Examining change using regression analysis: Three approaches compared

Dalecki, Michael G.; Willits, Fern K.

doi:10.1080/02732173.1991.9981960

Cited by 110 publications

(78 citation statements)

References 7 publications

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“…Also, we sought to identify predictive relationships between the changes in technology integration self-efficacy and the changes in intentions. According to Dalecki and Willits (1991), studying change (i.e., difference) scores is a viable way to assess the impact of varying events on a desired outcome. In the current study, if a significant predictive relationship was found between changes in self-efficacy and changes in intentions, that could build a case for continued research into using authentic learning exercises to improve preservice teachers' self-efficacy.…”

Section: Purposementioning

confidence: 99%

“…Change in technology integration self-efficacy was measured as change in perceived technological knowledge (TK), pedagogical knowledge (PK), pedagogical content knowledge (PCK), technological pedagogical knowledge (TPK), and technological pedagogical and content knowledge (TPACK) from pre-to postassessment, as were changes in intentions. Again, the value in studying change (i.e., difference) scores is to assess the impact of events on a desired outcome (Dalecki & Willits, 1991). In the current study, we sought to find a positive relationship between improved technology integration self-efficacy and changes in intentions to integrate technology.…”

Section: Relationship Between Change In Technology Integration Self-ementioning

confidence: 99%

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Authentic learning exercises as a means to influence preservice teachers’ technology integration self-efficacy and intentions to integrate technology

Banas

York

2014

AJET

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This study explored the impact of authentic learning exercises, as an instructional strategy, on preservice teachers' technology integration self-efficacy and intentions to integrate technology. Also explored was the predictive relationship between change in preservice teachers' technology integration self-efficacy and change in intentions to integrate technology. Participants included 104 preservice teachers enrolled into a professional preparation methods course. Technology integration self-efficacy was measured as perceived technological knowledge (TK), pedagogical knowledge (PK), technological pedagogical knowledge (TPK), pedagogical content knowledge (PCK), and technological pedagogical content knowledge (TPACK). A paired samples t-test revealed a significant increase in self-efficacy for all TPACK constructs, as well as intentions to integrate technology. The effect was greatest for PCK. Also, multiple regression analysis revealed change in technology integration self-efficacy as a model, predicted change in intentions to integrate. Particularly change in perceived technology knowledge, predicted change in intentions. Implications for professional preparation programs are shared.

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Section: Purposementioning

confidence: 99%

Section: Relationship Between Change In Technology Integration Self-ementioning

confidence: 99%

Authentic learning exercises as a means to influence preservice teachers’ technology integration self-efficacy and intentions to integrate technology

Banas

York

2014

AJET

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“…Next, T 1 and T 2 indicators were computed as a daily average in order to standardize the differences across the T 1 reporting interval (past 30 days) and the T 2 reporting interval (number of days since arrival at the SB site). We conducted a set of ordinary least square (OLS) regression analyses to identify potential predictors of risk behaviors during T 2 (as outlined by Allison (1990) and Dalecki and Willits (1991)). For this set of analyses, the average daily occurrence of risk behaviors among each respondent during T 2 was the dependent variable while controlling for: (1) the average number of days since the respondent had arrived at the SB site (as a measure of opportunity to participate in risk behaviors) and (2) the average daily occurrence of risk behaviors among each respondent during T 1 .…”

Section: Analytic Proceduresmentioning

confidence: 99%

High-Risk Drinking Among Female College Drinkers at Two Reporting Intervals: Comparing Spring Break to the 30 Days Prior

et al. 2011

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Alcohol use has increased among college women in the past decade, and although event-specific high-risk drinking is common among college students, few studies have focused on Spring Break-specific high-risk drinking and other risk behaviors among female college drinkers. As such, we compared substance use (e.g., alcohol and marijuana use) and engagement in other risk behaviors (e.g., riding with an impaired driver) in the 30 days prior (T 1 ) and during Spring Break (T 2 ) in a sample of 86 female college drinkers (mean age=19.9, SD=1.3, 18-23 years). Participants were recruited on a public beach during their Spring Break vacation and were administered a survey that included the TWEAK, a brief screener to identify alcohol use disorder (AUD) risk. Initial results indicated that heavy alcohol use, riding with an alcohol-impaired driver, and marijuana use were high during T 1 and increased significantly during T 2 . The TWEAK results indicated that 78% of respondents were at high AUD risk, yet only 25% felt the need to reduce their alcohol consumption. OLS regression results indicated significant relationships between changes in engagement in risk behaviors at T 2 as compared to T 1 across a number of sample characteristics and TWEAK indicators (e.g., age at first drink; age at first sex). Implications for prevention programming targeting female college drinkers are discussed.Keywords Spring Break . Female college drinkers . High-risk drinking . TWEAK High-risk drinking (e.g., heavy episodic drinking or "binge drinking") is prevalent among US college students and is associated with a variety of adverse behaviors such as

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“…Using a change score as a dependent variable in regression models has generated some debate (Allison 1990;Bohrnstedt 1969;Cronbach & Furby 1970;Dalecki & Willits 1991;Kessler 1977). Econometricians argue that raw change scores (i.e., T 2 )T 1 ) are insufficient because of their low reliability and correlation to T 1 , and they propose using either residualized change score models or not using change scores at all (Bohrnstedt 1969;Cronbach & Furby 1970;Kessler 1977).…”

Section: Analysis and Strategymentioning

confidence: 99%

The Consequences of Caregiving: Effects on Women’s Employment and Earnings

Wakabayashi

Donato

2005

Popul Res Policy Rev

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Caregiving to family members comprises a major part of familial obligations in the United States. Informal caregiving is unevenly distributed in society, with women performing most of the work and bearing the burden of its costs. This paper addresses the cost dimension of informal caregiving to family members by examining whether and how it penalizes women's employment. Drawing data from the 1987 and 1992 National Survey of Families and Households, we examine whether and how caregiving transitions affect changes in women's labor force participation and the implications of this caregiving transitions for their earnings. We calculate how these effects vary for demographically different groups of women: those older and younger, with and without high levels of education, and married and not married. Our findings reveal that for most women, the initiation of caregiving led to a substantial reduction in their weekly hours worked and annual earnings. However, the effects were different for various subgroups of women: those older, with fewer skills, and more competing roles paid substantial costs if they began caregiving between 1987 and 1992.

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Examining change using regression analysis: Three approaches compared

Cited by 110 publications

References 7 publications

Authentic learning exercises as a means to influence preservice teachers’ technology integration self-efficacy and intentions to integrate technology

Authentic learning exercises as a means to influence preservice teachers’ technology integration self-efficacy and intentions to integrate technology

High-Risk Drinking Among Female College Drinkers at Two Reporting Intervals: Comparing Spring Break to the 30 Days Prior

The Consequences of Caregiving: Effects on Women’s Employment and Earnings

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