledgements 81

82. Lifetime Trauma and Later-Life Health: Age Cohort and Trauma Type Effects Kimberly Miller-Tolbert Jacqui Smith, PhD University of Michigan University of Michigan Health and Retirement Study (HRS) 82

83. Presentation Outline Background Research Questions Measures and Methods Results Conclusion Future Directions 83

84. Topic Background Personally interested in clinical psychology and public health Life course approach: How experiences in life affect later life health If someone lost a parent as a child how would that affect them later? 84

85. Health & Retirement Study Background Longitudinal study that has been conducted every two years since 1992 Sponsored by the National Institute of Aging 2006 � First year for the psychosocial questionnaire 2006 data - 7139 individuals Mean age of 69.27 years Mean years of school : 12.52 56.8% Female 22.3% Minority 85

86. Questions about the Data Is there a relationship between total lifetime trauma and later life health? Is there a difference in prevalence of trauma between age cohorts? Is the type of trauma an important factor? 86 Why am I interested in this/ asking these specific questions? Longitudinal study would be best but that�s kind of impossible Why am I interested in this/ asking these specific questions? Longitudinal study would be best but that�s kind of impossible

87. Measures of Lifetime Trauma Total lifetime trauma: composite of eleven questions from the SAQ (Krause, Cairney and Shaw, 2004) Child: trauma occurring before the age of 18 Before you were 18 years old, were you ever physically abused by either of your parents? Self: trauma experienced by the respondent after the age of 18 Were you ever the victim of a serious physical attack or assault in your life? Family: trauma experienced by a family member Did your spouse or a child of yours ever have a life-threatening illness or accident? 87

88. Health Measures Subjective Health (5 point scale): Would you say your health is excellent, very good, good, fair, or poor? Functional Limitations (24 limitations) Chronic Illnesses (8 illnesses) Depressive symptoms Count of all CES-D items Psychiatric Condition Have you ever had or has a doctor ever told you that you had any emotional, nervous, or psychiatric problems? Methods: Descriptive analyses and stepwise hierarchical regression 88

89. Age Cohort Differences 89

90. Age Cohort and Trauma Type Differences 90

91. Trauma Type Effects on Health We first controlled for demographics and subjective health All three trauma subgroups significant for: Psychiatric condition, functional limitations, number of chronic diseases Only family trauma was significant for depression 91

92. Multiple Regression Analysis Summary 92

93. Conclusion The oldest age cohort had the largest percentage of lifetime trauma. There were some interesting trends among the age cohorts and trauma subgroups. There was a differential association between types of trauma and health outcomes. 93 How to present w/out simply restating what�s been said?How to present w/out simply restating what�s been said?

94. Future Directions Who suffers from lifetime trauma the most? What interventions can be developed so that those who have experienced trauma won�t be at risk for health problems in later life? 94

95. Acknowledgements All HRS staff George Myers Anita Johnson The other SRC interns 95

96. Daily Stress and Salivary Cortisol: The Role of Age and Neuroticism Elvina Wardjiman Dr. Kira Birditt & Dr. Toni Antonucci Life Course Development Program 96 Hello everyone, my name is Elvina Wardjiman and this summer I have been working in the Life Course Development Program on many data sets that deal with the stress hormone, cortisol. The study I will discuss with you today focuses on the role of age and neuroticism on daily stress and salivary cortisol using the National Study of Daily Experiences II. Hello everyone, my name is Elvina Wardjiman and this summer I have been working in the Life Course Development Program on many data sets that deal with the stress hormone, cortisol. The study I will discuss with you today focuses on the role of age and neuroticism on daily stress and salivary cortisol using the National Study of Daily Experiences II.

97. Background Cortisol Indicator of hypothalamic-pituitary-adrenal (HPA) axis activity (stress response) Prolonged activation is harmful Daily diary studies 97 Cortisol is a hormonal indicator of a biological system associated with the stress response referred to as the hypothalamic-pituitary-adrenal (HPA) axis. Cortisol produces a burst of energy during the flight-or-fight situation, but can be harmful when elevated levels of cortisol are prolonged. Elevated levels of cortisol are related to distress, withdrawal, negative emotions (worry/stress & anger/frustration), poor well-being, and poor health outcomes such as immunosuppression and cardiovascular disease* Cortisol has a normal daily pattern that is characterized by a peak 30 minutes after wakeup and a gradual decline throughout the remainder of the day, with the lowest expected at bedtime. *SHOW GRAPH* Two common measures of cortisol are morning rise and area under the curve (AUC). Morning rise (the oval area) is the difference between the cortisol values at wakeup and at 30 minutes after wakeup. AUC (the triangular area) is the �total cortisol concentration over the day� (John D. and Catherine T. MacArthur, 2000). Daily diary studies, in which individuals are asked to report their experiences of daily stressors and their appraisals of how stressful these experiences are, are important to study the daily pattern of cortisol and individual differences. The exposure-reactivity model is often used to understand the patterns of cortisol. ---- * (Dickerson & Kemeny, 2004; Adam, 2006; Adam, Hawkley, Kudielka, & Cacioppo, 2006; Adam, Klimes-Dougan, & Gunnar, 2007; Evans et al., in press) Cortisol is a hormonal indicator of a biological system associated with the stress response referred to as the hypothalamic-pituitary-adrenal (HPA) axis. Cortisol produces a burst of energy during the flight-or-fight situation, but can be harmful when elevated levels of cortisol are prolonged. Elevated levels of cortisol are related to distress, withdrawal, negative emotions (worry/stress & anger/frustration), poor well-being, and poor health outcomes such as immunosuppression and cardiovascular disease* Cortisol has a normal daily pattern that is characterized by a peak 30 minutes after wakeup and a gradual decline throughout the remainder of the day, with the lowest expected at bedtime. *SHOW GRAPH* Two common measures of cortisol are morning rise and area under the curve (AUC). Morning rise (the oval area) is the difference between the cortisol values at wakeup and at 30 minutes after wakeup. AUC (the triangular area) is the �total cortisol concentration over the day� (John D. and Catherine T. MacArthur, 2000). Daily diary studies, in which individuals are asked to report their experiences of daily stressors and their appraisals of how stressful these experiences are, are important to study the daily pattern of cortisol and individual differences. The exposure-reactivity model is often used to understand the patterns of cortisol. ---- * (Dickerson & Kemeny, 2004; Adam, 2006; Adam, Hawkley, Kudielka, & Cacioppo, 2006; Adam, Klimes-Dougan, & Gunnar, 2007; Evans et al., in press)

98. Exposure-Reactivity Model* 98 According to the exposure-reactivity model that is adapted from Almeida (2005), daily well-being differs by individual characteristics as well as contextual factors that occur on a day-to-day basis. Exposure refers to the number of daily stressors experienced. And reactivity refers to how stressful the individual appraises the situation to be. This study focused on age and neuroticism because both are associated with variations in daily stress. Neuroticism is a personality trait that is characterized by negative emotions, worry, and irrational thinking (Eysenck, & Eysenck, 1985; Portella et al., 2005; Costa & McCrae, 1992 as cited in Hutchinson & Williams, 2007). Previous research has found that as people age, they are better at regulating their emotions and may deal with stress better (Mroczek & Almeida, 2004). Older people report experiencing fewer numbers of stressors and appraise them as less stressful. Prior research has linked neuroticism to greater exposures to and higher appraisals of stress. Accordingly, the purpose of this study is to explore whether a similar pattern occurs with cortisol in relation to age and neuroticism. According to the exposure-reactivity model that is adapted from Almeida (2005), daily well-being differs by individual characteristics as well as contextual factors that occur on a day-to-day basis. Exposure refers to the number of daily stressors experienced. And reactivity refers to how stressful the individual appraises the situation to be. This study focused on age and neuroticism because both are associated with variations in daily stress. Neuroticism is a personality trait that is characterized by negative emotions, worry, and irrational thinking (Eysenck, & Eysenck, 1985; Portella et al., 2005; Costa & McCrae, 1992 as cited in Hutchinson & Williams, 2007). Previous research has found that as people age, they are better at regulating their emotions and may deal with stress better (Mroczek & Almeida, 2004). Older people report experiencing fewer numbers of stressors and appraise them as less stressful. Prior research has linked neuroticism to greater exposures to and higher appraisals of stress. Accordingly, the purpose of this study is to explore whether a similar pattern occurs with cortisol in relation to age and neuroticism.

99. Previous Research Cortisol in older people� After exposed to stressor, decreased ability to return to baseline Higher cortisol concentration Less variation in cortisol level over the day Cortisol in people who are higher in neuroticism� Higher cortisol level Age, neuroticism, stress, and well-being Stronger association between daily stress and negative affect among highly neurotic older adults 99 Only limited research has been done on cortisol in relation to age and neuroticism. Past research has found that after an exposure to stressor, the ability for cortisol level to return to baseline decreases as people age (Sapolsky, 2004, p. 244). Additionally, cortisol pattern may alter as people age, in which older people have higher cortisol concentrations and there is less variation in their cortisol levels over the day* In terms of neuroticism, past research has found that individuals who are high in neuroticism were more likely to have higher levels of cortisol (Portella et al., 2005). A study of daily stress and self-reported negative affect found that negative implications of stress on well-being vary by age and neuroticism with stronger association between daily stress and negative affect among highly neurotic older adults (Mroczek & Almeida, 2004). However, it is not well-understood whether the same is true for cortisol. In addition, most studies of stress and cortisol have been conducted used a small sample with a smaller age range. Accordingly, this study attempts to extend these findings using a large, national daily diary study. --- * (Adam et al., 2006, p. 17061; Raff et al., 1999; Touitou et al., 1982; Maes et al., 1994; Nicholson et al., 1997; Copinschi and Van-Cuter; 1995; Jensen and Blichert-Toft, 1971 as cited in Simpson et al., 2007). Only limited research has been done on cortisol in relation to age and neuroticism. Past research has found that after an exposure to stressor, the ability for cortisol level to return to baseline decreases as people age (Sapolsky, 2004, p. 244). Additionally, cortisol pattern may alter as people age, in which older people have higher cortisol concentrations and there is less variation in their cortisol levels over the day* In terms of neuroticism, past research has found that individuals who are high in neuroticism were more likely to have higher levels of cortisol (Portella et al., 2005). A study of daily stress and self-reported negative affect found that negative implications of stress on well-being vary by age and neuroticism with stronger association between daily stress and negative affect among highly neurotic older adults (Mroczek & Almeida, 2004). However, it is not well-understood whether the same is true for cortisol. In addition, most studies of stress and cortisol have been conducted used a small sample with a smaller age range. Accordingly, this study attempts to extend these findings using a large, national daily diary study. --- * (Adam et al., 2006, p. 17061; Raff et al., 1999; Touitou et al., 1982; Maes et al., 1994; Nicholson et al., 1997; Copinschi and Van-Cuter; 1995; Jensen and Blichert-Toft, 1971 as cited in Simpson et al., 2007).

100. Research Questions 100 The present study examined: Whether the experience of daily stress (exposure & appraisal) and cortisol vary by age H1: I expect age to be associated with lower daily stress and cortisol Whether the experience of daily stress (exposure & appraisal) and cortisol vary by neuroticism H2: I expect neuroticism to be associated with greater daily stress and cortisol Whether associations between daily stress and cortisol vary by age and neuroticism H3: I expect the associations between daily stress and cortisol will be greater among younger people and people with higher neuroticismThe present study examined: Whether the experience of daily stress (exposure & appraisal) and cortisol vary by age H1: I expect age to be associated with lower daily stress and cortisol Whether the experience of daily stress (exposure & appraisal) and cortisol vary by neuroticism H2: I expect neuroticism to be associated with greater daily stress and cortisol Whether associations between daily stress and cortisol vary by age and neuroticism H3: I expect the associations between daily stress and cortisol will be greater among younger people and people with higher neuroticism

101. National Study of Daily Experiences II* 101 Participants were from the 2nd wave of a longitudinal study called the National Study of Daily Experiences (NSDE). MIDUS I (1994-1995); MIDUS II (2003-2004) NSDE was conducted as part of the Midlife Development in the United States survey, which sought to �investigate [various] factors in understanding age-related differences in physical and mental health� (MIDUS website). � There were a total of 1265 participants who ranged in age from 33 to 84 and over half of them were females. The mean of highest level of education completed is around �3 OR MORE YEARS OF COLLEGE, NO DEGREE YET.� Participants reported their experiences of daily stressors and their appraisals of how stressful these experiences were via telephone interviews over 8 consecutive days. For 4 of the days, they provided saliva samples 4x/day (at wakeup, 30 min after wakeup, lunchtime, and bedtime). Participants were from the 2nd wave of a longitudinal study called the National Study of Daily Experiences (NSDE). MIDUS I (1994-1995); MIDUS II (2003-2004) NSDE was conducted as part of the Midlife Development in the United States survey, which sought to �investigate [various] factors in understanding age-related differences in physical and mental health� (MIDUS website). � There were a total of 1265 participants who ranged in age from 33 to 84 and over half of them were females. The mean of highest level of education completed is around �3 OR MORE YEARS OF COLLEGE, NO DEGREE YET.� Participants reported their experiences of daily stressors and their appraisals of how stressful these experiences were via telephone interviews over 8 consecutive days. For 4 of the days, they provided saliva samples 4x/day (at wakeup, 30 min after wakeup, lunchtime, and bedtime).

102. Measures Predictors Age Neuroticism (4 items; moody, worrying, nervous, calm*) Outcomes Stress exposure: number of daily stressors (e.g., argument) Stress appraisal: rating of stress from 0 (none at all) to 3 (very) Cortisol: morning rise & area under the curve (AUC) Control variables Gender Highest level of education completed Self-reported physical health 102 Predictors included: AGE and NEUROTICISM For neuroticism, 4 items were asked that taps into how much moodiness, worrying, nervousness, and calmness describe the individuals Outcomes included: stress exposure, stress appraisal, and cortisol Stress exposure is the number of daily stressors that an individual encounters in terms of arguments, at work/school, at home, etc. Stress appraisal is how stressful an individual rated the events to be Cortisol (morning rise & AUC) Control variables included: gender, education, and health Predictors included: AGE and NEUROTICISM For neuroticism, 4 items were asked that taps into how much moodiness, worrying, nervousness, and calmness describe the individuals Outcomes included: stress exposure, stress appraisal, and cortisol Stress exposure is the number of daily stressors that an individual encounters in terms of arguments, at work/school, at home, etc. Stress appraisal is how stressful an individual rated the events to be Cortisol (morning rise & AUC) Control variables included: gender, education, and health

103. Does the experience of daily stress and cortisol vary by age? * 103 - First, using multiple regression models I examined whether the experience of daily stress and cortisol varied by age. This graph shows the associations between age and the daily stress and cortisol variables. - As you can see here, older individuals reported experiencing fewer daily stressors, appraised the events as less stressful, but have higher morning rise and area under the curve (AUC) cortisol scores. - First, using multiple regression models I examined whether the experience of daily stress and cortisol varied by age. This graph shows the associations between age and the daily stress and cortisol variables. - As you can see here, older individuals reported experiencing fewer daily stressors, appraised the events as less stressful, but have higher morning rise and area under the curve (AUC) cortisol scores.

104. Does the experience of daily stress and cortisol vary by neuroticism? * 104 - Next, I explained whether the experience of daily stress and cortisol vary by neuroticism. This graph shows the associations between neuroticism and the daily stress and cortisol variables. As you can see here, more neurotic individuals reported experiencing more daily stressors, appraised the events as more stressful, but there were no significant associations with cortisol scores. - Next, I explained whether the experience of daily stress and cortisol vary by neuroticism. This graph shows the associations between neuroticism and the daily stress and cortisol variables. As you can see here, more neurotic individuals reported experiencing more daily stressors, appraised the events as more stressful, but there were no significant associations with cortisol scores.

105. Do associations between stress exposure and morning rise vary by age?? 105 ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** Next, I examined whether the links between self-reported stress and cortisol varied by age. Among younger individuals, greater exposures to daily stressors was associated with higher morning rise cortisol scores. The association among older individuals was in the same direction, but more moderate. ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** Next, I examined whether the links between self-reported stress and cortisol varied by age. Among younger individuals, greater exposures to daily stressors was associated with higher morning rise cortisol scores. The association among older individuals was in the same direction, but more moderate.

106. Do associations between stress appraisal and AUC vary by age?? 106 ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** - Among younger individuals, higher appraisals of stress was associated with higher AUC cortisol scores, whereas the opposite association occurred among older individuals. ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** - Among younger individuals, higher appraisals of stress was associated with higher AUC cortisol scores, whereas the opposite association occurred among older individuals.

107. Do associations between stress exposure and morning rise vary by neuroticism?? 107 ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** - Next, I examined whether the links between self-reported stress and cortisol varied by neuroticism. Among individuals with higher neuroticism scores, greater exposures to daily stressors was associated with higher morning rise cortisol scores. The association among individuals with lower neuroticism scores was in the same direction, but more moderate. ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** - Next, I examined whether the links between self-reported stress and cortisol varied by neuroticism. Among individuals with higher neuroticism scores, greater exposures to daily stressors was associated with higher morning rise cortisol scores. The association among individuals with lower neuroticism scores was in the same direction, but more moderate.

108. Do associations between stress appraisal and morning rise vary by neuroticism?? 108 ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** - Among individuals with higher neuroticism scores, higher appraisals of stress was associated with higher morning rise cortisol scores, whereas the opposite associations occurred among individuals with lower neuroticism scores. ***Controlled for: stress exposure, stress appraisal, neuroticism, age, gender, education, and health*** - Among individuals with higher neuroticism scores, higher appraisals of stress was associated with higher morning rise cortisol scores, whereas the opposite associations occurred among individuals with lower neuroticism scores.

109. Summary and Implications Older ages�. Lower self-reported daily stress Higher cortisol Cortisol less sensitive to daily stress More neurotic individuals�. Greater self-reported daily stress No association with cortisol Cortisol more sensitive to stress Cortisol may have different interpretations (e.g., among some individuals, higher stress linked with lower cortisol) 109 *** 3 sub-bullet describes interactions*** Consistent with the exposure-reactivity model, older people experience less self-reported daily stress. Older people had higher cortisol, but appeared less sensitive to stress. Also consistent with the exposure-reactivity model, more neurotic individuals experience more self-reported daily stress. There was no association between neuroticism and cortisol, but neurotic individuals� cortisol levels appeared more sensitive to stress (stronger links between daily stress and cortisol) - Overall, findings support the exposure-reactivity model, but indicate associations among self-reports of stress and biological indicator of stress vary by age and personality. Since cortisol patterns revealed more complicated associations, cortisol may have different interpretations depending on individual differences. *** 3 sub-bullet describes interactions*** Consistent with the exposure-reactivity model, older people experience less self-reported daily stress. Older people had higher cortisol, but appeared less sensitive to stress. Also consistent with the exposure-reactivity model, more neurotic individuals experience more self-reported daily stress. There was no association between neuroticism and cortisol, but neurotic individuals� cortisol levels appeared more sensitive to stress (stronger links between daily stress and cortisol) - Overall, findings support the exposure-reactivity model, but indicate associations among self-reports of stress and biological indicator of stress vary by age and personality. Since cortisol patterns revealed more complicated associations, cortisol may have different interpretations depending on individual differences.

110. Future Research Within-person examination of daily stress processes Other personality traits Mental health Interpersonal relationships 110 I will use multilevel modeling to examine within-person variation of daily stress processes. I will examine other personality traits such as extraversion and agreeableness I am also interested in examining mental health such as the experience of depression to understand whether they also predict experiences of daily stress and cortisol. Interpersonal relationships- research shows that daily interpersonal tensions are more highly associated with well-being than other stressors. Thus, we would like to conduct a more focused analysis to examine whether people�s reactions to relationship problems vary by age and neuroticism. I will use multilevel modeling to examine within-person variation of daily stress processes. I will examine other personality traits such as extraversion and agreeableness I am also interested in examining mental health such as the experience of depression to understand whether they also predict experiences of daily stress and cortisol. Interpersonal relationships- research shows that daily interpersonal tensions are more highly associated with well-being than other stressors. Thus, we would like to conduct a more focused analysis to examine whether people�s reactions to relationship problems vary by age and neuroticism.

111. 111

112. Teen Fathers: Involvement with Child and Mental Health Outcomes Chlo� Gurin-Sands Cleopatra Caldwell, Ph.D. University of Michigan School of Public Health University of Michigan Jacqueline Smith, Ph.D. ISR, University of Michigan 112

113. Overview of Presentation Background Research Questions Methods Sample Measures Findings Conclusion Implications 113

114. Background Teen parent literature is centered around the mother. Father involvement is of growing interest, and multiple dimensions have been identified (Lamb 1986). Literature that includes the father focuses on his affect on the mother and child (Kalil 2004). Very few studies examine fathers� mental health in connection with parenthood. 114 Literature that does focus on parenthood and involvement from the father�s point of view (that does focus on �psychological and emotional aspects of fathering�) is mostly about the father�s understanding of the Role of a father, and his commitment to various aspects of fatherhood. Not about his actual psychological well-being. (Coley & Hernandez 2006).Literature that does focus on parenthood and involvement from the father�s point of view (that does focus on �psychological and emotional aspects of fathering�) is mostly about the father�s understanding of the Role of a father, and his commitment to various aspects of fatherhood. Not about his actual psychological well-being. (Coley & Hernandez 2006).

115. Research Question & Hypotheses Is there a relationship between involvement with his child and psychological well-being, among teenage fathers? Hypotheses Fathers who provide financial support will have better psychological well-being Fathers who have more contact with child will have better psychological well-being Fathers concerned about being a good father will have worse psychological well-being Fathers emotionally satisfied with their role in fatherhood will have better psychological well-being. 115

116. Dataset & Sample Teen Dad�s Study Longitudinal Data taken 6 weeks after birth N= 64 African American First-time fathers Average age: 18 years (sd: 1.38) 66% unemployed Average household income: ~$15,000-$20,000 Average education: ~11th grade (sd: 1.19) 116 Survey taken 4 times: 6 weeks before birth, 6 weeks after birth, 6 months after birth, and 1 year after birthSurvey taken 4 times: 6 weeks before birth, 6 weeks after birth, 6 months after birth, and 1 year after birth

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118. Measures Mental Health Outcomes CES-D* (a .855) Rosenberg Self Esteem Scale (a .748) Life Satisfaction Scale (single-item) Predictor Variables Financial Commitment (responsibility) Contact (interaction) Worry about parenting (availability) Satisfaction with Involvement Scale (a .690) (availability) Control Variables Household Income Education 118 CES-D: 20 measure scale. Self-Esteem: 9 measure scale, reversed for positivity. Satisfaction with Involvement Scale: 3 measure scale, satisfaction with being a father, satisfaction with amount of financial help give baby, satisfaction with current amount of involvement with baby Responsibility � welfare of the child Interaction � physical contact with child Availability � emotional ability to relate to child / fatherhoodCES-D: 20 measure scale. Self-Esteem: 9 measure scale, reversed for positivity. Satisfaction with Involvement Scale: 3 measure scale, satisfaction with being a father, satisfaction with amount of financial help give baby, satisfaction with current amount of involvement with baby Responsibility � welfare of the child Interaction � physical contact with child Availability � emotional ability to relate to child / fatherhood

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120. Multiple Regression Analysis Summary1 120

121. Conclusions Emotional involvement is associated with fathers� psychological well-being. Satisfaction with involvement is consistently related to all three outcomes, positive connotation. Worry is only related to CES-D and Self-Esteem, negative connotation. In this population, socio-economic status factors are not associated with mental health outcomes. 121

122. Policy Implications Further research on teen fathers � for teen fathers. Fatherhood policies that go beyond financial responsibilities. Programs for teen fathers. 122

123. Acknowledgements Dr. Cleo Caldwell Dr. Jacqui Smith George Myers Anita Johnson SRC Interns Ama, Katie, Aneesa, Lindsay, Ryan, Frank, Jennifer, Moyra, and Tim 123

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