Good evening,

I merged m:1 a BP dataset with BP and heart rate variables with a demographic dataset with age, gender, and education variables (code below). Afterward, in my new merged file, I have repeated observations of the age, gender, and education variables as shown in the data example below. I would like to know what I am doing wrong in this merge process so I can fix it such that I (1) do not have repeated observations of the demographic variables or (2) figure out a way to examine my data as in a Table 1 or analyze my data e.g. in mixed-level models such that I am not using the repeated observations of age and gender in my analysis. Should I have done it the other way round and merged 1:m with the BP dataset being the using file.

Here is the code I used in merging:

Code:
use  "/Volumes/Datasets/BP Data/BP.dta" 
merge m:1 id visit using "/Volumes/Datasets/Demographic Data/Demo.dta"
drop _merge
save "/Volumes/Datasets/Merged Datasets/Demo_BP_merged.dta", replace

Here is the sample of the merged dataset:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long id byte age str6 gender float education int bp float map int hr double(time datetime) float visit
1 61 "Female" 1 113    88  60          82800000      1.8498204e+12 0
1 61 "Female" 1 129    91  78          32400000        1.84977e+12 0
1 61 "Female" 1  84    61  69          68400000       1.849806e+12 0
1 61 "Female" 1 131    95  73          70380000      1849807980000 0
1 61 "Female" 1 104    76  59          12600000      1.8498366e+12 0
1 61 "Female" 1 112    78  69          79200000      1.8498168e+12 0
1 61 "Female" 1 136    99  65          27540000      1849765140000 0
1 61 "Female" 1 117    81  76          54000000      1.8497916e+12 0
1 61 "Female" 1 117    93  61          84600000      1.8498222e+12 0
1 61 "Female" 1 110    79  85          52380000      1849789980000 0
1 61 "Female" 1 123    80  62           7320000      1849831320000 0
1 61 "Female" 1 119    86  81          55800000      1.8497934e+12 0
1 61 "Female" 1  95    68  61          19800000      1.8498438e+12 0
1 61 "Female" 1  99    71  57          18120000      1849842120000 0
1 61 "Female" 1 117    78  73          41400000       1.849779e+12 0
1 61 "Female" 1 125    92  74          66600000      1.8498042e+12 0
1 61 "Female" 1 111    83  58           1800000      1.8498258e+12 0
1 61 "Female" 1 123    86  70          72000000      1.8498096e+12 0
1 61 "Female" 1 112    84  61          16200000      1.8498402e+12 0
1 61 "Female" 1  98    73  60          81000000      1.8498186e+12 0
1 61 "Female" 1 104    83  67          37800000      1.8497754e+12 0
1 61 "Female" 1  98    68  80          45000000      1.8497826e+12 0
1 61 "Female" 1 103    72  69          61200000      1.8497988e+12 0
1 61 "Female" 1 112    50  54          43200000      1.8497808e+12 0
1 61 "Female" 1 105    83 125            180000      1849824180000 0
1 61 "Female" 1 122    94  77          64980000      1849802580000 0
1 61 "Female" 1  93    57  74          73800000      1.8498114e+12 0
1 61 "Female" 1 102    75  57           3600000      1.8498276e+12 0
1 61 "Female" 1  88    65  74          75600000      1.8498132e+12 0
1 61 "Female" 1  96    72  61          77400000       1.849815e+12 0
1 61 "Female" 1  89    68  60           9000000       1.849833e+12 0
1 61 "Female" 1  98    62  77          59400000       1.849797e+12 0
1 61 "Female" 1 101    68  60          10800000      1.8498348e+12 0
1 61 "Female" 1 112    79  63          14400000      1.8498384e+12 0
1 61 "Female" 1 117    83  57           5400000      1.8498294e+12 0
1 61 "Female" 1 110    81  74          57600000      1.8497952e+12 0
1 61 "Female" 1  89    74  71          46980000      1849784580000 0
1  . ""       . 115    86  66 11339999.99975586 1859684939999.9998 1
1  . ""       . 112    86  75          83520000      1859670720000 1
1  . ""       . 112    73  70          85140000      1859672340000 1
1  . ""       . 125    83  76 76139999.99975586 1859663339999.9998 1
1  . ""       . 111    83  65 6120000.000244141 1859679720000.0002 1
1  . ""       . 101    73  64          63540000      1859650740000 1
1  . ""       . 124    91  63          38760000      1859625960000 1
1  . ""       .  81    54  66           4140000      1859677740000 1
1  . ""       . 113    78  72           2340000      1859675940000 1
1  . ""       . 121    93  78          58320000      1859645520000 1
1  . ""       . 103    75  66 16739999.99975586 1859690339999.9998 1
1  . ""       . 116    84  67          50940000      1859638140000 1
1  . ""       . 105    78  71           7740000      1859681340000 1
1  . ""       .  98    70  61 22139999.99975586 1859695739999.9998 1
1  . ""       .  98    63  72 81539999.99975586 1859668739999.9998 1
1  . ""       .  95    61  71          79740000      1859666940000 1
1  . ""       . 121    93  79          74520000      1859661720000 1
1  . ""       .  96    67  73          47340000      1859634540000 1
1  . ""       . 107    70  68          52920000      1859640120000 1
1  . ""       . 100    65  62          23940000      1859697540000 1
1  . ""       . 101    75  95          67140000      1859654340000 1
1  . ""       . 112    75  67          78120000      1859665320000 1
1  . ""       . 128    93  60          38580000      1859625780000 1
1  . ""       . 118    91  71           9540000      1859683140000 1
1  . ""       . 107    72  72 49139999.99975586 1859636339999.9998 1
1  . ""       . 101    68  66          45540000      1859632740000 1
1  . ""       . 102    71  62          25740000      1859699340000 1
1  . ""       . 113    81  60          68940000      1859656140000 1
1  . ""       . 120    90  60          38700000      1.8596259e+12 1
1  . ""       . 113    85  64          61740000      1859648940000 1
1  . ""       .  99    69  58          20340000      1859693940000 1
1  . ""       .  91    60  71 65339999.99975586 1859652539999.9998 1
1  . ""       . 100    68  65 539999.9997558594 1859674139999.9998 1
1  . ""       . 125    93  79 43739999.99975586 1859630939999.9998 1
1  . ""       . 137    98  88          41940000      1859629140000 1
1  . ""       . 100    79  91 70920000.00024414 1859658120000.0002 1
1  . ""       . 111    78  69          13140000      1859686740000 1
1  . ""       . 112    71  61          15120000      1859688720000 1
1  . ""       . 118    86  75 59939999.99975586 1859647139999.9998 1
2 55 "Female" 1 117 87.52  73          51360000      1877609760000 0
2 55 "Female" 1  91 62.19  72           2760000      1877647560000 0
2 55 "Female" 1 121 90.85  73          49800000      1.8776082e+12 0
2 55 "Female" 1 118 87.85  71          31560000      1877676360000 0
2 55 "Female" 1 121  94.2  74          33360000      1877678160000 0
2 55 "Female" 1 120 85.16  69          35160000      1877679960000 0
2 55 "Female" 1 110 85.21  83          65760000      1877624160000 0
2 55 "Female" 1 113 84.86  74          53160000      1877611560000 0
2 55 "Female" 1 113 89.55  80          29940000      1877674740000 0
2 55 "Female" 1 104 77.87  72          85560000      1877643960000 0
2 55 "Female" 1 139 97.46  71          40560000      1877685360000 0
2 55 "Female" 1 125 93.51  72          42360000      1877600760000 0
2 55 "Female" 1 103  76.2  75           8160000      1877652960000 0
2 55 "Female" 1  99  72.2  60          18960000      1877663760000 0
2 55 "Female" 1  90  69.9  72           9960000      1877654760000 0
2 55 "Female" 1 106 81.88  74            960000      1877645760000 0
2 55 "Female" 1 127 92.16  82          38940000      1877683740000 0
2 55 "Female" 1  98 72.54  69          24360000      1877669160000 0
2 55 "Female" 1 109 85.55  70          38760000      1877597160000 0
2 55 "Female" 1 101 73.53  78          83940000      1877642340000 0
2 55 "Female" 1 112 89.89  69          38940000      1877597340000 0
2 55 "Female" 1 119 90.86  78          74760000      1877633160000 0
2 55 "Female" 1 114 99.26  87          71340000      1877629740000 0
2 55 "Female" 1 126 93.84  92          73140000      1877631540000 0
end
format %tc_HH:MM time
format %tcNN-DD-CCYY_HH:MM:SS datetime
label values id id
label values education education
label def education 1 "College", modify
label values visit visit
label def visit 0 "0: Baseline", modify
label def visit 1 "1: Follow-up", modify
Question 3, what if I have to merge the merged file produced above with a file containing at 3 observations for BP medication for each person/id. would that then be a 1:1 merge or a 1:m merge?

Thank you