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I'm confused about which predictor should have the "0" and which should have the "1" when using dummy codes for regression. For example:

Y: time spent at current job X: type of assessment --> interview (I), competency exam (C), written test (W)

Question: a researcher is interested in comparing differences in time spent at current job (Y) between those given an interview (X1) and those given competency exams (X2).

I designed my table like this:

1. New variable 1: I --> +1, C --> 0, W --> 0 (Because I am comparing competency individually to interview)
2. New variable 2: I --> 0, C --> 0, W --> +1 (Because I am comparing competency individually to written)

However, these new variables are not orthogonal. Is that OK? (I'm not sure I understand what the point of variables being orthogonal is. I'm so lost. Also when do I use dummy coding instead of contrast coding? Is there a hard and fast rule for when to default to either?

I'm confused about which predictor should have the "0" and which should have the "1" when using dummy codes for regression. For example:

Y: time spent at current job 
X: type of assessment --> interview (I), competency exam (C), written test (W)

Question: a researcher is interested in comparing differences in time spent at a current job (Y) between those given an interview (X1) and those given competency exams (X2).

I designed my table like this:

1. New variable 1: I --> +1, C --> 0, W --> 0 (Because I am comparing competency individually to interview)
2. New variable 2: I --> 0, C --> 0, W --> +1 (Because I am comparing competency individually to written)

However, these new variables are not orthogonal. Is that OK? (I'm not sure I understand what the point of variables being orthogonal is. I'm so lost. Also, when do I use dummy coding instead of contrast coding? Is there a hard and fast rule for when to default to either?

I'm confused about which predictor should have the "O" and which should have the "1" when using dummy codes for regression. For example:

Y: time spent at current job X: type of assessment --> interview (I), competency exam (C), written test (W)

Question: a researcher is interested in comparing differences in time spent at current job (Y) between those given an interview (X1) and those given competency exams (X2).

I designed my table like this:

1. New variable 1: I --> +1, C --> 0, W --> 0 (Because I am comparing competency individually to interview)
2. New variable 2: I --> 0, C --> 0, W --> +1 (Because I am comparing competency individually to written)

However, these new variables are not orthogonal. Is that OK? (I'm not sure I understand what the point of variables being orthogonal is. I'm so incredibly lost. Also when do I use dummy coding instead of contrast coding? Is there a hard and fast rule for when to default to either?

Any clarification on this is greatly, greatly appreciated!

I'm confused about which predictor should have the "0" and which should have the "1" when using dummy codes for regression. For example:

Y: time spent at current job X: type of assessment --> interview (I), competency exam (C), written test (W)

Question: a researcher is interested in comparing differences in time spent at current job (Y) between those given an interview (X1) and those given competency exams (X2).

I designed my table like this:

1. New variable 1: I --> +1, C --> 0, W --> 0 (Because I am comparing competency individually to interview)
2. New variable 2: I --> 0, C --> 0, W --> +1 (Because I am comparing competency individually to written)

However, these new variables are not orthogonal. Is that OK? (I'm not sure I understand what the point of variables being orthogonal is. I'm so lost. Also when do I use dummy coding instead of contrast coding? Is there a hard and fast rule for when to default to either?