# Simple effects in mixed design with R [closed]

My experiment consists of two factors, one between and one within subject. Time is the between-subjects factor and hold two levels: (low and high). Times is the within-subject factors and hold three levels: 1, 4, 5. dv is my dependent variable, and id an identifier for each participant. I attached the data for the 6 first participants.

Using R, I ran an ANOVA that yielded significant results for both factors. I have two planned contrasts:

1) In time 1, I want to compare the two age groups (low vs high). That is, a between subject comparison.

2) In age low, I want to compare Time 1 and Time 5. That is, a within subject comparison.

Of course I could perform t-tests, yet it seems not appropriate as I can base my standard error estimate on more cells here. My question is how can I perform the above contrasts?

Here is my model using aov()

model= aov(dv ~ age*time + Error(id/time),
data = Data)
summary(model)


And here is the data

Data = structure(list(id = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "5", "6",
"7", "8", "11", "12", "13", "15", "17", "18", "19", "20", "21",
"22", "23", "24", "25", "27", "28", "29", "31", "32", "34", "35",
"36", "37", "38", "39", "40", "42", "43", "44", "45", "46", "47",
"48", "49", "52", "53", "54", "55", "56", "58", "59", "60", "62",
"63", "64", "66", "67", "68", "69", "70", "71", "72", "73", "74",
"75", "77", "79", "80", "81", "83", "84", "85", "86", "87", "88",
"89", "90", "91", "92", "93", "94", "96", "97", "98", "99", "100",
"101", "102", "103", "104", "105", "106", "107", "108", "109",
"110", "111", "112", "113", "114", "115", "116", "117", "118",
"119", "120", "121", "122", "123", "124", "125", "126", "127",
"128", "129", "130", "132", "133", "134", "135", "136", "137",
"138", "139", "140", "142", "143", "144", "145", "146", "147",
"148", "149", "150", "151", "152", "153", "154", "156", "157",
"158", "159", "160", "161", "162", "163", "165", "166", "167",
"168", "169", "171", "172", "174", "175", "176", "177", "178",
"179", "180", "181", "182", "183", "184", "185", "186", "187",
"188", "189", "190", "191", "192", "193", "194", "195", "196",
"200", "201", "202", "203", "204", "205", "206", "208", "209",
"210", "212", "213", "214", "215", "216", "217", "218", "219",
"220", "222", "223", "224", "226", "228", "230", "231", "232",
"233", "234", "236", "237", "238", "239", "240", "241", "242",
"243", "244", "246", "247", "248", "249", "250", "251", "252",
"253", "254", "255", "256", "257", "258", "260", "261", "262",
"263", "266", "267", "269", "270", "271", "272", "273", "274",
"275", "276", "277", "278", "279", "280", "281", "282", "283",
"284", "285", "286", "287", "288", "289", "290", "291", "292",
"293", "294", "295", "296", "298", "299", "300"), class = "factor"),
age = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("high", "low"), class = "factor"),
time = structure(c(3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 2L,
1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 3L,
3L, 2L, 2L, 1L, 1L), .Label = c("1", "4", "5"), class = "factor"),
dv = c(104, 102, 104, 103, 104, 104, 102, 102, 102, 102,
106, 106, 106, 106, 107, 107, 106, 106, 106, 107, 105, 104,
106, 107, 104, 101, 104, 101, 104, 106)), row.names = c(NA,
-30L), class = c("tbl_df", "tbl", "data.frame"))


## closed as off-topic by Stefan, Michael Chernick, Peter Flom♦Jan 24 at 10:12

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• Did your model include an interaction between age and time? Also, can you share the command you used to assess the significance of age and time? – Isabella Ghement Jan 23 at 16:05
• I just added the code. The model included the interaction. – Rtist Jan 23 at 16:28