Monday, October 14, 2019
Thursday, October 10, 2019
Tuesday, October 8, 2019
Psychological tests
മനശാസ്ത്രപഠനോപാധികള്‍
1. ചെക് ലിസ്റ്റ് (check list)
വിവിധവ്യവഹാരങ്ങള്‍, കഴിവുകള്‍, താത്പര്യമേഖലകള്‍ തുടങ്ങിയവ വിലയിരുത്താനും കണ്ടെത്താനും ഉപയോഗിക്കുന്നു.
പഠനോദ്ദേശവുമായി ബന്ധപ്പെട്ട ഘടകങ്ങളുടെ / സവിശേഷതകളുടെ ചെക് ലിസ്റ്റ് തയ്യാറാക്കുന്നു.
ഒരു ഇനം ബാധകമെങ്കില്‍ അതിനു നേരെ ടിക് ചെയ്യുന്നു.
ഇങ്ങനെ ലഭിക്കുന്ന വിവരങ്ങളുടെ അടിസ്ഥാനത്തിന്‍ ചില നിഗമനങ്ങളിലെത്തുന്നു
2. റേറ്റിങ്ങ് സ്കെയില്‍ (rating scale)
ഇതില്‍ ഒരോ സവിശേഷതയുടെയും നിലയെ സൂചിപ്പിക്കുന്ന പോയിന്റുകള്‍ / ഗ്രേഡ് / നിലവാരസൂചിക നല്‍കിയിരിക്കും. 3,5,7തുടങ്ങിയ പോയിന്റുകള്‍ ആണ് സാധാരണ നല്‍കാറുള്ളത്.

3. ചോദ്യാവലി ( questionnaire)
ഒരു വലിയ പ്രശ്നത്തിന്റെ വിശദാംശങ്ങള്‍ തേടാന്‍ കുറേയേറെ ചോദ്യങ്ങള്‍ തയ്യാറാക്കിയാല്‍ ചോദ്യാവലിയായി. സര്‍വേകളില്‍ ഇത്തരം ചോദ്യാവലികളാണ് ഉപയോഗിക്കുന്നത്. പ്രതികരിക്കുന്ന വ്യക്തിക്ക് എഴുതിയോ പറഞ്ഞോ മറുപടി നല്‍കാം.
4. മന:ശാസ്ത്രശോധകം ( psychological tests)
വ്യക്തികളുടെ ബുദ്ധി, വ്യക്തിത്വം, വികാരം തുടങ്ങിയവ കണ്ടെത്താന്‍ ശാസ്ത്രീയമായി തയ്യാറാക്കിയ ശോധകങ്ങള്‍ ഉപയോഗിക്കാറുണ്ട്. ഇവയാണ് മന:ശാസ്ത്രശോധകങ്ങള്‍. ഇവ വാചികം, ലിഖിതം,നിര്‍വഹണം എന്നിങ്ങനെ മൂന്നു രീതികളിലാവാം.
5. സാമൂഹ്യാലേഖനരീതി ( sociometry)

വ്യക്തികള്‍ക്കിടയില്‍ നിലനില്‍ക്കുന്ന ബന്ധങ്ങളെ സംബന്ധിച്ച വിവരം ലഭിക്കാന്‍ ഈ രീതി പ്രയോജനപ്പെടും. വ്യക്തികള്‍ തങ്ങള്‍ക്ക് ചേര്‍ന്നു പ്രവര്‍ത്തിക്കാന്‍ താത്പര്യമുള്ളവരുടെ പേരുകള്‍ എഴുതുകയാണെങ്കില്‍
കൂടുതല്‍ പേര്‍ ആരെയാണ് തെരഞ്ഞടുത്തതെന്ന് അറിയാനാകും. ഇവരാണ് stars.
പരസ്പരം തെരഞ്ഞെടുത്ത ചെറുഗ്രൂപ്പുകളെയും ഇതിലൂടെ കണ്ടെത്താം. അത്തരം ഗ്രൂപ്പുകളാണ് cliques.
ആരും തെരഞ്ഞടുക്കാത്തവരും ഉണ്ടായേക്കാം.അവരാണ് isolates.
6. പ്രക്ഷേപണ രീതികള്‍
റോഷാ മഷിയൊപ്പ് പരീക്ഷ

തീമാറ്റിക് അപ്പര്‍സെപ്ഷന്‍ ടെസ്റ്റ് TAT . മുപ്പത്തിയൊന്നു ചിത്രകാര്‍ഡുകളാണ് ഉപയോഗിക്കുക.ചിത്രങ്ങളുടെ അടിസ്ഥാനത്തില്‍ കഥകള്‍ നിര്‍മിക്കാനാണ് ആവശ്യപ്പെടുക. കഥ വിശകലനം ചെയ്ത് വിധേയനായ വ്യക്തിയുടെ മനോവ്യാപാരങ്ങള്‍ കണ്ടെത്തുന്നു.

പദസഹചരത്വ പരീക്ഷ ( പദക്കൂട്ടം നല്‍കും അതുണര്‍ത്തുന്ന ചിന്തകളുമായി ബന്ധപ്പെട്ട വാക്കുകള്‍ പറയിച്ച് വിശകലനം ചെയ്യും)
Tuesday, October 1, 2019
Tuesday, September 10, 2019
Monday, September 9, 2019
Sunday, September 8, 2019
Wednesday, August 28, 2019
ICT Workshop Report
Tuesday, August 27, 2019
Saturday, August 24, 2019
Study notes on Qualitative Research
Qualitative
Research: Sampling & Sample Size Considerations
The goal of
qualitative research is to provide in-depth understanding and therefore,
targets a specific group, type of individual, event or process. There are three
main types of qualitative sampling: purposeful sampling, quota sampling, and
snowballing sampling.
Purposeful
Sampling
Purposeful sampling is
the most common sampling strategy. In this type of sampling, participants are
selected or sought after based on pre-selected criteria based on the research
question. For example, the study may be attempting to collect data from
lymphoma patients in a particular city or country. The sample size may be
predetermined or based on theoretical saturation, which is the point at which
the newly collected longer provides additional insights. Click on the
following link for a description of purposeful sampling: Types of Purposeful
Sampling.
Quota
Sampling
It is a sampling technique
whereby participant quotas are preset prior to sampling. Typically, the
researcher is attempting to gather data from a certain number of participants
that meet certain characteristics that may include things such as age, sex,
class, marital status, HIV status, etc. Click here for more information on this
type of sampling: Quota Sampling.
Snowball
Sampling
It is also known as
chain referral sampling. In this method, the participants refer the researcher
to others who may be able to potentially contribute or participate in the
study. This method offer helps researchers find and recruit participants that
may otherwise be hard to reach. For more information, click here: Snowball
Sampling.
Figure1. Snowball sampling
Common
Qualitative Sampling Strategies
Extreme
or Deviant Case Sampling.
Looks at highly unusual manifestations of the phenomenon of interest, such as
outstanding success/notable failures, top of the class/dropouts, exotic events,
and crises. This strategy tries to select particular cases that would glean the
most information, given the research question. One example of an extreme/ the deviant case related to battered women would be battered women who kill their
abusers.
Intensity Sampling. Chooses information-rich cases that manifest the
phenomenon intensely, but not extremely, such as good students/poor students,
above average/below average. This strategy is very similar to extreme/deviant
case sampling as it uses the same logic. The difference is that the cases
selected are not as extreme. This type of sampling requires that you have prior
information on the variation of the phenomena under study so that you can
choose intense, although not extreme, examples. For example, heuristic research
uses the intense, personal experience(s) of the researcher. If one were
studying jealousy, you would need to have had an intense experience with this
particular emotion; a mild or pathologically extreme experience would not
likely elucidate the phenomena in the same way as an intense experience.
Maximum Variation Sampling.
Selects a wide range of variations on dimensions of interest. The purpose is to
discover/uncover central themes, core elements, and/or shared dimensions that
cut across a diverse sample while at the same time offering the opportunity to
document unique or diverse variations. For example, to implement this strategy,
you might create a matrix (of communities, people, etc.) where each item on the
matrix is as different (on relevant dimensions) as possible from all other
items.
Homogeneous Sampling. Brings together people of similar backgrounds and
experiences. It reduces variation, simplifies the analysis, and facilitates group
interviewing. This strategy is used most often when conducting focus groups.
For example, if you are studying participation in a parenting program, you
might sample all single-parent, female heads of households.
Typical
Case Sampling. It focuses on what is typical, normal, and/or average. This
strategy may be adopted when one needs to present a qualitative profile of one
or more typical cases. When using this strategy you must have a broad consensus
about what is “average.” For example, if you were working to begin development
projects in Third World countries, you might conduct a typical case sampling of
“average” villages. Such a study would uncover critical issues to be addressed
for most villages by looking at the ones you sampled.
Critical
Case. Look at cases that will
produce critical information. In order to use this method, you must know what
constitutes a critical case. This method permits logical generalization and
maximum application of information to other cases because if it’s true of this
one case, it’s likely to be true of all other cases. For example, if you want to
know if people understand a particular set of federal regulations, you may
present the regulations to a group of highly educated people (“If they can’t understand
them, then most people probably cannot”) and/or you might present them to a
group of under-educated people (“If they can understand them, then most people
probably can”).
Snowball or Chain Sampling. Identifies cases of interest
from people who know people who know what cases are information-rich, that is,
who would be a good interview participant. Thus, this is an approach used for
locating information-rich cases. You would begin by asking relevant people
something like: “Who knows a lot about ___?” For example, you would ask for
nominations, until the nominations snowball, getting bigger and bigger.
Eventually, there should be a few key names that are mentioned repeatedly(‘Sampling in
Qualitative Research’, n.d.).
Criterion Sampling. Selects all cases that meet some criteria. This strategy
is typically applied when considering quality assurance issues. In essence, you
choose cases that are information-rich and that might reveal a major system
weakness that could be improved. For example, if the average length of stay for
a certain surgical procedure is three days, you might set a criterion for being
in the study as anyone whose stay exceeded three days. Interviewing these cases
may offer information related to aspects of the process/system improved that
could be based or Operational Construct or Theoretical Sampling defines
manifestations of a theoretical construct of interest so as to elaborate and
examine the construct. This strategy is similar to criterion sampling, except
it is more conceptually focused. This strategy is used in grounded theory
studies. You would sample people/incidents, etc., based on whether or not they
manifest/represent an important theoretical or operational construct. For
example, if you were interested in studying the theory of “resiliency” in
adults who were physically abused as children, you would sample people who meet
theory-driven criteria for “resiliency.”
Confirming and Disconfirming Sampling. Seeks cases that are both “expected” and the
“exception” to what is expected. In this way, this strategy deepens initial the analysis seeks exceptions and tests variation. In this strategy, you find both
confirming cases (those that add depth, richness, credibility) as well as
disconfirming cases (an example that does not fit and is the source of rival
interpretations). This strategy is typically adopted after initial fieldwork
has established what a confirming case would be. For example, if you are
studying certain negative academic outcomes related to environmental factors,
like low SES, low parental involvement, high teacher to student ratios, lack of
funding for a school, etc. you would look for both confirming cases (cases that
evidence the negative impact of these factors on academic performance) and
disconfirming cases (cases where there is no apparent negative association
between these factors and academic performance).
Stratified Purposeful Sampling.
Focuses on characteristics of particular subgroups of interest; facilitates
comparisons. This strategy is similar to stratified random sampling (samples
are taken within samples), except the sample size is typically much smaller. In
stratified sampling you “stratify” a sample based on a characteristic. Thus, if
you are studying academic performance, you would sample a group of below-average performers, average performers, and above-average performers. The main
goal of this strategy is to capture major variations (although common themes
may emerge).
Opportunistic or Emergent Sampling. Follows new leads during fieldwork, takes
advantage of the unexpected, and are flexible. This strategy takes advantage of
whatever unfolds as it is unfolding and may be used after fieldwork has begun
and as a researcher becomes open to sampling a group or person they may not
have initially planned to interview. For example, you might be studying 6th
grade students’ awareness of a topic and realize you will gain additional
understanding by including 5th-grade students’ as well.
Purposeful
Random Sampling. Looks at a random sample. This strategy adds credibility
to a sample when the potential purposeful sample is larger than one can handle.
While this is a type of random sampling, it uses small sample sizes, thus the
goal is credibility, not representativeness or the ability to generalize. For
example, if you want to study clients at a drug rehabilitation program, you may
randomly select 10 of 300 current cases to follow. This reduces judgment within
a purposeful category because the cases are picked randomly and without regard
to the program outcome.
Sampling Politically Important Cases.
Seeks cases that will increase the usefulness and relevance of information
gained based on the politics of the moment. This strategy attracts attention to
the study (or avoids attracting undesired attention by purposefully eliminating
from the sample politically sensitive cases). This strategy is a variation on
critical case sampling. For example, when studying voter behaviour, one might
choose the 2000 election, not only because it would provide insight, but also
because it would likely attract attention.
Convenience
Sampling. Selects cases based on ease of accessibility.
This strategy saves time, money, and effort, however, it has the weakest rationale
along with the lowest credibility. This strategy may yield information-poor
cases because cases are picked simply because they are easy to access, rather
than on a specific strategy/rationale. Sampling your co-workers, family members
or neighbours simply because they are “there” is an example of convenience
sampling.
Combination or Mixed Purposeful Sampling. Combines two or more strategies listed above. Basically,
using more than one strategy above is considered combination or mixed
purposeful sampling. This type of sampling meets multiple interests and needs.
For example, you might use chain sampling in order to identify extreme or
deviant cases. That is, you might ask people to identify cases that would be
considered extreme/deviant and do this until you have consensus on a set of
cases that you would sample.
Table 1.
Sampling
methods in qualitative and quantitative research
Assumptions of quantitative sampling
|
Assumptions of qualitative sampling
|
|
· We
want to generalize to the population
· Random events are predictable
· We can compare random events to our
results
· Probability sampling is the best
approach
|
· Social
actors are not predictable like objects
· Randomized events are irrelevant to social
life
· Probability
sampling is expensive and inefficient
· Non-probability sampling is the best
approach
|
Voice note on Qualitative Sampling in Research
References
Sampling in Qualitative
Research. (n.d.). Retrieved 20 August 2019, from
https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/s10-02-sampling-in-qualitative-resear.html
Friday, August 23, 2019
Thursday, August 22, 2019
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