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IAT and Ageism Becky Nixon and Alexis Rose Hanover College Fall 2005.

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Presentation on theme: "IAT and Ageism Becky Nixon and Alexis Rose Hanover College Fall 2005."— Presentation transcript:

1 IAT and Ageism Becky Nixon and Alexis Rose Hanover College Fall 2005

2 Previous Research IAT IAT Greenwald and McGhee (1998) Greenwald and McGhee (1998) –found black to be negative and white to be positive (white participants) Ageism Ageism Hought (2002) Hought (2002) –used ASD on first-year and third-year med students –interest in geriatrics decreased

3 Hypothesis We predict that our participants already have implicit associations towards the elderly; therefore an old face paired with a positive word will have a slower reaction time than an old face paired with a negative word. We predict that our participants already have implicit associations towards the elderly; therefore an old face paired with a positive word will have a slower reaction time than an old face paired with a negative word.

4 Methods Participants: Participants: –13 participants: all intro psych volunteers  12 female, 1 male  All Caucasian  Ages Materials: Materials: –Gateway Computer, LCD monitors –Internet Explorer, Java

5 Design Condition 1: Condition 1: –Positive words press “d,” negative words press “k” Condition 2: Condition 2: –Old faces press “k,” young faces press “d” Condition 3: Condition 3: –Young faces/positive words “d” –Old faces/negative words “k” Condition 4: Condition 4: –Old faces press “d,” young faces press “k” Condition 5: Condition 5: –Young faces/negative words “d” –Old faces/positive words “k”

6 FACES

7 WORDS Positive Positive –WONDERFUL –LIBERATION –LAUGHTER Negative Negative –LOAD –SUPRESSION –HAMPER

8 Accuracy The mean of young faces and negative words paired was percent, (SD = 20.06), the mean of young faces and positive words was percent, (SD = 10.90), t(12) = 13.21, p <.001 The mean of young faces and negative words paired was percent, (SD = 20.06), the mean of young faces and positive words was percent, (SD = 10.90), t(12) = 13.21, p <.001 The mean of old faces and negative words was percent, (SD = 14.75), the mean of old faces and positive was percent, (SD = 25.61), t(12) = 8.01, p <.001 The mean of old faces and negative words was percent, (SD = 14.75), the mean of old faces and positive was percent, (SD = 25.61), t(12) = 8.01, p <.001

9 Reaction Time The mean of young faces and negative words was ms, (SD = ), the mean of young faces and positive words was ms, (SD = ), t(12) = 1017, p =.265 The mean of young faces and negative words was ms, (SD = ), the mean of young faces and positive words was ms, (SD = ), t(12) = 1017, p =.265 The mean of old faces and positive was ms, (SD = ), the mean of old faces and negative words was ms, (SD = ), t(12) = 2.39, p =.035 The mean of old faces and positive was ms, (SD = ), the mean of old faces and negative words was ms, (SD = ), t(12) = 2.39, p =.035

10 Discussion Baby-boomers retiring soon Baby-boomers retiring soon –Doctors aren’t interested in this field, when needed the most  Not enough to meet growing geriatric needs  Elderly viewed as burden –Social Security running out  Baby-boomers will be seen as a burden if not modified

11 Limitations Ugly faces Ugly faces –One participant reported she felt negatively towards all faces because of appearance Confused participants Confused participants –Forgot which letter was paired with which face/word –More detailed directions –Difficult words (e.g. saddle, encumber, emancipation) Square problem-wrong answers, more data needed Square problem-wrong answers, more data needed

12 Future Research Control for conditions with attractive faces Control for conditions with attractive faces Attractive elderly vs. sick elderly appearance Attractive elderly vs. sick elderly appearance Change in Social Security…effect on ageism? Change in Social Security…effect on ageism?


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