Qualitative and quantitative sampling

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Qualitative and quantitative
sampling
Who are they
Black/Blue/Green/Red
Thin/Bold
Smiling/Normal/Sad
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         
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     
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        
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    
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  
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    
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       
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 
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Non-Probability Sampling
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Haphazard sampling
Quota sampling
Purposive sampling
Snowball sampling
Deviant case sampling
Sequential sampling
Theoretical sampling
Haphazard / Accidental / Convenience sample
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Quota sample
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    
         

   
     
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     
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Purposive sample
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    
         

   
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   
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        


   
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       
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
   
       
 
  


    
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       
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











Snowball sample

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

 



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
    
         

   
     
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    

  
 

 





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
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
     
 





   

 
        


   



       
 









 


   
       
 
  

    




    


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       






 
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
 
   
       

 





Deviant case sampling




 





  



 





 
 
 
 
 






    
         

   
     







   
   




     

      
  
        


  


     




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
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
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

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

 
 

   
    

  
 

 









 

     
 





   

 
        


   



       
 









 


   
       
 
  


    




    


 
       






 



 
   
       

 








Sequential sampling
(= purposive + collect cases until marginal utility drops significantly)




 





  



 





 
 
 
 
 






    
         

   
     







   
   




     

      
  
        


  


     


















 
 

   
    

  
 

 









 

     
 





   

 
        


   



       
 









 


   
       
 
  


    




    


 
       






 



 
   
       

 













Theoretical sampling
• Based on (grounded) theory
– Theory develops from initial research
– Cases are selected that are expected to
further deepen the theory
• Eg. Theory developed from data collected during
day time  next collect data at night
Probability Sampling





 
   
  
 




 
 

    



  
    



 

   
Target population     














(thieves)
   
     


       
      
    
 











Sampling frame

   

  










(known as

 
  
   



  



“thieves” by
 
  
 

 
        
police)

 



 

 

    
  
      

   
Sample
        


   
n

     
   


 
 
 








 
 


Sampling ratio
   

  

n
 





   
N
Population
(delinquents)
N
Probability Sampling
• Population is often (usually) unknown,
therefor a population is described with theoretical
values (parameters) eg: N(,)
• Random sample
– Equal chance for all elements to figure in the sample
• Sampling error (deviation from representativeness)
• Sampling distribution
• Central limit theorem
Other demos : 1, 2
Theoretical sampling distribution
Population
Sample
CASE
A
B
C
D
E
F
G
H
I
J
Mean
SCORE
0
1
2
3
4
5
6
7
8
9
4.5
Sample Means
Samples
of 2
Cases
.5
1
1.0
1
15 -1.75
2
2.0 - 2.67
5
2.75 - 3.25
3
3.33 - 4.00
8
4.17 - 4.83
5
5.00 - 5.67
8
5.75 - 6.25
3
6.33 - 7.0
5
7.25 - 7.5
2
8.0
1
8.5
1
No. of samples
45
Mean of s. means
4.5
% sample means
11
> 4.00 and < 5.00
Samples
of 4
Cases
Samples
of 6
Cases
2
10
25
43
50
43
25
10
2
2
10
52
82
52
10
2
210
4.5
24
210
4.5
39
Probability sampling techniques
• Simple Random Sample (SRS)
– Random numbers
• Systematic sampling
(danger: cycles or patterns in sampling frame)
– Random start
– Sampling interval
• Stratified sampling
– Subpopulating (stratification)
– SRS from all strata
• Cluster sampling
– Identify clusters and draw SRS from those
– Then draw SRS of elements from the selected clusters
– Within-Household sample: whom should researcher ask? (always first
to pick up the phone or open the door...?)
– Probability Proportionate to Size (equal probabilities)
• Random Digit Dialling
A link
Simple random sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
Systematic sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
Stratified sampling
Cluster sampling
How large should a sample be?
• ... It depends on...
– population size ,
characteristics
(homogeneousless /
heterogeneousmore)
– spread of the data (sd)
– Purposes (descriptive?
testing? (power))
Population
Sample
Sampling
ratio
200
171
85.5%
500
352
70.4%
1000
543
54.3%
2000
745
37.2%
5000
960
19.2%
10000
1061
10.6%
20000
1121
5.6%
50000
1160
2.3%
100000
1173
1.2%
Haphazard sampling
Random sampling
= Accidental-, Convenience-,
Availability -
Cluster sampling
Snowball sampling
Multi stage sampling
Stratified sampling
Quota sampling
Systematic sampling
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