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11-11-2017 05:27 PM - edited 11-11-2017 06:23 PM

Hello everyone,

I'm modeling revenues of different stores with/without marketing in a linear mixed model. The stores vary in size and location. The data is for 5 years in a strongly balanced panel structure. Could you please help me interpret these results?

In the Solution for fixed effects, I get:

Effect | Geo | Marketing | Estimate | Standard Error | DF | t Value | Pr > |t| |

Marketing | 0 | 2510887 | 117028 | 4354 | 21.46 | <.0001 | |

Marketing | 1 | 2739612 | 106464 | 4354 | 25.73 | <.0001 |

In the LS-Means table I get:

Effect | Geo | Marketing | Estimate | Standard Error | DF | t Value | Pr > |t| |

Marketing | 0 | 1083537 | 24129 | 4354 | 44.91 | <.0001 | |

Marketing | 1 | 1252747 | 21511 | 4354 | 58.24 | <.0001 |

So, in the former, there's a 9% increase in revenues with marketing, while in the latter it's a 15 % increase.

To add to my confusion, when I plot the simple arithmetic mean for every year, the difference between the marketing and no-marketing groups is way higher than the 9% or 15% figures.

My question is:

1- Why are the coefficients and the ls-means different?

2- Why are both of them very different than the impression from graphing the annual simple arithmetic means?

3- Finally, Which of these results I should include in my conclusion?

Thank you in advance.

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Posted in reply to mragaa

11-12-2017 03:44 PM

You haven't provided enough information. Refer to

At a minimum, please show your MIXED code and an example of your data set.

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11-12-2017 05:21 PM

And we also need to see all of the parameter estimates from MIXED (including the intercept).

--

Paige Miller

Paige Miller

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Posted in reply to mragaa

02-10-2018 04:42 AM

Greetings,

I'm writing to thank you all for paying attention to my inquiry and to apologize for I couldn't share the data as requested for its confidentiality. It took a while to get back to you because I have been unsuccessfully trying to take the permission for sharing a de-identified sample.

Thank you for your understanding.

Respectfully.

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Posted in reply to mragaa

02-12-2018 09:28 AM

No one asked to see your real data. The request was to see your PROC MIXED statement and, to help understand the code, an EXAMPLE of the structure of your data. Feel free to make up the data so that no sensitive information is revealed. For example, the revenue for the stores can be 1, 2, 3, ... Often we can infer the structure of the data, so the most important information is the PROC MIXED code.

Basically, the answer to your question is that the statistics mean different things. The parameter estimates are the coefficients of the fixed effects. LSMEANS are more complicated, but you can Google the SAS documentation or conference papers to find many examples that interpret LSMEANS. Two papers I like are

Dickey (2010: https://www.lexjansen.com/mwsug/2010/stats/MWSUG-2010-108.pdf

and

Kiernan et al (2011): https://support.sas.com/resources/papers/proceedings11/351-2011.pdf

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Posted in reply to Rick_SAS

02-12-2018 05:41 PM

Even just the code would be helpful. What kept me (and possibly @PaigeMiller) from being able to provide an answer to the initial post is that the "Solution for fixed effects" table does not show one of the Marketing level estimates being set to zero. Possibly this is a no-intercept model, but then I would expect the values in the two tables to match. So I was puzzled then, and still am.